Mathematical and statistical software Books

Book SynopsisA practical guide simplifying discrete math for curious minds and demonstrating its application in solving problems related to software development, computer algorithms, and data scienceKey Features Apply the math of countable objects to practical problems in computer science Explore modern Python libraries such as scikit-learn, NumPy, and SciPy for performing mathematics Learn complex statistical and mathematical concepts with the help of hands-on examples and expert guidance Book DescriptionDiscrete mathematics deals with studying countable, distinct elements, and its principles are widely used in building algorithms for computer science and data science. The knowledge of discrete math concepts will help you understand the algorithms, binary, and general mathematics that sit at the core of data-driven tasks. Practical Discrete Mathematics is a comprehensive introduction for those who are new to the mathematics of countable objects. This book will help you get up to speed with using discrete math principles to take your computer science skills to a more advanced level. As you learn the language of discrete mathematics, you'll also cover methods crucial to studying and describing computer science and machine learning objects and algorithms. The chapters that follow will guide you through how memory and CPUs work. In addition to this, you'll understand how to analyze data for useful patterns, before finally exploring how to apply math concepts in network routing, web searching, and data science. By the end of this book, you'll have a deeper understanding of discrete math and its applications in computer science, and be ready to work on real-world algorithm development and machine learning.What you will learn Understand the terminology and methods in discrete math and their usage in algorithms and data problems Use Boolean algebra in formal logic and elementary control structures Implement combinatorics to measure computational complexity and manage memory allocation Use random variables, calculate descriptive statistics, and find average-case computational complexity Solve graph problems involved in routing, pathfinding, and graph searches, such as depth-first search Perform ML tasks such as data visualization, regression, and dimensionality reduction Who this book is forThis book is for computer scientists looking to expand their knowledge of discrete math, the core topic of their field. University students looking to get hands-on with computer science, mathematics, statistics, engineering, or related disciplines will also find this book useful. Basic Python programming skills and knowledge of elementary real-number algebra are required to get started with this book.Table of ContentsTable of Contents Key Concepts, Notation, Set Theory, Relations, and Functions Formal Logic and Constructing Mathematical Proofs Computing with Base-n Numbers Combinatorics Using SciPy Elements of Discrete Probability Computational Algorithms in Linear Algebra Computational Requirements for Algorithms Storage and Feature Extraction of Graphs, Trees, and Networks Searching Data Structures and Finding Shortest Paths Regression Analysis with NumPy and Scikit-Learn Web Searches with PageRank Principal Component Analysis with Scikit-Learn

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Book SynopsisBased on the new 'guided-tour' concept that eliminates the start-up transient encountered in learning new programming languages, this beginner's introduction to MATLAB teaches a sufficient subset of the functionality and gives the reader practical experience on how to find more information. Recent developments in MATLAB to advance programming are described using realistic examples in order to prepare students for larger programming projects. In addition, a large number of exercises, tips, and solutions mean that the course can be followed with or without a computer. The development of MATLAB programming and its use in engineering courses makes this a valuable self-study guide for both engineering students and practicing engineers.Trade ReviewFrom the reviews: "The book consists of three parts: an initiation in matlab, more advanced programming in matlab, and some elaborated applications. … The text gives a bottom-up learning-by-example approach. … Thus the reader is forced to sit at the computer and do experiments, which is in my opinion the best and fastest way to learn matlab. The excellent help tool of matlab should do the rest. … The command summaries in the appendices make it … a substitute for the matlab manuals." (Adhemar Bultheel, Bulletin of the Belgian Mathematical Society, Vol. 12 (1), 2005)Table of Contents1 Learning MATLAB.- 1 Introduction.- 2 Interactive computation and elementary functions.- 3 Manipulating matrices.- 4 Strings and workspace administration.- 5 Graphical illustrations.- 6 Matrix algebra and polynomials.- 7 Advanced graphics.- 8 MATLAB Scripts.- 9 MATLAB Functions.- 10 Functions of functions.- 2 Advanced Programming.- 11 Data Structures.- 11.1 Sparse Matrices.- 11.2 Multidimensional Arrays and Cell Arrays.- 11.3 Structs.- 12 Object Orientation.- 13 Graphical Object Orientation and User Interfaces.- 13.1 Graphical objects.- 13.2 Default settings.- 13.3 Graphical User Interface (GUI).- 13.4 Constructing a GUI using guide.- 14 Optimizing MATLAB Code.- 15 Calling C-routines from MATLAB.- 3 Applications of MATLAB.- 16 Calculus.- 17 Data interpolation.- 18 Linear Algebra.- 19 Optimization.- 20 Numerical Accuracy and Number Representation.- 21 Statistics.- 22 Control Theory and the LTI Object.- 23 Dynamical Simulation with SIMULlNK.- 24 Ordinary Differential Equations.- 25 Signal processing.- 26 Communication Systems.- 27 Documentation, presentation and animation.- A Answers to the exercises.- B Command reference.- C Summary of mathematical functions.- D Toolbox Summaries.- E Graphics summary.

