Mathematical and statistical software Books
O'Reilly Media R in a Nutshell
Book SynopsisR is rapidly becoming the standard for developing statistical software, and R in a Nutshell provides a quick and practical way to learn this increasingly popular open source language and environment.
£35.99
Springer New York An Introduction to Modern Mathematical Computing
Book Synopsisand the building of the Three “M’s” Maple, Mathematica and Matlab. We intend to persuade that Maple and other like tools are worth knowing assuming only that one wishes to be a mathematician, a mathematics educator, a computer scientist, an engineer or scientist, or anyone else who wishes/needs to use mathematics better.Trade ReviewFrom the reviews:“This book is intended to teach the reader the usage of the computer algebra system Maple. … The book is readable and valuable to mathematics, science, and engineering undergraduates at the sophomore or above level. It could also be valuable to practitioners in those fields who want to learn Maple in situ. … Summing Up: Recommended. Lower-division undergraduates through graduate students; professionals.” (D. Z. Spicer, Choice, Vol. 49 (5), January, 2012)“This is a Maple-application book which illustrates some basic areas of mathematics by symbolic computation examples. … The presentation is clear with all necessary details and comments for ensuring a full understanding of the considered examples. The intended beneficiaries are undergraduate students, teachers giving courses to undergraduate students, as well as programmers interested in using Maple for several classes of mathematical problems.” (Octavian Pastravanu, Zentralblatt MATH, Vol. 1228, 2012)“In An Introduction to Modern Mathematical Computing with Maple, Borwein and Skerritt show that computers are an excellent companion for learning mathematics. … The theme of the book is that Maple can supplement mathematics learning and, what is more, can do much of the mathematics for the students. … The temptation is tremendous for students to skip the real work to have a true understanding of mathematics.” (David S. Mazel, The Mathematical Association of America, June, 2012)Table of Contents-Preface. -Conventions and Notation.-1. Number Theory (Introduction to Maple, Putting it together, Enough code, already. Show me some maths!, Problems and Exercises, Further Explorations). -2. Calculus(Revision and Introduction, Univariate Calculus, Multivariate Calculus, Exercises, Further Explorations). -3. Linear Algebra (Introduction and Review, Vector Spaces, Linear Transformations, Exercises, Further Explorations). -4. Visualisation and Geometry: a postscript (Useful Visualisation Tools, Geometry and Geometric Constructions). –A. Sample Quizzes (Number Theory, Calculus, Linear Algebra). –Index. –References
£56.35
Springer-Verlag New York Inc. R for SAS and SPSS Users
Book SynopsisR is a powerful and free software system for data analysis and graphics, with over 5,000 add-on packages available. This book introduces R using SAS and SPSS terms with which you are already familiar. It demonstrates which of the add-on packages are most like SAS and SPSS and compares them to R''s built-in functions. It steps through over 30 programs written in all three packages, comparing and contrasting the packages'' differing approaches. The programs and practice datasets are available for download.The glossary defines over 50 R terms using SAS/SPSS jargon and again using R jargon. The table of contents and the index allow you to find equivalent R functions by looking up both SAS statements and SPSS commands. When finished, you will be able to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses.This new edition has updated programming, an expanded index, and even more statistical methods covered in overTrade ReviewFrom the reviews of the second edition:“This is a greatly expanded second edition of a text that has already proved widely popular. The explanation is careful and detailed. It uses SAS and SPSS terminology, matching it with R terminology … . A glossary translates R terminology into terminology that is likely to be more familiar to SAS and SPSS users. … a wide-ranging and carefully compiled source of information on R. It is a strongly recommended addition to the library of anyone who comes to R from SAS or SPSS.” (John H. Maindonald, International Statistical Review, Vol. 80 (1), 2012)Table of ContentsIntroduction.- Installing and Updating R.- Running R.- Help and Documentation.- Programming Language Basics.- Data Acquisition.- Selecting Variables.- Selecting Observations.- Selecting Variables and Observations.- Data Management.- Enhancing Your Output.- Generating Data.- Managing Your Files and Workspace.- Graphics Overview.- Traditional Graphics- Graphics with ggplot2.- Statistics.- Conclusion.
£112.49
Springer New York Bayesian Networks and Influence Diagrams A Guide to Construction and Analysis 22 Information Science and Statistics
Book SynopsisThe techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide.Trade ReviewFrom the book reviews:“The monograph concentrates on intelligent systems for decision support based on probabilistic models, including Bayesian networks and influence diagrams. … This monograph provides a review of recent state affairs of probabilistic networks that can be useful for professionals, practitioners, and researchers from diverse fields of statistics and related disciplines. I think it can be used as a textbook in its own right for an upper level undergraduate course, especially for a reading course.” (Technometrics, Vol. 55 (2), May, 2013)Table of ContentsIntroduction.- Networks.- Probabilities.- Probabilistic Networks.- Solving Probabilistic Networks.- Eliciting the Model.- Modeling Techniques.- Data-Driven Modeling.- Conflict Analysis.- Sensitivity Analysis.- Value of Information Analysis.- Quick Reference to Model Construction.- List of Examples.- List of Figures.- List of Tables.- List of Symbols.- References.- Index.
£82.49
Springer-Verlag New York Inc. Applied Statistics for Business and Management
Book SynopsisApplied Business Statistics for Business and Management using Microsoft Excelis the firstbook to illustrate the capabilities of Microsoft Excel to teach applied statistics effectively.It is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical statistical problems in industry.If understanding statistics isn't your strongest suit, you are not especially mathematically-inclined, or if you are wary of computers, this is the right book for you.Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in statistics courses.Its powerful computational ability and graphical functions make learning statistics much easier than in years past.However, Applied Business Statistics for Business and Managementcapitalizes on these improvements by teaching students and practitioners how to apply Excel to statistical techniques necessary in theirTable of ContentsStatistics and Data.- Summarizing Data.- Descriptive Statistics and Graphing.- Normal World.- Survey Design.- Sampling.- Inference.- Probability.- Correlation.- Simple Linear Regression.- Multiple Regression.- Significance Tests.- Non Linear Regression.- Survey Reports.
£80.99
Springer-Verlag New York Inc. The R Software Fundamentals of Programming and
Book SynopsisEach statistical chapter in the second part relies on one or more real biomedical data sets, kindly made available by the Bordeaux School of Public Health (Institut de Santé Publique, d'Épidémiologie et de Développement - ISPED) and described at the beginning of the book.Trade ReviewFrom the book reviews:“This is a great addition to the chorus of books on R. It is a clear an excellent resource for teaching courses on data analysis and statistical computing using R at the graduate and advanced undergraduate levels. The book can be an asset for data scientists, and even more broadly for a wide variety of users including students, teachers, researchers, software engineers, and others whose work involves statistics, mathematics, and computer science.” (Yousri El Fattah, Computing Reviews, January, 2015)Table of ContentsForeward.- Basic Concepts and Data Organisation.- Importing, Exporting and Producing Data.- Data Manipulation, Functions.- R and its Documentation.- Drawing Curves and Plots.- Programming in R.- Managing Sessions.- Basic Mathematics.- Descriptive Statistics.- A Better Understanding of Random Variables.- Confidence Intervals and Hypothesis Testing.- Simple and Multiple Linear Regression.- Elementary Analysis of Variance.- Installing R and R Packages.- References.- Indices.- Solutions.
