{"title":"Mathematical and statistical software Books","description":"","products":[{"product_id":"introduction-to-static-analysis-an-abstract-interpretation-perspective-the-mit-press-9780262043410","title":"Introduction to Static Analysis An Abstract","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eA self-contained introduction to abstract interpretation-based static analysis, an essential resource for students, developers, and users.\u003c\/b\u003e\u003cp\u003eStatic program analysis, or static analysis, aims to discover semantic properties of programs without running them. It plays an important role in all phases of development, including verification of specifications and programs, the synthesis of optimized code, and the refactoring and maintenance of software applications. This book offers a self-contained introduction to static analysis, covering the basics of both theoretical foundations and practical considerations in the use of static analysis tools. By offering a quick and comprehensive introduction for nonspecialists, the book fills a notable gap in the literature, which until now has consisted largely of scientific articles on advanced topics.\u003c\/p\u003e\u003cp\u003eThe text covers the mathematical foundations of static analysis, including semantics, semantic abstraction, and computation of program inv\u003c\/p\u003e","brand":"MIT Press Ltd","offers":[{"title":"Default Title","offer_id":48733452206423,"sku":"9780262043410","price":68.4,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780262043410.jpg?v=1720000130"},{"product_id":"advanced-issues-in-partial-least-squares-structural-equation-modeling-9781071862506","title":"Advanced Issues in Partial Least Squares","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe \u003cstrong\u003eSecond Edition\u003c\/strong\u003e of \u003cem\u003e\u003cstrong\u003eAdvanced Issues in Partial Least Squares Structural Equation Modeling\u003c\/strong\u003e\u003c\/em\u003e offers a straightforward and practical guide to PLS-SEM for users ready to go further than the basics of \u003cem\u003e\u003cstrong\u003eA Primer on Partial Least Squares Structural Equation Modeling\u003c\/strong\u003e\u003c\/em\u003e\u003cstrong\u003e\u003cem\u003e,Third Edition\u003c\/em\u003e\u003c\/strong\u003e. Even in this advanced guide, the authors have limited the emphasis on equations, formulas, and Greek symbols, and instead rely on detailed explanations of the fundamentals of PLS-SEM and provide general guidelines for understanding and evaluating the results of applying the method. A single study on corporate reputation features as an example throughout the book, along with a single software package (SmartPLS 4.0) to provide a seamless learning experience. The approach of this book is based on the authors' many years of conducting research and teaching methodology courses, including developing the SmartPLS software. The pr\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Excellent guide on how to use smart pls. Good starter product for understanding the underlying concepts.\" -- Saurabh Gupta\u003cbr\u003e\"Must have if you want to do PLS\" -- Jason Xiong\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eChapter 1: An Overview of Recent and Emerging Developments in PLS-SEM Chapter 2: Higher-order Constructs Chapter 3: Advanced Modeling and Model Assessment Chapter 4: Advanced Results Illustration Chapter 5: Modeling Observed Heterogeneity Chapter 6: Modeling Unobserved Heterogeneity","brand":"SAGE Publications Inc","offers":[{"title":"Default Title","offer_id":48738220376407,"sku":"9781071862506","price":55.1,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781071862506.jpg?v=1723811830"},{"product_id":"essential-math-for-ai-9781098107635","title":"Essential Math for AI","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory. Engineers, data scientists, and students alike will examine mathematical topics critical for AI-including regression, neural networks, optimization, backpropagation, and Markov chains.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48738226733399,"sku":"9781098107635","price":47.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098107635.jpg?v=1723811836"},{"product_id":"the-design-and-statistical-analysis-of-animal-experiments-9781107690943","title":"The Design and Statistical Analysis of Animal","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis is the first book to provide life scientists with a practical guide to using experimental design and statistics when running animal experiments. The chapters cover a range of design types and analysis techniques employed by practitioners, using non-mathematical terms and drawing on real-life examples.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'At last, a readable statistics book focusing solely on preclinical experimental designs, data and its analysis that should form part of an in-vivo scientist's personal library. The author's unique insight into the statistical needs of preclinical scientists has allowed them to compile a non-technical guide that can facilitate sound experimental design, meaningful data analysis and appropriate scientific conclusions. I would also encourage all readers to download and explore 'InVivoStat', a powerful software package that both my group and I use on a daily basis.' Darrel J. Pemberton, Janssen Research and Development\u003cbr\u003e'This book provides an indispensable reference for any in-vivo scientist. It addresses common pitfalls in animal experiments and provides tangible advice to address sources of bias, thus increasing the robustness of the data. … The text links experimental design and statistical analysis in a practical way, easily accessible without any prior statistical knowledge. The statistical concepts are described in plain English, avoiding overuse of mathematical formulas and illustrated with numerous examples relevant to biomedical scientists. … This book will help scientists improve the design of animal experiments and give them the confidence to use more complex designs, enabling more efficient use of animals and reducing the number of experimental animals needed overall.' Nathalie Percie du Sert, National Centre for the Replacement, Refinement and Reduction of Animals in Research\u003cbr\u003e'This book will transform the way biomedical scientists plan their work and interpret their results. Although the subject matter covers complex points, it is easy to read and packed with relevant examples. There are two particularly striking features. First, at no point do the authors resort to mathematical equations as a substitute for explaining the concepts. Secondly, they explain why the choice of experimental design is so important, why the design affects the statistical analysis and how to ensure the choice of the most appropriate statistical test. The final section describes how to use InvivoStat (a software package, assembled by the authors), which enables researchers to put into practice all the points covered in this book. This is an invaluable combination of resources that should be within easy reach of anyone carrying out experiments in the biomedical sciences, especially if their work involves using live animals.' Clare Stanford, University College London\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface; Acknowledgements; 1. Introduction; 2. Statistical concepts; 3. Experimental design; 4. Randomisation; 5. Statistical analysis; 6. Analysis using InVivoStat; 7. Conclusion; Glossary; References; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738277032279,"sku":"9781107690943","price":47.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781107690943.jpg?v=1723811881"},{"product_id":"generalized-linear-models-with-examples-in-r-9781441901170","title":"Generalized Linear Models With Examples in R","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis textbook presents an introduction to multiple linear regression, providing real-world data sets and practice problems. A practical working knowledge of applied statistical practice is developed through the use of these data sets and numerous case studies. The authors include a set of practice problems both at the end of each chapter and at the end of the book. Each example in the text is cross-referenced with the relevant data set, so that readers can load the data and follow the analysis in their own R sessions. The balance between theory and practice is evident in the list of problems, which vary in difficulty and purpose.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eThis book is designed with teaching and learning in mind, featuring chapter introductions and summaries, exercises, short answers, and simple, clear examples. Focusing on the connections between generalized linear models (GLMs) and linear regression, the book also references advanced topics and tools that have not typically been included in\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“This is a great book … . The book comprehensively covers almost everything you need to know or teach in this area. This book is an invaluable reference either as a classroom text or for the researcher’s bookshelf.” (Pablo Emilio Verde, ISCB News, iscb.info, Issue 69, July, 2020)\u003cbr\u003e“I congratulate the authors for making an important contribution in this field. … the book represents an excellent and very comprehensible introduction into the world of generalized linear models and is recommended for all readers who are looking for a practical introduction to this topic using R.” (Dominic Edelmann, Biometrical Journal, Vol. 62, 2020)\u003cbr\u003e“The book is targeted at students and notes it is appropriate for graduate students. It is also useful to the junior statistician needing to learn how to work a model they are unfamiliar with. The practicing and experienced statistician can use this as a quick reference for working a model they may have forgotten the specific of.” (James P. Howard II, zbMath 1416.62020, 2019)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eStatistical models.- Linear regression models.-  Linear regression models: diagnostics and model-building.- Beyond linear regression: the method of maximum likelihood.- Generalized linear models: structure.- Generalized linear models: estimation.- Generalized linear models: inference.- Generalized linear models: diagnostics.- Models for proportions: binomial GLMs.- Models for counts: Poisson and negative binomial GLMs.- Positive continuous data: gamma and inverse Gaussian GLMs.- Tweedie GLMs.- Extra problems.- Appendix A: Using R for data analysis.- Appendix B: The GLMsData package.- Index: Data sets.- Index: R commands.- Index: General Topics. ","brand":"Springer-Verlag New York Inc.","offers":[{"title":"Default Title","offer_id":48739209347415,"sku":"9781441901170","price":79.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781441901170.jpg?v=1720051498"},{"product_id":"a-course-in-mathematical-statistics-and-large-sample-theory-9781493940301","title":"A Course in Mathematical Statistics and Large","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cdiv\u003eThis graduate-level textbook is primarily aimed at graduate students of statistics, mathematics, science, and engineering who have had an undergraduate course in statistics, an upper division course in analysis, and some acquaintance with measure theoretic probability. It provides a rigorous presentation of the core of mathematical statistics.\u003c\/div\u003e\u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003ePart I of this book constitutes a one-semester course on basic parametric mathematical statistics. Part II deals with the large sample theory of statistics - parametric and nonparametric, and its contents may be covered in one semester as well. Part III provides brief accounts of a number of topics of current interest for practitioners and other disciplines whose work involves statistical methods.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“It deals with advanced statistical theory with a special focus on statistical inference and large sample theory, aiming to cover the material for a modern two-semester graduate course in mathematical statistics. … Overall, the book is very advanced and is recommended to graduate students with sound statistical backgrounds, as well as to teachers, researchers, and practitioners who wish to acquire more knowledge on mathematical statistics and large sample theory.” (Lefteris Angelis, Computing Reviews, March, 2017)\u003cp\u003e\u003c\/p\u003e\u003cp\u003e“This is a very nice book suitable for a theoretical statistics course after having worked through something at the level of Casella \u0026amp; Berger, as well as some measure theory. … In addition to the exercises, which range from doable to interesting, there are several projects scattered throughout the text. The explanations are clear and crisp, and the presentation is interesting. … the book would be a worthy addition to your statistics library.” (Peter Rabinovitch, MAA Reviews, maa.org, March, 2017)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1 Introduction.- 2 Decision Theory.- 3 Introduction to General Methods of Estimation.- 4 Sufficient Statistics, Exponential Families, and Estimation.- 5 Testing Hypotheses.- 6 Consistency and Asymptotic Distributions and Statistics.- 7 Large Sample Theory of Estimation in Parametric Models.- 8 Tests in Parametric and Nonparametric Models.- 9 The Nonparametric Bootstrap.- 10 Nonparametric Curve Estimation.- 11 Edgeworth Expansions and the Bootstrap.- 12 Frechet Means and Nonparametric Inference on Non-Euclidean Geometric Spaces.- 13 Multiple Testing and the False Discovery Rate.- 14 Markov Chain Monte Carlo (MCMC) Simulation and Bayes Theory.- 15 Miscellaneous Topics.- Appendices.- Solutions of Selected Exercises in Part 1.","brand":"Springer-Verlag New York Inc.","offers":[{"title":"Default Title","offer_id":48739724853591,"sku":"9781493940301","price":82.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781493940301.jpg?v=1720053001"},{"product_id":"applied-statistics-using-stata-a-guide-for-the-social-sciences-9781529742565","title":"Applied Statistics Using Stata: A Guide for the","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eStraightforward, clear, and applied, this book will give you the theoretical and practical basis you need to apply data analysis techniques to real data. \u003cbr\u003e \u003cbr\u003e Combining key statistical concepts with detailed technical advice, it addresses common themes and problems presented by real research, and shows you how to adjust your techniques and apply your statistical knowledge to a range of datasets. It also embeds code and software output throughout and is supported by online resources to enable practice and safe experimentation. \u003cbr\u003e \u003cbr\u003e The book includes: \u003cbr\u003e ·       Original case studies and data sets \u003cbr\u003e ·       Practical exercises and lists of commands for each chapter \u003cbr\u003e ·       Downloadable Stata programmes created to work alongside chapters \u003cbr\u003e ·       A wide range of detailed applications using Stata \u003cbr\u003e ·       Step-by-step guidance on writing the relevant code. \u003cbr\u003e \u003cbr\u003e This is the perfect text for anyone doing statistical research in the social sciences getting started using Stata for data analysis. \u003cbr\u003e \u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eNewly updated, now with more advanced content, this book remains a must have for those studying applied statistics. The book is practically orientated with intuitive theoretical explanations, a wide array  \"how-to-do-it\" examples and an engaging narrative.  You won’t’ be sorry! -- Franz Buscha\u003cbr\u003eThis is a most impressive teaching and learning resource. Mehmetoglu and Jakobsen expertly introduce introductory to advanced social science data analysis skills in a clear and engaging manner. This text teaches students how to do data analysis in a transparent and principled manner. -- Roxanne Connelly\u003cbr\u003eMehmetoglu and Jakobsen′s book offers a concise, yet comprehensive, introduction to the statistical methods that are widely used in data analysis. In addition to presenting a thorough overview of the basics of conducting empirical research, the book also emphasizes how to use Stata to analyze data in practice. This book is an excellent starting point for those who are interested in empirical work. -- Hector H. Sandoval\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eChapter 1. Research and Statistics Chapter 2. Introduction to Stata Chapter 3. Simple (Bivariate) Regression Chapter 4. Multiple Regression Chapter 5. Dummy-Variable Regression Chapter 6. Interaction\/Moderation Effects Using Regression Chapter 7. Linear Regression Assumptions and Diagnostics Chapter 8. Logistic Regression Chapter 9. Survival Analysis Chapter 10. Multilevel Analysis Chapter 11. Panel Data Analysis Chapter 12. Time Series Analysis Chapter 13. Exploratory Factor Analysis Chapter 14. Structural Equation Modelling and Confirmatory Factor Analysis Chapter 15. Advanced Statistical Techniques Chapter 16. Programming and Dynamic Reporting Using Stata","brand":"Sage Publications Ltd","offers":[{"title":"Default Title","offer_id":48740314251607,"sku":"9781529742565","price":41.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781529742565.jpg?v=1720054386"},{"product_id":"adventures-in-social-research-data-analysis-using-ibm-spss-statistics-9781544398006","title":"Adventures in Social Research: Data Analysis","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis text provides a practical, hands-on introduction to data conceptualization, measurement, and association through active learning. Students get step-by-step instruction on data analysis using the latest version of SPSS and the most current General Social Survey data. The text starts with an introduction to computerized data analysis and the social research process, then walks users through univariate, bivariate, and multivariate analysis using SPSS. The book contains applications from across the social sciences—sociology, political science, social work, criminal justice, health—so it can be used in courses offered in any of these departments. The Eleventh Edition uses the latest general Social Survey (GSS) data, and the latest available version of SPSS. The GSS datasets now offer additional variables for more possibilities in the demonstrations and exercises within each chapter.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eThis text has been a lifesaver! Although the material is challenging, I have been continually impressed with my student’s ability to come away from this course with the ability to perform their own (small) data analysis project in the final week using what they learned. . . . Many start with zero knowledge or experience with research, and in a very short time period are able to get up to speed with the terminology, and to sift through all of the various ‘rules’ of data analysis (which measures of association, tests of significance, etc. to use based on their variables) like pros. -- Kristie Vise\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart I Preparing for Data Analysis\t Chapter 1 Introduction: The Theory and Practice of Social Research\t Chapter 2 The Logic of Measurement\t Chapter 3 Description of Data Sets: The General Social Survey\t Part II Univariate Analysis\t Chapter 4 Using SPSS Statistics: Some Basics\t Chapter 5 Describing Your Data: Religiosity\t Chapter 6 Presenting Your Data in Graphic Form: Political Orientations\t Chapter 7 Recoding Your Data: Religiosity and Political Orientations\t Chapter 8 Creating Composite Measures: Exploring Attitudes Toward Abortion in More Depth\t Chapter 9 Suggestions for Further Analysis\t Part III Bivariate Analysis\t Chapter 10 Examining the Sources of Religiosity\t Chapter 11 Political Orientations as Cause and Effect\t Chapter 12 What Causes Different Attitudes Toward Abortion?\t Chapter 13 Measures of Association for Nominal and Ordinal Variables\t Chapter 14 Correlation and Regression Analysis\t Chapter 15 Tests of Significance\t Chapter 16 Suggestions for Further Bivariate Analyses\t Part IV Multivariate Analysis\t Chapter 17 Multiple Causation: Examining Religiosity in Greater Depth\t Chapter 18 Dissecting the Political Factor\t Chapter 19 A Powerful Prediction of Attitudes Toward Abortion\t Chapter 20 Suggestions for Further Multivariate Analyses\t Part V The Adventure Continues\t Chapter 21 Designing and Executing Your Own Survey\t Chapter 22 Further Opportunities for Social Research","brand":"SAGE Publications Inc","offers":[{"title":"Default Title","offer_id":48740483334487,"sku":"9781544398006","price":104.96,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781544398006.jpg?v=1720054823"},{"product_id":"analyzing-qualitative-data-with-maxqda-text-audio-and-video-9783030156701","title":"Analyzing Qualitative Data with MAXQDA: Text,","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book presents strategies for analyzing qualitative and mixed methods data with MAXQDA software, and provides guidance on implementing a variety of research methods and approaches, e.g. grounded theory, discourse analysis and qualitative content analysis, using the software. In addition, it explains specific topics, such as transcription, building a coding frame, visualization, analysis of videos, concept maps, group comparisons and the creation of literature reviews. The book is intended for masters and PhD students as well as researchers and practitioners dealing with qualitative data in various disciplines, including the educational and social sciences, psychology, public health, business or economics.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction: Analyzing Qualitative Data with Software.- Getting to Know the Interface of MAXQDA.- Setting up a Project and Importing Data.- Transcribing Audio and Video Recordings.- Exploring the Data.- Coding Text and PDF Files.- Coding Video Data, Audio Data, and Images.- Building a Coding Frame.- Working with Coded Segments and Memos.- Adding Variables and Quantifying Codes.- Working with Paraphrases and Summaries, Creating Case Overviews.- Comparing Cases and Groups, Discovering Interrelations and Using Visualizations.- Analyzing Mixed Methods Data.- Working with Bibliographic Information and Creating Literature Reviews.- Analyzing Focus Group Data.- Analyzing (Online) Survey Data with Closed and Open-Ended Questions.- MAXMaps: Creating Infographics and Concept Maps.- Collaborating in Teams.- Analyzing Intercoder Agreement.- Documenting and Archiving the Research Process.\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743025443159,"sku":"9783030156701","price":71.24,"currency_code":"GBP","in_stock":true}]},{"product_id":"a-beginner-s-guide-to-statistics-for-criminology-and-criminal-justice-using-r-9783030506278","title":"A Beginner’s Guide to Statistics for Criminology","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book provides hands-on guidance for researchers and practitioners in criminal justice and criminology to perform statistical analyses and data visualization in the free and open-source software R. It offers a step-by-step guide for beginners to become familiar with the RStudio platform and tidyverse set of packages. \u003c\/p\u003e  This volume will help users master the fundamentals of the R programming language, providing tutorials in each chapter that lay out research questions and hypotheses centering around a real criminal justice dataset, such as data from the National Survey on Drug Use and Health, National Crime Victimization Survey, Youth Risk Behavior Surveillance System, The Monitoring the Future Study, and The National Youth Survey. Users will also learn how to manipulate common sources of agency data, such as calls-for-service (CFS) data. The end of each chapter includes exercises that reinforce the R tutorial examples, designed to help master the software as well as to provide practice on statistical concepts, data analysis, and interpretation of results.\u003cp\u003e\u003c\/p\u003e  \u003cp\u003eThe text can be used as a stand-alone guide to learning R or it can be used as a companion guide to an introductory statistics textbook, such as \u003ci\u003eBasic Statistics in Criminal Justice\u003c\/i\u003e (2020).\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Getting started.2. Managing your data.3. Data visualization.4. Spatiotemporal data visualization and basic crime analysis.5. Descriptive statistics: measures of central tendency.6. Descriptive statistics: measures of dispersion.7. Statistical inference in criminal justice research.8. Defining the observed significance level of a test.9. Hypothesis testing using the binomial distribution.10. Chi-square: a test commonly used for nominal-level measures.11. The normal distribution and its application to tests of statistical significance.12. Comparing means in two samples.13. Analysis of variance.14. Measures of association for nominal and ordinal variables.15. Measuring association for interval data.16. Introduction to regression analysis.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743038288215,"sku":"9783030506278","price":47.