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Book SynopsisThis textbook offers an easy-to-follow, practical guide to modern data analysis using the programming language R. The chapters cover topics such as the fundamentals of programming in R, data collection and preprocessing, including web scraping, data visualization, and statistical methods, including multivariate analysis, and feature exercises at the end of each section. The text requires only basic statistics skills, as it strikes a balance between statistical and mathematical understanding and implementation in R, with a special emphasis on reproducible examples and real-world applications. This textbook is primarily intended for undergraduate students of mathematics, statistics, physics, economics, finance and business who are pursuing a career in data analytics. It will be equally valuable for master students of data science and industry professionals who want to conduct data analyses.Trade Review“It was very interesting to go through the pages of this book. The authors should be commended for writing a thorough book about complex concepts of data analysis in R that could, however, be read easily. I warmly recommend this book to students of statistics but also to professionals who would like to acquire advanced analytical skills or improve their competencies in R, especially nowadays with R very popular amongst data analysts.” (Georgios Nikolopoulos, ISCB News, iscb.info, Issue 71, June, 2021)Table of ContentsPreface.- 1 Introduction.- 2 Introduction to R.- 3 Databases in R.- 4 Visualization.- 5 Data Analysis with R.- R Packages and Funtions.

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Book SynopsisThis 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. 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. The 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 Basic Statistics in Criminal Justice (2020).Table of Contents1. 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.

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Book SynopsisThis 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. Table of Contents1 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.

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Book Synopsis​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. 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.Table of Contents1. 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.

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Book SynopsisThese 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. Table of ContentsUncertainty 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.

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Book SynopsisThis book discusses questions of numerical solutions of applied problems on parallel computing systems. Nowadays, engineering and scientific computations are carried out on parallel computing systems, which provide parallel data processing on a few computing nodes. In the development of up-to-date applied software, this feature of computers must be taken into account for the maximum efficient usage of their resources. In constructing computational algorithms, we should separate relatively independent subproblems in order to solve them on a single computing node.

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Book SynopsisThis volume, the third of a series, consists of applications of Mathematica® to a potpourri of more advanced topics. These include differential geometry of curves and surfaces, differential equations and special functions and complex analysis. Some of the newest features of Mathematica® are demonstrated and explained and some problems with the current implementation pointed out and possible future improvements suggested. Contains a large number of worked out examples. Explains some of the most recent mathematical features of Mathematica®. Considers topics discussed rarely or not at all in the context of Mathematica®. Can be used to supplement several different courses. Based on actual university courses.

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Book SynopsisPractical LaTeX covers the material that is needed for everyday LaTeX documents. This accessible manual is friendly, easy to read, and is designed to be as portable as LaTeX itself.A short chapter, Mission Impossible, introduces LaTeX documents and presentations. Read these 30 pages; you then should be able to compose your own work in LaTeX. The remainder of the book delves deeper into the topics outlined in Mission Impossible while avoiding technical subjects. Chapters on presentations and illustrations are a highlight, as is the introduction of LaTeX on an iPad.Students, faculty, and professionals in the worlds of mathematics and technology will benefit greatly from this new, practical introduction to LaTeX. George Grätzer, author of More Math into LaTeX (now in its 4th edition) and First Steps in LaTeX, has been a LaTeX guru for over a quarter of century.From the reviews of More Math into LaTeX:``There are several LaTeX guides, but this one wins hands down for the elegance of its approach and breadth of coverage.''—Amazon.com, Best of 2000, Editors Choice``A very helpful and useful tool for all scientists and engineers.''—Review of Astronomical Tools``A novice reader will be able to learn the most essential features of LaTeX sufficient to begin typesetting papers within a few hours of time…An experienced TeX user, on the other hand, will find a systematic and detailed discussion of all LaTeX features, supporting software, and many other advanced technical issues.''—Reports on Mathematical PhysicsTrade ReviewFrom the book reviews:“I’ve been looking for a friendly and accessible manual that I could recommend to students as a way to get over the initial learning curve, and this book seemed like it would fit the bill. … The emphasis is on skills necessary for writing and presenting in an academic setting, and in particular it is geared toward students in math, physics, and numerate disciplines. … Overall, it is a well-presented volume and pleasant to read.” (Sara Kalvala, Computing Reviews, December, 2014)“The book starts with a quick survey, and then explores a bit deeper how to typeset the text, the use of environments, (mathematical) formulas and arrays, and finally the global structure of the document (top matter, body, back matter). … this book might be interesting to read, not only for the beginner, but also for the experienced LaTeX user.” (Adhemar Bultheel, euro-math-soc.eu, December, 2014)“If I really, really have to learn LaTeX, this is the book I’ll go to in a flash. … Even at a first glance or at first browse it’s abundantly clear that this is a very good book for a TeXtyro like me … . It’s eminently practical and therefore eminently worthwhile.” (Michael Berg, MAA Reviews, November, 2014)Table of Contents​Introduction.- 1. Getting LaTex.- 2. Typing Text.- 3. Text environments.- 4. Typing Formulas.- 5. Displayed Formulas.- 6. Articles.- 7. Making Presentations.- 8. Customization.- 9. The Symbol Tables.- Index.

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Book SynopsisThis 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. Regression Modelling Strategies presents 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.As 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. Trade Review“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)Table of ContentsIntroduction.- 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.