£125.99
Taylor & Francis Inc Statistical Computing with R Second Edition
Book SynopsisPraise for the First Edition:. . . the book serves as an excellent tutorial on the R language, providing examples that illustrate programming concepts in the context of practical computational problems. The book will be of great interest for all specialists working on computational statistics and Monte Carlo methods for modeling and simulation. Tzvetan Semerdjiev, Zentralblatt MathComputational statistics and statistical computing are two areas within statistics that may be broadly described as computational, graphical, and numerical approaches to solving statistical problems. Like its bestselling predecessor, Statistical Computing with R, Second Edition covers the traditional core material of these areas with an emphasis on using the R language via an examples-based approach. The new edition is up-to-date with the many advances that have been made in recent years. Features Provides an overview of cTrade ReviewPraise for the First Edition:"… an excellent tutorial on the R language, providing examples that illustrate programming concepts in the context of practical computational problems. The book will be of great interest for all specialists working on computational statistics and Monte Carlo methods for modeling and simulation." —Tzvetan Semerdjiev, Zentralblatt Math, 2008, Vol. 1137 "Statistical computing and computational statistics are two areas of statistics described as computational, graphical, and numerical approaches to solving statistical problems. Statistical Computing with R comprises, thorough and examples-based approach, the conventional core material of computational statistics with an emphasis on R... This book includes standard statistical computing topics using the R language... All examples in the text are realised in R. Software is actively maintained, it has good connectivity to various types of data and other systems, and it is versatile. In addition, R is very stable and reliable... The book also includes exercises and applications in all chapters, as well as coverage of recent advances including R Studio. Many examples are included, fully implemented in the R statisticalcomputing environment, and the R code for the examples can be downloaded from the author’s website. Most examples and exercises apply datasets accessible in the R distribution or simulated data. The author, Maria L. Rizzo, is a Full Professor at the Department of Mathematics and Statistics of Bowling Green State University (US) and is an expert on Applied Statistics, Statistical Computing, and Energy Statistics... After finishing the book, I feel that it is a well-written text useful for biostatisticians and graduate teachers, principally because it is written by a leading expert who is engaged in statistical modelling and methodological developments and applications in the real world. In my opinion, the book is a must-have for the interested biostatistician audience."- Luca Bertolaccini, ISCB December 2019 Praise for the First Edition:"… an excellent tutorial on the R language, providing examples that illustrate programming concepts in the context of practical computational problems. The book will be of great interest for all specialists working on computational statistics and Monte Carlo methods for modeling and simulation." —Tzvetan Semerdjiev, Zentralblatt Math, 2008, Vol. 1137 "Statistical computing and computational statistics are two areas of statistics described as computational, graphical, and numerical approaches to solving statistical problems. Statistical Computing with R comprises, thorough and examples-based approach, the conventional core material of computational statistics with an emphasis on R... This book includes standard statistical computing topics using the R language... All examples in the text are realised in R. Software is actively maintained, it has good connectivity to various types of data and other systems, and it is versatile. In addition, R is very stable and reliable... The book also includes exercises and applications in all chapters, as well as coverage of recent advances including R Studio. Many examples are included, fully implemented in the R statistical computing environment, and the R code for the examples can be downloaded from the author’s website. Most examples and exercises apply datasets accessible in the R distribution or simulated data. The author, Maria L. Rizzo, is a Full Professor at the Department of Mathematics and Statistics of Bowling Green State University (US) and is an expert on Applied Statistics, Statistical Computing, and Energy Statistics... After finishing the book, I feel that it is a well-written text useful for biostatisticians and graduate teachers, principally because it is written by a leading expert who is engaged in statistical modelling and methodological developments and applications in the real world. In my opinion, the book is a must-have for the interested biostatistician audience."- Luca Bertolaccini, ISCB December 2019 "...This book tries to keep a balance between theory and practice, with more focus on the latter...also provides plenty of R codes to help the readers practice what they learned from the book. As stated in the preface, the targeted readers of this book are graduate students and advanced undergraduates with preparation in the relevant mathematics foundations. From this point of view, the content of the book fits well to the anticipated audience...I really appreciate the section on “finding source code” in Chapter 15. A lot of the libraries in R are written in C or Fortran. Occasionally, we need to dig into those codes and make changes to suit our needs. It is very helpful in our daily research to be able to find the source code and compile the changes...Finally, I would like to give credit to the author on making their code available on github. This makes it convenient for readers to try the code themselves without lots of typing. It also allows the authors to easily make updated code available to readers."- Ling Leng, JASA, September 2020 Table of ContentsIntroduction. Probability and Statistics Review. Methods for Generating Random Variables. Visualization of Multivariate Data. Monte Carlo Integration and Variance Reduction. Monte Carlo Methods in Inference. Bootstrap and Jackknife. Permutation Tests. Markov Chain Monte Carlo Methods. Probability Density Estimation. Smoothing and Nonparametric Regression. High Dimensional Data. Numerical Methods in R. Optimization.
£65.54
Taylor & Francis Inc Differential Equations with MATLAB
Book SynopsisA unique textbook for an undergraduate course on mathematical modeling, Differential Equations with MATLAB: Exploration, Applications, and Theory provides students with an understanding of the practical and theoretical aspects of mathematical models involving ordinary and partial differential equations (ODEs and PDEs). The text presents a unifying picture inherent to the study and analysis of more than 20 distinct models spanning disciplines such as physics, engineering, and finance.The first part of the book presents systems of linear ODEs. The text develops mathematical models from ten disparate fields, including pharmacokinetics, chemistry, classical mechanics, neural networks, physiology, and electrical circuits. Focusing on linear PDEs, the second part covers PDEs that arise in the mathematical modeling of phenomena in ten other areas, including heat conduction, wave propagation, fluid flow through fissured rocks, pattern formation, and financial Trade Review"The purpose of this book is to illustrate the use of MATLAB in the study of several models involving ordinary and partial differential equations. It includes different disciplines such as physics, engineering and finance. It may be useful for engineers, physicists and applied mathematicians and also for advanced undergraduate (or beginning graduate) students who are interested in the utilization of MATLAB in differential equations. ... The volume incorporates many figures and MATLAB exercises and many questions are raised throughout the text so that readers can do their own computer experiments."—Antonio Cańada Villar (Granada), writing in Zentralblatt MATH 1320 – 1Table of ContentsORDINARY DIFFERENTIAL EQUATIONS: Welcome! A Basic Analysis Toolbox. A First Wave of Mathematical Models. Finite-Dimensional Theory—Ground Zero: The Homogenous Case. Finite-Dimensional Theory—Next Step: The Non-Homogenous Case. A Second Wave of Mathematical Models—Now, with Nonlinear Interactions. Finite-Dimensional Theory—Last Step: The Semi-Linear Case. ABSTRACT ORDINARY DIFFERENTIAL EQUATIONS: Getting the Lay of a New Land. Three New Mathematical Models. Formulating a Theory for (A-HCP). The Next Wave of Mathematical Models—With Forcing. Remaining Mathematical Models. Formulating a Theory for (A-NonCP). A Final Wave of Models—Accounting for Semilinear Effects. Appendix. Bibliography. Index.
£104.50
Taylor & Francis Inc Implementing Reproducible Research
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.
£68.39
Taylor & Francis Inc Statistical Studies of Income Poverty and
Book SynopsisThere is no shortage of incentives to study and reduce poverty in our societies. Poverty is studied in economics and political sciences, and population surveys are an important source of information about it. The design and analysis of such surveys is principally a statistical subject matter and the computer is essential for their data compilation and processing.Focusing on The European Union Statistics on Income and Living Conditions (EU-SILC), a program of annual national surveys which collect data related to poverty and social exclusion, Statistical Studies of Income, Poverty and Inequality in Europe: Computing and Graphics in R presents a set of statistical analyses pertinent to the general goals of EU-SILC. The contents of the volume are biased toward computing and statistics, with reduced attention to economics, political and other social sciences. The emphasis is on methods and procedures as opposed to results, because the data from aTrade Review"In this book, the analyses of surveys conducted by EU-SILC are carried out using the statistical language R. … One noteworthy section … is devoted to Horvitz–Thompson estimation and is methodologically solid. … The presented methods … are illustrative in the use of software codes, figures, tables, and graphics."—International Statistical Review, 2015Table of ContentsPoverty Rate. Statistical Background. Poverty Indices. Mixtures of Distributions. Regions. Transitions. Multivariate Mixtures. Social Transfers. Causes and Effects. Education and Income. Epilogue. Bibliography. Subject Index. Index of User-Defined R Functions.
£104.50
Springer Us Essentials of Statistics for Scientists and Technologists
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.
£40.49
Taylor & Francis Inc Sharpening Your Advanced SAS Skills
Book SynopsisSharpening Your Advanced SAS Skills presents sophisticated SAS programming techniques, procedures, and tools, such as Proc SQL, hash tables, and SAS Macro programming, for any industry. Drawing on his more than 20 years' experience of SAS programming in the pharmaceutical industry, the author provides a unique approach that empowers both advanced programmers who need a quick refresher and programmers interested in learning new techniques.The book helps you easily search for key points by summarizing and differentiating the syntax between similar SAS statements and options. Each chapter begins with an overview so you can quickly locate the detailed examples and syntax. The basic syntax, expected data, and descriptions are organized in summary tables to facilitate better memory recall. General rules list common points about similar statements or options. Real-world examples of SAS programs and code statements are line numbeTable of ContentsAccessing Data Using SQL. SAS Macro Processing. Advanced Programming Techniques . What's New in SAS Version 9.3. Answers to Chapter Review Questions.