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030506278.jpg?v=1720063840"},{"product_id":"a-beginner-s-guide-to-statistics-for-criminology-and-criminal-justice-using-r-9783030506247","title":"A Beginner’s Guide to Statistics for Criminology","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book provides hands-on guidance for researchers and practitioners in criminal justice and criminology to perform statistical analyses and data visualization in the free and open-source software R. It offers a step-by-step guide for beginners to become familiar with the RStudio platform and tidyverse set of packages. \u003c\/p\u003e  This volume will help users master the fundamentals of the R programming language, providing tutorials in each chapter that lay out research questions and hypotheses centering around a real criminal justice dataset, such as data from the National Survey on Drug Use and Health, National Crime Victimization Survey, Youth Risk Behavior Surveillance System, The Monitoring the Future Study, and The National Youth Survey. Users will also learn how to manipulate common sources of agency data, such as calls-for-service (CFS) data. The end of each chapter includes exercises that reinforce the R tutorial examples, designed to help master the software as well as to provide practice on statistical concepts, data analysis, and interpretation of results.\u003cp\u003e\u003c\/p\u003e  \u003cp\u003eThe text can be used as a stand-alone guide to learning R or it can be used as a companion guide to an introductory statistics textbook, such as \u003ci\u003eBasic Statistics in Criminal Justice\u003c\/i\u003e (2020).\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Getting started.2. Managing your data.3. Data visualization.4. Spatiotemporal data visualization and basic crime analysis.5. Descriptive statistics: measures of central tendency.6. Descriptive statistics: measures of dispersion.7. Statistical inference in criminal justice research.8. Defining the observed significance level of a test.9. Hypothesis testing using the binomial distribution.10. Chi-square: a test commonly used for nominal-level measures.11. The normal distribution and its application to tests of statistical significance.12. Comparing means in two samples.13. Analysis of variance.14. Measures of association for nominal and ordinal variables.15. Measuring association for interval data.16. Introduction to regression analysis.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743038517591,"sku":"9783030506247","price":66.49,"currency_code":"GBP","in_stock":true}]},{"product_id":"a-course-on-small-area-estimation-and-mixed-models-methods-theory-and-applications-in-r-9783030637569","title":"A Course on Small Area Estimation and Mixed","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis advanced textbook explores small area estimation techniques, covers the underlying mathematical and statistical theory and offers hands-on support with their implementation. It presents the theory in a rigorous way and compares and contrasts various statistical methodologies, helping readers understand how to develop new methodologies for small area estimation. It also includes numerous sample applications of small area estimation techniques. The underlying R code is provided in the text and applied to four datasets that mimic data from labor markets and living conditions surveys, where the socioeconomic indicators include the small area estimation of total unemployment, unemployment rates, average annual household incomes and poverty indicators. Given its scope, the book will be useful for master and PhD students, and for official and other applied statisticians.\u003c\/p\u003e   \u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1 Small Area Estimation.- 2 Design-based Direct Estimation.- 3 Design-based Indirect Estimation.- 4 Prediction Theory.- 5 Linear Models.- 6 Linear Mixed Models.- 7 Nested Error Regression Models.- 8 EBLUPs under Nested Error Regression Models.- 9 Mean Squared Error of EBLUPs.- 10 EBPs under Nested Error Regression Models.- 11 EBLUPs under Two-fold Nested Error Regression Models.- 12 EBPs under Two-fold Nested Error Regression Models.- 13 Random Regression Coefficient Models.- 14 EBPs under Unit-level Logit Mixed Models.- 15 EBPs under Unit-level Two-fold Logit Mixed Models.- 16 Fay-Herriot Models.- 17 Area-level Temporal Linear Mixed Models.- 18 Area-level Spatio-temporal Linear Mixed Models.- 19 Area-level Bivariate Linear Mixed Models.- 20 Area-level Poisson Mixed Models.- 21 Area-level Temporal Poisson Mixed Models.- A Some Useful Formulas.- Index.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743043400023,"sku":"9783030637569","price":104.49,"currency_code":"GBP","in_stock":true}]},{"product_id":"luminescence-data-analysis-and-modeling-using-r-9783030673109","title":"Luminescence: Data Analysis and Modeling Using R","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e​This book covers applications of R to the general discipline of radiation dosimetry and to the specific areas of luminescence dosimetry, luminescence dating, and radiation protection dosimetry. It features more than 90 detailed worked examples of R code fully integrated into the text, with extensive annotations. The book shows how researchers can use available R packages to analyze their experimental data, and how to extract the various parameters describing mathematically the luminescence signals.\u003cbr\u003e In each chapter, the theory behind the subject is summarized, and references are given from the literature, so that researchers can look up the details of the theory and the relevant experiments. Several chapters are dedicated to Monte Carlo methods, which are used to simulate the luminescence processes during the irradiation, heating, and optical stimulation of solids, for a wide variety of materials. This book will be useful to those who use the tools of luminescence dosimetry, including physicists, geologists, archaeologists, and for all researchers who use radiation in their research.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction.- 2. Analysis and Modeling of TL Data.- 3. Analysis of Experimental OSL Data.- 4. Dose Response of Dosimetric Materials.- 5. Monte Carlo Simulations With Fixed Time Interval.- 6. Luminescence as a Stochastic Life-and-Death Process.- 7. Delocalized Transitions: The R Package RLumCarlo.- 8. Localized Transitions: The R Package RLumCarlo.- 9. Quantum Tunneling and Luminescence Models.- 10. Quantum Tunneling: The R Package RLumCarlo.- 11. Comprehensive Quartz Models Using Program KMS.- 12. Quartz Models Using the R-Package RLumModel.\u003cbr\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743044645207,"sku":"9783030673109","price":66.49,"currency_code":"GBP","in_stock":true}]},{"product_id":"an-introduction-to-statistics-with-python-with-applications-in-the-life-sciences-9783030973704","title":"An Introduction to Statistics with Python: With","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eNow in its second edition, this textbook provides an introduction to Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics.\u003c\/p\u003e\u003cp\u003eFor this new edition, the introductory chapters on Python, data input and visualization have been reworked and updated. The chapter on experimental design has been expanded, and programs for the determination of confidence intervals commonly used in quality control have been introduced. The book also features a new chapter on finding patterns in data, including time series. A new appendix describes useful programming tools, such as testing tools, code repositories, and GUIs.\u003c\/p\u003eThe provided working code for Python solutions, together with easy-to-follow examples, will reinforce the reader’s immediate understanding of the topic. Accompanying data sets and Python programs are also available online. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis.\u003cp\u003e\u003c\/p\u003e\u003cp\u003eWith examples drawn mainly from the life and medical sciences, this book is intended primarily for masters and PhD students. As it provides the required statistics background, the book can also be used by anyone who wants to perform a statistical data analysis. \u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eI Python and Statistics.- 1 Introduction.- 2 Python.- 3 Data Input.- 4 Data Display.- II Distributions and Hypothesis Tests.- 5 Basic Statistical Concepts.- 6 Distributions of One Variable.- 7 Hypothesis Tests.- 8 Tests of Means of Numerical Data.- 9 Tests on Categorical Data.- 10 Analysis of Survival Times.- III Statistical Modelling.- 11 Finding Patterns in Signals.- 12 Linear Regression Models.- 13 Generalized Linear Models.- 14 Bayesian Statistics.- Appendices.- A Useful Programming Tools.- B Solutions.- C Equations for Confidence Intervals.- D Web Ressources.- Glossary.- Bibliography.- Index.\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743062831447,"sku":"9783030973704","price":71.24,"currency_code":"GBP","in_stock":true}]},{"product_id":"bayes-factors-for-forensic-decision-analyses-with-r-9783031098383","title":"Bayes Factors for Forensic Decision Analyses with","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003ci\u003eBayes Factors for Forensic Decision Analyses with R\u003c\/i\u003e provides a self-contained introduction to computational Bayesian statistics using R. With its primary focus on Bayes factors supported by data sets, this book features an operational perspective, practical relevance, and applicability—keeping theoretical and philosophical justifications limited. It offers a balanced approach to three naturally interrelated topics:\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\u003cli\u003eProbabilistic Inference - Relies on the core concept of Bayesian inferential statistics, to help practicing forensic scientists in the logical and balanced evaluation of the weight of evidence.\u003c\/li\u003e\u003c\/ul\u003e\u003cul\u003e\u003cli\u003eDecision Making - Features how Bayes factors are interpreted in practical applications to help address questions of decision analysis involving the use of forensic science in the law.\u003c\/li\u003e\u003c\/ul\u003e\u003cul\u003e\u003cli\u003eOperational Relevance - Combines inference and decision, backed up with practical examples and complete sample code in R, including sensitivity analyses and discussion on how to interpret results in context.\u003c\/li\u003e\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eOver the past decades, probabilistic methods have established a firm position as a reference approach for the management of uncertainty in virtually all areas of science, including forensic science, with Bayes' theorem providing the fundamental logical tenet for assessing how new information—scientific evidence—ought to be weighed. Central to this approach is the Bayes factor, which clarifies the evidential meaning of new information, by providing a measure of the change in the odds in favor of a proposition of interest, when going from the prior to the posterior distribution. Bayes factors should guide the scientist's thinking about the value of scientific evidence and form the basis of logical and balanced reporting practices, thus representing essential foundations for rational decision making under uncertainty.\u003c\/p\u003e\u003cp\u003eThis book would be relevant to students, practitioners, and applied statisticians interested in inference and decision analyses in the critical field of forensic science. It could be used to support practical courses on Bayesian statistics and decision theory at both undergraduate and graduate levels, and will be of equal interest to forensic scientists and practitioners of Bayesian statistics for driving their evaluations and the use of R for their purposes.\u003c\/p\u003e\u003cp\u003eThis book is Open Access.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePart I - Introduction to the Bayes Factor (Likelihood Ratio)\u003c\/b\u003ePresents the principal statistic discussed throughout this book:  the Bayes factor, in the context of forensic science, more often known as the likelihood ratio.  Subsections of this part:\u003cbr\u003e\u003cul\u003e\n\u003cli\u003eclarify the different roles (known as, respectively, the ‘investigative’ and ‘evaluative’ role) that forensic scientists may assume in their daily work\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003earticulate the reasons why forensic scientists should adhere to a Bayesian framework of inference in order to ensure coherence in their inferential and decision-making tasks\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eformally describe what the Bayes factor is and how it relates to coherent decision analysis\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003edescribe the advantages that Bayes factors offer in assessing, articulating and communicating the value of scientific evidence in general, and in legal proceedings in particular\u003cbr\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cb\u003ePart II - Bayes Factor for I\u003c\/b\u003envestigative PurposesDeals with a peculiar task of the forensic scientist, known as the ‘investigative mode’ (i.e., one of the two main modes of functioning introduced in Part I). That is, in forensic settings, it may well be the case that a potential source (i.e., a suspect) is not available for comparative purposes, in particular in early stages of the legal process.  Notwithstanding, data and measurements on recovered material (e.g., seized on a crime scene) can be used for an investigative purpose.  In this mode of working, scientists can offer to investigative authorities (or, in a more general perspective, mandating parties) information to help discriminate between general propositions concerning, for instance, the characterizing features of the source that left the recovered material (e.g., gender, externally visible traits such as hair and eye color, handedness, etc.).  At this stage in the process, the scientist tries to help answer questions such as ‘what  happened?’ in the case under investigation, or ‘what can we infer about the offender?’. In this context, the Bayes factor can be used as a statistic to measure and help decide how to classify, for example, objects and substances on which measurements have been made. This use of the Bayes factor will be explained through practical examples involving topics such as handwriting characteristics, toner from printers in questioned document examination, drugs of abuse, toxicology, forensic anthropology and forensic DNA profiling (listing is not exhaustive and may evolve during the writing of the book).  Both univariate and multivariate data will be considered, with or without replicates, and involving different statistical distributions (i.e. Binomial, Poisson, Normal, etc.).  The examples refer to realistic forensic applications as they may be encountered in judicial contexts and the forensic practitioner’s own field of activity.  Data will be selected from published literature or from the author’s own records.  R sample code will be specified and explanations will be included on how to interpret results in context and convey their meaning appropriately.\u003cbr\u003e\u003cbr\u003e\u003cb\u003ePart III - Bayes Factor for Evaluative Purposes\u003c\/b\u003eFocuses on the scientist’s role in a more advanced stage of the legal process.  That is, situations in which the evaluation of scientific findings will take into account a potential source of the recovered material (e.g., a suspect or an  object\/tool).  This kind of reporting is typically required when scientists need to communicate their results for use at trial.  It is of utmost importance at this juncture that scientists express the value of the observed data and findings under competing hypotheses, focusing on a potential (i.e., known) source versus an  alternative source (e.g., propositions such as ‘the recovered item comes from the same source as the control material’, and ‘the recovered item is from a source that is different from that of the control material’).  The Bayes factor is the central inferential concept for such expressions of weight of evidence.  In this part of the book, too, examples will be chosen with the intention to reflect realistic scenarios as they may arise in current judicial practice.  In particular, the outline will consider uni- and multi­-variate data from scenarios related to microtraces (e.g., glass and paint fragments), handwriting and drugs of abuse.  Besides computational R code, this chapter will also include (i) sensitivity analyses to provide readers with a means to further investigate the properties of the proposed evaluative procedures based on the Bayes factor, and (ii) decision theoretic extensions to outline how to interface expressions of weight of evidence with the broader perspective of coherent decision-making.  \u003cbr\u003e\u003cbr\u003e\u003cb\u003ePart IV - Conclusion\u003c\/b\u003eSummarizes the key messages developed throughout this book, emphasizing (i) the contribution of an extended use of the Bayes factor in a normative decision framework, and (ii) the role of the Bayes factor as the relevant statistic for both investigative and evaluative tasks that characterize current forensic science.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743069352279,"sku":"9783031098383","price":35.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031098383.jpg?v=1720063974"},{"product_id":"the-fundamentals-of-people-analytics-with-applications-in-r-9783031286766","title":"The Fundamentals of People Analytics: With","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis open access book prepares current and aspiring analytics professionals to effectively address this need by curating key concepts spanning the entire analytics lifecycle, along with step-by-step instructions for their applications to real-world problems, using ubiquitous and freely available open-source software. This book does not assume prior knowledge of statistics, how to query databases, or how to write performant code; early chapters include an introduction to R and SQL as well as an overview of statistical foundations.\u003cbr\u003e\u003cbr\u003eHuman capital is an organization’s most important asset. Without the knowledge and skills of people, an organization can accomplish nothing. The acquisition, development, and retention of critical talent has become increasingly more complex and challenging, and organizations are making significant investments to gain a deeper, data-informed understanding of organizational phenomena impacting the bottom line. \u003cbr\u003e\u003cbr\u003eBy the end of this book, readers will be able to: \u003cbr\u003e• Design and conduct empirical research \u003cbr\u003e• Query and wrangle data using SQL \u003cbr\u003e• Profile, clean, and analyze data using R \u003cbr\u003e• Apply appropriate statistical and ML models to a range of people analytics use cases \u003cbr\u003e• Package and present analyses to communicate impactful insights to stakeholders\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Getting Started.- 2. Introduction to R.- 3. Introduction to SQL.- 4. Research Design.- 5. Measurement \u0026amp; Sampling.- 6. Data Preparation.- 7. Descriptive Statistics.- 8. Statistical Inference.- 9. Analysis of Differences.- 10. Linear Regression.- 11. Linear Model Extensions.- 12. Logistic Regression.- 13. Predictive Modeling.- 14. Unsupervised Learning.- 15. Data Visualization.- 16. Data Storytelling.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743079248215,"sku":"9783031286766","price":31.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031286766.jpg?v=1720064019"},{"product_id":"optimal-experimental-design-a-concise-introduction-for-researchers-9783031359170","title":"Optimal Experimental Design: A Concise","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis textbook provides a concise introduction to optimal experimental design and efficiently prepares the reader for research in the area. It presents the common concepts and techniques for linear and nonlinear models as well as Bayesian optimal designs. The last two chapters are devoted to particular themes of interest, including recent developments and hot topics in optimal experimental design, and real-world applications. Numerous examples and exercises are included, some of them with solutions or hints, as well as references to the existing software for computing designs. The book is primarily intended for graduate students and young researchers in statistics and applied mathematics who are new to the field of optimal experimental design. Given the applications and the way concepts and results are introduced, parts of the text will also appeal to engineers and other applied researchers.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface.- Motivating Introduction.- Linear Models.- Nonlinear Models.- Bayesian Optimal Designs.- Hot Topics.- Real Case Examples.- Appendices.- References.- Index.\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743083376983,"sku":"9783031359170","price":59.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"regression-modeling-strategies-with-applications-to-linear-models-logistic-and-ordinal-regression-and-survival-analysis-9783319194240","title":"Regression Modeling Strategies: With Applications","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modelling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasises problem solving strategies that address the many issues arising when developing multi-variable models using real data and not standard textbook examples. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eRegression Modelling Strategies \u003c\/i\u003epresents full-scale case studies of non-trivial data-sets instead of over-simplified illustrations of each method. These case studies use freely available R functions that make the multiple imputation, model building, validation and interpretation tasks described in the book relatively easy to do. Most of the methods in this text apply to all regression models, but special emphasis is given to multiple regression using generalised least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models and the Cox semi parametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression.\u003c\/p\u003eAs in the first edition, this text is intended for Masters' or PhD. level graduate students who have had a general introductory probability and statistics course and who are well versed in ordinary multiple regression and intermediate algebra. The book will also serve as a reference for data analysts and statistical methodologists, as it contains an up-to-date survey and bibliography of modern statistical modelling techniques. \u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“The aim and scope of this edition to provide graduate students and professional and early career researchers with insights, understandings and working knowledge of regression modelling. … . The book is sequentially organized and well structured and many chapters are self-contained. It includes many useful topics and techniques for graduate .students and researchers alike. This book can be used as a textbook and equally as a reference book.” (Technometrics, Vol. 58 (2), February, 2016)\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction.- General Aspects of Fitting Regression Models.- Missing Data.- Multivariable Modeling Strategies.- Describing, Resampling, Validating and Simplifying the Model.- R Software.- Modeling Longitudinal Responses using Generalized Least Squares.- Case Study in Data Reduction.- Overview of Maximum Likelihood Estimation.- Binary Logistic Regression.- Binary Logistic Regression Case Study 1.- Logistic Model Case Study 2: Survival of Titanic Passengers.- Ordinal Logistic Regression.- Case Study in Ordinal Regression, Data Reduction and Penalization.- Regression Models for Continuous Y and Case Study in Ordinal Regression.- Transform-Both-Sides Regression.- Introduction to Survival Analysis.- Parametric Survival Models.- Case Study in Parametric Survival Modeling and Model Approximation.- Cox Proportional Hazards Regression Model.- Case Study in Cox Regression.- Appendix.    \u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743092617559,"sku":"9783319194240","price":94.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"a-tiny-handbook-of-r-9783642179792","title":"A Tiny Handbook of R","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis Brief provides a roadmap for the R language and programming environment with signposts to further resources and documentation.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eIntroduction to R.- Data Structures.- Tables and Graphs.- Hypothesis Tests.- Linear Models.","brand":"Springer-Verlag Berlin and Heidelberg GmbH \u0026 Co. KG","offers":[{"title":"Default Title","offer_id":48743134363991,"sku":"9783642179792","price":39.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783642179792.jpg?v=1720064259"},{"product_id":"statistics-applied-with-excel-data-analysis-is-not-an-art-9783662643181","title":"Statistics Applied With Excel: Data Analysis Is","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book shows you how to analyze data sets systematically and to use Excel 2019 to extract information from data almost effortlessly. Both are (not) an art!