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Book SynopsisDas Arbeitsbuch führt in die Nutzung der Software Statistiklabor ein. Die Funktionalität wird im ersten Teil detailliert beschrieben, der zweite Teil illustriert Standardauswertungen. Die Software kann kostenfrei unter www.statistiklabor.de heruntergeladen werden. Sie bietet eine interaktive Arbeitsumgebung, um statistische Funktionen und Darstellungsmöglichkeiten leicht und intuitiv bearbeiten zu können, und erlaubt einen wesentlich einfacheren Zugang zu der umfangreichen Funktionalität der Statistik-Programmierumgebung R.Table of ContentsEine erste Beispielauswertung.- Die Oberfläche.- Ein- und Ausgabe.- Statistische Objekte.- Der Kalkulator.- Einiges zu R.- R-Grafik.- Spezielle Aspekte des Labors.- Beschreibung von Daten.- Wahrscheinlichkeitsrechnung.- Stichproben und Punktschätzungen.- Tests und Konfidenzintervalle.- Regression.- Tabellarische Überblicke.- Referenzen von R-Funktionen.- Liste typischer Auswertungen

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Book SynopsisThis book focuses on the applications of convex optimization and highlights several topics, including support vector machines, parameter estimation, norm approximation and regularization, semi-definite programming problems, convex relaxation, and geometric problems. All derivation processes are presented in detail to aid in comprehension. The book offers concrete guidance, helping readers recognize and formulate convex optimization problems they might encounter in practice.Trade Review“Selected Applications of Convex Optimization is a brief book, only 140 pages, and includes exercises with each chapter. It would be a good supplemental text for an optimization or machine learning course.” (John D. Cook, MAA Reviews, maa.org, December, 2015)Table of ContentsPreliminary Knowledge.- Support Vector Machines.- Parameter Estimations.- Norm Approximation and Regulariztion.- Semi-Definite Programing and Linear Matrix Inequalities.- Convex Relaxation.- Geometric Problems.

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Book SynopsisTable of Contents1. 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

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Book SynopsisFunctional and Phylogenetic Ecology in R is designed to teach readers to use R for phylogenetic and functional trait analyses. Researchers getting started in R can use this volume as a step-by-step entryway into phylogenetic and functional analyses for ecology in R.Trade ReviewFrom the book reviews:“This book is structured in nine interlinked chapters … . Each chapter is built in a lecture-style incremental manner and does not assume an extensive previous knowledge of R. All chapters conclude with a series of exercises that consolidate the presented notions. This approach makes the book suitable for undergraduates and postgraduates, as well as researchers with an interest in the field. Its structure and detailed examples supported with exercises make it a timely addition for the scientific community.” (Irina Ioana Mohorianu, zbMATH, Vol. 1300, 2015)“This book is based on a course taught by the author and has therefore gone through rigorous user testing, which shows in the clear layout and detailed step-by-step guidance through sophisticated statistical analyses. … Anyone embarking on related research will benefit from this.” (Markus Eichhorn, Frontiers of Biogeography, Vol. 6 (2), 2014)Table of Contents​​​Preface.- Introduction.- Phylogenetic Data in R.- Phylogenetic Diversity.- Functional Diversity.- Phylogenetic & Functional Beta Diversity.- Null Models.- Comparative Methods & Phylogenetic Signal.- Partitioning the Phylogenetic, Functional, Environmental and Spatial Components of Community Diversity.- Integrating R with Other Phylogenetic and Functional Trait Analytical Software.- References.- Index

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Book SynopsisIn computational science, reproducibility requires that researchers make code and data available to others so that the data can be analyzed in a similar manner as in the original publication. Code must be available to be distributed, data must be accessible in a readable format, and a platform must be available for widely distributing the data and code. In addition, both data and code need to be licensed permissively enough so that others can reproduce the work without a substantial legal burden.Implementing Reproducible Research covers many of the elements necessary for conducting and distributing reproducible research. It explains how to accurately reproduce a scientific result. Divided into three parts, the book discusses the tools, practices, and dissemination platforms for ensuring reproducibility in computational science. It describes: Computational tools, such as Sweave, knitr, VisTrails, Sumatra, CDE, and the Declaratron syTrade Review"This collection brings together the expertise and experience of numerous authors and is likely to be valuable to scientists and statisticians alike. … This book should have broad appeal … introduces some extremely useful tools and practices from leaders in the field. On top of that, it also contains an exciting vision for the future of scientific research. … The challenge of reproducibility in the computational era is being confronted across the sciences, with each field developing its own tools and best practices. This book is an important step in bringing together a broad group of scientists to share what has been learned."—Journal of the American Statistical Association, June 2015 "The book as a whole has something for everybody and provides an interesting snapshot of the available tools, platforms, and good practices for researchers as the scientific community aims to be more self-correcting."—Journal of Statistical Software, October 2014 "Three recent books have significantly influenced how I use R in reproducible work: Dynamic Documents with R and knitr by Yihui Xie, Reproducible Research with R and RStudio by Christopher Gandrud, and Implementing Reproducible Research edited by Victoria Stodden, Friedrich Leisch, and Roger D. Peng … I recommend all three books to R users at any level. There really is something here for everyone."—Richard Layton, PhD, PE, Rose-Hulman Institute of Technology, Terre Haute, Indiana, USA "In total, this book provides information on almost all aspects of reproducible research in the open science environment … I would recommend this book to anybody who wants to learn more about reproducible research in the context of open science."—Biometrical Journal Table of ContentsTools: knitr: A Comprehensive Tool for Reproducible Research in R. Reproducibility Using VisTrails. Sumatra: A Toolkit for Reproducible Research. CDE: Automatically Package and Reproduce Computational Experiments. Reproducible Physical Science and the Declaratron. Practices and Guidelines: Developing Open-Source Scientific Practice. Reproducible Bioinformatics Research for Biologists. Reproducible Research for Large-Scale Data Analysis. Practicing Open Science. Reproducibility, Virtual Appliances, and Cloud Computing. The Reproducibility Project: A Model of Large-Scale Collaboration for Empirical Research on Reproducibility—Open Science Collaboration. What Computational Scientists Need to Know about Intellectual Property Law: A Primer. Platforms: Open Science in Machine Learning. RunMyCode.org: A Research-Reproducibility Tool for Computational Sciences. Open Science and the Role of Publishers in Reproducible Research. Index.