£68.39
APress MATLAB Control Systems Engineering
Book SynopsisIn addition to giving an introduction to the MATLAB environment and MATLAB programming, this book provides all the material needed to design and analyze control systems using MATLAB's specialized Control Systems Toolbox.Table of Contents1. Introduction to the MATLAB Environment2. MATLAB Variables, Numbers, Operators, and Functions3 MATLAB Control Systems – using the MATLAB Control System Toolbox4. Robust Predictive Control Strategies
£47.49
APress R 4 Quick Syntax Reference
Book SynopsisThis handy reference book detailing the intricacies of R covers version 4.x features, including numerous and significant changes to syntax, strings, reference counting, grid units, and more. Starting with the basic structure of R, the book takes you on a journey through the terminology used in R and the syntax required to make R work. You will find looking up the correct form for an expression quick and easy. Some of the new material includes information on RStudio, S4 syntax, working with character strings, and an example using the Twitter API. With a copy of the R 4 Quick Syntax Reference in hand, you will find that you are able to use the multitude of functions available in R and are even able to write your own functions to explore and analyze data. What You Will LearnDiscover the modes and classes of R objects and how to use themUse both packaged and user-created functions in RImport/export data and create new data objects in RCreate descriptive functions and manipulate objecTable of ContentsPart 1: R Basics1. Downloading R and Setting Up a File System2. The R Prompt3. Assignments and OperatorsPart 2: Kinds of Objects4. Modes of Objects5. Classes of ObjectsPart 3: Functions6. Packaged Functions7. User Created Functions8. How to Use a FunctionPart 4: I/O and Manipulating Objects9. Importing/Creating Data10. Exporting from R11. Descriptive Functions and Manipulating ObjectsPart 5: Flow control12. Flow Control13. Examples of Flow Control14. The Functions ifelse() and switch()Part 6: Some Common Functions, Packages and Techniques15. Some Common Functions16. The Packages base, stats and graphics17. The Tricks of the Trade
£42.49
O'Reilly Media Graphing Data with R
Book SynopsisAnyone who wants to analyze data will find something useful here-even if you don't have a background in mathematics, statistics, or computer programming. If you want to examine data related to your work, this book is the ideal way to start.
£23.99
O'Reilly Media Tidy Modeling with R
Book SynopsisGet going with tidymodels, a collection of R packages for modeling and machine learning. Whether you're just starting out or have years of experience with modeling, this practical introduction shows data analysts, business analysts, and data scientists how the tidymodels framework offers a consistent, flexible approach for your work.
£39.74
Springer New York Bayesian Networks and Influence Diagrams A Guide to Construction and Analysis
Book SynopsisThe techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide.Trade ReviewFrom the book reviews:“The monograph concentrates on intelligent systems for decision support based on probabilistic models, including Bayesian networks and influence diagrams. … This monograph provides a review of recent state affairs of probabilistic networks that can be useful for professionals, practitioners, and researchers from diverse fields of statistics and related disciplines. I think it can be used as a textbook in its own right for an upper level undergraduate course, especially for a reading course.” (Technometrics, Vol. 55 (2), May, 2013)Table of ContentsIntroduction.- Networks.- Probabilities.- Probabilistic Networks.- Solving Probabilistic Networks.- Eliciting the Model.- Modeling Techniques.- Data-Driven Modeling.- Conflict Analysis.- Sensitivity Analysis.- Value of Information Analysis.- Quick Reference to Model Construction.- List of Examples.- List of Figures.- List of Tables.- List of Symbols.- References.- Index.
£59.99
Springer-Verlag New York Inc. Introduction to Stochastic Programming
Book SynopsisThe aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. At the same time, it is now being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors aim to present a broad overview of the main themes and methods of the subject. Its prime goal is to help students develop an intuition on how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems.In this extensively updated new edition there is more material onTrade ReviewFrom the reviews of the second edition:“Help the students to understand how to model uncertainty into mathematical optimization problems, what uncertainty brings to the decision process and which techniques help to manage uncertainty in solving the problems. … certainly attract also the wide spectrum of readers whose main interest lies in possible exploitation of stochastic programming methodology and will help them to find their own way to treat actual problems using stochastic programming methods. As a whole, the three main building blocks of stochastic programming … are well represented and balanced.” (Jitka Dupačová, Zentralblatt MATH, Vol. 1223, 2011)Table of ContentsIntroduction and Examples.- Uncertainty and Modeling Issues.- Basic Properties and Theory.- The Value of Information and the Stochastic Solution.- Two-Stage Recourse Problems.- Multistage Stochastic Programs.- Stochastic Integer Programs.- Evaluating and Approximating Expectations.- Monte Carlo Methods.- Multistage Approximations.- Sample Distribution Functions.- References.
£52.24
Springer-Verlag New York Inc. A Course in Mathematical Statistics and Large
Book SynopsisThis 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.Part 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.Trade Review“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)“This is a very nice book suitable for a theoretical statistics course after having worked through something at the level of Casella & 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)Table of Contents1 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.
£98.99
Springer-Verlag New York Inc. Applied Predictive Modeling
Book SynopsisApplied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning.Trade Review“…In teaching a data science course…I use a range of different resources because I need to cover working with data, model evaluation, and machine learning methods. The next time I teach this course, I will use only this book because it covers all of these aspects of the field.” (Louis Luangkesorn, lugerpitt.blogspot.com, June 2015) “There are a wide variety of books available on predictive analytics and data modeling around the web…we’ve carefully selected the following 10 books, based on relevance, popularity, online ratings, and their ability to add value to your business. 1. Applied Predictive Modeling.” (Timothy King, Business Intelligence Solutions Review, solutions-review.com, June 2015) "Applied Predictive Modeling aims to expose many of these techniques in a very readable and self-contained book. This is a very applied and hands-on book. It guides the reader through many examples that serve to illustrate main points, and it raises possible issues and considerations that are oftentimes overlooked or not sufficiently reflected upon. Highly recommended." (Bojan Tunguz, tunguzreview.com, June 2015)“This monograph presents a very friendly practical course on prediction techniques for regression and classification models… It is a well-written book very useful to students and practitioners who need an immediate and helpful way to apply complex statistical techniques.” (Stan Lipovetsky, Technometrics, Vol. 56 (3), August 2014)“In my judgment, Applied Predictive Modeling by Max Kuhn and Kjell Johnson (Springer 2013) ought to be at the very top of the reading list …They come across like coaches who really, really want you to be able to do this…Applied Predictive Modeling is a remarkable text…it is the succinct distillation of years of experience of two expert modelers…” (Joseph Rickert, blog.revolutionanalytics.com, June 2014)Table of ContentsGeneral Strategies.- Regression Models.- Classification Models.- Other Considerations.- Appendix.- References.- Indices.