\u003c\/p\u003e\u003cp\u003eThe statistical methods are presented and discussed using a single data set. This makes it clear how the methods build on each other and gradually more and more information can be extracted from the data. The Excel functions used are explained in detail - the procedure can be easily transferred to other data sets. \u003c\/p\u003eVarious didactic elements facilitate orientation and working with the book: At the checkpoints, the most important aspects from each chapter are briefly summarized. In the freak knowledge section, more advanced aspects are addressed to whet the appetite for more. All examples are calculated with hand and Excel. Numerous applications and solutions as well as further data sets are available on the author's internet platform. \u003cp\u003e \u003c\/p\u003e\u003cp\u003eThis book is a translation of the original German 2\u003csup\u003end\u003c\/sup\u003e edition \u003ci\u003eStatistik angewandt mit Excel \u003c\/i\u003eby Franz Kronthaler, published by Springer-Verlag GmbH Germany, part of Springer Nature in 2021. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart 1 - Basic knowledge and tools to apply statistics.- Statistics is fun.- Excel: A brief introduction and the statistical possibilities.- Part 2 - Describe, nothing but describe.- Mean values: How people and objects behave on average.- Scatter: The deviation from average behavior.- Graphs: The possibility to represent data visually.- Correlation: Of the correlation.- Ratio and index numbers: The chance to generate new things from old knowledge.- Part 3 - From Few to All.- Of Data and the Truth.- Hypotheses: Just a specification of the question.- Normal distribution and other test distributions.- Hypothesis testing: What is Valid?.- Part 4 - Procedures for Testing Hypotheses.- The Mean Test.- The Test for Difference of Means in Independent Samples.- The Test for Difference of Means in Dependent Samples.- The Analysis of Variance for Testing for Group Differences in More than Two Groups.- The Test for Correlation in Metric, Ordinal, and Nominal Data.- Further Test Procedures for Nominal Variables.- Summary Part IV - Overview of testing procedures.- Part 5 - Regression analysis.- The linear single regression.- The multiple regression analysis.- Part 6 - What's next.- Brief report on a research question.- Further statistical procedures.- Interesting and further statistics books.- Another data set to practice on - Intern of a company.- Appendix.","brand":"Springer-Verlag Berlin and Heidelberg GmbH \u0026 Co. KG","offers":[{"title":"Default Title","offer_id":48743142752599,"sku":"9783662643181","price":61.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783662643181.jpg?v=1720064297"},{"product_id":"bayesian-statistical-modeling-with-stan-r-and-python-9789811947544","title":"Bayesian Statistical Modeling with Stan, R, and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book provides a highly practical introduction to Bayesian statistical modeling with Stan, which has become the most popular probabilistic programming language.\u003c\/p\u003e\u003cp\u003eThe book is divided into four parts. The first part reviews the theoretical background of modeling and Bayesian inference and presents a modeling workflow that makes modeling more engineering than art. The second part discusses the use of Stan, CmdStanR, and CmdStanPy from the very beginning to basic regression analyses. The third part then introduces a number of probability distributions, nonlinear models, and hierarchical (multilevel) models, which are essential to mastering statistical modeling. It also describes a wide range of frequently used modeling techniques, such as censoring, outliers, missing data, speed-up, and parameter constraints, and discusses how to lead convergence of MCMC. Lastly, the fourth part examines advanced topics for real-world data: longitudinal data analysis, state space models, spatial data analysis, Gaussian processes, Bayesian optimization, dimensionality reduction, model selection, and information criteria, demonstrating that Stan can solve any one of these problems in as little as 30 lines.\u003c\/p\u003e\u003cp\u003eUsing numerous easy-to-understand examples, the book explains key concepts, which continue to be useful when using future versions of Stan and when using other statistical modeling tools. The examples do not require domain knowledge and can be generalized to many fields. The book presents full explanations of code and math formulas, enabling readers to extend models for their own problems. All the code and data are on GitHub.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface\u003cbr\u003ePart I: Introduction Chapter 1: Overview of Statistical Modeling and StanChapter 2: Review of Bayesian InferenceChapter 3: Before Starting Statistical Modeling\u003cbr\u003ePart II: Introduction of StanChapter 4: Start with Stan, RStan and PyStanChapter 5: Elementary Regression and Model Check\u003cbr\u003ePart III: Essential Components and Techniques for ExpertsChapter 6: Introduction of Distributions from Modeling ViewpointsChapter 7: Issues of RegressionChapter 8: Nonlinear ModelChapter 9: Hierarchical ModelChapter 10: Advanced GrammarsChapter 11: How to Lead ConvergenceChapter 12: Discrete ParametersChapter 13: Usage of MCMC Samples\u003cbr\u003ePart IV: Advanced  Topics for Real-world DataChapter 14: Longitudinal Data Analysis with State Space Model Chapter 15: Spatial Data Analysis with Markov Field ModelChapter 16: Survival AnalysisChapter 17: Causal InferenceChapter 18: Model selection\u003cbr\u003eAppendix: Differences between Stan and BUGSReferenceIndex","brand":"Springer Verlag, Singapore","offers":[{"title":"Default Title","offer_id":48743294337367,"sku":"9789811947544","price":98.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789811947544.jpg?v=1720064964"},{"product_id":"getting-started-in-mathematical-life-sciences-from-matlab-programming-to-computer-simulations-9789811982569","title":"Getting Started in Mathematical Life Sciences:","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book helps the reader make use of the mathematical models of biological phenomena starting from the basics of programming and computer simulation. Computer simulations based on a mathematical model enable us to find a novel biological mechanism and predict an unknown biological phenomenon. Mathematical biology could further expand the progress of modern life sciences. Although many biologists are interested in mathematical biology, they do not have experience in mathematics and computer science. An educational course that combines biology, mathematics, and computer science is very rare to date. Published books for mathematical biology usually explain the theories of established mathematical models, but they do not provide a practical explanation for how to solve the differential equations included in the models, or to establish such a model that fits with a phenomenon of interest. \u003cbr\u003eMATLAB is an ideal programming platform for the beginners of computer science. This book starts from the very basics about how to write a programming code for MATLAB (or Octave), explains how to solve ordinary and partial differential equations, and how to apply mathematical models to various biological phenomena such as diabetes, infectious diseases, and heartbeats. Some of them are original models, newly developed for this book. Because MATLAB codes are embedded and explained throughout the book, it will be easy to catch up with the text. In the final chapter, the book focuses on the mathematical model of the proneural wave, a phenomenon that guarantees the sequential differentiation of neurons in the brain. This model was published as a paper from the author’s lab (Sato et al., PNAS 113, E5153, 2016), and was intensively explained in the book chapter “Notch Signaling in Embryology and Cancer”, published by Springer in 2020. \u003cbr\u003eThis book provides the reader who has a biological background with invaluable opportunities to learn and practice mathematical biology.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e1. Preparation.- 2. Introduction to MATLAB   programming .-  3. Simulating time variations in   life phenomena.-  4. Simulating temporal and   spatial changes in biological phenomena.\u003cbr\u003e","brand":"Springer Verlag, Singapore","offers":[{"title":"Default Title","offer_id":48743295385943,"sku":"9789811982569","price":39.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789811982569.jpg?v=1720064971"},{"product_id":"phylogenetic-comparative-methods-in-r-9780691219035","title":"Phylogenetic Comparative Methods in R","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":48865550074199,"sku":"9780691219035","price":40.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691219035.jpg?v=1722274514"},{"product_id":"patterns-predictions-and-actions-9780691233734","title":"Patterns Predictions and Actions","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"A thorough, very clearly written overview on the subject of machine learning for those with the prerequisite mathematical tools of calculus, linear algebra and probability.\"\u003cb\u003e---Jonathan Shock, \u003ci\u003eMathemafrica\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e\"Valuable.\"\u003cb\u003e---J. Brzezinski, \u003ci\u003eChoice\u003c\/i\u003e\u003c\/b\u003e","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":48865552499031,"sku":"9780691233734","price":45.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691233734.jpg?v=1722274526"},{"product_id":"an-introduction-to-statistical-learning-9781071614204","title":"An Introduction to Statistical Learning","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface.- 1 Introduction.- 2 Statistical Learning.- 3 Linear Regression.- 4 Classification.- 5 Resampling Methods.- 6 Linear Model Selection and Regularization.- 7 Moving Beyond Linearity.- 8 Tree-Based Methods.- 9 Support Vector Machines.- 10 Deep Learning.- 11 Survival Analysis and Censored Data.- 12 Unsupervised Learning.- 13 Multiple Testing.- Index.","brand":"Springer-Verlag New York Inc.","offers":[{"title":"Default Title","offer_id":48866324709719,"sku":"9781071614204","price":49.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781071614204.jpg?v=1722278136"},{"product_id":"tinspire-for-dummies-9781118004661","title":"TINspire For Dummies","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe updated guide to the newest graphing calculator from Texas Instruments    The TI-Nspire graphing calculator is popular among high school and college students as a valuable tool for calculus, AP calculus, and college-level algebra courses. Its use is allowed on the major college entrance exams.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction 1\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I: Getting to Know Your TI-Nspire Handheld 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 1: Using TI-Nspire for the First Time 11\u003c\/p\u003e \u003cp\u003eChapter 2: Understanding the Document Structure 25\u003c\/p\u003e \u003cp\u003eChapter 3: Creating and Editing Documents 37\u003c\/p\u003e \u003cp\u003eChapter 4: Linking Handhelds 47\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: The Calculator Application 51\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 5: Entering and Evaluating Expressions 53\u003c\/p\u003e \u003cp\u003eChapter 6: Working with Variables 69\u003c\/p\u003e \u003cp\u003eChapter 7: Using the Calculator Application with Other Applications 77\u003c\/p\u003e \u003cp\u003eChapter 8: Using the Calculator Application with TI-Nspire CAS 85\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III: The Graphs Application 99\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 9: Working with Graphs 101\u003c\/p\u003e \u003cp\u003eChapter 10: Using the Graphs Application to Do Calculus 131\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV: The Geometry Application 135\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 11: Working with Geometric Objects 137\u003c\/p\u003e \u003cp\u003eChapter 12: Using an Analytic Window in the Geometry Application 159\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V: The Lists \u0026amp; Spreadsheet Application 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 13: Applying What You Already Know about Spreadsheets 167\u003c\/p\u003e \u003cp\u003eChapter 14: Working with Data 177\u003c\/p\u003e \u003cp\u003eChapter 15: Constructing Scatter Plots and Performing Regressions 189\u003c\/p\u003e \u003cp\u003eChapter 16: Manual and Automatic Data Capture 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VI: The Data \u0026amp; Statistics and Vernier DataQuest Applications 209\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 17: Constructing Statistical Graphs 211\u003c\/p\u003e \u003cp\u003eChapter 18: Working with Single-Variable Data 215\u003c\/p\u003e \u003cp\u003eChapter 19: Working with Two-Variable Data 227\u003c\/p\u003e \u003cp\u003eChapter 20: Data Collection 237\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VII: The Notes Application 249\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 21: The Why and How of Using Notes 251\u003c\/p\u003e \u003cp\u003eChapter 22: Taking Notes to a Whole New Level 255\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VIII: TI-Nspire Computer Software 261\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 23: Getting Started with TI-Nspire Computer Software 263\u003c\/p\u003e \u003cp\u003eChapter 24: File Creation and Display in Documents Workspace 271\u003c\/p\u003e \u003cp\u003eChapter 25: File Management with Content Workspace 287\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IX: The Part of Tens 295\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 26: Ten Great Tips and Shortcuts 297\u003c\/p\u003e \u003cp\u003eChapter 27: Ten Common Problems Resolved 305\u003c\/p\u003e \u003cp\u003eAppendix A: Safeguarding in Press-to-Test Mode 311\u003c\/p\u003e \u003cp\u003eAppendix B: Basic Programming 315\u003c\/p\u003e \u003cp\u003eAppendix C: Working with Libraries 331\u003c\/p\u003e \u003cp\u003eIndex 337\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866363441495,"sku":"9781118004661","price":17.09,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781118004661.jpg?v=1722278294"},{"product_id":"statistical-data-cleaning-with-applications-in-r-9781118897157","title":"Statistical Data Cleaning with Applications in R","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eForeword xi\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Data Cleaning 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 The Statistical Value Chain 1\u003c\/p\u003e \u003cp\u003e1.1.1 Raw Data 2\u003c\/p\u003e \u003cp\u003e1.1.2 Input Data 2\u003c\/p\u003e \u003cp\u003e1.1.3 Valid Data 3\u003c\/p\u003e \u003cp\u003e1.1.4 Statistics 3\u003c\/p\u003e \u003cp\u003e1.1.5 Output 3\u003c\/p\u003e \u003cp\u003e1.2 Notation and Conventions Used in this Book 3\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 A Brief Introduction to R 5\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 R on the Command Line 5\u003c\/p\u003e \u003cp\u003e2.1.1 Getting Help and Learning R 6\u003c\/p\u003e \u003cp\u003e2.2 Vectors 7\u003c\/p\u003e \u003cp\u003e2.2.1 Computing with Vectors 9\u003c\/p\u003e \u003cp\u003e2.2.2 Arrays and Matrices 10\u003c\/p\u003e \u003cp\u003e2.3 Data Frames 11\u003c\/p\u003e \u003cp\u003e2.3.1 The Formula-Data Interface 12\u003c\/p\u003e \u003cp\u003e2.3.2 Selecting Rows and Columns; Boolean Operators 12\u003c\/p\u003e \u003cp\u003e2.3.3 Selection with Indices 13\u003c\/p\u003e \u003cp\u003e2.3.4 Data Frame Manipulation:The dplyr Package 14\u003c\/p\u003e \u003cp\u003e2.4 Special Values 15\u003c\/p\u003e \u003cp\u003e2.4.1 Missing Values 17\u003c\/p\u003e \u003cp\u003e2.5 Getting Data into and out of R 18\u003c\/p\u003e \u003cp\u003e2.5.1 File Paths in R 19\u003c\/p\u003e \u003cp\u003e2.5.2 Formats Provided by Packages 20\u003c\/p\u003e \u003cp\u003e2.5.3 Reading Data from a Database 20\u003c\/p\u003e \u003cp\u003e2.5.4 Working with Data External to R 21\u003c\/p\u003e \u003cp\u003e2.6 Functions 21\u003c\/p\u003e \u003cp\u003e2.6.1 Using Functions 22\u003c\/p\u003e \u003cp\u003e2.6.2 Writing Functions 22\u003c\/p\u003e \u003cp\u003e2.7 Packages Used in this Book 23\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Technical Representation of Data 27\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Numeric Data 28\u003c\/p\u003e \u003cp\u003e3.1.1 Integers 28\u003c\/p\u003e \u003cp\u003e3.1.2 Integers in R 30\u003c\/p\u003e \u003cp\u003e3.1.3 Real Numbers 31\u003c\/p\u003e \u003cp\u003e3.1.4 Double Precision Numbers 31\u003c\/p\u003e \u003cp\u003e3.1.5 The Concept of Machine Precision 33\u003c\/p\u003e \u003cp\u003e3.1.6 Consequences ofWorking with Floating Point Numbers 34\u003c\/p\u003e \u003cp\u003e3.1.7 Dealing with the Consequences 35\u003c\/p\u003e \u003cp\u003e3.1.8 Numeric Data in R 37\u003c\/p\u003e \u003cp\u003e3.2 Text Data 38\u003c\/p\u003e \u003cp\u003e3.2.1 Terminology and Encodings 38\u003c\/p\u003e \u003cp\u003e3.2.2 Unicode 39\u003c\/p\u003e \u003cp\u003e3.2.3 Some Popular Encodings 40\u003c\/p\u003e \u003cp\u003e3.2.4 Textual Data in R: Objects of Class Character 43\u003c\/p\u003e \u003cp\u003e3.2.5 Encoding in R 44\u003c\/p\u003e \u003cp\u003e3.2.6 Reading andWriting of Data with Non-Local Encoding 46\u003c\/p\u003e \u003cp\u003e3.2.7 Detecting Encoding 48\u003c\/p\u003e \u003cp\u003e3.2.8 Collation and Sorting 49\u003c\/p\u003e \u003cp\u003e3.3 Times and Dates 50\u003c\/p\u003e \u003cp\u003e3.3.1 AIT, UTC, and POSIX Seconds Since the Epcoch 50\u003c\/p\u003e \u003cp\u003e3.3.2 Time and Date Notation 52\u003c\/p\u003e \u003cp\u003e3.3.3 Time and Date Storage in R 54\u003c\/p\u003e \u003cp\u003e3.3.4 Time and Date Conversion in R 55\u003c\/p\u003e \u003cp\u003e3.3.5 Leap Days, Time Zones, and Daylight Saving Times 57\u003c\/p\u003e \u003cp\u003e3.4 Notes on Locale Settings 58\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Data Structure 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 61\u003c\/p\u003e \u003cp\u003e4.2 Tabular Data 61\u003c\/p\u003e \u003cp\u003e4.2.1 data.frame 61\u003c\/p\u003e \u003cp\u003e4.2.2 Databases 62\u003c\/p\u003e \u003cp\u003e4.2.3 dplyr 64\u003c\/p\u003e \u003cp\u003e4.3 Matrix Data 65\u003c\/p\u003e \u003cp\u003e4.4 Time Series 66\u003c\/p\u003e \u003cp\u003e4.5 Graph Data 68\u003c\/p\u003e \u003cp\u003e4.6 Web Data 69\u003c\/p\u003e \u003cp\u003e4.6.1 Web Scraping 69\u003c\/p\u003e \u003cp\u003e4.6.2 Web API 70\u003c\/p\u003e \u003cp\u003e4.7 Other Data 72\u003c\/p\u003e \u003cp\u003e4.8 Tidying Tabular Data 72\u003c\/p\u003e \u003cp\u003e4.8.1 Variable Per Column 74\u003c\/p\u003e \u003cp\u003e4.8.2 Single Observation Stored in Multiple Tables 75\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Cleaning Text Data 77\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Character Normalization 78\u003c\/p\u003e \u003cp\u003e5.1.1 Encoding Conversion and Unicode Normalization 78\u003c\/p\u003e \u003cp\u003e5.1.2 Character Conversion and Transliteration 80\u003c\/p\u003e \u003cp\u003e5.2 Pattern Matching with Regular Expressions 81\u003c\/p\u003e \u003cp\u003e5.2.1 Basic Regular Expressions 82\u003c\/p\u003e \u003cp\u003e5.2.2 Practical Regular Expressions 85\u003c\/p\u003e \u003cp\u003e5.2.3 Generating Regular Expressions in R 92\u003c\/p\u003e \u003cp\u003e5.3 Common String Processing Tasks in R 93\u003c\/p\u003e \u003cp\u003e5.4 Approximate Text Matching 98\u003c\/p\u003e \u003cp\u003e5.4.1 String Metrics 100\u003c\/p\u003e \u003cp\u003e5.4.2 String Metrics and Approximate Text Matching in R 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Data Validation 119\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 119\u003c\/p\u003e \u003cp\u003e6.2 A First Look at the validate Package 120\u003c\/p\u003e \u003cp\u003e6.2.1 Quick Checks with check_that 120\u003c\/p\u003e \u003cp\u003e6.2.2 The BasicWorkflow: validator and confront 122\u003c\/p\u003e \u003cp\u003e6.2.3 A Little Background on validate and DSLs 124\u003c\/p\u003e \u003cp\u003e6.3 Defining Data Validation 125\u003c\/p\u003e \u003cp\u003e6.3.1 Formal Definition of Data Validation 126\u003c\/p\u003e \u003cp\u003e6.3.2 Operations on Validation Functions 128\u003c\/p\u003e \u003cp\u003e6.3.3 Validation and Missing Values 130\u003c\/p\u003e \u003cp\u003e6.3.4 Structure of Validation Functions 131\u003c\/p\u003e \u003cp\u003e6.3.5 Demarcating Validation Rules in validate 132\u003c\/p\u003e \u003cp\u003e6.4 A Formal Typology of Data Validation Functions 134\u003c\/p\u003e \u003cp\u003e6.4.1 A Closer Look at Measurement 134\u003c\/p\u003e \u003cp\u003e6.4.2 Classification of Validation Rules 135\u003c\/p\u003e \u003cp\u003e6.5 Validating Data with the validate Package 137\u003c\/p\u003e \u003cp\u003e6.5.1 Validation Rules in the Console and the validator Object 137\u003c\/p\u003e \u003cp\u003e6.5.2 Validating in the Pipeline 139\u003c\/p\u003e \u003cp\u003e6.5.3 Raising Errors orWarnings 140\u003c\/p\u003e \u003cp\u003e6.5.4 Tolerance for Testing Linear Equalities 140\u003c\/p\u003e \u003cp\u003e6.5.5 Setting and Resetting Options 141\u003c\/p\u003e \u003cp\u003e6.5.6 Importing and Exporting Validation Rules from and to File 142\u003c\/p\u003e \u003cp\u003e6.5.7 Checking Variable Types and Metadata 145\u003c\/p\u003e \u003cp\u003e6.5.8 Checking Value Ranges and Code Lists 146\u003c\/p\u003e \u003cp\u003e6.5.9 Checking In-Record Consistency Rules 146\u003c\/p\u003e \u003cp\u003e6.5.10 Checking Cross-Record Validation Rules 148\u003c\/p\u003e \u003cp\u003e6.5.11 Checking Functional Dependencies 149\u003c\/p\u003e \u003cp\u003e6.5.12 Cross-Dataset Validation 150\u003c\/p\u003e \u003cp\u003e6.5.13 Macros, Variable Groups, Keys 152\u003c\/p\u003e \u003cp\u003e6.5.14 Analyzing Output: validation Objects 152\u003c\/p\u003e \u003cp\u003e6.5.15 Output Dimensionality and Output Selection 155\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Localizing Errors in Data Records 157\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Error Localization 157\u003c\/p\u003e \u003cp\u003e7.2 Error Localization with R 160\u003c\/p\u003e \u003cp\u003e7.2.1 The Errorlocate Package 160\u003c\/p\u003e \u003cp\u003e7.3 Error Localization as MIP-Problem 163\u003c\/p\u003e \u003cp\u003e7.3.1 Error Localization and Mixed-Integer Programming 163\u003c\/p\u003e \u003cp\u003e7.3.2 Linear Restrictions 164\u003c\/p\u003e \u003cp\u003e7.3.3 Categorical Restrictions 165\u003c\/p\u003e \u003cp\u003e7.3.4 Mixed-Type Restrictions 167\u003c\/p\u003e \u003cp\u003e7.4 Numerical Stability Issues 170\u003c\/p\u003e \u003cp\u003e7.4.1 A Short Overview of MIP Solving 170\u003c\/p\u003e \u003cp\u003e7.4.2 Scaling Numerical Records 172\u003c\/p\u003e \u003cp\u003e7.4.3 Setting NumericalThreshold Values 173\u003c\/p\u003e \u003cp\u003e7.5 Practical Issues 174\u003c\/p\u003e \u003cp\u003e7.5.1 Setting ReliabilityWeights 174\u003c\/p\u003e \u003cp\u003e7.5.2 Simplifying Conditional Validation Rules 176\u003c\/p\u003e \u003cp\u003e7.6 Conclusion 180\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Rule Set Maintenance and Simplification 183\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Quality of Validation Rules 183\u003c\/p\u003e \u003cp\u003e8.1.1 Completeness 183\u003c\/p\u003e \u003cp\u003e8.1.2 Superfluous Rules and Infeasibility 184\u003c\/p\u003e \u003cp\u003e8.2 Rules in the Language of Logic 184\u003c\/p\u003e \u003cp\u003e8.2.1 Using Logic to Rewrite Rules 185\u003c\/p\u003e \u003cp\u003e8.3 Rule Set Issues 186\u003c\/p\u003e \u003cp\u003e8.3.1 Infeasible Rule Set 186\u003c\/p\u003e \u003cp\u003e8.3.2 Fixed Value 187\u003c\/p\u003e \u003cp\u003e8.3.3 Redundant Rule 188\u003c\/p\u003e \u003cp\u003e8.3.4 Nonrelaxing Clause 189\u003c\/p\u003e \u003cp\u003e8.3.5 Nonconstraining Clause 189\u003c\/p\u003e \u003cp\u003e8.4 Detection and Simplification Procedure 190\u003c\/p\u003e \u003cp\u003e8.4.1 Mixed-Integer Programming 190\u003c\/p\u003e \u003cp\u003e8.4.2 Detecting Feasibility 191\u003c\/p\u003e \u003cp\u003e8.4.3 Finding Rules Causing Infeasibility 191\u003c\/p\u003e \u003cp\u003e8.4.4 Detecting Conflicting Rules 191\u003c\/p\u003e \u003cp\u003e8.4.5 Detect Partial Infeasibility 192\u003c\/p\u003e \u003cp\u003e8.4.6 Detect Fixed Values 192\u003c\/p\u003e \u003cp\u003e8.4.7 Detect Nonrelaxing Clauses 192\u003c\/p\u003e \u003cp\u003e8.4.8 Detecting Nonconstraining Clauses 193\u003c\/p\u003e \u003cp\u003e8.4.9 Detecting Redundant Rules 193\u003c\/p\u003e \u003cp\u003e8.5 Conclusion 194\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Methods Based on Models for Domain Knowledge 195\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Correction with Data Modifying Rules 195\u003c\/p\u003e \u003cp\u003e9.1.1 Modifying Functions 196\u003c\/p\u003e \u003cp\u003e9.1.2 A Class of Modifying Functions on Numerical Data 201\u003c\/p\u003e \u003cp\u003e9.2 Rule-Based Correction with dcmodify 205\u003c\/p\u003e \u003cp\u003e9.2.1 Reading Rules from File 206\u003c\/p\u003e \u003cp\u003e9.2.2 Modifying Rule Syntax 207\u003c\/p\u003e \u003cp\u003e9.2.3 Missing Values 208\u003c\/p\u003e \u003cp\u003e9.2.4 Sequential and Sequence-Independent Execution 208\u003c\/p\u003e \u003cp\u003e9.2.5 Options Settings Management 209\u003c\/p\u003e \u003cp\u003e9.3 Deductive Correction 209\u003c\/p\u003e \u003cp\u003e9.3.1 Correcting Typing Errors in Numeric Data 209\u003c\/p\u003e \u003cp\u003e9.3.2 Deductive Imputation Using Linear Restrictions 213\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Imputation and Adjustment 219\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Missing Data 219\u003c\/p\u003e \u003cp\u003e10.1.1 Missing Data Mechanisms 219\u003c\/p\u003e \u003cp\u003e10.1.2 Visualizing and Testing for Patterns in Missing Data Using R 220\u003c\/p\u003e \u003cp\u003e10.2 Model-Based Imputation 224\u003c\/p\u003e \u003cp\u003e10.3 Model-Based Imputation in R 226\u003c\/p\u003e \u003cp\u003e10.3.1 Specifying ImputationMethods with simputation 226\u003c\/p\u003e \u003cp\u003e10.3.2 Linear