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Book SynopsisStatistics is of ever-increasing importance in Science and Technology and this book presents the essentials of the subject in a form suitable either as the basis of a course of lectures or to be read and/or used on its own.Table of Contents1 Introduction or ‘What is statistics?’.- 2 The presentation of data.- 3 Probability, its meaning, real and theoretical populations.- 4 Basic properties of the normal distribution.- 5 Some properties of sampling distributions.- 6 Applications of normal sampling theory; significance tests.- 7 Normal sampling theory: test for difference between several sample means, analysis of variance, design of experiments.- 8 Normal sampling theory: estimation of ‘parameters’ by confidence intervals, by maximum likelihood.- 9 The binomial distribution: laws of probability, applications of the binomial distribution, the multinomial distribution.- 10 The Poisson, negative exponential, and rectangular distributions.- 11 The ?2 test for ‘goodness of fit’: test for ‘association’.- 12 Fitting lines and curves to data, least squares method.- 13 Regression curves and lines, correlation coefficient, normal bivariate distribution.- 14 Some distribution-independent (or ‘distribution-free’ or ‘non-parametric’) tests.- 15 Note on sampling techniques and quality control.- 16 Some problems of practical origin.- Answers.

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Book SynopsisThe Mata Book: A Book for Serious Programmers and Those Who Want to Be is the book that Stata programmers have been waiting for. Mata is a serious programming language for developing small- and large-scale projects and for adding features to Stata. What makes Mata serious is that it provides structures, classes, and pointers along with matrix capabilities. The book is serious in that it covers those advanced features, and teaches them. The reader is assumed to have programming experience, but only some programming experience. That experience could be with Stata's ado language, or with Python, Java, C++, Fortran, or other languages like them. As the book says, "being serious is a matter of attitude, not current skill level or knowledge".The author of the book is William Gould, who is also the designer and original programmer of Mata, of Stata, and who also happens to be the president of StataCorp. Table of ContentsThe mechanics of using Mata. A programmer’s tour of Mata. Mata’s programming statements. Mata’s expressions. Mata’s variable types. Mata’s strict option and Mata’s pragmas. Mata’s function arguments. Programming example: n_choose_k() three ways. Mata’s structures. Programming example: Linear regression. Mata’s classes. Programming example: Linear regression 2. Better variable types. Programming constants. Mata’s associative arrays. Programming example: Sparse matrices. Programming example: Sparse matrices, continued. The Mata Reference Manual. Writing Mata code to add new commands to Stata. Mata’s storage type for complex numbers. How Mata differs from C and C++. Three-dimensional arrays (advanced use of pointers).

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Book SynopsisThis textbook teaches crucial statistical methods to answer research questions using a unique range of statistical software programs, including MINITAB and R. This textbook is developed for undergraduate students in agriculture, nursing, biology and biomedical research. Graduate students will also find it to be a useful way to refresh their statistics skills and to reference software options. The unique combination of examples is approached using MINITAB and R for their individual strengths. Subjects covered include among others data description, probability distributions, experimental design, regression analysis, randomized design and biological assay. Unlike other biostatistics textbooks, this text also includes outliers, influential observations in regression and an introduction to survival analysis. Material is taken from the author's extensive teaching and research in Africa, USA and the UK. Sample problems, references and electronic supplementary material accompany each chapter.Table of Contents​Table of Contents attached as well. Introduction.- Frequency Distributions.- Numerical Description of Data.- Probability and Probability Distributions.- Estimation and Hypothesis Testing.- Regression Analysis.- Categorical Data Analysis.- Experimental Design.- The Completely Randomized Design.- The Randomized Complete Block Design.- Multiple Blocking Designs.- Analysis of Covariance.- Factorial Treatments Designs.- The Split-Plot Design.- Incomplete Block Design.- Quantal-Bioassay.- Repeated Measures Design.- Survival Analysis.

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Book SynopsisComputer Simulation.- Basic Probability.- Conditional Probability.- Discrete Random Variables.- Expected Values for Discrete Random Variables.- Multiple Discrete Random Variables.- Conditional Probability Mass Functions.- Discrete N-Dimensional Random Variables.- Continuous Random Variables.- Expected Values for Continuous Random Variables.- Multiple Continuous Random Variables.- Conditional Probability Density Functions.- Continuous N-Dimensional Random Variables.- Probability and Moment Approximations Using Limit Theorems.- Basic Random Processes.- Wide Sense Stationary Random Processes.- Linear Systems and Wide Sense Stationary Random Processes.- Multiple Wide Sense Stationary Random Processes.- Gaussian Random Processes.- Poisson Random Processes.- Markov Chains.Trade ReviewFrom the reviews:"The book is composed of 22 chapters. … This is a very readable book. … Kay’s book undoubtedly will see its greatest use in engineering schools, but I think it would work nicely in other settings as well. … It is written in a clear and informal style that students will appreciate, its coverage is excellent, and the author’s stated objective (to lessen the difficulty that students usually experience assimilating and applying probability and random processes) will, I predict, be met." (Ralph P. Russo, The American Statistician, Vol. 62 (2), May, 2008)“Kay’s book occupies a unique place in the overcrowded market of textbooks on probability and random processes. … This new textbook is a breath of fresh air in the market of books devoted to probability and random processes. The book lives up to its ambition of setting a new standard for a modern, computer-based treatment of the subject. … I fully recommend its use in undergraduate and first-year graduate courses.” (Osvaldo Simeone, IEEE Control Systems Magazine, Vol. 27, June, 2007)Table of ContentsComputer Simulation.- Basic Probability.- Conditional Probability.- Discrete Random Variables.- Expected Values for Discrete Random Variables.- Multiple Discrete Random Variables.- Conditional Probability Mass Functions.- Discrete N-Dimensional Random Variables.- Continuous Random Variables.- Expected Values for Continuous Random Variables.- Multiple Continuous Random Variables.- Conditional Probability Density Functions.- Continuous N-Dimensional Random Variables.- Probability and Moment Approximations Using Limit Theorems.- Basic Random Processes.- Wide Sense Stationary Random Processes.- Linear Systems and Wide Sense Stationary Random Processes.- Multiple Wide Sense Stationary Random Processes.- Gaussian Random Processes.- Poisson Random Processes.- Markov Chains.