£49.49
Taylor & Francis Inc R for Programmers
Book SynopsisThis book discusses advanced topics such as R core programing, object oriented R programing, parallel computing with R, and spatial data types. The author leads readers to merge mature and effective methdologies in traditional programing to R programing. It shows how to interface R with C, Java, and other popular programing laguages and platforms. Table of ContentsPreface, Acknowledgments, About the Author, About the Translator, Translator’s Words, SECTION I. APPLYING R MATHEMATIC CALCULATIONS AND ALGORITHMS, 1. The Knowledge System and Mathematical Functions of R, 2. The Algorithm Implementations in R, SECTION II. R PROGRAMMING DEPTH, 3. The Kernel Programming in R, 4. Object-Oriented Programming, SECTION III. DEVELOPING R PACKAGE, 5. Developing R Packages, 6. Journey on R Game Development, Bibliography, Index
£71.24
Sage Publications Ltd Discovering Statistics Using IBM SPSS Statistics
Book SynopsisWith an exciting new look, new characters to meet, and its unique combination of humour and step-by-step instruction, this award-winning book is the statistics lifesaver for everyone. From initial theory through to regression, factor analysis and multilevel modelling, Andy Field animates statistics and SPSS software with his famously bizarre examples and activities. What’s brand new: A radical new design with original illustrations and even more colour A maths diagnostic tool to help students establish what areas they need to revise and improve on. A revamped online resource that uses video, case studies, datasets, testbanks and more to help students negotiate project work, master data management techniques, and apply key writing and employability skills New sections on replication, open science and Bayesian thinking Now fully up to date with latest versions of IBM SPSS Statistics©. All the online resources above (video, case studies, datasets, testbanks) can be easily integrated into your institution′s virtual learning environment or learning management system. This allows you to customize and curate content for use in module preparation, delivery and assessment. Please note that ISBN: 9781526445780 comprises the paperback edition of the Fifth Edition and the student version of IBM SPSS Statistics. Trade Review This book turned my hatred of stats and SPSS into love. -- Sharmina AugustI love it! It′s the first text I′ve come across that has been written in such a captivating way. There′s humor, tons of information, and awesome resources both within and on the companion website. Kudos to Prof. Field! -- Raymond GroganI also appreciate how the author made the text interesting to read, but the content is rich enough to provide readers good knowledge on how to draw insights from stats and data. Also, it provides a lot of practical guides for reporting results and findings for research paper. Can′t wait to take a deeper dive into the text -- Hsing-Chi Hwang * Harvard University *I never thought I would find a statistics textbook amusing but somehow our text pulls it off. I also appreciated the online supplementary tools provided by the publisher. They provide a good synthesis of each of the chapters and some easy options to review -- Rickelle Mathis * Harvard University *I really really love the book, it′s the main reason why I′m not curled up in bed with my cats sobbing in fear at the moment. Speaking of cats, I gotta say the correcting cat/misconception mutt framing is very cute, and it almost broke my heart finding out the origin of that orange spiritual feline. I′m having a blast reading about stats, who would′ve thunk it? -- Joao Matos Amara Da SilveriaI am enjoying the book, which I would never have imagined! I am not afraid of statistics anymore. -- Lineke, Language Teacher & Psychology PhD studentTable of ContentsChapter 1: Why is my evil lecturer forcing me to learn statistics? What the hell am I doing here? I don’t belong here The research process Initial observation: finding something that needs explaining Generating and testing theories and hypotheses Collecting data: measurement Collecting data: research design Reporting Data Chapter 2: The SPINE of statistics What is the SPINE of statistics? Statistical models Populations and Samples P is for parameters E is for Estimating parameters S is for standard error I is for (confidence) Interval N is for Null hypothesis significance testing, NHST Reporting significance tests Chapter 3: The phoenix of statistics Problems with NHST NHST as part of wider problems with science A phoenix from the EMBERS Sense, and how to use it Preregistering research and open science Effect sizes Bayesian approaches Reporting effect sizes and Bayes factors Chapter 4: The IBM SPSS Statistics environment Versions of IBM SPSS Statistics Windows, MacOS and Linux Getting started The Data Editor Entering data into IBM SPSS Statistics Importing Data The SPSS Viewer Exporting SPSS Output The Syntax Editor Saving files Opening files Extending IBM SPSS Statistics Chapter 5: Exploring data with graphs The art of presenting data The SPSS Chart Builder Histograms Boxplots (box-whisker diagrams) Graphing means: bar charts and error bars Line charts Graphing relationships: the scatterplot Editing graphs Chapter 6: The beast of bias What is bias? Outliers Overview of assumptions Additivity and Linearity Normally distributed something or other Homoscedasticity/Homogeneity of Variance Independence Spotting outliers Spotting normality Spotting linearity and heteroscedasticity/heterogeneity of variance Reducing Bias Chapter 7: Non-parametric models When to use non-parametric tests General procedure of non-parametric tests in SPSS Comparing two independent conditions: the Wilcoxon rank-sum test and Mann– Whitney test Comparing two related conditions: the Wilcoxon signed-rank test Differences between several independent groups: the Kruskal–Wallis test Differences between several related groups: Friedman’s ANOVA Chapter 8: Correlation Modelling relationships Data entry for correlation analysis Bivariate correlation Partial and semi-partial correlation Comparing correlations Calculating the effect size How to report correlation coefficents Chapter 9: The Linear Model (Regression) An Introduction to the linear model (regression) Bias in linear models? Generalizing the model Sample size in regression Fitting linear models: the general procedure Using SPSS Statistics to fit a linear model with one predictor Interpreting a linear model with one predictor The linear model with two of more predictors (multiple regression) Using SPSS Statistics to fit a linear model with several predictors Interpreting a linear model with several predictors Robust regression Bayesian regression Reporting linear models Chapter 10: Comparing two means Looking at differences An example: are invisible people mischievous? Categorical predictors in the linear model The t-test Assumptions of the t-test Comparing two means: general procedure Comparing two independent means using SPSS Statistics Comparing two related means using SPSS Statistics Reporting comparisons between two means Between groups or repeated measures? Chapter 11: Moderation, mediation and multicategory predictors The PROCESS tool Moderation: Interactions in the linear model Mediation Categorical predictors in regression Chapter 12: GLM 1: Comparing several independent means Using a linear model to compare several means Assumptions when comparing means Planned contrasts (contrast coding) Post hoc procedures Comparing several means using SPSS Statistics Output from one-way independent ANOVA Robust comparisons of several means Bayesian comparison of several means Calculating the effect size Reporting results from one-way independent ANOVA Chapter 13: GLM 2: Comparing means adjusted for other predictors (analysis of covariance) What is ANCOVA? ANCOVA and the general linear model Assumptions and issues in ANCOVA Conducting ANCOVA using SPSS Statistics Interpreting ANCOVA Testing the assumption of homogeneity of regression slopes Robust ANCOVA Bayesian analysis with covariates Calculating the effect size Reporting results Chapter 14: GLM 3: Factorial designs Factorial designs Independent factorial designs and the linear model Model assumptions in factorial designs Factorial designs using SPSS Statistics Output from factorial designs Interpreting interaction graphs Robust models of factorial designs Bayesian models of factorial designs Calculating effect sizes Reporting the results of two-way ANOVA Chapter 15: GLM 4: Repeated-measures designs Introduction to repeated-measures designs A grubby example Repeated-measures and the linear model The ANOVA approach to repeated-measures designs The F-statistic for repeated-measures designs Assumptions in repeated-measures designs One-way repeated-measures designs using SPSS Output for one-way repeated-measures designs Robust tests of one-way repeated-measures designs Effect sizes for one-way repeated-measures designs Reporting one-way repeated-measures designs A boozy example: a factorial repeated-measures design Factorial repeated-measures designs using SPSS Statistics Interpreting factorial repeated-measures designs Effect Sizes for factorial repeated-measures designs Reporting the results from factorial repeated-measures designs Chapter 16: GLM 5: Mixed designs Mixed designs Assumptions in mixed designs A speed dating example Mixed designs using SPSS Statistics Output for mixed factorial designs Calculating effect sizes Reporting the results of mixed designs Chapter 17: Multivariate analysis of variance (MANOVA) Introducing MANOVA Introducing matrices The theory behind MANOVA MANOVA using SPSS Statistics Interpreting MANOVA Reporting results from MANOVA Following up MANOVA with discriminant analysis Interpreting discriminant analysis Reporting results from discriminant analysis The final interpretation Chapter 18: Exploratory factor analysis When to use factor analysis Factors and Components Discovering factors An anxious example Factor analysis using SPSS statistics Interpreting factor analysis How to report factor analysis Reliability analysis Reliability analysis using SPSS Statistics Interpreting Reliability analysis How to report reliability analysis Chapter 19: Categorical outcomes: chi-square and loglinear analysis Analysing categorical data Associations between two categorical variables Associations between several categorical variables: loglinear analysis Assumptions when analysing categorical data General procedure for analysing categorical outcomes Doing chi-square using SPSS Statistics Interpreting the chi-square test Loglinear analysis using SPSS Statistics Interpreting loglinear analysis Reporting the results of loglinear analysis Chapter 20: Categorical outcomes: logistic regression What is logistic regression? Theory of logistic regression Sources of bias and common problems Binary logistic regression Interpreting logistic regression Reporting logistic regression Testing assumptions: another example Predicting several categories: multinomial logistic regression Chapter 21: Multilevel linear models Hierarchical data Theory of multilevel linear models The multilevel model Some practical issues Multilevel modelling using SPSS Statistics Growth models How to report a multilevel model A message from the octopus of inescapable despair Chapter 22: Epilogue