Regression-Based Imputation 227\u003c\/p\u003e \u003cp\u003e10.3.3 M-Estimation 230\u003c\/p\u003e \u003cp\u003e10.3.4 Lasso, Ridge, and Elasticnet Regression 231\u003c\/p\u003e \u003cp\u003e10.3.5 Classification and Regression Trees 232\u003c\/p\u003e \u003cp\u003e10.3.6 Random Forest 235\u003c\/p\u003e \u003cp\u003e10.4 Donor Imputation with R 236\u003c\/p\u003e \u003cp\u003e10.4.1 Random and Sequential Hot Deck Imputation 237\u003c\/p\u003e \u003cp\u003e10.4.2 k Nearest Neighbors and Predictive Mean Matching 238\u003c\/p\u003e \u003cp\u003e10.5 Other Methods in the simputation Package 239\u003c\/p\u003e \u003cp\u003e10.6 Imputation Based on the EM Algorithm 240\u003c\/p\u003e \u003cp\u003e10.6.1 The EM Algorithm 241\u003c\/p\u003e \u003cp\u003e10.6.2 EM Imputation Assuming the Multivariate Normal Distribution 243\u003c\/p\u003e \u003cp\u003e10.7 Sampling Variance under Imputation 244\u003c\/p\u003e \u003cp\u003e10.8 Multiple Imputations 246\u003c\/p\u003e \u003cp\u003e10.8.1 Multiple Imputation Based on the EM Algorithm 248\u003c\/p\u003e \u003cp\u003e10.8.2 The Amelia Package 249\u003c\/p\u003e \u003cp\u003e10.8.3 Multivariate Imputation with Chained Equations (Mice) 252\u003c\/p\u003e \u003cp\u003e10.8.4 Imputation with the mice Package 254\u003c\/p\u003e \u003cp\u003e10.9 Analytic Approaches to Estimate Variance of Imputation 256\u003c\/p\u003e \u003cp\u003e10.9.1 Imputation as Part of the Estimator 256\u003c\/p\u003e \u003cp\u003e10.10 Choosing an ImputationMethod 257\u003c\/p\u003e \u003cp\u003e10.11 Constraint Value Adjustment 259\u003c\/p\u003e \u003cp\u003e10.11.1 Formal Description 259\u003c\/p\u003e \u003cp\u003e10.11.2 Application to Imputed Data 262\u003c\/p\u003e \u003cp\u003e10.11.3 Adjusting Imputed Values with the rspa Package 263\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Example: A Small Data-Cleaning System 265\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Setup 266\u003c\/p\u003e \u003cp\u003e11.1.1 DeterministicMethods 266\u003c\/p\u003e \u003cp\u003e11.1.2 Error Localization 269\u003c\/p\u003e \u003cp\u003e11.1.3 Imputation 269\u003c\/p\u003e \u003cp\u003e11.1.4 Adjusting Imputed Data 271\u003c\/p\u003e \u003cp\u003e11.2 Monitoring Changes in Data 273\u003c\/p\u003e \u003cp\u003e11.2.1 Data Diff (Daff) 274\u003c\/p\u003e \u003cp\u003e11.2.2 Summarizing Cell Changes 275\u003c\/p\u003e \u003cp\u003e11.2.3 Summarizing Changes in Conformance to Validation Rules 277\u003c\/p\u003e \u003cp\u003e11.2.4 Track Changes in Data Automatically with lumberjack 278\u003c\/p\u003e \u003cp\u003e11.3 Integration and Automation 282\u003c\/p\u003e \u003cp\u003e11.3.1 Using RScript 283\u003c\/p\u003e \u003cp\u003e11.3.2 The docopt Package 283\u003c\/p\u003e \u003cp\u003e11.3.3 Automated Data Cleaning 285\u003c\/p\u003e \u003cp\u003eReferences 287\u003c\/p\u003e \u003cp\u003eIndex 297\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866380382551,"sku":"9781118897157","price":62.65,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781118897157.jpg?v=1722278377"},{"product_id":"statistics-9781118941096","title":"Statistics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003e\u003ci\u003e...I know of no better book of its kind... (Journal of the Royal Statistical Society, Vol 169 (1), January 2006)\u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA revised and updated edition of this bestselling introductory textbook to statistical analysis using the leading free software package R\u003c\/p\u003e \u003cp\u003eThis new edition of a bestselling title offers a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to awide range of disciplines. Step-by-step instructionshelp the non-statistician to fully understand the methodology. The book covers the full range of statistical techniques likely to be needed to analyse the data from research projects, including elementary material like t--tests and chi--squared tests, intermediate methods like regression and analysis of variance, and more advanced techniques like generalized linear modelling.\u003c\/p\u003e \u003cp\u003eIncludes numerous worked examples and exercises within each chapter.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Fundamentals 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEverything Varies 2\u003c\/p\u003e \u003cp\u003eSignificance 3\u003c\/p\u003e \u003cp\u003eGood and Bad Hypotheses 3\u003c\/p\u003e \u003cp\u003eNull Hypotheses 3\u003c\/p\u003e \u003cp\u003ep Values 3\u003c\/p\u003e \u003cp\u003eInterpretation 4\u003c\/p\u003e \u003cp\u003eModel Choice 4\u003c\/p\u003e \u003cp\u003eStatistical Modelling 5\u003c\/p\u003e \u003cp\u003eMaximum Likelihood 6\u003c\/p\u003e \u003cp\u003eExperimental Design 7\u003c\/p\u003e \u003cp\u003eThe Principle of Parsimony (Occam’s Razor) 8\u003c\/p\u003e \u003cp\u003eObservation, Theory and Experiment 8\u003c\/p\u003e \u003cp\u003eControls 8\u003c\/p\u003e \u003cp\u003eReplication: It’s the ns that Justify the Means 8\u003c\/p\u003e \u003cp\u003eHow Many Replicates? 9\u003c\/p\u003e \u003cp\u003ePower 9\u003c\/p\u003e \u003cp\u003eRandomization 10\u003c\/p\u003e \u003cp\u003eStrong Inference 14\u003c\/p\u003e \u003cp\u003eWeak Inference 14\u003c\/p\u003e \u003cp\u003eHow Long to Go On? 14\u003c\/p\u003e \u003cp\u003ePseudoreplication 15\u003c\/p\u003e \u003cp\u003eInitial Conditions 16\u003c\/p\u003e \u003cp\u003eOrthogonal Designs and Non-Orthogonal Observational Data 16\u003c\/p\u003e \u003cp\u003eAliasing 16\u003c\/p\u003e \u003cp\u003eMultiple Comparisons 17\u003c\/p\u003e \u003cp\u003eSummary of Statistical Models in R 18\u003c\/p\u003e \u003cp\u003eOrganizing Your Work 19\u003c\/p\u003e \u003cp\u003eHousekeeping within R 20\u003c\/p\u003e \u003cp\u003eReferences 22\u003c\/p\u003e \u003cp\u003eFurther Reading 22\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Dataframes 23\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSelecting Parts of a Dataframe: Subscripts 26\u003c\/p\u003e \u003cp\u003eSorting 27\u003c\/p\u003e \u003cp\u003eSummarizing the Content of Dataframes 29\u003c\/p\u003e \u003cp\u003eSummarizing by Explanatory Variables 30\u003c\/p\u003e \u003cp\u003eFirst Things First: Get to Know Your Data 31\u003c\/p\u003e \u003cp\u003eRelationships 34\u003c\/p\u003e \u003cp\u003eLooking for Interactions between Continuous Variables 36\u003c\/p\u003e \u003cp\u003eGraphics to Help with Multiple Regression 39\u003c\/p\u003e \u003cp\u003eInteractions Involving Categorical Variables 39\u003c\/p\u003e \u003cp\u003eFurther Reading 41\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Central Tendency 42\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFurther Reading 49\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Variance 50\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDegrees of Freedom 53\u003c\/p\u003e \u003cp\u003eVariance 53\u003c\/p\u003e \u003cp\u003eVariance: A Worked Example 55\u003c\/p\u003e \u003cp\u003eVariance and Sample Size 58\u003c\/p\u003e \u003cp\u003eUsing Variance 59\u003c\/p\u003e \u003cp\u003eA Measure of Unreliability 60\u003c\/p\u003e \u003cp\u003eConfidence Intervals 61\u003c\/p\u003e \u003cp\u003eBootstrap 62\u003c\/p\u003e \u003cp\u003eNon-constant Variance: Heteroscedasticity 65\u003c\/p\u003e \u003cp\u003eFurther Reading 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Single Samples 66\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Summary in the One-Sample Case 66\u003c\/p\u003e \u003cp\u003eThe Normal Distribution 70\u003c\/p\u003e \u003cp\u003eCalculations Using z of the Normal Distribution 76\u003c\/p\u003e \u003cp\u003ePlots for Testing Normality of Single Samples 79\u003c\/p\u003e \u003cp\u003eInference in the One-Sample Case 81\u003c\/p\u003e \u003cp\u003eBootstrap in Hypothesis Testing with Single Samples 81\u003c\/p\u003e \u003cp\u003eStudent’s t Distribution 82\u003c\/p\u003e \u003cp\u003eHigher-Order Moments of a Distribution 83\u003c\/p\u003e \u003cp\u003eSkew 84\u003c\/p\u003e \u003cp\u003eKurtosis 86\u003c\/p\u003e \u003cp\u003eReference 87\u003c\/p\u003e \u003cp\u003eFurther Reading 87\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Two Samples 88\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eComparing Two Variances 88\u003c\/p\u003e \u003cp\u003eComparing Two Means 90\u003c\/p\u003e \u003cp\u003eStudent’s t Test 91\u003c\/p\u003e \u003cp\u003eWilcoxon Rank-Sum Test 95\u003c\/p\u003e \u003cp\u003eTests on Paired Samples 97\u003c\/p\u003e \u003cp\u003eThe Binomial Test 98\u003c\/p\u003e \u003cp\u003eBinomial Tests to Compare Two Proportions 100\u003c\/p\u003e \u003cp\u003eChi-Squared Contingency Tables 100\u003c\/p\u003e \u003cp\u003eFisher’s Exact Test 105\u003c\/p\u003e \u003cp\u003eCorrelation and Covariance 108\u003c\/p\u003e \u003cp\u003eCorrelation and the Variance of Differences between Variables 110\u003c\/p\u003e \u003cp\u003eScale-Dependent Correlations 112\u003c\/p\u003e \u003cp\u003eReference 113\u003c\/p\u003e \u003cp\u003eFurther Reading 113\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Regression 114\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eLinear Regression 116\u003c\/p\u003e \u003cp\u003eLinear Regression in R 117\u003c\/p\u003e \u003cp\u003eCalculations Involved in Linear Regression 122\u003c\/p\u003e \u003cp\u003ePartitioning Sums of Squares in Regression: SSY = SSR + SSE 125\u003c\/p\u003e \u003cp\u003eMeasuring the Degree of Fit, r\u003csup\u003e 2\u003c\/sup\u003e 133\u003c\/p\u003e \u003cp\u003eModel Checking 134\u003c\/p\u003e \u003cp\u003eTransformation 135\u003c\/p\u003e \u003cp\u003ePolynomial Regression 140\u003c\/p\u003e \u003cp\u003eNon-Linear Regression 142\u003c\/p\u003e \u003cp\u003eGeneralized Additive Models 146\u003c\/p\u003e \u003cp\u003eInfluence 148\u003c\/p\u003e \u003cp\u003eFurther Reading 149\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Analysis of Variance 150\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOne-Way ANOVA 150\u003c\/p\u003e \u003cp\u003eShortcut Formulas 157\u003c\/p\u003e \u003cp\u003eEffect Sizes 159\u003c\/p\u003e \u003cp\u003ePlots for Interpreting One-Way ANOVA 162\u003c\/p\u003e \u003cp\u003eFactorial Experiments 168\u003c\/p\u003e \u003cp\u003ePseudoreplication: Nested Designs and Split Plots 173\u003c\/p\u003e \u003cp\u003eSplit-Plot Experiments 174\u003c\/p\u003e \u003cp\u003eRandom Effects and Nested Designs 176\u003c\/p\u003e \u003cp\u003eFixed or Random Effects? 177\u003c\/p\u003e \u003cp\u003eRemoving the Pseudoreplication 178\u003c\/p\u003e \u003cp\u003eAnalysis of Longitudinal Data 178\u003c\/p\u003e \u003cp\u003eDerived Variable Analysis 179\u003c\/p\u003e \u003cp\u003eDealing with Pseudoreplication 179\u003c\/p\u003e \u003cp\u003eVariance Components Analysis (VCA) 183\u003c\/p\u003e \u003cp\u003eReferences 184\u003c\/p\u003e \u003cp\u003eFurther Reading 184\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Analysis of Covariance 185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFurther Reading 192\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Multiple Regression 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Steps Involved in Model Simplification 195\u003c\/p\u003e \u003cp\u003eCaveats 196\u003c\/p\u003e \u003cp\u003eOrder of Deletion 196\u003c\/p\u003e \u003cp\u003eCarrying Out a Multiple Regression 197\u003c\/p\u003e \u003cp\u003eA Trickier Example 203\u003c\/p\u003e \u003cp\u003eFurther Reading 211\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Contrasts 212\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eContrast Coefficients 213\u003c\/p\u003e \u003cp\u003eAn Example of Contrasts in R 214\u003c\/p\u003e \u003cp\u003eA Priori Contrasts 215\u003c\/p\u003e \u003cp\u003eTreatment Contrasts 216\u003c\/p\u003e \u003cp\u003eModel Simplification by Stepwise Deletion 218\u003c\/p\u003e \u003cp\u003eContrast Sums of Squares by Hand 222\u003c\/p\u003e \u003cp\u003eThe Three Kinds of Contrasts Compared 224\u003c\/p\u003e \u003cp\u003eReference 225\u003c\/p\u003e \u003cp\u003eFurther Reading 225\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Other Response Variables 226\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction to Generalized Linear Models 228\u003c\/p\u003e \u003cp\u003eThe Error Structure 229\u003c\/p\u003e \u003cp\u003eThe Linear Predictor 229\u003c\/p\u003e \u003cp\u003eFitted Values 230\u003c\/p\u003e \u003cp\u003eA General Measure of Variability 230\u003c\/p\u003e \u003cp\u003eThe Link Function 231\u003c\/p\u003e \u003cp\u003eCanonical Link Functions 232\u003c\/p\u003e \u003cp\u003eAkaike’s Information Criterion (AIC) as a Measure of the Fit of a Model 233\u003c\/p\u003e \u003cp\u003eFurther Reading 233\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 Count Data 234\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Regression with Poisson Errors 234\u003c\/p\u003e \u003cp\u003eAnalysis of Deviance with Count Data 237\u003c\/p\u003e \u003cp\u003eThe Danger of Contingency Tables 244\u003c\/p\u003e \u003cp\u003eAnalysis of Covariance with Count Data 247\u003c\/p\u003e \u003cp\u003eFrequency Distributions 250\u003c\/p\u003e \u003cp\u003eFurther Reading 255\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 Proportion Data 256\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAnalyses of Data on One and Two Proportions 257\u003c\/p\u003e \u003cp\u003eAverages of Proportions 257\u003c\/p\u003e \u003cp\u003eCount Data on Proportions 257\u003c\/p\u003e \u003cp\u003eOdds 259\u003c\/p\u003e \u003cp\u003eOverdispersion and Hypothesis Testing 260\u003c\/p\u003e \u003cp\u003eApplications 261\u003c\/p\u003e \u003cp\u003eLogistic Regression with Binomial Errors 261\u003c\/p\u003e \u003cp\u003eProportion Data with Categorical Explanatory Variables 264\u003c\/p\u003e \u003cp\u003eAnalysis of Covariance with Binomial Data 269\u003c\/p\u003e \u003cp\u003eFurther Reading 272\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15 Binary Response Variable 273\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIncidence Functions 275\u003c\/p\u003e \u003cp\u003eANCOVA with a Binary Response Variable 279\u003c\/p\u003e \u003cp\u003eFurther Reading 284\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16 Death and Failure Data 285\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSurvival Analysis with Censoring 287\u003c\/p\u003e \u003cp\u003eFurther Reading 290\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix Essentials of the R Language 291\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eR as a Calculator 291\u003c\/p\u003e \u003cp\u003eBuilt-in Functions 292\u003c\/p\u003e \u003cp\u003eNumbers with Exponents 294\u003c\/p\u003e \u003cp\u003eModulo and Integer Quotients 294\u003c\/p\u003e \u003cp\u003eAssignment 295\u003c\/p\u003e \u003cp\u003eRounding 295\u003c\/p\u003e \u003cp\u003eInfinity and Things that Are Not a Number (NaN) 296\u003c\/p\u003e \u003cp\u003eMissing Values (NA) 297\u003c\/p\u003e \u003cp\u003eOperators 298\u003c\/p\u003e \u003cp\u003eCreating a Vector 298\u003c\/p\u003e \u003cp\u003eNamed Elements within Vectors 299\u003c\/p\u003e \u003cp\u003eVector Functions 299\u003c\/p\u003e \u003cp\u003eSummary Information from Vectors by Groups 300\u003c\/p\u003e \u003cp\u003eSubscripts and Indices 301\u003c\/p\u003e \u003cp\u003eWorking with Vectors and Logical Subscripts 301\u003c\/p\u003e \u003cp\u003eAddresses within Vectors 304\u003c\/p\u003e \u003cp\u003eTrimming Vectors Using Negative Subscripts 304\u003c\/p\u003e \u003cp\u003eLogical Arithmetic 305\u003c\/p\u003e \u003cp\u003eRepeats 305\u003c\/p\u003e \u003cp\u003eGenerate Factor Levels 306\u003c\/p\u003e \u003cp\u003eGenerating Regular Sequences of Numbers 306\u003c\/p\u003e \u003cp\u003eMatrices 307\u003c\/p\u003e \u003cp\u003eCharacter Strings 309\u003c\/p\u003e \u003cp\u003eWriting Functions in R 310\u003c\/p\u003e \u003cp\u003eArithmetic Mean of a Single Sample 310\u003c\/p\u003e \u003cp\u003eMedian of a Single Sample 310\u003c\/p\u003e \u003cp\u003eLoops and Repeats 311\u003c\/p\u003e \u003cp\u003eThe ifelse Function 312\u003c\/p\u003e \u003cp\u003eEvaluating Functions with apply 312\u003c\/p\u003e \u003cp\u003eTesting for Equality 313\u003c\/p\u003e \u003cp\u003eTesting and Coercing in R 314\u003c\/p\u003e \u003cp\u003eDates and Times in R 315\u003c\/p\u003e \u003cp\u003eCalculations with Dates and Times 319\u003c\/p\u003e \u003cp\u003eUnderstanding the Structure of an R Object Using str 320\u003c\/p\u003e \u003cp\u003eReference 322\u003c\/p\u003e \u003cp\u003eFurther Reading 322\u003c\/p\u003e \u003cp\u003eIndex 323 \u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866381562199,"sku":"9781118941096","price":31.3,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781118941096.jpg?v=1722278384"},{"product_id":"statistics-with-jmp-hypothesis-tests-anova-and-regression-9781119097150","title":"Statistics with JMP Hypothesis Tests ANOVA and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eStatistics with JMP: Hypothesis Tests, ANOVA and Regression\u003c\/p\u003e \u003cp\u003e\u003ci\u003ePeter Goos, University of Leuven and University of Antwerp, Belgium\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003e\u003c\/i\u003e\u003ci\u003eDavid Meintrup, University of Applied Sciences Ingolstadt, \u003c\/i\u003e\u003ci\u003eGermany\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA first course on basic statistical methodology using JMP\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis book provides a first course on parameter estimation (point estimates and confidence interval estimates), hypothesis testing, ANOVA and simple linear regression. The authors approach combines mathematical depth with numerous examples and demonstrations using the JMP software.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eKey features:\u003c\/i\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eProvides a comprehensive and rigorous presentation of introductory statistics that has been extensively classroom tested.\u003c\/li\u003e \u003cli\u003ePays attention to the usual parametric hypothesis tests as well as to non-parametric tests (including the calculation of exact p-values).\u003c\/li\u003e \u003cli\u003eDiscusses the power of various statistical tests, along with examples in JMP to \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Masters and advanced students in applied statistics, industrial engineering, business engineering, civil engineering and bio-science engineering will find this book beneficial. It also provides a useful resource for teachers of statistics particularly in the area of engineering.\" (Zentralblatt MATH 2016)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eDedication iii\u003c\/p\u003e \u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003eAcknowledgements xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart One Estimators and tests 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1 Estimating population parameters 3\u003c\/p\u003e \u003cp\u003e2 Interval estimators 37\u003c\/p\u003e \u003cp\u003e3 Hypothesis tests 71\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Two One population 103\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4 Hypothesis tests for a population mean, proportion or variance 105\u003c\/p\u003e \u003cp\u003e5 Two hypothesis tests for the median of a population 149\u003c\/p\u003e \u003cp\u003e6 Hypothesis tests for the distribution of a population 175\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Three Two populations\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7 Independent versus paired samples 213\u003c\/p\u003e \u003cp\u003e8 Hypothesis tests for means, proportions and variances of two independent samples 219\u003c\/p\u003e \u003cp\u003e9 A nonparametric hypothesis test for the medians of two independent samples 263\u003c\/p\u003e \u003cp\u003e10 Hypothesis tests for the population mean of two paired samples 285\u003c\/p\u003e \u003cp\u003e11 Two nonparametric hypothesis tests for paired samples 305\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Four More than two populations 325\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12 Hypothesis tests for more than two population means: one-way analysis of variance 327\u003c\/p\u003e \u003cp\u003e13 Nonparametric alternatives to an analysis of variance 375\u003c\/p\u003e \u003cp\u003e14 Hypothesis tests for more than two population variances 401\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Five More useful tests and procedures 417\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15 Design of experiments and data collection 419\u003c\/p\u003e \u003cp\u003e16 Testing equivalence 427\u003c\/p\u003e \u003cp\u003e17 Estimation and testing of correlation and association 445\u003c\/p\u003e \u003cp\u003e18 An introduction to regression modeling 481\u003c\/p\u003e \u003cp\u003e19 Simple linear regression 493\u003c\/p\u003e \u003cp\u003eA Binomial distribution 589\u003c\/p\u003e \u003cp\u003eB Standard normal distribution 593\u003c\/p\u003e \u003cp\u003eC \u003ci\u003eX\u003c\/i\u003e\u003csub\u003e2\u003c\/sub\u003e-distribution 595\u003c\/p\u003e \u003cp\u003eD Student’s \u003ci\u003et\u003c\/i\u003e-distribution 597\u003c\/p\u003e \u003cp\u003eE Wilcoxon signed-rank test 599\u003c\/p\u003e \u003cp\u003eF Critical values for the Shapiro-Wilk test 605\u003c\/p\u003e \u003cp\u003eG Fisher’s \u003ci\u003eF\u003c\/i\u003e-distribution 607\u003c\/p\u003e \u003cp\u003eH Wilcoxon rank-sum test 615\u003c\/p\u003e \u003cp\u003eI Studentized range or \u003ci\u003eQ\u003c\/i\u003e-distribution 625\u003c\/p\u003e \u003cp\u003eJ Two-sided Dunnett test 629\u003c\/p\u003e \u003cp\u003eK One-sided Dunnett test 633\u003c\/p\u003e \u003cp\u003eL Kruskal-Wallis-Test 637\u003c\/p\u003e \u003cp\u003eM Rank correlation test 641\u003c\/p\u003e \u003cp\u003eIndex 643\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866386510167,"sku":"9781119097150","price":57.9,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119097150.jpg?v=1722278403"},{"product_id":"exploring-arduino-tools-and-techniques-for-engineering-wizardry-second-edition-9781119405375","title":"Exploring Arduino  Tools and Techniques for","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe bestselling beginner Arduino guide, updated with new projects!   Exploring Arduino makes electrical engineering and embedded software accessible. Learn step by step everything you need to know about electrical engineering, programming, and human-computer interaction through a series of increasingly complex projects.