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Book SynopsisThe fun and easy way to learn to use this leading business intelligence tool Written 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. SAS For Dummies, 2nd Edition gives you the necessary background on what SAS can do for you and explains how to use the Enterprise Guide. SAS provides statistical and data analysis tools to help you deal with all kinds of data: operational, financial, performance, and more Places special emphasis on Enterprise Guide and other analytical tools, covering all commonly used features Covers all commonly used features and shows you the practical applications you can put to worTable of ContentsIntroduction. Part I: Welcome to SAS! Chapter 1:Touring the Wonderful World of SAS. Chapter 2: Your Connection to SAS: Using SAS Enterprise Guide. Chapter 3: Six-Minute Abs: Getting Miraculous Results with SAS. Part II: Gathering Data and Presenting Information. Chapter 4: Accessing Data: Oh, the Choices! Chapter 5: Managing Data: I Can Do That? Chapter 6: Show Me a Report in Less Than a Minute. Chapter 7: Graphs: More Value with SAS. Part III: Impressing Your Boss with Your SAS Business Intelligence. Chapter 8: A Painless Introduction to Analytics. Chapter 9: More Analytics to Enlighten and Entertain. Chapter 10: Data Mining: Making the Leap from Guesses to Smart Choices. Part IV: Enhancing and Sharing Your SAS Masterpieces. Chapter 11: Leveraging Work from SAS to Those Less Fortunate. Chapter 12: Use OLAP and Impress Your Coworkers. Chapter 13: Supercharge Microsoft Offi ce with SAS. Chapter 14: Web Reporting Fever: SAS Has That Covered. Part V: Getting SAS Ready to Rock and Roll. Chapter 15: Setting Up SAS. Chapter 16: SAS Programming for the Faint of Heart. Chapter 17: The New World Meets the Old: Programmers and SAS Enterprise Guide. Part VI: The Part of Tens. Chapter 18: Ten SAS Enterprise Guide Productivity Tips. Chapter 19: Ten Tips for Administrators. Chapter 20: Ten (or More) Web Resources for Extra Information. Index.