£152.00
Sage Publications Ltd Building Experiments in PsychoPy
Book SynopsisPsychoPy is an open-source software package for creating rich, dynamic experiments in psychology, neuroscience and linguistics. Written by its creator, this book walks you through the steps of building experiments in PsychoPy, from using images to discovering lesser-known features, and from analysing data to debugging your experiment. Divided into three parts and with unique extension exercises to guide you at whatever level you are at, this textbook is the perfect tool for teaching practical undergraduate classes on research methods, as well as acting as a comprehensive reference text for the professional scientist. Essential reading for anyone using PsychoPy software, the second edition has been fully updated and includes multiple new chapters about features included in recent versions of PsychoPy, including running studies online and collecting survey data. Part I teaches you all the basic skills you need (and some more advanced tips along the way) to design experiments in behavioral sciences. Each chapter introduces anew concept but will offer a series of working experiments that you can build on. Part II presents more details important for professional scientists intending to use PsychoPy for published research. This part is recommended reading for science professionals in any discipline. Part III covers a range of specialist topics, such as those doing fMRI research, or those studying visual perception. "This book fills an incredibly important gap in the field. Many users of PsychoPy will be excited to learn that there is now a highly accessible and well-designed written guide to refine their skills." – Susanne Quadflieg, University of BristolTrade ReviewThe 2020 pandemic has forced a lot of researchers to move their physical lab experiments online. If you are already doing this, or thinking about it, the second edition of "Building Experiments in PsychoPy" is a must have. The new edition offers sage advice on online data collection and provides a walkthrough on how to use some of its new components (e.g., creating surveys via Forms. ). -- Jason GellerIn ye olden days when I was a student, we had to script our experiments, which was tedious and error-prone, or use proprietary software, which was expensive and inflexible. This is why I love PsychoPy Builder, and Building Experiments in PsychoPy is a great resource for today‘s budding experimenters. -- Heiða María SigurðardóttirTable of ContentsChapter 1: Introduction PART I: FOR THE BEGINNER Chapter 2: Building your first experiment Chapter 3: Using images: A study into face perception Chapter 4: Timing and brief stimuli: Posner cueing Chapter 5: Running studies online Chapter 6: Creating dynamic stimuli (revealing text and moving stimuli) Chapter 7: Providing feedback: Simple code components Chapter 8: Collecting survey data using forms Chapter 9: Using sliders Chapter 10: Randomizing and counterbalancing blocks of trials: A bilingual Stroop task Chapter 11: Using the mouse for input: Creating a visual search task PART II: FOR THE PROFESSIONAL Chapter 12: Implementing research designs with randomization Chapter 13: Coordinates and color spaces Chapter 14: Understanding your computer timing issues Chapter 15: Monitors and monitor center Chapter 16: Debugging your experiment Chapter 17: Pro tips, tricks, and lesser-known features PART III: FOR THE SPECIALIST Chapter 18: Psychophysics, stimuli and staircases Chapter 19: Building an FMRI study Chapter 20: Building an EEG study Chapter 21: Add eye tracking to your experiment Appendices
£34.19
Sage Publications Ltd Quantitative Social Science Data with R: An
Book SynopsisRelevant, engaging, and packed with student-focused learning features, this book provides the basic step-by-step introduction to quantitative research and data every student needs. Gradually introducing applied statistics and the language and functionality of R and R Studio software, it uses examples from across the social sciences to show students how to apply abstract statistical and methodological principles to their own work. Maintaining a student-friendly pace, it goes beyond a normal introductory statistics book and shows students where data originates and how to: - Understand and use quantitative data to answer questions - Approach surrounding ethical issues - Collect quantitative data - Manage, write about, and share the data effectively Supported by incredible digital resources with online tutorials, videos, datasets, and multiple choice questions, this book gives students not only the tools they need to understand statistics, quantitative data, and R software, but also the chance to practice and apply what they have learned. Table of ContentsIntroduction Introduction To R And R Studio Finding Data Data Management Variables And Manipulation Developing Hypotheses Univariate And Descriptive Statistics Data Visualisation Hypothesis Testing Bivariate Analysis Linear Regression And Model Building OLS Assumptions And Diagnostic Testing Generalised Linear Models Count Models Putting It All Together
£35.14
SAGE Publications Inc Using Stata for Quantitative Analysis
Book SynopsisUsing Stata for Quantitative Analysis offers a brief but thorough introduction to analyzing data in undergraduate and graduate level research methods, statistics, and data analysis courses using Stata software. Kyle C. Longest teaches the language of Stata from an intuitive perspective, allowing students with no experience in statistical software to start working with data quickly and complete a basic quantitative research project from start to finish. The Third Edition covers the use of Stata 15 and includes more information on data management and non-linear regression techniques. Enhanced layouts make finding important commands easy. Trade Review"Learning to use statistical software can be an intimidating experience that is often made excessively difficult by textbooks that provide unclear explanations and overwhelming detail. In Using Stata for Quantitative Analysis, Longest well balances overview and detail of Stata’s components, commands, and instructional resources that will help new users." -- Charles PlanteTable of ContentsPart I: Foundations for Working with Stata Chapter 1: Getting to Know Stata 15 What You See Getting Started With Data Files Chapter 2: The Essentials Intuition and Stata Commands The Structure of Stata Commands The 5 Essential Commands Nonessential, Everyday Commands Chapter 3: Do Files and Data Management What Is a Do File? Data Management Part II: Quantitative Analysis With Stata Chapter 4: Descriptive Statistics Frequency Distributions Measures of Central Tendency and Variability Chapter 5: Relationships Between Nominal and Ordinal Variables Cross-Tabulations Chapter 6: Relationships Between Different Measurement Levels Testing Means Analysis of Variance (ANOVA) Chapter 7: Relationships Between Interval-Ratio Variables Correlation Linear Regression Chapter 8: Enhancing Your Command Repertoire Stata Help Files Advanced Convenience Commands Expanding Stata′s Capabilities
£70.52
SAGE Publications Inc Multilevel Modeling: Applications in STATA®, IBM®
Book SynopsisMultilevel Modeling: Applications in STATA®, IBM® SPSS®, SAS®, R & HLM™ provides a gentle, hands-on illustration of the most common types of multilevel modeling software, offering instructors multiple software resources for their students and an applications-based foundation for teaching multilevel modeling in the social sciences. Author G. David Garson’s step-by-step instructions for software walk readers through each package. The instructions for the different platforms allow students to get a running start using the package with which they are most familiar while the instructor can start teaching the concepts of multilevel modeling right away. Instructors will find this text serves as both a comprehensive resource for their students and a foundation for their teaching alike.Trade Review"The practical and hands-on approach in addition to using several software make this book appealing to a wide range of readers." -- Amin Mousavi"This is a solid treatment of MLMs which illustrates implementation across all major MLM software." -- J.M. Pogodzinski"This text effectively balances depth, complexity, and readability of a number of challenging topics related to multilevel modeling. The wealth of examples in many different software environments are fantastic." -- Michael BrodaTable of ContentsPreface Acknowledgments About the Author Chapter 1 • Introduction to Multilevel Modeling Overview What Multilevel Modeling Does The Importance of Multilevel Theory Types of Multilevel Data Common Types of Multilevel Model Mediation and Moderation Models in Multilevel Analysis Alternative Statistical Packages Multilevel Modeling