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction xxv\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Arduino Engineering Basics 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Getting Started and Understanding the Arduino Landscape 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExploring the Arduino Ecosystem 4\u003c\/p\u003e \u003cp\u003eArduino Functionality 5\u003c\/p\u003e \u003cp\u003eThe Microcontroller 7\u003c\/p\u003e \u003cp\u003eProgramming Interfaces 8\u003c\/p\u003e \u003cp\u003eInput\/Output: GPIO, ADCs, and Communication Busses 9\u003c\/p\u003e \u003cp\u003ePower 9\u003c\/p\u003e \u003cp\u003eArduino Boards 11\u003c\/p\u003e \u003cp\u003eCreating Your First Program 15\u003c\/p\u003e \u003cp\u003eDownloading and Installing the Arduino IDE 16\u003c\/p\u003e \u003cp\u003eRunning the IDE and Connecting to the Arduino 17\u003c\/p\u003e \u003cp\u003eBreaking Down Your First Program 18\u003c\/p\u003e \u003cp\u003eSummary 21\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Digital Inputs, Outputs, and Pulse-Width Modulation 23\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDigital Outputs 24\u003c\/p\u003e \u003cp\u003eWiring Up an LED and Using Breadboards 24\u003c\/p\u003e \u003cp\u003eWorking with Breadboards 24\u003c\/p\u003e \u003cp\u003eWiring LEDs 25\u003c\/p\u003e \u003cp\u003eProgramming Digital Outputs 29\u003c\/p\u003e \u003cp\u003eUsing For Loops 30\u003c\/p\u003e \u003cp\u003ePulse-Width Modulation with \u003ci\u003eanalogWrite()\u003c\/i\u003e 31\u003c\/p\u003e \u003cp\u003eReading Digital Inputs 35\u003c\/p\u003e \u003cp\u003eReading Digital Inputs with Pull-Down Resistors 35\u003c\/p\u003e \u003cp\u003eWorking with “Bouncy” Buttons 38\u003c\/p\u003e \u003cp\u003eBuilding a Controllable RGB LED Nightlight 42\u003c\/p\u003e \u003cp\u003eSummary 46\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Interfacing with Analog Sensors 47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding Analog and Digital Signals 48\u003c\/p\u003e \u003cp\u003eComparing Analog and Digital Signals 48\u003c\/p\u003e \u003cp\u003eConverting an Analog Signal to Digital 49\u003c\/p\u003e \u003cp\u003eReading Analog Sensors with the Arduino: \u003ci\u003eanalogRead()\u003c\/i\u003e 51\u003c\/p\u003e \u003cp\u003eReading a Potentiometer 51\u003c\/p\u003e \u003cp\u003eUsing Analog Sensors 56\u003c\/p\u003e \u003cp\u003eUsing Variable Resistors to Make Your Own Analog Sensors 60\u003c\/p\u003e \u003cp\u003eUsing Resistive Voltage Dividers 61\u003c\/p\u003e \u003cp\u003eUsing Analog Inputs to Control Analog Outputs 64\u003c\/p\u003e \u003cp\u003eSummary 66\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Interfacing with Your Environment\u003c\/b\u003e\u003cb\u003e 67\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4 Using Transistors and Driving DC Motors 69\u003c\/p\u003e \u003cp\u003eDriving DC Motors 70\u003c\/p\u003e \u003cp\u003eHandling High-Current Inductive Loads 71\u003c\/p\u003e \u003cp\u003eUsing Transistors as Switches 72\u003c\/p\u003e \u003cp\u003eUsing Protection Diodes73\u003c\/p\u003e \u003cp\u003eUsing a Secondary Power Source 74\u003c\/p\u003e \u003cp\u003eWiring the Motor 74\u003c\/p\u003e \u003cp\u003eControlling Motor Speed with PWM 76\u003c\/p\u003e \u003cp\u003eUsing an H-Bridge to Control DC Motor Direction 78\u003c\/p\u003e \u003cp\u003eBuilding an H-Bridge Circuit 80\u003c\/p\u003e \u003cp\u003eOperating an H-Bridge Circuit 82\u003c\/p\u003e \u003cp\u003eBuilding a Roving Robot 86\u003c\/p\u003e \u003cp\u003eChoosing the Robot Parts 87\u003c\/p\u003e \u003cp\u003eSelecting a Motor and Gearbox 87\u003c\/p\u003e \u003cp\u003ePowering Your Robot 87\u003c\/p\u003e \u003cp\u003eConstructing the Robot 89\u003c\/p\u003e \u003cp\u003eWriting the Robot Software 92\u003c\/p\u003e \u003cp\u003eBringing It Together 96\u003c\/p\u003e \u003cp\u003eSummary 97\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Driving Stepper and Servo Motors 99\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDriving Servo Motors 100\u003c\/p\u003e \u003cp\u003eUnderstanding the Difference between Continuous Rotation and Standard Servos 100\u003c\/p\u003e \u003cp\u003eUnderstanding Servo Control 101\u003c\/p\u003e \u003cp\u003eControlling a Servo 104\u003c\/p\u003e \u003cp\u003eBuilding a Sweeping Distance Sensor 105\u003c\/p\u003e \u003cp\u003eUnderstanding and Driving Stepper Motors 109\u003c\/p\u003e \u003cp\u003eHow Bipolar Stepper Motors Work 111\u003c\/p\u003e \u003cp\u003eMaking Your Stepper Move 113\u003c\/p\u003e \u003cp\u003eBuilding a “One-Minute Chronograph” 117\u003c\/p\u003e \u003cp\u003eWiring and Building the Chronograph 117\u003c\/p\u003e \u003cp\u003eProgramming the Chronograph 119\u003c\/p\u003e \u003cp\u003eSummary 124\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Making Sounds and Music 125\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding How Speakers Work 126\u003c\/p\u003e \u003cp\u003eThe Properties of Sound 126\u003c\/p\u003e \u003cp\u003eHow a Speaker Produces Sound 128\u003c\/p\u003e \u003cp\u003eUsing tone() to Make Sounds 129\u003c\/p\u003e \u003cp\u003eIncluding a Definition File 129\u003c\/p\u003e \u003cp\u003eWiring the Speaker 130\u003c\/p\u003e \u003cp\u003eMaking Sound Sequences 133\u003c\/p\u003e \u003cp\u003eUsing Arrays 133\u003c\/p\u003e \u003cp\u003eMaking Note and Duration Arrays 134\u003c\/p\u003e \u003cp\u003eCompleting the Program 134\u003c\/p\u003e \u003cp\u003eUnderstanding the Limitations of the tone() Function 136\u003c\/p\u003e \u003cp\u003eBuilding a Micro Piano 136\u003c\/p\u003e \u003cp\u003eSummary 139\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 USB Serial Communication 141\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding the Arduino’s Serial Communication Capabilities 142\u003c\/p\u003e \u003cp\u003eArduino Boards with an Internal or External FTDI or Silicon Labs USB-to-Serial Converter 143\u003c\/p\u003e \u003cp\u003eArduino Boards with a Secondary USB-Capable ATmega MCU Emulating a Serial Converter 146\u003c\/p\u003e \u003cp\u003eArduino Boards with a Single USB-Capable MCU 147\u003c\/p\u003e \u003cp\u003eArduino Boards with USB-Host Capabilities 147\u003c\/p\u003e \u003cp\u003eListening to the Arduino 148\u003c\/p\u003e \u003cp\u003eUsing \u003ci\u003eprint\u003c\/i\u003e Statements 148\u003c\/p\u003e \u003cp\u003eUsing Special Characters 150\u003c\/p\u003e \u003cp\u003eChanging Data Type Representations 152\u003c\/p\u003e \u003cp\u003eTalking to the Arduino 152\u003c\/p\u003e \u003cp\u003eConfiguring the Arduino IDE’s Serial Monitor to Send Command Strings 152\u003c\/p\u003e \u003cp\u003eReading Incoming Data from a Computer or Other Serial Device 153\u003c\/p\u003e \u003cp\u003eTelling the Arduino to Echo Incoming Data 153\u003c\/p\u003e \u003cp\u003eUnderstanding the Differences between Chars and Ints 154\u003c\/p\u003e \u003cp\u003eSending Single Characters to Control an LED 156\u003c\/p\u003e \u003cp\u003eSending Lists of Values to Control an RGB LED 158\u003c\/p\u003e \u003cp\u003eTalking to a Desktop App 161\u003c\/p\u003e \u003cp\u003eInstalling Processing 162\u003c\/p\u003e \u003cp\u003eControlling a Processing Sketch from Your Arduino 163\u003c\/p\u003e \u003cp\u003eSending Data from Processing to Your Arduino 166\u003c\/p\u003e \u003cp\u003eSummary 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Emulating USB Devices 171\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEmulating a Keyboard 173\u003c\/p\u003e \u003cp\u003eTyping Data into the Computer 173\u003c\/p\u003e \u003cp\u003eCommanding Your Computer to Do Your Bidding 177\u003c\/p\u003e \u003cp\u003eEmulating a Mouse 178\u003c\/p\u003e \u003cp\u003eSummary 182\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Shift Registers 183\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding Shift Registers 184\u003c\/p\u003e \u003cp\u003eSending Parallel and Serial Data 185\u003c\/p\u003e \u003cp\u003eWorking with the 74HC595 Shift Register 186\u003c\/p\u003e \u003cp\u003eUnderstanding the Shift Register pin Functions 186\u003c\/p\u003e \u003cp\u003eUnderstanding How the Shift Register Works 187\u003c\/p\u003e \u003cp\u003eShifting Serial Data from the Arduino 189\u003c\/p\u003e \u003cp\u003eConverting Between Binary and Decimal Formats 192\u003c\/p\u003e \u003cp\u003eControlling Light Animations with a Shift Register 192\u003c\/p\u003e \u003cp\u003eBuilding a “Light Rider” 192\u003c\/p\u003e \u003cp\u003eResponding to Inputs with an LED Bar Graph 194\u003c\/p\u003e \u003cp\u003eSummary 197\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Communication Interfaces 199\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 The I\u003csup\u003e2\u003c\/sup\u003eC Bus 201\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHistory of the I\u003csup\u003e2\u003c\/sup\u003eC Bus 202\u003c\/p\u003e \u003cp\u003eI\u003csup\u003e2\u003c\/sup\u003eC Hardware Design 203\u003c\/p\u003e \u003cp\u003eCommunication Scheme and ID Numbers 203\u003c\/p\u003e \u003cp\u003eHardware Requirements and Pull-Up Resistors 206\u003c\/p\u003e \u003cp\u003eCommunicating with an I\u003csup\u003e2\u003c\/sup\u003eC Temperature Probe 208\u003c\/p\u003e \u003cp\u003eSetting Up the Hardware208\u003c\/p\u003e \u003cp\u003eReferencing the Datasheet 210\u003c\/p\u003e \u003cp\u003eWriting the Software 212\u003c\/p\u003e \u003cp\u003eCombining Shift Registers, Serial Communication, and I\u003csup\u003e2\u003c\/sup\u003eC Communications 214\u003c\/p\u003e \u003cp\u003eBuilding the Hardware for a Temperature Monitoring System 214\u003c\/p\u003e \u003cp\u003eModifying the Embedded Program 215\u003c\/p\u003e \u003cp\u003eWriting the Processing Sketch 218\u003c\/p\u003e \u003cp\u003eSummary 221\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 The SPI Bus and Third-Party Libraries 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOverview of the SPI Bus 224\u003c\/p\u003e \u003cp\u003eSPI Hardware and Communication Design 225\u003c\/p\u003e \u003cp\u003eHardware Configuration 225\u003c\/p\u003e \u003cp\u003eCommunication Scheme 227\u003c\/p\u003e \u003cp\u003eComparing SPI to I\u003csup\u003e2\u003c\/sup\u003eC and UART 227\u003c\/p\u003e \u003cp\u003eCommunicating with an SPI Accelerometer 228\u003c\/p\u003e \u003cp\u003eWhat is an Accelerometer? 229\u003c\/p\u003e \u003cp\u003eGathering Information from the Datasheet 231\u003c\/p\u003e \u003cp\u003eSetting Up the Hardware233\u003c\/p\u003e \u003cp\u003eWriting the Software 235\u003c\/p\u003e \u003cp\u003eInstalling the Adafruit Sensor Libraries 236\u003c\/p\u003e \u003cp\u003eLeveraging the Library 237\u003c\/p\u003e \u003cp\u003eCreating an Audiovisual Instrument Using a 3-Axis Accelerometer 241\u003c\/p\u003e \u003cp\u003eSetting Up the Hardware242\u003c\/p\u003e \u003cp\u003eModifying the Software 242\u003c\/p\u003e \u003cp\u003eSummary 246\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Interfacing with Liquid Crystal Displays 247\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSetting Up the LCD 248\u003c\/p\u003e \u003cp\u003eUsing the LiquidCrystal Library to Write to the LCD 251\u003c\/p\u003e \u003cp\u003eAdding Text to the Display 252\u003c\/p\u003e \u003cp\u003eCreating Special Characters and Animations 254\u003c\/p\u003e \u003cp\u003eBuilding a Personal Thermostat 258\u003c\/p\u003e \u003cp\u003eSetting Up the Hardware 258\u003c\/p\u003e \u003cp\u003eDisplaying Data on the LCD 261\u003c\/p\u003e \u003cp\u003eAdjusting the Set Point with a Button 264\u003c\/p\u003e \u003cp\u003eAdding an Audible Warning and a Fan 265\u003c\/p\u003e \u003cp\u003eBringing It All Together: The Complete Program 266\u003c\/p\u003e \u003cp\u003eTaking This Project to the Next Level 270\u003c\/p\u003e \u003cp\u003eSummary 271\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Digging Deeper and Combining Functions\u003c\/b\u003e\u003cb\u003e 273\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Interrupts and Other Special Functions 275\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUsing Hardware Interrupts 276\u003c\/p\u003e \u003cp\u003eKnowing the Tradeoffs Between Polling and Interrupting 277\u003c\/p\u003e \u003cp\u003eEase of Implementation (Software) 277\u003c\/p\u003e \u003cp\u003eEase of Implementation (Hardware) 277\u003c\/p\u003e \u003cp\u003eMultitasking 278\u003c\/p\u003e \u003cp\u003eAcquisition Accuracy 278\u003c\/p\u003e \u003cp\u003eUnderstanding the Arduino Hardware Interrupt Capabilities 278\u003c\/p\u003e \u003cp\u003eBuilding and Testing a Hardware-Debounced Button Interrupt Circuit 279\u003c\/p\u003e \u003cp\u003eCreating a Hardware-Debouncing Circuit 280\u003c\/p\u003e \u003cp\u003eAssembling the Complete Test Circuit 284\u003c\/p\u003e \u003cp\u003eWriting the Software 285\u003c\/p\u003e \u003cp\u003eUsing Timer Interrupts 288\u003c\/p\u003e \u003cp\u003eUnderstanding Timer Interrupts 288\u003c\/p\u003e \u003cp\u003eGetting the Library 289\u003c\/p\u003e \u003cp\u003eExecuting Two Tasks Simultaneously(ish) 289\u003c\/p\u003e \u003cp\u003eBuilding an Interrupt-Driven Sound Machine 290\u003c\/p\u003e \u003cp\u003eSound Machine Hardware 291\u003c\/p\u003e \u003cp\u003eSound Machine Software 291\u003c\/p\u003e \u003cp\u003eSummary 294\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Data Logging with SD Cards 295\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGetting Ready for Data Logging 296\u003c\/p\u003e \u003cp\u003eFormatting Data with CSV Files 297\u003c\/p\u003e \u003cp\u003ePreparing an SD Card for Data Logging 297\u003c\/p\u003e \u003cp\u003eFormatting Your SD Card Using a Windows PC 298\u003c\/p\u003e \u003cp\u003eFormatting Your SD Card Using Mac OS 300\u003c\/p\u003e \u003cp\u003eFormatting Your SD Card Using Linux 302\u003c\/p\u003e \u003cp\u003eInterfacing the Arduino with an SD Card 304\u003c\/p\u003e \u003cp\u003eSD Card Shields 304\u003c\/p\u003e \u003cp\u003eSD Card SPI Interface 307\u003c\/p\u003e \u003cp\u003eWriting to an SD Card 307\u003c\/p\u003e \u003cp\u003eReading from an SD Card 312\u003c\/p\u003e \u003cp\u003eReal-Time Clocks 317\u003c\/p\u003e \u003cp\u003eUnderstanding Real-Time Clocks 317\u003c\/p\u003e \u003cp\u003eCommunicating with a Real-Time Clock 317\u003c\/p\u003e \u003cp\u003eUsing the RTC Arduino Third-Party Library 318\u003c\/p\u003e \u003cp\u003eUsing a Real-Time Clock 319\u003c\/p\u003e \u003cp\u003eInstalling the RTC and SD Card Modules 319\u003c\/p\u003e \u003cp\u003eUpdating the Software 320\u003c\/p\u003e \u003cp\u003eBuilding an Entrance Logger 327\u003c\/p\u003e \u003cp\u003eLogger Hardware 328\u003c\/p\u003e \u003cp\u003eLogger Software 329\u003c\/p\u003e \u003cp\u003eData Analysis 334\u003c\/p\u003e \u003cp\u003eSummary 335\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V Going Wireless 337\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Wireless RF Communications 339\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Electromagnetic Spectrum 340\u003c\/p\u003e \u003cp\u003eThe Spectrum 342\u003c\/p\u003e \u003cp\u003eHow Your RF Link Will Send and Receive Data 343\u003c\/p\u003e \u003cp\u003eReceiving Key Presses with the RF Link 346\u003c\/p\u003e \u003cp\u003eConnecting Your Receiver 346\u003c\/p\u003e \u003cp\u003eProgramming Your Receiver 347\u003c\/p\u003e \u003cp\u003eMaking a Wireless Doorbell 351\u003c\/p\u003e \u003cp\u003eWiring the Receiver 351\u003c\/p\u003e \u003cp\u003eProgramming the Receiver 351\u003c\/p\u003e \u003cp\u003eThe Start of Your Smart Home—Controlling a Lamp 354\u003c\/p\u003e \u003cp\u003eYour Home’s AC Power 356\u003c\/p\u003e \u003cp\u003eHow a Relay Works 356\u003c\/p\u003e \u003cp\u003eProgramming the Relay Control 358\u003c\/p\u003e \u003cp\u003eHooking up Your Lamp and Relay to the Arduino 360\u003c\/p\u003e \u003cp\u003eSummary 361\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Bluetooth Connectivity\u003c\/b\u003e\u003cb\u003e 363\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDemystifying Bluetooth 364\u003c\/p\u003e \u003cp\u003eBluetooth Standards and Versions 364\u003c\/p\u003e \u003cp\u003eBluetooth Profiles and BTLE GATT Services 365\u003c\/p\u003e \u003cp\u003eCommunication between Your Arduino and Your Phone 366\u003c\/p\u003e \u003cp\u003eReading a Sensor over BTLE 366\u003c\/p\u003e \u003cp\u003eAdding Support for Third-Party Boards to the Arduino IDE 367\u003c\/p\u003e \u003cp\u003eInstalling the BTLE Module Library 369\u003c\/p\u003e \u003cp\u003eProgramming the Feather Board 369\u003c\/p\u003e \u003cp\u003eConnecting Your Smartphone to Your BTLE Transmitter 377\u003c\/p\u003e \u003cp\u003eSending Commands from Your Phone over BTLE 379\u003c\/p\u003e \u003cp\u003eParsing Command Strings 380\u003c\/p\u003e \u003cp\u003eCommanding Your BTLE Device with Natural Language 384\u003c\/p\u003e \u003cp\u003eControlling an AC Lamp with Bluetooth 389\u003c\/p\u003e \u003cp\u003eHow Your Phone “Pairs” to BTLE Devices 389\u003c\/p\u003e \u003cp\u003eWriting the Proximity Control Software 390\u003c\/p\u003e \u003cp\u003ePairing Your Phone 394\u003c\/p\u003e \u003cp\u003ePairing an Android Phone 394\u003c\/p\u003e \u003cp\u003ePairing an iPhone 395\u003c\/p\u003e \u003cp\u003eMake Your Lamp React to Your Presence 396\u003c\/p\u003e \u003cp\u003eSummary 397\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Wi-Fi and the Cloud\u003c\/b\u003e\u003cb\u003e 399\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Web, the Arduino, and You 400\u003c\/p\u003e \u003cp\u003eNetworking Lingo 401\u003c\/p\u003e \u003cp\u003eThe Internet vs. the World Wide Web vs. the Cloud 401\u003c\/p\u003e \u003cp\u003eIP Address 401\u003c\/p\u003e \u003cp\u003eNetwork Address Translation 402\u003c\/p\u003e \u003cp\u003eMAC Address 402\u003c\/p\u003e \u003cp\u003eHTML 402\u003c\/p\u003e \u003cp\u003eHTTP and HTTPS 402\u003c\/p\u003e \u003cp\u003eGET\/POST 403\u003c\/p\u003e \u003cp\u003eDHCP 403\u003c\/p\u003e \u003cp\u003eDNS 403\u003c\/p\u003e \u003cp\u003eClients and Servers 403\u003c\/p\u003e \u003cp\u003eYour Wi-Fi–Enabled Arduino 404\u003c\/p\u003e \u003cp\u003eControlling Your Arduino from the Web 404\u003c\/p\u003e \u003cp\u003eSetting Up the I\/O Control Hardware 404\u003c\/p\u003e \u003cp\u003ePreparing the Arduino IDE for Use with the Feather Board.406\u003c\/p\u003e \u003cp\u003eEnsuring the Wi-Fi Library is Matched to the Wi-Fi Module’s Firmware 407\u003c\/p\u003e \u003cp\u003eChecking the WINC1500’s Firmware Version 408\u003c\/p\u003e \u003cp\u003eUpdating the WINC1500’s Firmware 408\u003c\/p\u003e \u003cp\u003eWriting an Arduino Server Sketch 408\u003c\/p\u003e \u003cp\u003eConnecting to the Network and Retrieving an IP Address via DHCP 409\u003c\/p\u003e \u003cp\u003eWriting the Code for a Bare-Minimum Web Server 412\u003c\/p\u003e \u003cp\u003eControlling Your Arduino from Inside and Outside Your Local Network 423\u003c\/p\u003e \u003cp\u003eControlling Your Arduino over the Local Network 423\u003c\/p\u003e \u003cp\u003eUsing Port Forwarding to Control Your Arduino from Anywhere 425\u003c\/p\u003e \u003cp\u003eInterfacing with Web APIs 427\u003c\/p\u003e \u003cp\u003eUsing a Weather API428\u003c\/p\u003e \u003cp\u003eCreating an Account with the API Service Provider 429\u003c\/p\u003e \u003cp\u003eUnderstanding How APIs are Structured 430\u003c\/p\u003e \u003cp\u003eJSON-Formatted Data and Your Arduino 430\u003c\/p\u003e \u003cp\u003eFetching and Parsing Weather Data 431\u003c\/p\u003e \u003cp\u003eGetting the Local Temperature from the Web on Your Arduino 433\u003c\/p\u003e \u003cp\u003eCompleting the Live Temperature Display 440\u003c\/p\u003e \u003cp\u003eWiring up the LED Readout Display 440\u003c\/p\u003e \u003cp\u003eDriving the Display with Temperature Data 443\u003c\/p\u003e \u003cp\u003eSummary 449\u003c\/p\u003e \u003cp\u003eAppendix A: Deciphering Datasheets and Schematics 451\u003c\/p\u003e \u003cp\u003eIndex 461\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866394669399,"sku":"9781119405375","price":24.8,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119405375.jpg?v=1722278447"},{"product_id":"statistical-analysis-with-excel-for-dummies-5th-e-dition-9781119844549","title":"Statistical Analysis with Excel For Dummies 5th E","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eIntroduction\u003c\/b\u003e\u003cb\u003e 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAbout This Book 2\u003c\/p\u003e \u003cp\u003eWhat’s New in This Edition 2\u003c\/p\u003e \u003cp\u003eWhat’s New in Excel (Microsoft 365) 3\u003c\/p\u003e \u003cp\u003eFoolish Assumptions 3\u003c\/p\u003e \u003cp\u003eIcons Used in This Book 4\u003c\/p\u003e \u003cp\u003eWhere to Go from Here 5\u003c\/p\u003e \u003cp\u003eBeyond This Book 5\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1: Getting Started With Statistical Analysis With Excel: A Marriage Made In Heaven\u003c\/b\u003e\u003cb\u003e 7\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1: Evaluating Data in the Real World\u003c\/b\u003e\u003cb\u003e 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Statistical (and Related) Notions You Just Have to Know 9\u003c\/p\u003e \u003cp\u003eSamples and populations 10\u003c\/p\u003e \u003cp\u003eVariables: Dependent and independent 11\u003c\/p\u003e \u003cp\u003eTypes of data 12\u003c\/p\u003e \u003cp\u003eA little probability 13\u003c\/p\u003e \u003cp\u003eInferential Statistics: Testing Hypotheses 14\u003c\/p\u003e \u003cp\u003eNull and alternative hypotheses 15\u003c\/p\u003e \u003cp\u003eTwo types of error 16\u003c\/p\u003e \u003cp\u003eSome Excel Fundamentals 18\u003c\/p\u003e \u003cp\u003eAutofilling cells 22\u003c\/p\u003e \u003cp\u003eReferencing cells 25\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2: Understanding Excel’s Statistical Capabilities\u003c\/b\u003e\u003cb\u003e 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGetting Started 30\u003c\/p\u003e \u003cp\u003eSetting Up for Statistics 32\u003c\/p\u003e \u003cp\u003eWorksheet functions 32\u003c\/p\u003e \u003cp\u003eQuickly accessing statistical functions 36\u003c\/p\u003e \u003cp\u003eArray functions 38\u003c\/p\u003e \u003cp\u003eWhat’s in a name? An array of possibilities 41\u003c\/p\u003e \u003cp\u003eCreating Your Own Array Formulas 50\u003c\/p\u003e \u003cp\u003eUsing data analysis tools 51\u003c\/p\u003e \u003cp\u003eAdditional data analysis tool packages 56\u003c\/p\u003e \u003cp\u003eAccessing Commonly Used Functions 58\u003c\/p\u003e \u003cp\u003eThe New Analyze Data Tool 59\u003c\/p\u003e \u003cp\u003eData from Pictures! 60\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2: Describing Data\u003c\/b\u003e\u003cb\u003e 63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3: Show-and-Tell: Graphing Data\u003c\/b\u003e\u003cb\u003e 65\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhy Use Graphs? 65\u003c\/p\u003e \u003cp\u003eExamining Some Fundamentals 67\u003c\/p\u003e \u003cp\u003eGauging Excel’s Graphics (Chartics?) Capabilities 68\u003c\/p\u003e \u003cp\u003eBecoming a Columnist 69\u003c\/p\u003e \u003cp\u003eStacking the Columns 73\u003c\/p\u003e \u003cp\u003eSlicing the Pie 74\u003c\/p\u003e \u003cp\u003eA word from the wise 76\u003c\/p\u003e \u003cp\u003eDrawing the Line 77\u003c\/p\u003e \u003cp\u003eAdding a Spark 80\u003c\/p\u003e \u003cp\u003ePassing the Bar 82\u003c\/p\u003e \u003cp\u003eThe Plot Thickens 84\u003c\/p\u003e \u003cp\u003eFinding Another Use for the Scatter Chart 88\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4: Finding Your Center 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMeans: The Lore of Averages 91\u003c\/p\u003e \u003cp\u003eCalculating the mean 92\u003c\/p\u003e \u003cp\u003eAVERAGE and AVERAGEA 93\u003c\/p\u003e \u003cp\u003eAVERAGEIF and AVERAGEIFS 95\u003c\/p\u003e \u003cp\u003eTRIMMEAN 99\u003c\/p\u003e \u003cp\u003eOther means to an end 100\u003c\/p\u003e \u003cp\u003eMedians: Caught in the Middle 102\u003c\/p\u003e \u003cp\u003eFinding the median 102\u003c\/p\u003e \u003cp\u003eMEDIAN 103\u003c\/p\u003e \u003cp\u003eStatistics à la Mode 104\u003c\/p\u003e \u003cp\u003eFinding the mode 104\u003c\/p\u003e \u003cp\u003eMODE.SNGL and MODE.MULT 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5: Deviating from the Average\u003c\/b\u003e\u003cb\u003e 107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMeasuring Variation 108\u003c\/p\u003e \u003cp\u003eAveraging squared deviations: Variance and how to calculate it 108\u003c\/p\u003e \u003cp\u003eVAR.P and VARPA 111\u003c\/p\u003e \u003cp\u003eSample variance 113\u003c\/p\u003e \u003cp\u003eVAR.S and VARA 114\u003c\/p\u003e \u003cp\u003eBack to the Roots: Standard Deviation 114\u003c\/p\u003e \u003cp\u003ePopulation standard deviation 115\u003c\/p\u003e \u003cp\u003eSTDEV.P and STDEVPA 115\u003c\/p\u003e \u003cp\u003eSample standard deviation 116\u003c\/p\u003e \u003cp\u003eSTDEV.S and STDEVA 116\u003c\/p\u003e \u003cp\u003eThe missing functions: STDEVIF and STDEVIFS 117\u003c\/p\u003e \u003cp\u003eRelated Functions 121\u003c\/p\u003e \u003cp\u003eDEVSQ 121\u003c\/p\u003e \u003cp\u003eAverage deviation 122\u003c\/p\u003e \u003cp\u003eAVEDEV 123\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6: Meeting Standards and Standings\u003c\/b\u003e\u003cb\u003e 125\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCatching Some Z’s 126\u003c\/p\u003e \u003cp\u003eCharacteristics of z-scores 126\u003c\/p\u003e \u003cp\u003eBonds versus the Bambino 127\u003c\/p\u003e \u003cp\u003eExam scores 128\u003c\/p\u003e \u003cp\u003eSTANDARDIZE 128\u003c\/p\u003e \u003cp\u003eWhere Do You Stand? 131\u003c\/p\u003e \u003cp\u003eRANK.EQ and RANK.AVG 131\u003c\/p\u003e \u003cp\u003eLARGE and SMALL 133\u003c\/p\u003e \u003cp\u003ePERCENTILE.INC and PERCENTILE.EXC 134\u003c\/p\u003e \u003cp\u003ePERCENTRANK.INC and PERCENTRANK.EXC 137\u003c\/p\u003e \u003cp\u003eData analysis tool: Rank and Percentile 138\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7: Summarizing It All\u003c\/b\u003e\u003cb\u003e 141\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCounting Out 141\u003c\/p\u003e \u003cp\u003eCOUNT, COUNTA, COUNTBLANK, COUNTIF, COUNTIFS 141\u003c\/p\u003e \u003cp\u003eThe Long and Short of It 144\u003c\/p\u003e \u003cp\u003eMAX, MAXA, MIN, and MINA 144\u003c\/p\u003e \u003cp\u003eGetting Esoteric 145\u003c\/p\u003e \u003cp\u003eSKEW and SKEW.P 146\u003c\/p\u003e \u003cp\u003eKURT 148\u003c\/p\u003e \u003cp\u003eTuning In the Frequency 150\u003c\/p\u003e \u003cp\u003eFREQUENCY 150\u003c\/p\u003e \u003cp\u003eData analysis tool: Histogram 152\u003c\/p\u003e \u003cp\u003eCan You Give Me a Description? 154\u003c\/p\u003e \u003cp\u003eData analysis tool: Descriptive Statistics 154\u003c\/p\u003e \u003cp\u003eBe Quick About It! 156\u003c\/p\u003e \u003cp\u003eInstant Statistics 159\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8: What’s Normal?