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Book SynopsisModern Analysis of Customer Surveys: with applications using R Customer 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. Key features: Provides an integrated case studies-based approach to analysing customer survey data. Presents a general introduction to customer surveys, within an organization's business cycle. Contains classical techniques with modern and non standard tools.Table of ContentsForeword xvii Preface xix Contributors xxiii Part I Basic Aspects of Customer Satisfaction Survey Data Analysis 1 Standards and Classical Techniques in Data Analysis of Customer Satisfaction Surveys 3 Silvia Salini and Ron S. Kenett 1.1 Literature on customer satisfaction surveys 4 1.2 Customer satisfaction surveys and the business cycle 4 1.3 Standards used in the analysis of survey data 7 1.4 Measures and models of customer satisfaction 12 1.4.1 The conceptual construct 12 1.4.2 The measurement process 13 1.5 Organization of the book 15 1.6 Summary 17 References 17 2 The ABC Annual Customer Satisfaction Survey 19 Ron S. Kenett and Silvia Salini 2.1 The ABC company 19 2.2 ABC 2010 ACSS: Demographics of respondents 20 2.3 ABC 2010 ACSS: Overall satisfaction 22 2.4 ABC 2010 ACSS: Analysis of topics 24 2.5 ABC 2010 ACSS: Strengths and weaknesses and decision drivers 27 2.6 Summary 28 References 28 Appendix 29 3 Census and Sample Surveys 37 Giovanna Nicolini and Luciana Dalla Valle 3.1 Introduction 37 3.2 Types of surveys 39 3.2.1 Census and sample surveys 39 3.2.2 Sampling design 40 3.2.3 Managing a survey 40 3.2.4 Frequency of surveys 41 3.3 Non-sampling errors 41 3.3.1 Measurement error 42 3.3.2 Coverage error 42 3.3.3 Unit non-response and non-self-selection errors 43 3.3.4 Item non-response and non-self-selection error 44 3.4 Data collection methods 44 3.5 Methods to correct non-sampling errors 46 3.5.1 Methods to correct unit non-response errors 46 3.5.2 Methods to correct item non-response 49 3.6 Summary 51 References 52 4 Measurement Scales 55 Andrea Bonanomi and Gabriele Cantaluppi 4.1 Scale construction 55 4.1.1 Nominal scale 56 4.1.2 Ordinal scale 57 4.1.3 Interval scale 58 4.1.4 Ratio scale 59 4.2 Scale transformations 60 4.2.1 Scale transformations referred to single items 61 4.2.2 Scale transformations to obtain scores on a unique interval scale 66 Acknowledgements 69 References 69 5 Integrated Analysis 71 Silvia Biffignandi 5.1 Introduction 71 5.2 Information sources and related problems 73 5.2.1 Types of data sources 73 5.2.2 Advantages of using secondary source data 73 5.2.3 Problems with secondary source data 74 5.2.4 Internal sources of secondary information 75 5.3 Root cause analysis 78 5.3.1 General concepts 78 5.3.2 Methods and tools in RCA 81 5.3.3 Root cause analysis and customer satisfaction 85 5.4 Summary 87 Acknowledgement 87 References 87 6 Web Surveys 89 Roberto Furlan and Diego Martone 6.1 Introduction 89 6.2 Main types of web surveys 90 6.3 Economic benefits of web survey research 91 6.3.1 Fixed and variable costs 92 6.4 Non-economic benefits of web survey research 94 6.5 Main drawbacks of web survey research 96 6.6 Web surveys for customer and employee satisfaction projects 100 6.7 Summary 102 References 102 7 The Concept and Assessment of Customer Satisfaction 107 Irena Ograjenšek and Iddo Gal 7.1 Introduction 107 7.2 The quality–satisfaction–loyalty chain 108 7.2.1 Rationale 108 7.2.2 Definitions of customer satisfaction 108 7.2.3 From general conceptions to a measurement model of customer satisfaction 110 7.2.4 Going beyond SERVQUAL: Other dimensions of relevance to the B2B context 112 7.2.5 From customer satisfaction to customer loyalty 113 7.3 Customer satisfaction assessment: Some methodological considerations 115 7.3.1 Rationale 115 7.3.2 Think big: An assessment programme 115 7.3.3 Back to basics: Questionnaire design 116 7.3.4 Impact of questionnaire design on interpretation 118 7.3.5 Additional concerns in the B2B setting 119 7.4 The ABC ACSS questionnaire: An evaluation 119 7.4.1 Rationale 119 7.4.2 Conceptual issues 119 7.4.3 Methodological issues 120 7.4.4 Overall ABC ACSS questionnaire asssessment 121 7.5 Summary 121 References 122 Appendix 126 8 Missing Data and Imputation Methods 129 Alessandra Mattei, Fabrizia Mealli and Donald B. Rubin 8.1 Introduction 129 8.2 Missing-data patterns and missing-data mechanisms 131 8.2.1 Missing-data patterns 131 8.2.2 Missing-data mechanisms and ignorability 132 8.3 Simple approaches to the missing-data problem 134 8.3.1 Complete-case analysis 134 8.3.2 Available-case analysis 135 8.3.3 Weighting adjustment for unit nonresponse 135 8.4 Single imputation 136 8.5 Multiple imputation 138 8.5.1 Multiple-imputation inference for a scalar estimand 138 8.5.2 Proper multiple imputation 139 8.5.3 Appropriately drawing imputations with monotone missing-data patterns 140 8.5.4 Appropriately drawing imputations with nonmonotone missing-data patterns 141 8.5.5 Multiple imputation in practice 142 8.5.6 Software for multiple imputation 143 8.6 Model-based approaches to the analysis of missing data 144 8.7 Addressing missing data in the ABC annual customer satisfaction survey: An example 145 8.8 Summary 149 Acknowledgements 150 References 150 9 Outliers and Robustness for Ordinal Data 155 Marco Riani, Francesca Torti and Sergio Zani 9.1 An overview of outlier detection methods 155 9.2 An example of masking 157 9.3 Detection of outliers in ordinal variables 159 9.4 Detection of bivariate ordinal outliers 160 9.5 Detection of multivariate outliers in ordinal regression 161 9.5.1 Theory 161 9.5.2 Results from the application 163 9.6 Summary 168 References 168 Part II Modern Techniques in Customer Satisfaction Survey Data Analysis 10 Statistical Inference for Causal Effects 173 Fabrizia Mealli, Barbara Pacini and Donald B. Rubin 10.1 Introduction to the potential outcome approach to causal inference 173 10.1.1 Causal inference primitives: Units, treatments, and potential outcomes 175 10.1.2 Learning about causal effects: Multiple units and the stable unit treatment value assumption 176 10.1.3 Defining causal estimands 177 10.2 Assignment mechanisms 179 10.2.1 The criticality of the assignment mechanism 179 10.2.2 Unconfounded and strongly ignorable assignment mechanisms 180 10.2.3 Confounded and ignorable assignment mechanisms 181 10.2.4 Randomized and observational studies 181 10.3 Inference in classical randomized experiments 182 10.3.1 Fisher’s approach and extensions 183 10.3.2 Neyman’s approach to randomization-based inference 183 10.3.3 Covariates, regression models, and Bayesian model-based inference 184 10.4 Inference in observational studies 185 10.4.1 Inference in regular designs 186 10.4.2 Designing observational studies: The role of the propensity score 186 10.4.3 Estimation methods 188 10.4.4 Inference in irregular designs 188 10.4.5 Sensitivity and bounds 189 10.4.6 Broken randomized experiments as templates for the analysis of some irregular designs 189 References 190 11 Bayesian Networks Applied to Customer Surveys 193 Ron S. Kenett, Giovanni Perruca and Silvia Salini 11.1 Introduction to Bayesian networks 193 11.2 The Bayesian network model in practice 197 11.2.1 Bayesian network analysis of the ABC 2010 ACSS 197 11.2.2 Transport data analysis 201 11.2.3 R packages and other software programs used for studying BNs 210 11.3 Prediction and explanation 211 11.4 Summary 213 References 213 12 Log-linear Model Methods 217 Stephen E. Fienberg and Daniel Manrique-Vallier 12.1 Introduction 217 12.2 Overview of log-linear models and methods 218 12.2.1 Two-way tables 218 12.2.2 Hierarchical log-linear models 220 12.2.3 