Versus GEE Summary Glossary Challenge Questions With Answers Chapter 2 • Assumptions of Multilevel Modeling About This Chapter Overview Model Specification Construct Operationalization and Validation Random Sampling Sample Size Balanced and Unbalanced Designs Data Level Linearity and Nonlinearity Independence Recursivity Missing Data Outliers Centered and Standardized Data Longitudinal Time Values Multicollinearity Homogeneity of Error Variance Normally Distributed Residuals Normal Distribution of Variables Normal Distribution of Random Effects Convergence Covariance Structure Assumptions Summary Glossary Challenge Questions With Answers Chapter 3 • The Null Model Overview Testing the Need for Multilevel Modeling Likelihood Ratio Tests Partition of Variance Components Examples Summary Glossary Challenge Questions With Answers Chapter 4 • Estimating Multilevel Models Fixed and Random Effects Why Not Just Use OLS Regression? Why Not Just Use GLM (ANOVA)? Types of Estimation Robust and Cluster-Robust Standard Errors Summary Glossary Challenge Questions With Answers Chapter 5 • Goodness of Fit and Effect Size in Multilevel Models Overview Goodness of Fit Measures and Tests Effect Size Measures Effect Size and Endogeneity Summary Glossary Challenge Questions With Answers Chapter 6 • The Two-Level Random Intercept Model Overview SPSS Stata SAS HLM 7 R Summary Glossary Challenge Questions With Answers Chapter 7 • The Two-Level Random Coefficients Model Overview SPSS Stata SAS HLM 7 R Significance (p) Values for Variance Components Summary Glossary Challenge Questions With Answers Chapter 8 • The Three-Level Unconditional Random Intercept Model with Longitudinal Data Overview SPSS Stata SAS HLM 7 R Summary Glossary Challenge Questions With Answers Chapter 9 • Repeated Measures and Heterogeneous Variance Models Overview SPSS SAS Stata R HLM 7 Summary Glossary Challenge Questions With Answers Chapter 10 • Residual and Influence Analysis for a Three-Level RC Model About This Chapter Overview Why Residual Analysis? Data Model Model Diagnostics SAS Stata SPSS HLM 7 R Summary Glossary Challenge Questions With Answers Chapter 11 • Cross-Classified Linear Mixed Models Overview Data Model Research Purpose Stata SPSS SAS HLM 7 R Summary Glossary Challenge Questions With Answers Chapter 12 • Generalized Linear Mixed Models Overview Estimation Methods Data Model Stata SAS SPSS HLM 7 R Summary Glossary Challenge Questions With Answers Appendix 1: Data Used in Examples. Refers to Student Companion Website Appendix 2: Reporting Multilevel Results References Index
£77.17
SAGE Publications Inc Adventures in Social Research: Data Analysis
Book SynopsisThis 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.Trade ReviewThis 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 ViseTable of ContentsPart I Preparing for Data Analysis Chapter 1 Introduction: The Theory and Practice of Social Research Chapter 2 The Logic of Measurement Chapter 3 Description of Data Sets: The General Social Survey Part II Univariate Analysis Chapter 4 Using SPSS Statistics: Some Basics Chapter 5 Describing Your Data: Religiosity Chapter 6 Presenting Your Data in Graphic Form: Political Orientations Chapter 7 Recoding Your Data: Religiosity and Political Orientations Chapter 8 Creating Composite Measures: Exploring Attitudes Toward Abortion in More Depth Chapter 9 Suggestions for Further Analysis Part III Bivariate Analysis Chapter 10 Examining the Sources of Religiosity Chapter 11 Political Orientations as Cause and Effect Chapter 12 What Causes Different Attitudes Toward Abortion? Chapter 13 Measures of Association for Nominal and Ordinal Variables Chapter 14 Correlation and Regression Analysis Chapter 15 Tests of Significance Chapter 16 Suggestions for Further Bivariate Analyses Part IV Multivariate Analysis Chapter 17 Multiple Causation: Examining Religiosity in Greater Depth Chapter 18 Dissecting the Political Factor Chapter 19 A Powerful Prediction of Attitudes Toward Abortion Chapter 20 Suggestions for Further Multivariate Analyses Part V The Adventure Continues Chapter 21 Designing and Executing Your Own Survey Chapter 22 Further Opportunities for Social Research
£103.10
Centre for the Study of Language & Information Time Warps, String Edits, and Macromolecules: The
Book SynopsisTime Warps, String Edits and Macromolecules is a young classic in computational science. The computational perspective is that of sequence processing, in particular the problem of recognizing related sequences. The book is the first, and still best compilation of papers explaining how to measure distance between sequences, and how to compute that measure effectively. This is called string distance, Levenshtein distance, or edit distance. The book contains lucid explanations of the basic techniques; well-annotated examples of applications; mathematical analysis of its computational (algorithmic) complexity; and extensive discussion of the variants needed for weighted measures, timed sequences (songs), applications to continuous data, comparison of multiple sequences and extensions to tree-structures. This theory finds applications in molecular biology, speech recognition, analysis of bird song and error correcting in computer software.Table of Contents1. An overview of sequence comparison Joseph B. Kruskal; Part I. Macromolecular Sequences: 2. Recognition of patterns in genetic sequences Bruce W. Erickson and Peter H. Sellers; 3. Fast algorithms to determine RNA secondary structures containing multiple loops David Sankoff, Joseph B. Kruskal, Sylvie Mainville and Robert J. Cedergren; Part II. Time-Warping, Continuous Functions, and Speech Processing; 4. The symmetric time-warping problem: from continuous to discrete Joseph B. Kruskal and Mark Liberman; 5. Use of dynamic programming in a syllable-based continuous speech recognition system Melvyn J. Hunt, Matthew Lennig and Paul Mermelstein; 6. Application of sequence comparison to the study of bird songs David W. Bradley and Richard A. Bradley; Part III. Variations on a Theme: Algorithms for Related Problems: 7. On the complexity of the extended string-to-string correction problem Robert A. Wagner; 8. An analysis of the general tree-editing problem Andrew S. Noetzel and Stanley M. Selkow; 9. Simultaneous comparison of three or more sequences related by a tree David Sankoff and Robert J. Cedergren; 10. An anthology of algorithms and concepts for sequence comparison Joseph B. Kruskal and David Sankoff; 11. Dissimilarity measures for clustering strings James M. Coggins; Part IV. Computational Complexity: 12. Recent results on the complexity of common-subsequence problems; 13. Formal-language error correction Robert A. Wagner; 14. How to computer string-edit distances quickly William J. Masek and Michael S. Paterson; Part V. Random Sequences: 15. An upper-bound technique for lengths of common subsequences Vaclav Chvatal and David Sankoff; 16. Probabilistic behavior of longest-common-subsequence length Joseph Deken; 17. Common subsequences and monotone subsequences David Sankoff and Sylvie Mainville; Author index; Subject index.
£34.83
Taylor & Francis Inc C++ for Mathematicians: An Introduction for
Book SynopsisFor problems that require extensive computation, a C++ program can race through billions of examples faster than most other computing choices. C++ enables mathematicians of virtually any discipline to create programs to meet their needs quickly, and is available on most computer systems at no cost. C++ for Mathematicians: An Introduction for Students and Professionals accentuates C++ concepts that are most valuable for pure and applied mathematical research.This is the first book available on C++ programming that is written specifically for a mathematical audience; it omits the language’s more obscure features in favor of the aspects of greatest utility for mathematical work. The author explains how to use C++ to formulate conjectures, create images and diagrams, verify proofs, build mathematical structures, and explore myriad examples. Emphasizing the essential role of practice as part of the learning process, the book is ideally designed for undergraduate coursework as well as self-study. Each chapter provides many problems and solutions which complement the text and enable you to learn quickly how to apply them to your own problems. Accompanying downloadable resources provide all numbered programs so that readers can easily use or adapt the code as needed. Presenting clear explanations and examples from the world of mathematics that develop concepts from the ground up, C++ for Mathematicians can be used again and again as a resource for applying C++ to problems that range from the basic to the complex.Trade Review“For a mathematician like myself, Scheinerman’s new book is ideal. It concentrates on the portion of C++ that will be most useful to a mathematician. While developing the necessary tools and syntax of C++, the book presents example programs relevant to interesting and somewhat sophisticated mathematical problems. The reader can proceed as far as he/she wants. Even just reading the first few chapters of the book and writing some programs using the constructs introduced, there is sufficient [material] for many purposes within undergraduate mathematics … The strength of this book is the intermingling of interesting mathematics with the ideas and syntax of the C++ language. … The writing is very fluent and does not bog down in endless detail as so many programming books do … In summary, I recommend this book highly to frustrated mathematicians wishing to learn C++ programming. You will really enjoy the well-chosen examples and the light touch in the exposition.” —Jeffrey Nunemacher, MAA ReviewsTable of ContentsPROCEDURES: The Basics. Numbers. Greatest Common Divisor. Random Numbers. Arrays. OBJECTS: Points in the Pane. Pythagorean Triples. Containers. Modular Arithmetic. Points in the Projective Plane. Permutations. Polynomials. Using Other Packages. Input/Output and Strings. APPENDICES: Your C++ Computing Environment. Using Doxygen. C++ Reference. Index.