\u003c\/b\u003e\u003cb\u003e 161\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHitting the Curve 161\u003c\/p\u003e \u003cp\u003eDigging deeper 162\u003c\/p\u003e \u003cp\u003eParameters of a normal distribution 163\u003c\/p\u003e \u003cp\u003eNORM.DIST 165\u003c\/p\u003e \u003cp\u003eNORM.INV 167\u003c\/p\u003e \u003cp\u003eA Distinguished Member of the Family 168\u003c\/p\u003e \u003cp\u003eNORM.S.DIST 169\u003c\/p\u003e \u003cp\u003eNORM.S.INV 170\u003c\/p\u003e \u003cp\u003ePHI and GAUSS 170\u003c\/p\u003e \u003cp\u003eGraphing a Standard Normal Distribution 171\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 3: Drawing Conclusions From Data\u003c\/b\u003e\u003cb\u003e 173\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9: The Confidence Game: Estimation \u003c\/b\u003e\u003cb\u003e175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding Sampling Distributions 176\u003c\/p\u003e \u003cp\u003eAn EXTREMELY Important Idea: The Central Limit Theorem 177\u003c\/p\u003e \u003cp\u003e(Approximately) simulating the Central Limit Theorem 178\u003c\/p\u003e \u003cp\u003eThe Limits of Confidence 183\u003c\/p\u003e \u003cp\u003eFinding confidence limits for a mean 183\u003c\/p\u003e \u003cp\u003eCONFIDENCE.NORM 186\u003c\/p\u003e \u003cp\u003eFit to a t 187\u003c\/p\u003e \u003cp\u003eCONFIDENCE.T 188\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10: One-Sample Hypothesis Testing\u003c\/b\u003e\u003cb\u003e 189\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHypotheses, Tests, and Errors 190\u003c\/p\u003e \u003cp\u003eHypothesis Tests and Sampling Distributions 191\u003c\/p\u003e \u003cp\u003eCatching Some Z’s Again 193\u003c\/p\u003e \u003cp\u003eZ.TEST 196\u003c\/p\u003e \u003cp\u003et for One 197\u003c\/p\u003e \u003cp\u003eT.DIST, T.DIST.RT, and T.DIST.2T 198\u003c\/p\u003e \u003cp\u003eT.INV and T.INV.2T 200\u003c\/p\u003e \u003cp\u003eVisualizing a t-Distribution 201\u003c\/p\u003e \u003cp\u003eTesting a Variance 203\u003c\/p\u003e \u003cp\u003eCHISQ.DIST and CHISQ.DIST.RT 205\u003c\/p\u003e \u003cp\u003eCHISQ.INV and CHISQ.INV.RT 206\u003c\/p\u003e \u003cp\u003eVisualizing a Chi-Square Distribution 208\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11: Two-Sample Hypothesis Testing\u003c\/b\u003e\u003cb\u003e 211\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHypotheses Built for Two 211\u003c\/p\u003e \u003cp\u003eSampling Distributions Revisited 212\u003c\/p\u003e \u003cp\u003eApplying the Central Limit Theorem 213\u003c\/p\u003e \u003cp\u003eZ’s once more 215\u003c\/p\u003e \u003cp\u003eData analysis tool: z-Test: Two Sample for Means 216\u003c\/p\u003e \u003cp\u003e\u003ci\u003et \u003c\/i\u003efor Two 219\u003c\/p\u003e \u003cp\u003eLike peas in a pod: Equal variances 220\u003c\/p\u003e \u003cp\u003eLike p’s and q’s: Unequal variances 221\u003c\/p\u003e \u003cp\u003eT.TEST 222\u003c\/p\u003e \u003cp\u003eData analysis tool: t-Test: Two Sample 223\u003c\/p\u003e \u003cp\u003eA Matched Set: Hypothesis Testing for Paired Samples 227\u003c\/p\u003e \u003cp\u003eT.TEST for matched samples 228\u003c\/p\u003e \u003cp\u003eData analysis tool: \u003ci\u003et\u003c\/i\u003e-Test: Paired Two Sample for Means 230\u003c\/p\u003e \u003cp\u003et-tests on the iPad with StatPlus 232\u003c\/p\u003e \u003cp\u003eTesting Two Variances 235\u003c\/p\u003e \u003cp\u003eUsing F in conjunction with t 237\u003c\/p\u003e \u003cp\u003eF.TEST 238\u003c\/p\u003e \u003cp\u003eF.DIST and F.DIST.RT 240\u003c\/p\u003e \u003cp\u003eF.INV and F.INV.RT 241\u003c\/p\u003e \u003cp\u003eData analysis tool: F-test: Two Sample for Variances 242\u003c\/p\u003e \u003cp\u003eVisualizing the F-Distribution 244\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12: Testing More Than Two Samples \u003c\/b\u003e\u003cb\u003e247\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTesting More than Two 247\u003c\/p\u003e \u003cp\u003eA thorny problem 248\u003c\/p\u003e \u003cp\u003eA solution 249\u003c\/p\u003e \u003cp\u003eMeaningful relationships 253\u003c\/p\u003e \u003cp\u003eAfter the F-test 254\u003c\/p\u003e \u003cp\u003eData analysis tool: Anova: Single Factor 258\u003c\/p\u003e \u003cp\u003eComparing the means 260\u003c\/p\u003e \u003cp\u003eAnother Kind of Hypothesis, Another Kind of Test 262\u003c\/p\u003e \u003cp\u003eWorking with repeated measures ANOVA 262\u003c\/p\u003e \u003cp\u003eGetting trendy 264\u003c\/p\u003e \u003cp\u003eData analysis tool: Anova: Two-Factor Without Replication 268\u003c\/p\u003e \u003cp\u003eAnalyzing trend 271\u003c\/p\u003e \u003cp\u003eANOVA on the iPad 272\u003c\/p\u003e \u003cp\u003eANOVA on the iPad: Another Way 274\u003c\/p\u003e \u003cp\u003eRepeated Measures ANOVA on the iPad 277\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13: Slightly More Complicated Testing\u003c\/b\u003e\u003cb\u003e 281\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCracking the Combinations 281\u003c\/p\u003e \u003cp\u003eBreaking down the variances 282\u003c\/p\u003e \u003cp\u003eData analysis tool: Anova: Two-Factor Without Replication 284\u003c\/p\u003e \u003cp\u003eCracking the Combinations Again 286\u003c\/p\u003e \u003cp\u003eRows and columns 286\u003c\/p\u003e \u003cp\u003eInteractions 287\u003c\/p\u003e \u003cp\u003eThe analysis 288\u003c\/p\u003e \u003cp\u003eData analysis tool: Anova: Two-Factor With Replication 289\u003c\/p\u003e \u003cp\u003eTwo Kinds of Variables — at Once 292\u003c\/p\u003e \u003cp\u003eUsing Excel with a Mixed Design 293\u003c\/p\u003e \u003cp\u003eGraphing the Results 298\u003c\/p\u003e \u003cp\u003eAfter the ANOVA 300\u003c\/p\u003e \u003cp\u003eTwo-Factor ANOVA on the iPad 300\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14: Regression: Linear and Multiple\u003c\/b\u003e\u003cb\u003e 303\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Plot of Scatter 303\u003c\/p\u003e \u003cp\u003eGraphing a line 305\u003c\/p\u003e \u003cp\u003eRegression: What a Line! 307\u003c\/p\u003e \u003cp\u003eUsing regression for forecasting 309\u003c\/p\u003e \u003cp\u003eVariation around the regression line 309\u003c\/p\u003e \u003cp\u003eTesting hypotheses about regression 311\u003c\/p\u003e \u003cp\u003eWorksheet Functions for Regression 317\u003c\/p\u003e \u003cp\u003eSLOPE, INTERCEPT, STEYX 318\u003c\/p\u003e \u003cp\u003eFORECAST.LINEAR 319\u003c\/p\u003e \u003cp\u003eArray function: TREND 319\u003c\/p\u003e \u003cp\u003eArray function: LINEST 323\u003c\/p\u003e \u003cp\u003eData Analysis Tool: Regression 325\u003c\/p\u003e \u003cp\u003eWorking with tabled output 327\u003c\/p\u003e \u003cp\u003eOpting for graphical output 329\u003c\/p\u003e \u003cp\u003eJuggling Many Relationships at Once: Multiple Regression 330\u003c\/p\u003e \u003cp\u003eExcel Tools for Multiple Regression 331\u003c\/p\u003e \u003cp\u003eTREND revisited 331\u003c\/p\u003e \u003cp\u003eLINEST revisited 333\u003c\/p\u003e \u003cp\u003eRegression data analysis tool revisited 336\u003c\/p\u003e \u003cp\u003eRegression Analysis on the iPad 338\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15: Correlation: The Rise and Fall of Relationships\u003c\/b\u003e\u003cb\u003e 341\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eScatterplots Again 341\u003c\/p\u003e \u003cp\u003eUnderstanding Correlation 342\u003c\/p\u003e \u003cp\u003eCorrelation and Regression 345\u003c\/p\u003e \u003cp\u003eTesting Hypotheses about Correlation 347\u003c\/p\u003e \u003cp\u003eIs a correlation coefficient greater than zero? 348\u003c\/p\u003e \u003cp\u003eDo two correlation coefficients differ? 349\u003c\/p\u003e \u003cp\u003eWorksheet Functions for Correlation 350\u003c\/p\u003e \u003cp\u003eCORREL and PEARSON 350\u003c\/p\u003e \u003cp\u003eRSQ 351\u003c\/p\u003e \u003cp\u003eCOVARIANCE.P and COVARIANCE.S 352\u003c\/p\u003e \u003cp\u003eData Analysis Tool: Correlation 353\u003c\/p\u003e \u003cp\u003eTabled output 354\u003c\/p\u003e \u003cp\u003eMultiple correlation 355\u003c\/p\u003e \u003cp\u003ePartial correlation 356\u003c\/p\u003e \u003cp\u003eSemipartial correlation 357\u003c\/p\u003e \u003cp\u003eData Analysis Tool: Covariance 358\u003c\/p\u003e \u003cp\u003eUsing Excel to Test Hypotheses about Correlation 358\u003c\/p\u003e \u003cp\u003eWorksheet functions: FISHER, FISHERINV 359\u003c\/p\u003e \u003cp\u003eCorrelation Analysis on the iPad 360\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16: It’s About Time\u003c\/b\u003e\u003cb\u003e 363\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Series and Its Components 363\u003c\/p\u003e \u003cp\u003eA Moving Experience 364\u003c\/p\u003e \u003cp\u003eLining up the trend 365\u003c\/p\u003e \u003cp\u003eData analysis tool: Moving Average 365\u003c\/p\u003e \u003cp\u003eHow to Be a Smoothie, Exponentially 368\u003c\/p\u003e \u003cp\u003eOne-Click Forecasting 369\u003c\/p\u003e \u003cp\u003eWorking with Time Series on the iPad 374\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17: Nonparametric Statistics\u003c\/b\u003e\u003cb\u003e 379\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIndependent Samples 380\u003c\/p\u003e \u003cp\u003eTwo samples: Mann-Whitney U test 380\u003c\/p\u003e \u003cp\u003eMore than two samples: Kruskal-Wallis one-way ANOVA 382\u003c\/p\u003e \u003cp\u003eMatched Samples 383\u003c\/p\u003e \u003cp\u003eTwo samples: Wilcoxon matched-pairs signed ranks 384\u003c\/p\u003e \u003cp\u003eMore than two samples: Friedman two-way ANOVA 386\u003c\/p\u003e \u003cp\u003eMore than two samples: Cochran’s Q 387\u003c\/p\u003e \u003cp\u003eCorrelation: Spearman’s rS 389\u003c\/p\u003e \u003cp\u003eA Heads-Up 391\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 4: Probability \u003c\/b\u003e\u003cb\u003e393\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 18: Introducing Probability\u003c\/b\u003e\u003cb\u003e 395\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is Probability? 395\u003c\/p\u003e \u003cp\u003eExperiments, trials, events, and sample spaces 396\u003c\/p\u003e \u003cp\u003eSample spaces and probability 396\u003c\/p\u003e \u003cp\u003eCompound Events 397\u003c\/p\u003e \u003cp\u003eUnion and intersection 397\u003c\/p\u003e \u003cp\u003eIntersection, again 398\u003c\/p\u003e \u003cp\u003eConditional Probability 399\u003c\/p\u003e \u003cp\u003eWorking with the probabilities 400\u003c\/p\u003e \u003cp\u003eThe foundation of hypothesis testing 400\u003c\/p\u003e \u003cp\u003eLarge Sample Spaces 400\u003c\/p\u003e \u003cp\u003ePermutations 401\u003c\/p\u003e \u003cp\u003eCombinations 402\u003c\/p\u003e \u003cp\u003eWorksheet Functions 403\u003c\/p\u003e \u003cp\u003eFACT 403\u003c\/p\u003e \u003cp\u003ePERMUT and PERMUTIONA 403\u003c\/p\u003e \u003cp\u003eCOMBIN and COMBINA 404\u003c\/p\u003e \u003cp\u003eRandom Variables: Discrete and Continuous 405\u003c\/p\u003e \u003cp\u003eProbability Distributions and Density Functions 405\u003c\/p\u003e \u003cp\u003eThe Binomial Distribution 407\u003c\/p\u003e \u003cp\u003eWorksheet Functions 409\u003c\/p\u003e \u003cp\u003eBINOM.DIST and BINOM.DIST.RANGE 409\u003c\/p\u003e \u003cp\u003eNEGBINOM.DIST 411\u003c\/p\u003e \u003cp\u003eHypothesis Testing with the Binomial Distribution 412\u003c\/p\u003e \u003cp\u003eBINOM.INV 413\u003c\/p\u003e \u003cp\u003eMore on hypothesis testing 414\u003c\/p\u003e \u003cp\u003eThe Hypergeometric Distribution 415\u003c\/p\u003e \u003cp\u003eHYPGEOM.DIST 416\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 19: More on Probability\u003c\/b\u003e\u003cb\u003e 419\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDiscovering Beta 419\u003c\/p\u003e \u003cp\u003eBETA.DIST 421\u003c\/p\u003e \u003cp\u003eBETA.INV 423\u003c\/p\u003e \u003cp\u003ePoisson 424\u003c\/p\u003e \u003cp\u003ePOISSON.DIST 425\u003c\/p\u003e \u003cp\u003eWorking with Gamma 427\u003c\/p\u003e \u003cp\u003eThe gamma function and GAMMA 427\u003c\/p\u003e \u003cp\u003eThe gamma distribution and GAMMA.DIST 428\u003c\/p\u003e \u003cp\u003eGAMMA.INV 430\u003c\/p\u003e \u003cp\u003eExponential 431\u003c\/p\u003e \u003cp\u003eEXPON.DIST 431\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 20: Using Probability: Modeling and Simulation\u003c\/b\u003e\u003cb\u003e 433\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eModeling a Distribution 434\u003c\/p\u003e \u003cp\u003ePlunging into the Poisson distribution 434\u003c\/p\u003e \u003cp\u003eVisualizing the Poisson distribution 435\u003c\/p\u003e \u003cp\u003eWorking with the Poisson distribution 436\u003c\/p\u003e \u003cp\u003eUsing POISSON.DIST again 437\u003c\/p\u003e \u003cp\u003eTesting the model’s fit 437\u003c\/p\u003e \u003cp\u003eA word about CHISQ.TEST 440\u003c\/p\u003e \u003cp\u003ePlaying ball with a model 441\u003c\/p\u003e \u003cp\u003eA Simulating Discussion 444\u003c\/p\u003e \u003cp\u003eTaking a chance: The Monte Carlo method 444\u003c\/p\u003e \u003cp\u003eLoading the dice 444\u003c\/p\u003e \u003cp\u003eData analysis tool: Random Number Generation 445\u003c\/p\u003e \u003cp\u003eSimulating the Central limit Theorem 448\u003c\/p\u003e \u003cp\u003eSimulating a business 452\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 21: Estimating Probability: Logistic Regression\u003c\/b\u003e\u003cb\u003e 457\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWorking Your Way Through Logistic Regression 458\u003c\/p\u003e \u003cp\u003eMining with XLMiner 460\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 5: The Part of Tens\u003c\/b\u003e\u003cb\u003e 465\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 22: Ten (12, Actually) Statistical and Graphical Tips and Traps\u003c\/b\u003e\u003cb\u003e 467\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSignificant Doesn’t Always Mean Important 467\u003c\/p\u003e \u003cp\u003eTrying to Not Reject a Null Hypothesis Has a Number of Implications 468\u003c\/p\u003e \u003cp\u003eRegression Isn’t Always Linear 468\u003c\/p\u003e \u003cp\u003eExtrapolating Beyond a Sample Scatterplot Is a Bad Idea 469\u003c\/p\u003e \u003cp\u003eExamine the Variability Around a Regression Line 469\u003c\/p\u003e \u003cp\u003eA Sample Can Be Too Large 470\u003c\/p\u003e \u003cp\u003eConsumers: Know Your Axes 470\u003c\/p\u003e \u003cp\u003eGraphing a Categorical Variable as a Quantitative Variable Is Just Plain Wrong 471\u003c\/p\u003e \u003cp\u003eWhenever Appropriate, Include Variability in Your Graph 472\u003c\/p\u003e \u003cp\u003eBe Careful When Relating Statistics Textbook Concepts to Excel 472\u003c\/p\u003e \u003cp\u003eIt’s Always a Good Idea to Use Named Ranges in Excel 472\u003c\/p\u003e \u003cp\u003eStatistical Analysis with Excel on the iPad Is Pretty Good! 473\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 23: Ten Topics (Thirteen, Actually) That Just Don’t Fit Elsewhere\u003c\/b\u003e\u003cb\u003e 475\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGraphing the Standard Error of the Mean 475\u003c\/p\u003e \u003cp\u003eProbabilities and Distributions 479\u003c\/p\u003e \u003cp\u003ePROB 479\u003c\/p\u003e \u003cp\u003eWEIBULL.DIST 479\u003c\/p\u003e \u003cp\u003eDrawing Samples 480\u003c\/p\u003e \u003cp\u003eTesting Independence: The True Use of CHISQ.TEST 481\u003c\/p\u003e \u003cp\u003eLogarithmica Esoterica 484\u003c\/p\u003e \u003cp\u003eWhat is a logarithm? 484\u003c\/p\u003e \u003cp\u003eWhat is e? 486\u003c\/p\u003e \u003cp\u003eLOGNORM.DIST 489\u003c\/p\u003e \u003cp\u003eLOGNORM.INV 490\u003c\/p\u003e \u003cp\u003eArray Function: LOGEST 491\u003c\/p\u003e \u003cp\u003eArray Function: \u003ci\u003eGROWTH\u003c\/i\u003e 494\u003c\/p\u003e \u003cp\u003eThe logs of Gamma 497\u003c\/p\u003e \u003cp\u003eSorting Data 498\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 6: Appendices\u003c\/b\u003e\u003cb\u003e 501\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A: When Your Data Live Elsewhere\u003c\/b\u003e\u003cb\u003e 503\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B: Tips for Teachers (and Learners)\u003c\/b\u003e\u003cb\u003e 507\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAugmenting Analyses Is a Good Thing 507\u003c\/p\u003e \u003cp\u003eUnderstanding ANOVA 508\u003c\/p\u003e \u003cp\u003eRevisiting regression 510\u003c\/p\u003e \u003cp\u003eSimulating Data Is Also a Good Thing 512\u003c\/p\u003e \u003cp\u003eWhen All You Have Is a Graph 514\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix C: More on Excel Graphics\u003c\/b\u003e\u003cb\u003e 515\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTasting the Bubbly 515\u003c\/p\u003e \u003cp\u003eTaking Stock 516\u003c\/p\u003e \u003cp\u003eScratching the Surface 518\u003c\/p\u003e \u003cp\u003eOn the Radar 519\u003c\/p\u003e \u003cp\u003eGrowing a Treemap and Bursting Some Sun 520\u003c\/p\u003e \u003cp\u003eBuilding a Histogram 521\u003c\/p\u003e \u003cp\u003eOrdering Columns: Pareto 522\u003c\/p\u003e \u003cp\u003eOf Boxes and Whiskers 523\u003c\/p\u003e \u003cp\u003e3D Maps 524\u003c\/p\u003e \u003cp\u003eFilled Maps 527\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix D: The Analysis of Covariance\u003c\/b\u003e\u003cb\u003e 529\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCovariance: A Closer Look 529\u003c\/p\u003e \u003cp\u003eWhy You Analyze Covariance 530\u003c\/p\u003e \u003cp\u003eHow You Analyze Covariance 531\u003c\/p\u003e \u003cp\u003eANCOVA in Excel 532\u003c\/p\u003e \u003cp\u003eMethod 1: ANOVA 533\u003c\/p\u003e \u003cp\u003eMethod 2: Regression 537\u003c\/p\u003e \u003cp\u003eAfter the ANCOVA 540\u003c\/p\u003e \u003cp\u003eAnd One More Thing 542\u003c\/p\u003e \u003cp\u003eIndex 545\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866420162903,"sku":"9781119844549","price":24.79,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119844549.jpg?v=1722278559"},{"product_id":"jamovi-for-psychologists-9781352011852","title":"Jamovi for Psychologists","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis textbook offers a refreshingly clear and digestible introduction to statistical analysis for psychology using the user-friendly jamovi software. The authors provide a concise, practical guide that takes students from the early stages of research design, with a jargon-free explanation of terminology, and walks them through key analyses such as the t-test, ANOVA, correlation, chi-square, and linear regression. The book features written interpretations to help learners identify relevant statistics along the way. With fascinating examples from psychological research, as well as screenshots and activities from jamovi, this text is sure to encourage even the most reluctant statistics student. The comprehensive companion website provides an extra helping hand, with practice datasets and a full suite of tutorial videos to help consolidate understanding.   This is essential reading for psychology students using jamovi for their courses in Research Methods and Statistics or Data Analysis.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eJamovi for Psychologists offers a complete overview of topics in introductory statistics in an easy, conversational tone. But what makes it especially valuable is its practical emphasis—how to use very accessible software, fully understand its output, and appropriately report the results. It’s the kind of book students will actually find useful! * Andy Luttrell, Ball State University, USA *\u003cbr\u003eJamovi for Psychologists is an excellent resource for those learning to use jamovi as part of a statistics course or for those seeking to better understand the wide range of statistical tests available in the software. The straightforward step-by-step instructions and conceptual framing of statistical analyses will help faculty make statistics and jamovi more accessible to students. * Andrew Mienaltowski, Western Kentucky University, USA *\u003cbr\u003eJamovi for Psychologists, is a friendly introduction to the accessible statistics package jamovi. It is well-pitched for psychologists beginning to learn about statistics and includes concise but thorough guides throughout. * Piers Fleming, University of East Anglia, UK *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Research Design  2. Data Preparation, Common Assumptions, and Descriptive Statistics  3. P-Values, Effect Sizes and 95% confidence intervals  4. Statistical Power  5. Reliability and Validity  6. Correlations  7. Chi Square  8. Independent T-Tests  9. Paired T-Tests  10. Comparing multiple means for Between-subjects designs (One-way ANOVA \u0026amp; Kruskal-Wallis)  11. Comparing multiple means for Repeated measures designs (one-way ANOVA and Friedman’s ANOVA)  12. Factorial ANOVA (assessing effects of multiple independent variables)  13. Simple, Multiple, and Hierarchical Linear Regression.","brand":"Bloomsbury Publishing PLC","offers":[{"title":"Default Title","offer_id":48866604384599,"sku":"9781352011852","price":28.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781352011852.jpg?v=1722279421"},{"product_id":"visualizing-mathematics-with-3d-printing-9781421420356","title":"Visualizing Mathematics with 3D Printing","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWith the book in one hand and a 3D printed model in the other, readers can find deeper meaning while holding a hyperbolic honeycomb, touching the twists of a torus knot, or caressing the curves of a Klein quartic.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eMy best advice is to go out and buy yourself a copy of the book. Chalkdust Magazine The breadth of Segerman's 3D printing explorations is impressive. Coupled with the clarity of his explanations of the mathematics behind those explorations, this book becomes an easy recommendation for any reader interested in learning some beautiful mathematical ideas. Journal of Mathematics and the Arts No previous mathematical maturity is required. The work is a good addition to any academic library. Highly recommended Choice I have great difficulty thinking about Visualizing Mathematics with 3D Printing as \"just a book.\" The careful choice, quality and effectiveness of the 140+ images in the book is outstanding. What Segerman has developed is much bigger than a book he has developed a whole platform to complement the book and explore mathematical concepts. Visualizing Mathematics with 3D printing allows the reader to manipulate with a computer or 3D print the objects discussed, making it possible to physically interact with the concepts. Mathematical Association of America\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface\u003cbr\u003eAcknowledgments\u003cbr\u003e1. Symmetry\u003cbr\u003e2. Polyhedra\u003cbr\u003e3. Four-Dimensional Space\u003cbr\u003e4. Tilings and Curvature\u003cbr\u003e5. Knots\u003cbr\u003e6. Surfaces\u003cbr\u003e7. Menagerie\u003cbr\u003eAppendix A\u003cbr\u003eAppendix B\u003cbr\u003eIndex\u003c\/p\u003e","brand":"Johns Hopkins University Press","offers":[{"title":"Default Title","offer_id":48866937241943,"sku":"9781421420356","price":54.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781421420356.jpg?v=1722280995"},{"product_id":"data-analysis-with-r-9781449359010","title":"Data Analysis with R","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eLearn how to program by diving into the R language, and then use your newfound skills to solve practical data science problems. 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This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. \u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eFour of the authors co-wrote \u003ci\u003eAn Introduction to Statistical Learning, With Applications in R \u003c\/i\u003e(ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. 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The book will help with: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e Manipulating and organizing data\u003c\/li\u003e\n\u003cli\u003e Generating statistics\u003c\/li\u003e\n\u003cli\u003e Interpreting results\u003c\/li\u003e\n\u003cli\u003e Presenting outputs \u003c\/li\u003e\n\u003c\/ul\u003e\u003ci\u003eThe Stata Survival Manual\u003c\/i\u003e is a lifesaver for both students and professionals who are using\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eIntroduction\u003cbr\u003eAbout the authors \u003cbr\u003eAcknowledgements\u003cp\u003eGetting started with Stata\u003cbr\u003eData in and out of Stata\u003cbr\u003eManipulating variables\u003cbr\u003eManipulating data \u003cbr\u003eDescriptive statistics and graphs\u003cbr\u003eTables and correlations\u003cbr\u003eDifferences in means, medians and proportions\u003cbr\u003eRegression\u003cbr\u003ePresenting your results\u003c\/p\u003e\u003cp\u003eReferences\u003cbr\u003eIndex  \u003c\/p\u003e","brand":"Open University Press","offers":[{"title":"Default Title","offer_id":48883990921559,"sku":"9780335223886","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"matlab-for-control-system-engineers-9781781830062","title":"Matlab for Control System Engineers","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"New Age International (UK) Ltd","offers":[{"title":"Default Title","offer_id":48887670735191,"sku":"9781781830062","price":47.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781781830062.jpg?v=1722545672"},{"product_id":"analysis-and-design-of-control-systems-using-matlab-9781906574192","title":"Analysis and Design of Control Systems Using","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"New Age International (UK) Ltd","offers":[{"title":"Default Title","offer_id":48888407261527,"sku":"9781906574192","price":38.0,"currency_code":"GBP","in_stock":true}]},{"product_id":"first-guide-to-statistical-computations-in-r-9788791319563","title":"First Guide to Statistical Computations in R","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eR is a statistical computer program used and developed by statisticians around the world. 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This book bridges these gaps by explaining the deep ideas of theoretical computer science in a clear and enjoyable fashion, making them accessible to non-computer scientists and to computer scientists who finally want to appreciate their field from a new point of view. The authors start with a lucid and playful explanation of the P vs. NP problem, explaining why it is so fundamental, and so hard to resolve. They then lead the reader through the complexity of mazes and games; optimization in theory and practice; randomized algorithms, interactive proofs, and pseudorandomness; Markov chains and phase transitions; and the outer reaches of quantum computing. At every turn, they use a minimum of formalism, providing explanations that are both deep and accessible. The book is intended for graduate and undergraduate students, scientists from other areas who have long wanted to understand this subject, and experts who want to fall in love with this field all over again.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eA creative, insightful, and accessible introduction to the theory of computing, written with a keen eye toward the frontiers of the field and a vivid enthusiasm for the subject matter. * Jon Kleinberg, Cornell University *\u003cbr\u003eTo put it bluntly: this book rocks! It's 900+ pages of awesome. It somehow manages to combine the fun of a popular book with the intellectual heft of a textbook, so much so that I don't know what to call it (but whatever the genre is, there needs to be more of it!). * Scott Aaronson, Massachusetts Institute of Technology *\u003cbr\u003eMoore and Mertens guide the reader through the interesting field of computational complexity in a clear, broadly accessible and informal manner, while systematically explaining the main concepts and approaches in this area and the existing links to other disciplines. The book is comprehensive and can be easily used as a textbook, at both advanced undergraduate and postgraduate levels, but is equally useful for researchers in neighbouring disciplines, such as statistical physics [...]