Model search and selection 222 12.2.4 Sparseness in contingency tables and its implications 223 12.2.5 Computer programs for log-linear model analysis 223 12.3 Application to ABC survey data 224 12.4 Summary 227 References 228 13 CUB Models: Statistical Methods and Empirical Evidence 231 Maria Iannario and Domenico Piccolo 13.1 Introduction 231 13.2 Logical foundations and psychological motivations 233 13.3 A class of models for ordinal data 233 13.4 Main inferential issues 236 13.5 Specification of CUB models with subjects’ covariates 238 13.6 Interpreting the role of covariates 240 13.7 A more general sampling framework 241 13.7.1 Objects’ covariates 241 13.7.2 Contextual covariates 243 13.8 Applications of CUB models 244 13.8.1 Models for the ABC annual customer satisfaction survey 245 13.8.2 Students’ satisfaction with a university orientation service 246 13.9 Further generalizations 248 13.10 Concluding remarks 251 Acknowledgements 251 References 251 Appendix 255 A program in R for CUB models 255 A.1 Main structure of the program 255 A.2 Inference on CUB models 255 A.3 Output of CUB models estimation program 256 A.4 Visualization of several CUB models in the parameter space 257 A.5 Inference on CUB models in a multi-object framework 257 A.6 Advanced software support for CUB models 258 14 The Rasch Model 259 Francesca De Battisti, Giovanna Nicolini and Silvia Salini 14.1 An overview of the Rasch model 259 14.1.1 The origins and the properties of the model 259 14.1.2 Rasch model for hierarchical and longitudinal data 263 14.1.3 Rasch model applications in customer satisfaction surveys 265 14.2 The Rasch model in practice 267 14.2.1 Single model 267 14.2.2 Overall model 268 14.2.3 Dimension model 272 14.3 Rasch model software 277 14.4 Summary 278 References 279 15 Tree-based Methods and Decision Trees 283 Giuliano Galimberti and Gabriele Soffritti 15.1 An overview of tree-based methods and decision trees 283 15.1.1 The origins of tree-based methods 283 15.1.2 Tree graphs, tree-based methods and decision trees 284 15.1.3 CART 287 15.1.4 CHAID 293 15.1.5 PARTY 295 15.1.6 A comparison of CART, CHAID and PARTY 297 15.1.7 Missing values 297 15.1.8 Tree-based methods for applications in customer satisfaction surveys 298 15.2 Tree-based methods and decision trees in practice 300 15.2.1 ABC ACSS data analysis with tree-based methods 300 15.2.2 Packages and software implementing tree-based methods 303 15.3 Further developments 304 References 304 16 PLS Models 309 Giuseppe Boari and Gabriele Cantaluppi 16.1 Introduction 309 16.2 The general formulation of a structural equation model 310 16.2.1 The inner model 310 16.2.2 The outer model 312 16.3 The PLS algorithm 313 16.4 Statistical interpretation of PLS 319 16.5 Geometrical interpretation of PLS 320 16.6 Comparison of the properties of PLS and LISREL procedures 321 16.7 Available software for PLS estimation 323 16.8 Application to real data: Customer satisfaction analysis 323 References 329 17 Nonlinear Principal Component Analysis 333 Pier Alda Ferrari and Alessandro Barbiero 17.1 Introduction 333 17.2 Homogeneity analysis and nonlinear principal component analysis 334 17.2.1 Homogeneity analysis 334 17.2.2 Nonlinear principal component analysis 336 17.3 Analysis of customer satisfaction 338 17.3.1 The setting up of indicator 338 17.3.2 Additional analysis 340 17.4 Dealing with missing data 340 17.5 Nonlinear principal component analysis versus two competitors 343 17.6 Application to the ABC ACSS data 344 17.6.1 Data preparation 344 17.6.2 The homals package 345 17.6.3 Analysis on the ‘complete subset’ 346 17.6.4 Comparison of NLPCA with PCA and Rasch analysis 350 17.6.5 Analysis of ‘entire data set’ for the comparison of missing data treatments 352 17.7 Summary 355 References 355 18 Multidimensional Scaling 357 Nadia Solaro 18.1 An overview of multidimensional scaling techniques 357 18.1.1 The origins of MDS models 358 18.1.2 MDS input data 359 18.1.3 MDS models 362 18.1.4 Assessing the goodness of MDS solutions 369 18.1.5 Comparing two MDS solutions: Procrustes analysis 371 18.1.6 Robustness issues in the MDS framework 371 18.1.7 Handling missing values in MDS framework 373 18.1.8 MDS applications in customer satisfaction surveys 373 18.2 Multidimensional scaling in practice 374 18.2.1 Data sets analysed 375 18.2.2 MDS analyses of overall satisfaction with a set of ABC features: The complete data set 375 18.2.3 Weighting objects or items 381 18.2.4 Robustness analysis with the forward search 382 18.2.5 MDS analyses of overall satisfaction with a set of ABC features: The incomplete data set 383 18.2.6 Package and software for MDS methods 384 18.3 Multidimensional scaling in a future perspective 386 18.4 Summary 386 References 387 19 Multilevel Models for Ordinal Data 391 Leonardo Grilli and Carla Rampichini 19.1 Ordinal variables 391 19.2 Standard models for ordinal data 393 19.2.1 Cumulative models 394 19.2.2 Other models 395 19.3 Multilevel models for ordinal data 395 19.3.1 Representation as an underlying linear model with thresholds 396 19.3.2 Marginal versus conditional effects 397 19.3.3 Summarizing the cluster-level unobserved heterogeneity 397 19.3.4 Consequences of adding a covariate 398 19.3.5 Predicted probabilities 399 19.3.6 Cluster-level covariates and contextual effects 399 19.3.7 Estimation of model parameters 400 19.3.8 Inference on model parameters 401 19.3.9 Prediction of random effects 402 19.3.10 Software 403 19.4 Multilevel models for ordinal data in practice: An application to student ratings 404 References 408 20 Quality Standards and Control Charts Applied to Customer Surveys 413 Ron S. Kenett, Laura Deldossi and Diego Zappa 20.1 Quality standards and customer satisfaction 413 20.2 ISO 10004 guidelines for monitoring and measuring customer satisfaction 414 20.3 Control Charts and ISO 7870 417 20.4 Control charts and customer surveys: Standard assumptions 420 20.4.1 Introduction 420 20.4.2 Standard control charts 420 20.5 Control charts and customer surveys: Non-standard methods 426 20.5.1 Weights on counts: Another application of the c chart 426 20.5.2 The χ2 chart 427 20.5.3 Sequential probability ratio tests 428 20.5.4 Control chart over items: A non-standard application of SPC methods 429 20.5.5 Bayesian control chart for attributes: A modern application of SPC methods 432 20.5.6 Control chart for correlated Poisson counts: When things become fairly complicated 433 20.6 The M-test for assessing sample representation 433 20.7 Summary 435 References 436 21 Fuzzy Methods and Satisfaction Indices 439 Sergio Zani, Maria Adele Milioli and Isabella Morlini 21.1 Introduction 439 21.2 Basic definitions and operations 440 21.3 Fuzzy numbers 441 21.4 A criterion for fuzzy transformation of variables 443 21.5 Aggregation and weighting of variables 445 21.6 Application to the ABC customer satisfaction survey data 446 21.6.1 The input matrices 446 21.6.2 Main results 448 21.7 Summary 453 References 455 Appendix an Introduction to R 457 Stefano Maria Iacus A.1 Introduction 457 A.2 How to obtain R 457 A.3 Type rather than ‘point and click’ 458 A.3.1 The workspace 458 A.3.2 Graphics 458 A.3.3 Getting help 459 A.3.4 Installing packages 459 A.4 Objects 460 A.4.1 Assignments 460 A.4.2 Basic object types 462 A.4.3 Accessing objects and subsetting 466 A.4.4 Coercion between data types 469 A.5 S4 objects 470 A.6 Functions 472 A.7 Vectorization 473 A.8 Importing data from different sources 475 A.9 Interacting with databases 476 A.10 Simple graphics manipulation 477 A.11 Basic analysis of the ABC data 481 A.12 About this document 496 A.13 Bibliographical notes 496 References 496 Index 499