£80.74
Taylor & Francis Inc The BUGS Book: A Practical Introduction to
Book SynopsisBayesian statistical methods have become widely used for data analysis and modelling in recent years, and the BUGS software has become the most popular software for Bayesian analysis worldwide. Authored by the team that originally developed this software, The BUGS Book provides a practical introduction to this program and its use. The text presents complete coverage of all the functionalities of BUGS, including prediction, missing data, model criticism, and prior sensitivity. It also features a large number of worked examples and a wide range of applications from various disciplines.The book introduces regression models, techniques for criticism and comparison, and a wide range of modelling issues before going into the vital area of hierarchical models, one of the most common applications of Bayesian methods. It deals with essentials of modelling without getting bogged down in complexity. The book emphasises model criticism, model comparison, sensitivity analysis to alternative priors, and thoughtful choice of prior distributions—all those aspects of the "art" of modelling that are easily overlooked in more theoretical expositions. More pragmatic than ideological, the authors systematically work through the large range of "tricks" that reveal the real power of the BUGS software, for example, dealing with missing data, censoring, grouped data, prediction, ranking, parameter constraints, and so on. Many of the examples are biostatistical, but they do not require domain knowledge and are generalisable to a wide range of other application areas. Full code and data for examples, exercises, and some solutions can be found on the book’s website.Trade Review"This is a beautiful book—it was a pleasure, and indeed great fun to read. … The authors succeeded in writing a very nicely readable yet concise and carefully balanced text. … It contains a lot of motivation, detailed explanations, necessary pieces of underlying theory, references to useful book-length treatments of various topics, and examples of the code illustrating how to implement concrete models in the BUGS language efficiently. … this book also has a substantial pedagogical value. By reading this book carefully, redoing the examples, and thinking about them, one can learn a lot not only about BUGS, but also about Bayesian methods and statistics in general. … highly recommended to a wide audience, from students of statistics [to] practicing statisticians to researchers from various fields."—ISCB News, 57, June 2014"… truly demonstrates the power and flexibility of the BUGS software and its broad range of applications, and that makes this book highly relevant not only for beginners but for advanced users as well. … a notable addition to the growing range of introductory Bayesian textbooks that have been published within the last decade. It is unique in its focus on explicating state-of-the-art computational Bayesian strategies in the WinBUGS software. Thus, practitioners may use it as an excellent, didactically enhanced BUGS manual that, unlike ordinary software manuals, presents detailed explanations of the underlying models with references to relevant literature [and] worked examples, including excerpts of WinBUGS code, as well as graphical illustrations of results and critical discussions. No doubt, The BUGS Book will become a classic Bayesian textbook and provide invaluable guidance to practicing statisticians, academics, and students alike."—Renate Meyer, Journal of Biopharmaceutical Statistics, 2014"In this book the developers of BUGS reveal the power of the BUGS software and how it can be used in Bayesian statistical modeling and inference. Many people will find it very useful for self-learning or as a supplement for a Bayesian inference course."—William M. Bolstad, Australian & New Zealand Journal of Statistics, 2013"If a book has ever been so much desired in the world of statistics, it is for sure this one. … the tens of thousands of users of WinBUGS are indebted to the leading team of the BUGS project for having eventually succeeded in finalizing the writing of this book and for making sure that the long-held expectations are not dashed. … it reflects very well the aims and spirit of the BUGS project and is meant to be a manual ‘for anyone who would like to apply Bayesian methods to real-world problems.’ … strikes the right distance between advanced theory and pure practice. I especially like the numerous examples given in the successive chapters which always help readers to figure out what is going on and give them new ideas to improve their BUGS skills. … The BUGS Book is not only a major textbook on a topical subject, but it is also a mandatory one for all statisticians willing to learn and analyze data with Bayesian statistics at any level. It will be the companion and reference book for all users (beginners or advanced) of the BUGS software. I have no doubt it will meet the same success as BUGS and become very soon a classic in the literature of computational Bayesian statistics."—Jean-Louis Fouley, CHANCE, 2013"… a two-in-one product that provides the reader with both a BUGS manual and a Bayesian analysis textbook, a combination that will likely appeal to many potential readers. … The strength of The BUGS Book is its rich collection of ambitiously constructed and thematically arranged examples, which often come with snippets of code and printouts, as well as illustrative plots and diagrams. … great value to many readers seeking to familiarize themselves with BUGS and its capabilities."—Joakim Ekström, Journal of Statistical Software, January 2013"MCMC freed Bayes from the shackles of conjugate priors and the curse of dimensionality; BUGS then brought MCMC-Bayes to the masses, yielding an astonishing explosion in the number, quality, and complexity of Bayesian inference over a vast array of application areas, from finance to medicine to data mining. The most anticipated applied Bayesian text of the last 20 years, The BUGS Book is like a wonderful album by an established rock supergroup: the pressure to deliver a high-quality product was enormous, but the authors have created a masterpiece well worth the wait. The book offers the perfect mix of basic probability calculus, Bayes and MCMC basics, an incredibly broad array of useful statistical models, and a BUGS tutorial and user manual complete with all the ‘tricks’ one would expect from the team that invented the language. BUGS is the dominant Bayesian software package of the post-MCMC era, and this book ensures it will remain so for years to come by providing accessible yet comprehensive instruction in its proper use. A must-own for any working applied statistical modeler."—Bradley P. Carlin, Professor and Head of Division of Biostatistics, University of Minnesota, Minneapolis, USA"This is a beautiful book—it was a pleasure, and indeed great fun to read. … The authors succeeded in writing a very nicely readable yet concise and carefully balanced text. … It contains a lot of motivation, detailed explanations, necessary pieces of underlying theory, references to useful book-length treatments of various topics, and examples of the code illustrating how to implement concrete models in the BUGS language efficiently. … this book also has a substantial pedagogical value. By reading this book carefully, redoing the examples, and thinking about them, one can learn a lot not only about BUGS, but also about Bayesian methods and statistics in general. … highly recommended to a wide audience, from students of statistics [to] practicing statisticians to researchers from various fields."—ISCB News, 57, June 2014"… truly demonstrates the power and flexibility of the BUGS software and its broad range of applications, and that makes this book highly relevant not only for beginners but for advanced users as well. … a notable addition to the growing range of introductory Bayesian textbooks that have been published within the last decade. It is unique in its focus on explicating state-of-the-art computational Bayesian strategies in the WinBUGS software. Thus, practitioners may use it as an excellent, didactically enhanced BUGS manual that, unlike ordinary software manuals, presents detailed explanations of the underlying models with references to relevant literature [and] worked examples, including excerpts of WinBUGS code, as well as graphical illustrations of results and critical discussions. No doubt, The BUGS Book will become a classic Bayesian textbook and provide invaluable guidance to practicing statisticians, academics, and students alike."—Renate Meyer, Journal of Biopharmaceutical Statistics, 2014"In this book the developers of BUGS reveal the power of the BUGS software and how it can be used in Bayesian statistical modeling and inference. Many people will find it very useful for self-learning or as a supplement for a Bayesian inference course."—William M. Bolstad, Australian & New Zealand Journal of Statistics, 2013"If a book has ever been so much desired in the world of statistics, it is for sure this one. … the tens of thousands of users of WinBUGS are indebted to the leading team of the BUGS project for having eventually succeeded in finalizing the writing of this book and for making sure that the long-held expectations are not dashed. … it reflects very well the aims and spirit of the BUGS project and is meant to be a manual ‘for anyone who would like to apply Bayesian methods to real-world problems.’ … strikes the right distance between advanced theory and pure practice. I especially like the numerous examples given in the successive chapters which always help readers to figure out what is going on and give them new ideas to improve their BUGS skills. … The BUGS Book is not only a major textbook on a topical subject, but it is also a mandatory one for all statisticians willing to learn and analyze data with Bayesian statistics at any level. It will be the companion and reference book for all users (beginners or advanced) of the BUGS software. I have no doubt it will meet the same success as BUGS and become very soon a classic in the literature of computational Bayesian statistics."—Jean-Louis Fouley, CHANCE, 2013"… a two-in-one product that provides the reader with both a BUGS manual and a Bayesian analysis textbook, a combination that will likely appeal to many potential readers. … The strength of The BUGS Book is its rich collection of ambitiously constructed and thematically arranged examples, which often come with snippets of code and printouts, as well as illustrative plots and diagrams. … great value to many readers seeking to familiarize themselves with BUGS and its capabilities."—Joakim Ekström, Journal of Statistical Software, January 2013"MCMC freed Bayes from the shackles of conjugate priors and the curse of dimensionality; BUGS then brought MCMC-Bayes to the masses, yielding an astonishing explosion in the number, quality, and complexity of Bayesian inference over a vast array of application areas, from finance to medicine to data mining. The most anticipated applied Bayesian text of the last 20 years, The BUGS Book is like a wonderful album by an established rock supergroup: the pressure to deliver a high-quality product was enormous, but the authors have created a masterpiece well worth the wait. The book offers the perfect mix of basic probability calculus, Bayes and MCMC basics, an incredibly broad array of useful statistical models, and a BUGS tutorial and user manual complete with all the ‘tricks’ one would expect from the team that invented the language. BUGS is the dominant Bayesian software package of the post-MCMC era, and this book ensures it will remain so for years to come by providing accessible yet comprehensive instruction in its proper use. A must-own for any working applied statistical modeler."—Bradley P. Carlin, Professor and Head of Division of Biostatistics, University of Minnesota, Minneapolis, USATable of ContentsIntroduction: Probability and Parameters. Monte Carlo Simulations using BUGS. Introduction to Bayesian Inference. Introduction to Markov Chain Monte Carlo Methods. Prior Distributions. Regression Models. Categorical Data. Model Checking and Comparison. Issues in Modeling. Hierarchical Models. Specialized Models. Different Implementations of BUGS. Appendices. Bibliography. Index.
£40.84
Nova Science Publishers Inc General Physics Problem Solving with Cas Derive
Book SynopsisDerive is a computer algebra system developed and marketed by the Soft Warehouse company. Magiera thinks that it has been unfairly overlooked in favour of glitzier software, and that the existing literature on it describes only purely mathematical and abstract problems, without recognising its utility to deal with a wide range of problems in physics. He demonstrates how to work out problems that are often encountered in undergraduate General Physics courses, and some that would also be suitable for high school. He says the software is very cheap, so simple that it can run on pre-Windows.
£99.19
Stata Press The Mata Book: A Book for Serious Programmers and
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).