. Some of the material covered, such as approximability issues and Probabilistically Checkable Proofs is typically not presented in books of this type, and the authors do an excellent job in presenting them very clearly and convincingly. * David Saad, Aston University, Birmingham *\u003cbr\u003eA treasure trove of ideas, concepts and information on algorithms and complexity theory. Serious material presented in the most delightful manner! * Vijay Vazirani, Georgia Instituute of Technology *\u003cbr\u003eIn a class by itself - in The Nature of Computation, Cristopher Moore and Stephan Mertens have produced one of the most successful attempts to capture the broad scope and intellectual depth of theoretical computer science as it is practiced today. 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With its broad and deep wealth of information, it would be a top contender for one of my desert island books.TNoC speaks directly, clearly, convincingly, and entetainingly, but also goes much further: it inspires. * Frederic Green, SIGACT *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Prologue ; 2. The Basics ; 3. Insights and Algorithms ; 4. Needles in a Haystack: The class NP ; 5. Who is the Hardest One of All: NP-Completeness ; 6. The Deep Question: P vs. NP ; 7. Memory, Paths and games ; 8. Grand Unified Theory of Computation ; 9. Simply the Best: Optimization ; 10. The Power of Randomness ; 11. Random Walks and Rapid Mixing ; 12. Counting, Sampling, and Statistical Physics ; 13. When Formulas Freeze: Phase Transitions in Computation ; 14. Quantum Computing ; 15. Epilogue ; 16. Appendix: Mathematical Tools","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":49083405009239,"sku":"9780199233212","price":77.9,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780199233212.jpg?v=1725548835"},{"product_id":"open-source-software-for-statistical-analysis-of-big-data-emerging-research-and-opportunities-9781799827689","title":"Open Source Software for Statistical Analysis of","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWith the development of computing technologies in today's modernized world, software packages have become easily accessible. Open source software, specifically, is a popular method for solving certain issues in the field of computer science. One key challenge is analyzing big data due to the high amounts that organizations are processing. 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It uniquely combines a hands-on approach to data analysis – supported by numerous real data examples and reusable [R] code – with a rigorous treatment of probability and statistical principles. \u003c\/p\u003eWhere contemporary undergraduate textbooks in probability theory or statistics often miss applications and an introductory treatment of modern methods (bootstrapping, Bayes, etc.), and where applied data analysis books often miss a rigorous theoretical treatment, this book provides an accessible but thorough introduction into data analysis, using statistical methods combining the two viewpoints. The book further focuses on methods for dealing with large data-sets and streaming-data and hence provides a single-course introduction of statistical methods for data science.\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“Having taught data analytics at the introductory graduate level, I welcome the authors’ textbook as an essential resource for training well-grounded entry-level data scientists. … A data scientist shall provide competent data science professional services to a client. … Training in both the theory and practice of data analytics is a requirement for such competence. The authors’ textbook definitely provides a valuable resource for such training.” (Harry J. Foxwell, Computing Reviews, July 7, 2022)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1 A First Look at Data.- 2 Sampling Plans and Estimates.- 3 Probability Theory.- 4 Random Variables and Distributions.- 5 Estimation.- 6 Multiple Random Variables.- 7 Making Decisions in Uncertainty.- 8 Bayesian Statistics.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49084748169559,"sku":"9783030105303","price":37.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030105303.jpg?v=1725553218"},{"product_id":"an-introduction-to-bayesian-inference-methods-and-computation-9783030828103","title":"An Introduction to Bayesian Inference, Methods","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThese lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy and principles of Bayesian inference, including the reasoning behind the prior\/likelihood model construction synonymous with Bayesian methods, through to advanced topics such as nonparametrics, Gaussian processes and latent factor models. These advanced modelling techniques can easily be applied using computer code samples written in Python and Stan which are integrated into the main text. Importantly, the reader will learn methods for assessing model fit, and to choose between rival modelling approaches.\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eUncertainty and Decisions.- Prior and Likelihood Representation.- Graphical Modeling.- Parametric Models.- Computational Inference.- Bayesian Software Packages.- Model choice.- Linear Models.- Nonparametric Models.- Nonparametric Regression.- Clustering and Latent Factor Models.- Conjugate Parametric Models.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49084751872343,"sku":"9783030828103","price":54.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"exploring-university-mathematics-with-python-9783031462696","title":"Exploring University Mathematics with Python","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book provides a unique tour of university mathematics with the help of Python. Written in the spirit of mathematical exploration and investigation, the book enables students to utilise Python to enrich their understanding of mathematics through:\u003c\/p\u003e  \u003cul\u003e\n\u003cli\u003eCalculation:      performing complex calculations and numerical simulations instantly\u003c\/li\u003e\n\u003cli\u003eVisualisation:      demonstrating key theorems with graphs, interactive plots and animations\u003c\/li\u003e\n\u003cli\u003eExtension:      using numerical findings as inspiration for making deeper, more general      conjectures.\u003c\/li\u003e\n\u003c\/ul\u003e This book is for all learners of mathematics, with the primary audience being mathematics undergraduates who are curious to see how Python can enhance their understanding of core university material. The topics chosen represent a mathematical overview of what students typically study in the first and second years at university, namely analysis, calculus, vector calculus and geometry, differential equations and dynamical systems, linear algebra, abstract algebra and number theory, probability and statistics. As such, it can also serve as a preview of university mathematics for high-school students. The prerequisites for reading the book are a familiarity with standard A-Level mathematics (or equivalent senior high-school curricula) and a willingness to learn programming.\u003cp\u003e\u003c\/p\u003e  \u003cp\u003eFor mathematics lecturers and teachers, this book is a useful resource on how Python can be seamlessly incorporated into the mathematics syllabus, assuming only basic knowledge of programming.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1 Analysis.- 2 Calculus.- 3 Vector Calculus and Geometry.- 4 Differential Equations and Dynamical Systems.- 5 Linear Algebra.- 6 Abstract Algebra and Number Theory.- 7 Probability.- 8 Statistics.- Appendix A: Python 101.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49084758786391,"sku":"9783031462696","price":61.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031462696.jpg?v=1725553246"},{"product_id":"system-assurances-9780323902403","title":"System Assurances","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Statistical analysis approach for the quality assessment of open-source software Yoshinobu Tamura and Shigeru Yamada 2. Analytical modeling and performance evaluation of SIP signaling protocol: Analytical modeling of SIP Nikesh Choudhary, Vandana Khaitan (nee Gupta), and Vaneeta Goel 3. An empirical validation for predicting bugs and the release time of open source software using entropy measures—Software reliability growth models Anjali Munde 4. Risk assessment of starting air system of marine diesel engine using fuzzy failure mode and effects analysis Rajesh S. Prabhu Gaonkar and Sunay P. Pai 5. Test scenario generator learning for model-based testing of mobile robots Gert Kanter and Marti Ingmar Liibert 6. Testing effort-dependent software reliability growth model using time lag functions under distributed environment Sudeept Singh Yadav, Avneesh Kumar, Prashant Johri, and J.N. Singh 7. Design and performance analysis of MIMO PID controllers for a paper machine subsystem Niharika Varshney, Parvesh Saini, and Ashutosh Dixit 8. Network and security leveraging IoT and image processing: A quantum leap forward Ajay Sudhir Bale, S. Saravana Kumar, S. Varun Yogi, Swetha Vura, R. Baby Chithra, N. Vinay, and P. Pravesh 9. Modeling software patching process inculcating the impact of vulnerabilities discovered and disclosed Deepti Aggrawal, Jasmine Kaur, and Adarsh Anand 10. Extension of software reliability growth models by several testing-time functions Yuka Minamino, Shinji Inoue, and Shigeru Yamada 11. A semi-Markov model of a system working under uncertainty R.K. Bhardwaj, Purnima Sonker, and Ravinder Singh 12. Design and evaluation of parallel-series IRM system Sridhar Akiri, P. Sasikala, Pavan Kumar Subbara, and VSS Yadavalli 13. Modeling and availability assessment of smart building automation systems with multigoal maintenance Yuriy Ponochovniy, Vyacheslav Kharchenko, and Olga Morozova 14. A study of bitcoin and Ethereum blockchains in the context of client types, transactions, and underlying network architecture Rohaila Naaz and Ashendra Kumar Saxena 15. High assurance software architecture and design Muhammad Ehsan Rana and Omar S. Saleh 16. Online condition monitoring and maintenance of photovoltaic system Neeraj Khera 17. Fault diagnosis and fault tolerance Afaq Ahmad and Sayyid Samir Al Busaidi 18. True power loss diminution by Improved Grasshopper Optimization Algorithm Lenin Kanagasabai 19. Security analytics Vani Rajasekar, J Premalatha, and Rajesh Kumar Dhanaraj 20. Stochastic modeling of the mean time between software failures: A review Gabriel Pena, Veronica Moreno, and Nestor Barraza 21. Inliers prone distributions: Perspectives and future scopes K. Muralidharan and Pratima Bavagosai 22. Integration of TPM, RCM, and CBM: A practical approach applied in Shipbuilding industry Rupesh Kumtekar, Swapnil Kamble, and Suraj Rane 23. Revolutionizing the internet of things with swarm intelligence Abhishek Kumar, Jyotir Moy Chatterjee, Manju Payal, and Pramod Singh Rathore 24. Security and challenges in IoT-enabled systems S. Kala and S. Nalesh 25. Provably correct aspect-oriented modeling with UPPAAL timed automata Juri Vain, Leonidas Tsiopoulos, and Gert Kanter 26. Relevance of data mining techniques in real life Palwinder Kaur Mangat and Kamaljit Singh Saini 27. D-PPSOK clustering algorithm with data sampling for clustering big data analysis C. Suresh Gnana Dhas, N. Yuvaraj, N.V. Kousik, and Tadele Degefa Geleto 28. A review on optimal placement of phasor measurement unit (PMU) Ashutosh Dixit, Arindam Chowdhury, and Parvesh Saini 29. Effective motivational factors and comprehensive study of information security and policy challenges M. Arvindhan 30. Integration of wireless communication technologies in internet of vehicles for handover decision and network selection Shaik Mazhar Hussain, Kamaludin Mohamad Yusof, Afaq Ahmad, and Shaik Ashfaq Hussain 31. Modeling HIV-TB coinfection with illegal immigrants and its stability analysis Rajinder Sharma","brand":"Elsevier Science \u0026 Technology","offers":[{"title":"Default Title","offer_id":49371674935639,"sku":"9780323902403","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"handbook-of-statistical-analysis-and-data-mining-applications-9780124166325","title":"Handbook of Statistical Analysis and Data Mining","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Data mining practitioners, here is your bible, the complete \"driver's manual\" for data mining. From starting the engine to handling the curves, this book covers the gamut of data mining techniques - including predictive analytics and text mining - illustrating how to achieve maximal value across business, scientific, engineering, and medical applications. What are the best practices through each phase of a data mining project? How can you avoid the most treacherous pitfalls? The answers are in here.   \"Going beyond its responsibility as a reference book, the heavily-updated second edition also provides all-new, detailed tutorials with step-by-step instructions to drive established data mining software tools across real world applications. This way, newcomers start their engines immediately and experience hands-on success.   \"What's more, this edition drills down on hot topics across seven new chapters, including deep learning and how to avert \"b---s---\" results. If you want to roll-up your sleeves and execute on predictive analytics, this is your definite, go-to resource. To put it lightly, if this book isn't on your shelf, you're not a data miner.\" --Eric Siegel, Ph.D., founder of Predictive Analytics World and author of \"Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die\"   \"Great introduction to the real-world process of data mining. The overviews, practical advice, tutorials, and extra CD material make this book an invaluable resource for both new and experienced data miners.\" --Karl Rexer, PhD (President and Founder of Rexer Analytics, Boston, Massachusetts)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart 1: History Of Phases Of Data Analysis, Basic Theory, And The Data Mining Process 1. The Background for Data Mining Practice 2. Theoretical Considerations for Data Mining 3. The Data Mining and Predictive Analytic Process 4. Data Understanding and Preparation 5. Feature Selection 6. Accessory Tools for Doing Data Mining   Part 2: The Algorithms And Methods In Data Mining And Predictive Analytics And Some Domain Areas 7. Basic Algorithms for Data Mining: A Brief Overview 8. Advanced Algorithms for Data Mining 9. Classification 10. Numerical Prediction 11. Model Evaluation and Enhancement 12. Predictive Analytics for Population Health and Care 13. Big Data in Education: New Efficiencies for Recruitment, Learning, and Retention of Students and Donors 14. Customer Response Modeling 15. Fraud Detection   Part 3: Tutorials And Case Studies Tutorial A Example of Data Mining Recipes Using Windows 10 and Statistica 13 Tutorial B Using the Statistica Data Mining Workspace Method for Analysis of Hurricane Data (Hurrdata.sta) Tutorial C Case Study—Using SPSS Modeler and STATISTICA to Predict Student Success at High-Stakes Nursing Examinations (NCLEX) Tutorial D Constructing a Histogram in KNIME Using MidWest Company Personality Data Tutorial E Feature Selection in KNIME Tutorial F Medical\/Business Tutorial Tutorial G A KNIME Exercise, Using Alzheimer’s Training Data of Tutorial F Tutorial H Data Prep 1-1: Merging Data Sources Tutorial I Data Prep 1–2: Data Description Tutorial J Data Prep 2-1: Data Cleaning and Recoding Tutorial K Data Prep 2-2: Dummy Coding Category Variables Tutorial L Data Prep 2-3: Outlier Handling Tutorial M Data Prep 3-1: Filling Missing Values With Constants Tutorial N Data Prep 3-2: Filling Missing Values With Formulas Tutorial O Data Prep 3-3: Filling Missing Values With a Model Tutorial P City of Chicago Crime Map: A Case Study Predicting Certain Kinds of Crime Using Statistica Data Miner and Text Miner Tutorial Q Using Customer Churn Data to Develop and Select a Best Predictive Model for Client Defection Using STATISTICA Data Miner 13 64-bit for Windows 10 Tutorial R Example With C\u0026amp;RT to Predict and Display Possible Structural Relationships Tutorial S Clinical Psychology: Making Decisions About Best Therapy for a Client   Part 4: Model Ensembles, Model Complexity; Using the Right Model for the Right Use, Significance, Ethics, and the Future, and Advanced Processes 16. The Apparent Paradox of Complexity in Ensemble Modeling 17. The \"Right Model\" for the \"Right Purpose\": When Less Is Good Enough 18. A Data Preparation Cookbook 19. Deep Learning 20. Significance versus Luck in the Age of Mining: The Issues of P-Value \"Significance\" and \"Ways to Test Significance of Our Predictive Analytic Models\" 21. Ethics and Data Analytics 22. IBM Watson","brand":"Elsevier Science Publishing Co Inc","offers":[{"title":"Default Title","offer_id":49399830085975,"sku":"9780124166325","price":75.04,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780124166325.jpg?v=1730468844"},{"product_id":"mathematica-by-example-9780128241639","title":"Mathematica by Example","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Getting Started 2. Numbers, Expressions and Functions 3. Calculus 4. Introduction to Lists and Tables 5. Nested Lists: Matrices and Vectors 6. Applications Related to Ordinary and Partial Differential Equations","brand":"Elsevier Science Publishing Co Inc","offers":[{"title":"Default Title","offer_id":49399840801111,"sku":"9780128241639","price":84.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780128241639.jpg?v=1730468880"},{"product_id":"matlab-for-brain-and-cognitive-scientists-9780262035828","title":"MATLAB for Brain and Cognitive Scientists","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eAn introduction to a popular programming language for neuroscience research, taking the reader from beginning to intermediate and advanced levels of MATLAB programming.\u003c\/b\u003e\u003cp\u003eMATLAB is one of the most popular programming languages for neuroscience and psychology research. Its balance of usability, visualization, and widespread use makes it one of the most powerful tools in a scientist's toolbox. In this book, Mike Cohen teaches brain scientists how to program in MATLAB, with a focus on applications most commonly used in neuroscience and psychology. Although most MATLAB tutorials will abandon users at the beginner's level, leaving them to sink or swim, \u003ci\u003eMATLAB for Brain and Cognitive Scientists\u003c\/i\u003e takes readers from beginning to intermediate and advanced levels of MATLAB programming, helping them gain real expertise in applications that they will use in their work.\u003c\/p\u003e\u003cp\u003eThe book offers a mix of instructive text and rigorous explanations of MATLAB code along with programming tips\u003c\/p\u003e","brand":"MIT Press","offers":[{"title":"Default Title","offer_id":49400685461847,"sku":"9780262035828","price":71.71,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780262035828.jpg?v=1730471292"},{"product_id":"sas-for-dummies-9780470539682","title":"SAS For Dummies","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eThe fun and easy way to learn to use this leading business intelligence tool\u003c\/b\u003e  \u003cp\u003eWritten by an author team who is directly involved with SAS, this easy-to-follow guide is fully updated for the latest release of SAS and covers just what you need to put this popular software to work in your business. SAS allows any business or enterprise to improve data delivery, analysis, reporting, movement across a company, data mining, forecasting, statistical analysis, and more. \u003ci\u003eSAS For Dummies, 2\u003csup\u003end\u003c\/sup\u003e Edition\u003c\/i\u003e gives you the necessary background on what SAS can do for you and explains how to use the Enterprise Guide.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eSAS provides statistical and data analysis tools to help you deal with all kinds of data: operational, financial, performance, and more\u003c\/li\u003e \u003cli\u003ePlaces special emphasis on Enterprise Guide and other analytical tools, covering all commonly used features\u003c\/li\u003e \u003cli\u003eCovers all commonly used features and shows you the practical applications you can put to wor\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eIntroduction.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003ePart I: Welcome to SAS!\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 1:Touring the Wonderful World of SAS.\u003c\/p\u003e \u003cp\u003eChapter 2: Your Connection to SAS: Using SAS Enterprise Guide.\u003c\/p\u003e \u003cp\u003eChapter 3: Six-Minute Abs: Getting Miraculous Results with SAS.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: Gathering Data and Presenting Information.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 4: Accessing Data: Oh, the Choices!\u003c\/p\u003e \u003cp\u003eChapter 5: Managing Data: I Can Do That?\u003c\/p\u003e \u003cp\u003eChapter 6: Show Me a Report in Less Than a Minute.\u003c\/p\u003e \u003cp\u003eChapter 7: Graphs: More Value with SAS.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III: Impressing Your Boss with Your SAS Business Intelligence.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 8: A Painless Introduction to Analytics.\u003c\/p\u003e \u003cp\u003eChapter 9: More Analytics to Enlighten and Entertain.\u003c\/p\u003e \u003cp\u003eChapter 10: Data Mining: Making the Leap from Guesses to Smart Choices.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV: Enhancing and Sharing Your SAS Masterpieces.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 11: Leveraging Work from SAS to Those Less Fortunate.\u003c\/p\u003e \u003cp\u003eChapter 12: Use OLAP and Impress Your Coworkers.\u003c\/p\u003e \u003cp\u003eChapter 13: Supercharge Microsoft Offi ce with SAS.\u003c\/p\u003e \u003cp\u003eChapter 14: Web Reporting Fever: SAS Has That Covered.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V: Getting SAS Ready to Rock and Roll.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 15: Setting Up SAS.\u003c\/p\u003e \u003cp\u003eChapter 16: SAS Programming for the Faint of Heart.\u003c\/p\u003e \u003cp\u003eChapter 17: The New World Meets the Old: Programmers and SAS Enterprise Guide.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VI: The Part of Tens.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 18: Ten SAS Enterprise Guide Productivity Tips.\u003c\/p\u003e \u003cp\u003eChapter 19: Ten Tips for Administrators.\u003c\/p\u003e \u003cp\u003eChapter 20: Ten (or More) Web Resources for Extra Information.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402360168791,"sku":"9780470539682","price":22.09,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470539682.jpg?v=1730480169"},{"product_id":"modern-analysis-of-customer-surveys-9780470971284","title":"Modern Analysis of Customer Surveys","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eModern Analysis of Customer Surveys: with applications using R\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eCustomer survey studies deal with customer, consumer and user satisfaction from a product or service. In practice, many of the customer surveys conducted by business and industry are analyzed in a very simple way, without using models or statistical methods. Typical reports include descriptive statistics and basic graphical displays. This book demonstrates how integrating such basic analysis with more advanced tools, provides insights into non-obvious patterns and important relationships between the survey variables. This knowledge can significantly affect the conclusions derived from a survey. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eKey features:\u003c\/b\u003e \u003cb\u003e\u003cul\u003e\u003cli\u003e Provides an integrated case studies-based approach to analysing customer survey data.\u003c\/li\u003e\u003c\/ul\u003e\u003c\/b\u003e \u003cb\u003e\u003cli\u003ePresents a general introduction to customer surveys, within an organization's business cycle.\u003c\/li\u003e\u003c\/b\u003e \u003cb\u003e\u003cli\u003eContains classical techniques with modern and non standard tools.