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Book SynopsisThe information contained in this book has served as the basis for a graduate-level biostatistics class at the University of North Carolina at Chapel Hill. The book focuses in the General Linear Model (GLM) theory, stated in matrix terms, which provides a more compact, clear, and unified presentation of regression of ANOVA than do traditional sums of squares and scalar equations. The book contains a balanced treatment of regression and ANOVA yet is very compact. Reflecting current computational practice, most sums of squares formulas and associated theory, especially in ANOVA, are not included. The text contains almost no proofs, despite the presence of a large number of basic theoretical results. Many numerical examples are provided, and include both the SAS code and equivalent mathematical representation needed to produce the outputs that are presented. All exercises involve only real data, collected in the course of scientific research. The book is divided Trade Review“…very useful to applied scientists and for graduate level courses in areas of non-mathematical statistics…” (Zentralblatt Math, Vol.1039, No.8, 2004)Table of ContentsPreface. Examples and Limits of the GLM. Statement of the Model, Estimation, and Testing. Some Distributions for the GLM. Multiple Regression: General Considerations. Testing Hypotheses in Multiple Regression. Correlations. GLM Assumption Diagnostics. GLM Computation Diagnostics. Polynomial Regression. Transformations. Selecting the Best Model. Coding Schemes for Regression. One-Way ANOVA. Complete, Two-Way Factorial ANOVA. Special Cases of Two-Way ANOVA and Random Effects Basics. The Full Model in Every Cell (ANCOVA as a Special Case). Understanding and Computing Power for the GLM. Appendix A. Matrix Algebra for Linear Models. Appendix B. Statistical Tables. Appendix C. Study Guide for Linear Model Theory. Appendix D. Homework and Example Data. Appendix E. Introduction to SAS/IML. Appendix F. A Brief Manual to LINMOD. Appendix G. SAS/IML Power Program User's Guide. Appendix H. Regression Model Selection Data. References. Index.

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