£56.99
Stata Press An Introduction to Stata for Health Researchers
Book SynopsisAn Introduction to Stata for Health Researchers, Fifth Edition updates this classic book that has become a standard reference for health researchers. As with previous editions, readers will learn to work effectively in Stata to perform data management, compute descriptive statistics, create meaningful graphs, fit regression models, and perform survival analysis. The fifth edition adds examples of performing power, precision, and sample-size analysis; working with Unicode characters; managing data with ICD-9 and ICD-10 codes; and creating customized tables.With many worked examples and downloadable datasets, this text is the ideal resource for hands-on learning, whether for students in a statistics course or for researchers in fields such as epidemiology, biostatistics, and public health who are learning to use Stata's tools for health research.Table of ContentsI The basics 1. Getting started 2. Getting help—and more 3. Command syntax II Data management 4. Variables 5. Getting data in and out of Stata 6. Adding explanatory text to data 7. Calculations 8. Commands affecting data structure 9. Taking good care of your data III Analysis 10. Description and simple analysis 11. Regression analysis 12. Time-to-event data 13. Power, precision, and sample-size analysis 14. Measurement and diagnosis 15. Miscellaneous IV Graphs 16. Graphs V Advanced topics 17. Advanced topics
£56.99
Stata Press Data Management Using Stata: A Practical Handbook
Book SynopsisThis second edition of Data Management Using Stata focuses on tasks that bridge the gap between raw data and statistical analysis. It has been updated throughout to reflect new data management features that have been added over the last 10 years. Such features include the ability to read and write a wide variety of file formats, the ability to write highly customized Excel files, the ability to have multiple Stata datasets open at once, and the ability to store and manipulate string variables stored as Unicode. Further, this new edition includes a new chapter illustrating how to write Stata programs for solving data management tasks. As in the original edition, the chapters are organized by data management areas: reading and writing datasets, cleaning data, labeling datasets, creating variables, combining datasets, processing observations across subgroups, changing the shape of datasets, and programming for data management. Within each chapter, each section is a self-contained lesson illustrating a particular data management task (for instance, creating date variables or automating error checking) via examples. This modular design allows you to quickly identify and implement the most common data management tasks without having to read background information first. In addition to the “nuts and bolts” examples, author Michael Mitchell alerts users to common pitfalls (and how to avoid them) and provides strategic data management advice. This book can be used as a quick reference for solving problems as they arise or can be read as a means for learning comprehensive data management skills. New users will appreciate this book as a valuable way to learn data management, while experienced users will find this information to be handy and time saving—there is a good chance that even the experienced user will learn some new tricks.Table of ContentsIntroduction. Reading and importing data files. Saving and exporting data files. Data cleaning. Labeling datasets. Creating variables. Combining datasets. Processing observations across subgroups. Changing the shape of your data. Programming for data management: Part I. Programming for data management: Part II.
£58.89
Stata Press Environmental Econometrics Using Stata
Book SynopsisAspects of environmental change are some of the greatest challenges faced by policymakers today. The key issues addressed by environmental science are often empirical, and in many instances very detailed, sizable datasets are available. Researchers in this field should have a solid understanding of the econometric tools best suited for analysis of these data. While complex and expensive physical models of the environment exist, it is becoming increasingly clear that reduced-form econometric models have an important role to play in modeling environmental phenomena. In short, successful environmental modeling does not necessarily require a structural model, but the econometric methods underlying a reduced-form approach must be competently executed. Environmental Econometrics Using Stata provides an important starting point for this journey by presenting a broad range of applied econometric techniques for environmental econometrics and illustrating how they can be applied in Stata. The emphasis is not only on how to formulate and fit models in Stata but also on the need to use a wide range of diagnostic tests in order to validate the results of estimation and subsequent policy conclusions. This focus on careful, reproducible research should be appreciated by academic and non-academic researchers who are seeking to produce credible, defensible conclusions about key issues in environmental science. Table of Contents1 Introduction 2 Linear regression models 3 Beyond ordinary least squares 4 Introducing dynamics 5 Multivariate time-series models 6 Testing for nonstationarity 7 Modeling nonstationary variables 8 Forecasting 9 Structural time-series models 10 Nonlinear time-series models 11 Modeling time-varying variance 12 Longitudinal data models 13 Spatial models 14 Discrete dependent variables 15 Fractional integration A Using Stata
£56.99
Stata Press A Visual Guide to Stata Graphics
Book SynopsisWhether you are new to Stata graphics or a seasoned veteran, this book will teach you how to use Stata to make publication-quality graphs that will stand out and enhance your statistical results. With over 1,200 illustrated examples and quick-reference tabs, this book quickly guides you to the information you need for creating and customizing high-quality graphs for any type of statistical data. Each graph is displayed in full color with simple and clear instructions that illustrate how to create and customize graphs using Stata commands. Whether you use this book as a learning tool or a quick reference, you will have the power of Stata graphics at your fingertips.Table of Contents1. Introduction 2. Twoway graphs 3. Scatterplot matrix graphs 4. Bar graphs 5. Box plots 6. Dot plots 7. Pie charts 8. Options available for most graphs 9. Standard options available for all graphs 10. Styles for changing the look of graphs 11. Appendix
£71.24
SAS Publishing Data Preparation for Analytics Using SAS
£65.30
SAS Publishing A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling, Second Edition
£42.01
SAS Institute Survival Analysis Using SAS: A Practical Guide
£58.19
SAS Publishing SAS Statistics by Example
£47.31
SIAM - Society for Industrial and Applied Mathematics MATLAB Guide
Book SynopsisThis third edition of MATLAB Guide completely revises and updates the best-selling second edition and is more than 25 per cent longer. The book remains a lively, concise introduction to the most popular and important features of MATLAB and the Symbolic Math Toolbox.
£56.25
Society for Industrial & Applied Mathematics,U.S. Fundamentals of Numerical Computation
Book Synopsis“If mathematical modeling is the process of turning real phenomena into mathematical abstractions, then numerical computation is largely about the transformation from abstract mathematics to concrete reality. Many science and engineering disciplines have long benefited from the tremendous value of the correspondence between quantitative information and mathematical manipulation.” -from the PrefaceFundamentals of Numerical Computation is an advanced undergraduate-level introduction to the mathematics and use of algorithms for the fundamental problems of numerical computation: linear algebra, finding roots, approximating data and functions, and solving differential equations. The book is organized with simpler methods in the first half and more advanced methods in the second half, allowing use for either a single course or a sequence of two courses. The authors take readers from basic to advanced methods, illustrating them with over 200 self-contained MATLAB functions and examples designed for those with no prior MATLAB experience. Although the text provides many examples, exercises, and illustrations, the aim of the authors is not to provide a cookbook per se, but rather an exploration of the principles of cooking.Professors Driscoll and Braun have developed an online resource that includes well-tested materials related to every chapter. Among these materials are lecture-related slides and videos, ideas for student projects, laboratory exercises, computational examples and scripts, and all the functions presented in the book.
£93.50
SAS Institute Multiple Imputation of Missing Data Using SAS
£19.59
£30.80
£44.67
£37.84
Manning Publications Data Munging with R
Book SynopsisData Munging with R shows readers how to take raw data and transform it for use in computations, tables, graphs, and more. Whether they already have some programming experience or they’re just a spreadsheet whiz looking for a more powerful data manipulation tool, this book will help programmers get started. Readers will discover the ins and outs of using the data-oriented R programming language and its many task-specific packages. By the end, readers will be master mungers, with a robust, reproducible workflow and the skills to use data to strengthen their conclusions! Key Features • Practical examples • Step-by-step guide • Introduction to R Audience If you have beginner programming skills or you're comfortable with writing spreadsheet formulas, you have everything you need to get the most out of this book. About the technology R is a statistical programming language in that it was made for the purpose of performing statistics calculations, but it has grown to be so much more through community contributions. As a general purpose language, it is flexible enough to work with almost any data you can interact with; stored or streaming, images, text, or numbers.
£39.99
Pragmatic Bookshelf Numerical Brain Teasers: Exercise Your Mind
Book SynopsisChallenge your brain with math! Using nothing more than basic arithmetic and logic, you'll be thrilled as answers slot into place. Whether purely for fun or to test your knowledge, you'll sharpen your problem-solving skills and flex your mental muscles. All you need is logical thought, a little patience, and a clear mind. There are no gotchas here. These puzzles are the perfect introduction to or refresher for math concepts you may have only just learned or long since forgotten. Get ready to have more fun with numbers than you've ever had before. Engage your analytical side with these numerical brain teasers. Math and logic puzzles help you stretch your mind to think in new ways. They flex your lateral thinking as you work through fresh problem styles. Each puzzle type comes with an explanation, a method for solving them, and solutions if you get stuck. The puzzles in this book are short, self-contained, and "gritty." They offer an enjoyable challenge and are designed to be solvable within a few minutes. You only need basic arithmetic to solve these puzzles; no advanced math required. There's plenty of variety to keep things fresh. From wandering digits to magic triangles, from summing grids to water pails, you'll find something that catches your interest. Each puzzle is brief, so use them as a warm-up to your daily work, for a delightful diversion on your coffee break, or solve a few while you wind down for the day. Grab a pencil and your thinking cap, and get solving!
£13.49