\u003c\/li\u003e\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eForeword xvii\u003c\/p\u003e \u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003eContributors xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Basic Aspects of Customer Satisfaction Survey Data Analysis\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Standards and Classical Techniques in Data Analysis of Customer Satisfaction Surveys 3\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSilvia Salini and Ron S. Kenett\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Literature on customer satisfaction surveys 4\u003c\/p\u003e \u003cp\u003e1.2 Customer satisfaction surveys and the business cycle 4\u003c\/p\u003e \u003cp\u003e1.3 Standards used in the analysis of survey data 7\u003c\/p\u003e \u003cp\u003e1.4 Measures and models of customer satisfaction 12\u003c\/p\u003e \u003cp\u003e1.4.1 The conceptual construct 12\u003c\/p\u003e \u003cp\u003e1.4.2 The measurement process 13\u003c\/p\u003e \u003cp\u003e1.5 Organization of the book 15\u003c\/p\u003e \u003cp\u003e1.6 Summary 17\u003c\/p\u003e \u003cp\u003eReferences 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The ABC Annual Customer Satisfaction Survey 19\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRon S. Kenett and Silvia Salini\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 The ABC company 19\u003c\/p\u003e \u003cp\u003e2.2 ABC 2010 ACSS: Demographics of respondents 20\u003c\/p\u003e \u003cp\u003e2.3 ABC 2010 ACSS: Overall satisfaction 22\u003c\/p\u003e \u003cp\u003e2.4 ABC 2010 ACSS: Analysis of topics 24\u003c\/p\u003e \u003cp\u003e2.5 ABC 2010 ACSS: Strengths and weaknesses and decision drivers 27\u003c\/p\u003e \u003cp\u003e2.6 Summary 28\u003c\/p\u003e \u003cp\u003eReferences 28\u003c\/p\u003e \u003cp\u003eAppendix 29\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Census and Sample Surveys 37\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGiovanna Nicolini and Luciana Dalla Valle\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 37\u003c\/p\u003e \u003cp\u003e3.2 Types of surveys 39\u003c\/p\u003e \u003cp\u003e3.2.1 Census and sample surveys 39\u003c\/p\u003e \u003cp\u003e3.2.2 Sampling design 40\u003c\/p\u003e \u003cp\u003e3.2.3 Managing a survey 40\u003c\/p\u003e \u003cp\u003e3.2.4 Frequency of surveys 41\u003c\/p\u003e \u003cp\u003e3.3 Non-sampling errors 41\u003c\/p\u003e \u003cp\u003e3.3.1 Measurement error 42\u003c\/p\u003e \u003cp\u003e3.3.2 Coverage error 42\u003c\/p\u003e \u003cp\u003e3.3.3 Unit non-response and non-self-selection errors 43\u003c\/p\u003e \u003cp\u003e3.3.4 Item non-response and non-self-selection error 44\u003c\/p\u003e \u003cp\u003e3.4 Data collection methods 44\u003c\/p\u003e \u003cp\u003e3.5 Methods to correct non-sampling errors 46\u003c\/p\u003e \u003cp\u003e3.5.1 Methods to correct unit non-response errors 46\u003c\/p\u003e \u003cp\u003e3.5.2 Methods to correct item non-response 49\u003c\/p\u003e \u003cp\u003e3.6 Summary 51\u003c\/p\u003e \u003cp\u003eReferences 52\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Measurement Scales 55\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAndrea Bonanomi and Gabriele Cantaluppi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Scale construction 55\u003c\/p\u003e \u003cp\u003e4.1.1 Nominal scale 56\u003c\/p\u003e \u003cp\u003e4.1.2 Ordinal scale 57\u003c\/p\u003e \u003cp\u003e4.1.3 Interval scale 58\u003c\/p\u003e \u003cp\u003e4.1.4 Ratio scale 59\u003c\/p\u003e \u003cp\u003e4.2 Scale transformations 60\u003c\/p\u003e \u003cp\u003e4.2.1 Scale transformations referred to single items 61\u003c\/p\u003e \u003cp\u003e4.2.2 Scale transformations to obtain scores on a unique interval scale 66\u003c\/p\u003e \u003cp\u003eAcknowledgements 69\u003c\/p\u003e \u003cp\u003eReferences 69\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Integrated Analysis 71\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSilvia Biffignandi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 71\u003c\/p\u003e \u003cp\u003e5.2 Information sources and related problems 73\u003c\/p\u003e \u003cp\u003e5.2.1 Types of data sources 73\u003c\/p\u003e \u003cp\u003e5.2.2 Advantages of using secondary source data 73\u003c\/p\u003e \u003cp\u003e5.2.3 Problems with secondary source data 74\u003c\/p\u003e \u003cp\u003e5.2.4 Internal sources of secondary information 75\u003c\/p\u003e \u003cp\u003e5.3 Root cause analysis 78\u003c\/p\u003e \u003cp\u003e5.3.1 General concepts 78\u003c\/p\u003e \u003cp\u003e5.3.2 Methods and tools in RCA 81\u003c\/p\u003e \u003cp\u003e5.3.3 Root cause analysis and customer satisfaction 85\u003c\/p\u003e \u003cp\u003e5.4 Summary 87\u003c\/p\u003e \u003cp\u003eAcknowledgement 87\u003c\/p\u003e \u003cp\u003eReferences 87\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Web Surveys 89\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRoberto Furlan and Diego Martone\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 89\u003c\/p\u003e \u003cp\u003e6.2 Main types of web surveys 90\u003c\/p\u003e \u003cp\u003e6.3 Economic benefits of web survey research 91\u003c\/p\u003e \u003cp\u003e6.3.1 Fixed and variable costs 92\u003c\/p\u003e \u003cp\u003e6.4 Non-economic benefits of web survey research 94\u003c\/p\u003e \u003cp\u003e6.5 Main drawbacks of web survey research 96\u003c\/p\u003e \u003cp\u003e6.6 Web surveys for customer and employee satisfaction projects 100\u003c\/p\u003e \u003cp\u003e6.7 Summary 102\u003c\/p\u003e \u003cp\u003eReferences 102\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 The Concept and Assessment of Customer Satisfaction 107\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eIrena Ograjenšek and Iddo Gal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 107\u003c\/p\u003e \u003cp\u003e7.2 The quality–satisfaction–loyalty chain 108\u003c\/p\u003e \u003cp\u003e7.2.1 Rationale 108\u003c\/p\u003e \u003cp\u003e7.2.2 Definitions of customer satisfaction 108\u003c\/p\u003e \u003cp\u003e7.2.3 From general conceptions to a measurement model of customer satisfaction 110\u003c\/p\u003e \u003cp\u003e7.2.4 Going beyond SERVQUAL: Other dimensions of relevance to the B2B context 112\u003c\/p\u003e \u003cp\u003e7.2.5 From customer satisfaction to customer loyalty 113\u003c\/p\u003e \u003cp\u003e7.3 Customer satisfaction assessment: Some methodological considerations 115\u003c\/p\u003e \u003cp\u003e7.3.1 Rationale 115\u003c\/p\u003e \u003cp\u003e7.3.2 Think big: An assessment programme 115\u003c\/p\u003e \u003cp\u003e7.3.3 Back to basics: Questionnaire design 116\u003c\/p\u003e \u003cp\u003e7.3.4 Impact of questionnaire design on interpretation 118\u003c\/p\u003e \u003cp\u003e7.3.5 Additional concerns in the B2B setting 119\u003c\/p\u003e \u003cp\u003e7.4 The ABC ACSS questionnaire: An evaluation 119\u003c\/p\u003e \u003cp\u003e7.4.1 Rationale 119\u003c\/p\u003e \u003cp\u003e7.4.2 Conceptual issues 119\u003c\/p\u003e \u003cp\u003e7.4.3 Methodological issues 120\u003c\/p\u003e \u003cp\u003e7.4.4 Overall ABC ACSS questionnaire asssessment 121\u003c\/p\u003e \u003cp\u003e7.5 Summary 121\u003c\/p\u003e \u003cp\u003eReferences 122\u003c\/p\u003e \u003cp\u003eAppendix 126\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Missing Data and Imputation Methods 129\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAlessandra Mattei, Fabrizia Mealli and Donald B. Rubin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 129\u003c\/p\u003e \u003cp\u003e8.2 Missing-data patterns and missing-data mechanisms 131\u003c\/p\u003e \u003cp\u003e8.2.1 Missing-data patterns 131\u003c\/p\u003e \u003cp\u003e8.2.2 Missing-data mechanisms and ignorability 132\u003c\/p\u003e \u003cp\u003e8.3 Simple approaches to the missing-data problem 134\u003c\/p\u003e \u003cp\u003e8.3.1 Complete-case analysis 134\u003c\/p\u003e \u003cp\u003e8.3.2 Available-case analysis 135\u003c\/p\u003e \u003cp\u003e8.3.3 Weighting adjustment for unit nonresponse 135\u003c\/p\u003e \u003cp\u003e8.4 Single imputation 136\u003c\/p\u003e \u003cp\u003e8.5 Multiple imputation 138\u003c\/p\u003e \u003cp\u003e8.5.1 Multiple-imputation inference for a scalar estimand 138\u003c\/p\u003e \u003cp\u003e8.5.2 Proper multiple imputation 139\u003c\/p\u003e \u003cp\u003e8.5.3 Appropriately drawing imputations with monotone missing-data patterns 140\u003c\/p\u003e \u003cp\u003e8.5.4 Appropriately drawing imputations with nonmonotone missing-data patterns 141\u003c\/p\u003e \u003cp\u003e8.5.5 Multiple imputation in practice 142\u003c\/p\u003e \u003cp\u003e8.5.6 Software for multiple imputation 143\u003c\/p\u003e \u003cp\u003e8.6 Model-based approaches to the analysis of missing data 144\u003c\/p\u003e \u003cp\u003e8.7 Addressing missing data in the ABC annual customer satisfaction survey: An example 145\u003c\/p\u003e \u003cp\u003e8.8 Summary 149\u003c\/p\u003e \u003cp\u003eAcknowledgements 150\u003c\/p\u003e \u003cp\u003eReferences 150\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Outliers and Robustness for Ordinal Data 155\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMarco Riani, Francesca Torti and Sergio Zani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 An overview of outlier detection methods 155\u003c\/p\u003e \u003cp\u003e9.2 An example of masking 157\u003c\/p\u003e \u003cp\u003e9.3 Detection of outliers in ordinal variables 159\u003c\/p\u003e \u003cp\u003e9.4 Detection of bivariate ordinal outliers 160\u003c\/p\u003e \u003cp\u003e9.5 Detection of multivariate outliers in ordinal regression 161\u003c\/p\u003e \u003cp\u003e9.5.1 Theory 161\u003c\/p\u003e \u003cp\u003e9.5.2 Results from the application 163\u003c\/p\u003e \u003cp\u003e9.6 Summary 168\u003c\/p\u003e \u003cp\u003eReferences 168\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Modern Techniques in Customer Satisfaction Survey Data Analysis\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Statistical Inference for Causal Effects 173\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eFabrizia Mealli, Barbara Pacini and Donald B. Rubin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction to the potential outcome approach to causal inference 173\u003c\/p\u003e \u003cp\u003e10.1.1 Causal inference primitives: Units, treatments, and potential outcomes 175\u003c\/p\u003e \u003cp\u003e10.1.2 Learning about causal effects: Multiple units and the stable unit treatment value assumption 176\u003c\/p\u003e \u003cp\u003e10.1.3 Defining causal estimands 177\u003c\/p\u003e \u003cp\u003e10.2 Assignment mechanisms 179\u003c\/p\u003e \u003cp\u003e10.2.1 The criticality of the assignment mechanism 179\u003c\/p\u003e \u003cp\u003e10.2.2 Unconfounded and strongly ignorable assignment mechanisms 180\u003c\/p\u003e \u003cp\u003e10.2.3 Confounded and ignorable assignment mechanisms 181\u003c\/p\u003e \u003cp\u003e10.2.4 Randomized and observational studies 181\u003c\/p\u003e \u003cp\u003e10.3 Inference in classical randomized experiments 182\u003c\/p\u003e \u003cp\u003e10.3.1 Fisher’s approach and extensions 183\u003c\/p\u003e \u003cp\u003e10.3.2 Neyman’s approach to randomization-based inference 183\u003c\/p\u003e \u003cp\u003e10.3.3 Covariates, regression models, and Bayesian model-based inference 184\u003c\/p\u003e \u003cp\u003e10.4 Inference in observational studies 185\u003c\/p\u003e \u003cp\u003e10.4.1 Inference in regular designs 186\u003c\/p\u003e \u003cp\u003e10.4.2 Designing observational studies: The role of the propensity score 186\u003c\/p\u003e \u003cp\u003e10.4.3 Estimation methods 188\u003c\/p\u003e \u003cp\u003e10.4.4 Inference in irregular designs 188\u003c\/p\u003e \u003cp\u003e10.4.5 Sensitivity and bounds 189\u003c\/p\u003e \u003cp\u003e10.4.6 Broken randomized experiments as templates for the analysis of some irregular designs 189\u003c\/p\u003e \u003cp\u003eReferences 190\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Bayesian Networks Applied to Customer Surveys 193\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRon S. Kenett, Giovanni Perruca and Silvia Salini\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction to Bayesian networks 193\u003c\/p\u003e \u003cp\u003e11.2 The Bayesian network model in practice 197\u003c\/p\u003e \u003cp\u003e11.2.1 Bayesian network analysis of the ABC 2010 ACSS 197\u003c\/p\u003e \u003cp\u003e11.2.2 Transport data analysis 201\u003c\/p\u003e \u003cp\u003e11.2.3 R packages and other software programs used for studying BNs 210\u003c\/p\u003e \u003cp\u003e11.3 Prediction and explanation 211\u003c\/p\u003e \u003cp\u003e11.4 Summary 213\u003c\/p\u003e \u003cp\u003eReferences 213\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Log-linear Model Methods 217\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eStephen E. Fienberg and Daniel Manrique-Vallier\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 217\u003c\/p\u003e \u003cp\u003e12.2 Overview of log-linear models and methods 218\u003c\/p\u003e \u003cp\u003e12.2.1 Two-way tables 218\u003c\/p\u003e \u003cp\u003e12.2.2 Hierarchical log-linear models 220\u003c\/p\u003e \u003cp\u003e12.2.3 Model search and selection 222\u003c\/p\u003e \u003cp\u003e12.2.4 Sparseness in contingency tables and its implications 223\u003c\/p\u003e \u003cp\u003e12.2.5 Computer programs for log-linear model analysis 223\u003c\/p\u003e \u003cp\u003e12.3 Application to ABC survey data 224\u003c\/p\u003e \u003cp\u003e12.4 Summary 227\u003c\/p\u003e \u003cp\u003eReferences 228\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 CUB Models: Statistical Methods and Empirical Evidence 231\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMaria Iannario and Domenico Piccolo\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 231\u003c\/p\u003e \u003cp\u003e13.2 Logical foundations and psychological motivations 233\u003c\/p\u003e \u003cp\u003e13.3 A class of models for ordinal data 233\u003c\/p\u003e \u003cp\u003e13.4 Main inferential issues 236\u003c\/p\u003e \u003cp\u003e13.5 Specification of CUB models with subjects’ covariates 238\u003c\/p\u003e \u003cp\u003e13.6 Interpreting the role of covariates 240\u003c\/p\u003e \u003cp\u003e13.7 A more general sampling framework 241\u003c\/p\u003e \u003cp\u003e13.7.1 Objects’ covariates 241\u003c\/p\u003e \u003cp\u003e13.7.2 Contextual covariates 243\u003c\/p\u003e \u003cp\u003e13.8 Applications of CUB models 244\u003c\/p\u003e \u003cp\u003e13.8.1 Models for the ABC annual customer satisfaction survey 245\u003c\/p\u003e \u003cp\u003e13.8.2 Students’ satisfaction with a university orientation service 246\u003c\/p\u003e \u003cp\u003e13.9 Further generalizations 248\u003c\/p\u003e \u003cp\u003e13.10 Concluding remarks 251\u003c\/p\u003e \u003cp\u003eAcknowledgements 251\u003c\/p\u003e \u003cp\u003eReferences 251\u003c\/p\u003e \u003cp\u003eAppendix 255\u003c\/p\u003e \u003cp\u003eA program in R for CUB models 255\u003c\/p\u003e \u003cp\u003eA.1 Main structure of the program 255\u003c\/p\u003e \u003cp\u003eA.2 Inference on CUB models 255\u003c\/p\u003e \u003cp\u003eA.3 Output of CUB models estimation program 256\u003c\/p\u003e \u003cp\u003eA.4 Visualization of several CUB models in the parameter space 257\u003c\/p\u003e \u003cp\u003eA.5 Inference on CUB models in a multi-object framework 257\u003c\/p\u003e \u003cp\u003eA.6 Advanced software support for CUB models 258\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 The Rasch Model 259\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eFrancesca De Battisti, Giovanna Nicolini and Silvia Salini\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 An overview of the Rasch model 259\u003c\/p\u003e \u003cp\u003e14.1.1 The origins and the properties of the model 259\u003c\/p\u003e \u003cp\u003e14.1.2 Rasch model for hierarchical and longitudinal data 263\u003c\/p\u003e \u003cp\u003e14.1.3 Rasch model applications in customer satisfaction surveys 265\u003c\/p\u003e \u003cp\u003e14.2 The Rasch model in practice 267\u003c\/p\u003e \u003cp\u003e14.2.1 Single model 267\u003c\/p\u003e \u003cp\u003e14.2.2 Overall model 268\u003c\/p\u003e \u003cp\u003e14.2.3 Dimension model 272\u003c\/p\u003e \u003cp\u003e14.3 Rasch model software 277\u003c\/p\u003e \u003cp\u003e14.4 Summary 278\u003c\/p\u003e \u003cp\u003eReferences 279\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Tree-based Methods and Decision Trees 283\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGiuliano Galimberti and Gabriele Soffritti\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 An overview of tree-based methods and decision trees 283\u003c\/p\u003e \u003cp\u003e15.1.1 The origins of tree-based methods 283\u003c\/p\u003e \u003cp\u003e15.1.2 Tree graphs, tree-based methods and decision trees 284\u003c\/p\u003e \u003cp\u003e15.1.3 CART 287\u003c\/p\u003e \u003cp\u003e15.1.4 CHAID 293\u003c\/p\u003e \u003cp\u003e15.1.5 PARTY 295\u003c\/p\u003e \u003cp\u003e15.1.6 A comparison of CART, CHAID and PARTY 297\u003c\/p\u003e \u003cp\u003e15.1.7 Missing values 297\u003c\/p\u003e \u003cp\u003e15.1.8 Tree-based methods for applications in customer satisfaction surveys 298\u003c\/p\u003e \u003cp\u003e15.2 Tree-based methods and decision trees in practice 300\u003c\/p\u003e \u003cp\u003e15.2.1 ABC ACSS data analysis with tree-based methods 300\u003c\/p\u003e \u003cp\u003e15.2.2 Packages and software implementing tree-based methods 303\u003c\/p\u003e \u003cp\u003e15.3 Further developments 304\u003c\/p\u003e \u003cp\u003eReferences 304\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 PLS Models 309\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGiuseppe Boari and Gabriele Cantaluppi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 309\u003c\/p\u003e \u003cp\u003e16.2 The general formulation of a structural equation model 310\u003c\/p\u003e \u003cp\u003e16.2.1 The inner model 310\u003c\/p\u003e \u003cp\u003e16.2.2 The outer model 312\u003c\/p\u003e \u003cp\u003e16.3 The PLS algorithm 313\u003c\/p\u003e \u003cp\u003e16.4 Statistical interpretation of PLS 319\u003c\/p\u003e \u003cp\u003e16.5 Geometrical interpretation of PLS 320\u003c\/p\u003e \u003cp\u003e16.6 Comparison of the properties of PLS and LISREL procedures 321\u003c\/p\u003e \u003cp\u003e16.7 Available software for PLS estimation 323\u003c\/p\u003e \u003cp\u003e16.8 Application to real data: Customer satisfaction analysis 323\u003c\/p\u003e \u003cp\u003eReferences 329\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Nonlinear Principal Component Analysis 333\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePier Alda Ferrari and Alessandro Barbiero\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 333\u003c\/p\u003e \u003cp\u003e17.2 Homogeneity analysis and nonlinear principal component analysis 334\u003c\/p\u003e \u003cp\u003e17.2.1 Homogeneity analysis 334\u003c\/p\u003e \u003cp\u003e17.2.2 Nonlinear principal component analysis 336\u003c\/p\u003e \u003cp\u003e17.3 Analysis of customer satisfaction 338\u003c\/p\u003e \u003cp\u003e17.3.1 The setting up of indicator 338\u003c\/p\u003e \u003cp\u003e17.3.2 Additional analysis 340\u003c\/p\u003e \u003cp\u003e17.4 Dealing with missing data 340\u003c\/p\u003e \u003cp\u003e17.5 Nonlinear principal component analysis versus two competitors 343\u003c\/p\u003e \u003cp\u003e17.6 Application to the ABC ACSS data 344\u003c\/p\u003e \u003cp\u003e17.6.1 Data preparation 344\u003c\/p\u003e \u003cp\u003e17.6.2 The homals package 345\u003c\/p\u003e \u003cp\u003e17.6.3 Analysis on the ‘complete subset’ 346\u003c\/p\u003e \u003cp\u003e17.6.4 Comparison of NLPCA with PCA and Rasch analysis 350\u003c\/p\u003e \u003cp\u003e17.6.5 Analysis of ‘entire data set’ for the comparison of missing data treatments 352\u003c\/p\u003e \u003cp\u003e17.7 Summary 355\u003c\/p\u003e \u003cp\u003eReferences 355\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Multidimensional Scaling 357\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNadia Solaro\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 An overview of multidimensional scaling techniques 357\u003c\/p\u003e \u003cp\u003e18.1.1 The origins of MDS models 358\u003c\/p\u003e \u003cp\u003e18.1.2 MDS input data 359\u003c\/p\u003e \u003cp\u003e18.1.3 MDS models 362\u003c\/p\u003e \u003cp\u003e18.1.4 Assessing the goodness of MDS solutions 369\u003c\/p\u003e \u003cp\u003e18.1.5 Comparing two MDS solutions: Procrustes analysis 371\u003c\/p\u003e \u003cp\u003e18.1.6 Robustness issues in the MDS framework 371\u003c\/p\u003e \u003cp\u003e18.1.7 Handling missing values in MDS framework 373\u003c\/p\u003e \u003cp\u003e18.1.8 MDS applications in customer satisfaction surveys 373\u003c\/p\u003e \u003cp\u003e18.2 Multidimensional scaling in practice 374\u003c\/p\u003e \u003cp\u003e18.2.1 Data sets analysed 375\u003c\/p\u003e \u003cp\u003e18.2.2 MDS analyses of overall satisfaction with a set of ABC features: The complete data set 375\u003c\/p\u003e \u003cp\u003e18.2.3 Weighting objects or items 381\u003c\/p\u003e \u003cp\u003e18.2.4 Robustness analysis with the forward search 382\u003c\/p\u003e \u003cp\u003e18.2.5 MDS analyses of overall satisfaction with a set of ABC features: The incomplete data set 383\u003c\/p\u003e \u003cp\u003e18.2.6 Package and software for MDS methods 384\u003c\/p\u003e \u003cp\u003e18.3 Multidimensional scaling in a future perspective 386\u003c\/p\u003e \u003cp\u003e18.4 Summary 386\u003c\/p\u003e \u003cp\u003eReferences 387\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Multilevel Models for Ordinal Data 391\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eLeonardo Grilli and Carla Rampichini\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Ordinal variables 391\u003c\/p\u003e \u003cp\u003e19.2 Standard models for ordinal data 393\u003c\/p\u003e \u003cp\u003e19.2.1 Cumulative models 394\u003c\/p\u003e \u003cp\u003e19.2.2 Other models 395\u003c\/p\u003e \u003cp\u003e19.3 Multilevel models for ordinal data 395\u003c\/p\u003e \u003cp\u003e19.3.1 Representation as an underlying linear model with thresholds 396\u003c\/p\u003e \u003cp\u003e19.3.2 Marginal versus conditional effects 397\u003c\/p\u003e \u003cp\u003e19.3.3 Summarizing the cluster-level unobserved heterogeneity 397\u003c\/p\u003e \u003cp\u003e19.3.4 Consequences of adding a covariate 398\u003c\/p\u003e \u003cp\u003e19.3.5 Predicted probabilities 399\u003c\/p\u003e \u003cp\u003e19.3.6 Cluster-level covariates and contextual effects 399\u003c\/p\u003e \u003cp\u003e19.3.7 Estimation of model parameters 400\u003c\/p\u003e \u003cp\u003e19.3.8 Inference on model parameters 401\u003c\/p\u003e \u003cp\u003e19.3.9 Prediction of random effects 402\u003c\/p\u003e \u003cp\u003e19.3.10 Software 403\u003c\/p\u003e \u003cp\u003e19.4 Multilevel models for ordinal data in practice: An application to student ratings 404\u003c\/p\u003e \u003cp\u003eReferences 408\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Quality Standards and Control Charts Applied to Customer Surveys 413\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRon S. Kenett, Laura Deldossi and Diego Zappa\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Quality standards and customer satisfaction 413\u003c\/p\u003e \u003cp\u003e20.2 ISO 10004 guidelines for monitoring and measuring customer satisfaction 414\u003c\/p\u003e \u003cp\u003e20.3 Control Charts and ISO 7870 417\u003c\/p\u003e \u003cp\u003e20.4 Control charts and customer surveys: Standard assumptions 420\u003c\/p\u003e \u003cp\u003e20.4.1 Introduction 420\u003c\/p\u003e \u003cp\u003e20.4.2 Standard control charts 420\u003c\/p\u003e \u003cp\u003e20.5 Control charts and customer surveys: Non-standard methods 426\u003c\/p\u003e \u003cp\u003e20.5.1 Weights on counts: Another application of the c chart 426\u003c\/p\u003e \u003cp\u003e20.5.2 The χ\u003csup\u003e2\u003c\/sup\u003e chart 427\u003c\/p\u003e \u003cp\u003e20.5.3 Sequential probability ratio tests 428\u003c\/p\u003e \u003cp\u003e20.5.4 Control chart over items: A non-standard application of SPC methods 429\u003c\/p\u003e \u003cp\u003e20.5.5 Bayesian control chart for attributes: A modern application of SPC methods 432\u003c\/p\u003e \u003cp\u003e20.5.6 Control chart for correlated Poisson counts: When things become fairly complicated 433\u003c\/p\u003e \u003cp\u003e20.6 The \u003ci\u003eM\u003c\/i\u003e-test for assessing sample representation 433\u003c\/p\u003e \u003cp\u003e20.7 Summary 435\u003c\/p\u003e \u003cp\u003eReferences 436\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Fuzzy Methods and Satisfaction Indices 439\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSergio Zani, Maria Adele Milioli and Isabella Morlini\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 439\u003c\/p\u003e \u003cp\u003e21.2 Basic definitions and operations 440\u003c\/p\u003e \u003cp\u003e21.3 Fuzzy numbers 441\u003c\/p\u003e \u003cp\u003e21.4 A criterion for fuzzy transformation of variables 443\u003c\/p\u003e \u003cp\u003e21.5 Aggregation and weighting of variables 445\u003c\/p\u003e \u003cp\u003e21.6 Application to the ABC customer satisfaction survey data 446\u003c\/p\u003e \u003cp\u003e21.6.1 The input matrices 446\u003c\/p\u003e \u003cp\u003e21.6.2 Main results 448\u003c\/p\u003e \u003cp\u003e21.7 Summary 453\u003c\/p\u003e \u003cp\u003eReferences 455\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix an Introduction to R 457\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eStefano Maria Iacus\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eA.1 Introduction 457\u003c\/p\u003e \u003cp\u003eA.2 How to obtain R 457\u003c\/p\u003e \u003cp\u003eA.3 Type rather than ‘point and click’ 458\u003c\/p\u003e \u003cp\u003eA.3.1 The workspace 458\u003c\/p\u003e \u003cp\u003eA.3.2 Graphics 458\u003c\/p\u003e \u003cp\u003eA.3.3 Getting help 459\u003c\/p\u003e \u003cp\u003eA.3.4 Installing packages 459\u003c\/p\u003e \u003cp\u003eA.4 Objects 460\u003c\/p\u003e \u003cp\u003eA.4.1 Assignments 460\u003c\/p\u003e \u003cp\u003eA.4.2 Basic object types 462\u003c\/p\u003e \u003cp\u003eA.4.3 Accessing objects and subsetting 466\u003c\/p\u003e \u003cp\u003eA.4.4 Coercion between data types 469\u003c\/p\u003e \u003cp\u003eA.5 S4 objects 470\u003c\/p\u003e \u003cp\u003eA.6 Functions 472\u003c\/p\u003e \u003cp\u003eA.7 Vectorization 473\u003c\/p\u003e \u003cp\u003eA.8 Importing data from different sources 475\u003c\/p\u003e \u003cp\u003eA.9 Interacting with databases 476\u003c\/p\u003e \u003cp\u003eA.10 Simple graphics manipulation 477\u003c\/p\u003e \u003cp\u003eA.11 Basic analysis of the ABC data 481\u003c\/p\u003e \u003cp\u003eA.12 About this document 496\u003c\/p\u003e \u003cp\u003eA.13 Bibliographical notes 496\u003c\/p\u003e \u003cp\u003eReferences 496\u003c\/p\u003e \u003cp\u003eIndex 499\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402467975511,"sku":"9780470971284","price":78.26,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470971284.jpg?v=1730480499"}],"url":"https:\/\/bookcurl.com\/collections\/mathematical-and-statistical-software.oembed?page=6","provider":"Book Curl","version":"1.0","type":"link"}