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An R Companion to Linear Statistical Models

An R Companion to Linear Statistical Models Author Christopher Hay-Jahans
ISBN-10 9781439873656
Release 2011-10-19
Pages 372
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Focusing on user-developed programming, An R Companion to Linear Statistical Models serves two audiences: those who are familiar with the theory and applications of linear statistical models and wish to learn or enhance their skills in R; and those who are enrolled in an R-based course on regression and analysis of variance. For those who have never used R, the book begins with a self-contained introduction to R that lays the foundation for later chapters. This book includes extensive and carefully explained examples of how to write programs using the R programming language. These examples cover methods used for linear regression and designed experiments with up to two fixed-effects factors, including blocking variables and covariates. It also demonstrates applications of several pre-packaged functions for complex computational procedures.

An R Companion to Applied Regression

An R Companion to Applied Regression Author John Fox
ISBN-10 9781412975148
Release 2011
Pages 449
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This book aims to provide a broad introduction to the R statistical environment in the context of applied regression analysis, which is typically studied by social scientists and others in a second course in applied statistics.

Applied Regression Analysis and Generalized Linear Models

Applied Regression Analysis and Generalized Linear Models Author John Fox
ISBN-10 9781483352527
Release 2008-04-16
Pages 688
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Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Second Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material throughout the book. Key Updates to the Second Edition: Provides greatly enhanced coverage of generalized linear models, with an emphasis on models for categorical and count data Offers new chapters on missing data in regression models and on methods of model selection Includes expanded treatment of robust regression, time-series regression, nonlinear regression, and nonparametric regression Incorporates new examples using larger data sets Includes an extensive Web site at that presents appendixes, data sets used in the book and for data-analytic exercises, and the data-analytic exercises themselves Intended Audience: This core text will be a valuable resource for graduate students and researchers in the social sciences (particularly sociology, political science, and psychology) and other disciplines that employ linear and related models for data analysis.

Applied Linear Statistical Models

Applied Linear Statistical Models Author John Neter
ISBN-10 0071145672
Release 1996
Pages 1408
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Focusing on applied statistical models, this text has an applied approach with an emphasis on understanding of concepts and exposition by means of examples. Theoretical foundations are provided so that applications of regression analysis can be carried out. There is expanded use of graphics, scatter plot metrics, and 3D rotating plots. Case studies feature throughout the text.

The R Book

The R Book Author Michael J. Crawley
ISBN-10 9781118448960
Release 2012-11-07
Pages 1080
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Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition: Features full colour text and extensive graphics throughout. Introduces a clear structure with numbered section headings to help readers locate information more efficiently. Looks at the evolution of R over the past five years. Features a new chapter on Bayesian Analysis and Meta-Analysis. Presents a fully revised and updated bibliography and reference section. Is supported by an accompanying website allowing examples from the text to be run by the user. Praise for the first edition: ‘…if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.’ (The American Statistician, August 2008) ‘The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book…’ (Professional Pensions, July 2007)

Applied Regression Analysis Linear Models and Related Methods

Applied Regression Analysis  Linear Models  and Related Methods Author John Fox
ISBN-10 080394540X
Release 1997
Pages 597
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An accessible, detailed, and up-to-date treatment of regression analysis, linear models, and closely related methods is provided in this book. Incorporating nearly 200 graphs and numerous examples and exercises that employ real data from the social sciences, the book begins with a consideration of the role of statistical data analysis in social research. It then moves on to cover the following topics: graphical methods for examining and transforming data; linear least-squares regression; dummy-variables regression; analysis of variance; diagnostic methods for discovering whether a linear model fit to data adequately represents the data; extensions to linear least squares, including logit and probit models, time-series regression, nonlinear

Applied Linear Regression

Applied Linear Regression Author Sanford Weisberg
ISBN-10 9781118625958
Release 2013-06-07
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Applied Linear Regression has been writing in one form or another for most of life. You can find so many inspiration from Applied Linear Regression also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Applied Linear Regression book for free.

A Course in Statistics with R

A Course in Statistics with R Author Prabhanjan N. Tattar
ISBN-10 9781119152750
Release 2016-03-15
Pages 696
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Integrates the theory and applications of statistics using R A Course in Statistics with R has been written to bridge the gap between theory and applications and explain how mathematical expressions are converted into R programs. The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject. With this dual goal in mind, the book begins with R basics and quickly covers visualization and exploratory analysis. Probability and statistical inference, inclusive of classical, nonparametric, and Bayesian schools, is developed with definitions, motivations, mathematical expression and R programs in a way which will help the reader to understand the mathematical development as well as R implementation. Linear regression models, experimental designs, multivariate analysis, and categorical data analysis are treated in a way which makes effective use of visualization techniques and the related statistical techniques underlying them through practical applications, and hence helps the reader to achieve a clear understanding of the associated statistical models. Key features: Integrates R basics with statistical concepts Provides graphical presentations inclusive of mathematical expressions Aids understanding of limit theorems of probability with and without the simulation approach Presents detailed algorithmic development of statistical models from scratch Includes practical applications with over 50 data sets

A Modern Approach to Regression with R

A Modern Approach to Regression with R Author Simon Sheather
ISBN-10 9780387096070
Release 2009-03-11
Pages 393
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This book focuses on tools and techniques for building regression models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to base inferences or conclusions only on valid models. Plots are shown to be an important tool for both building regression models and assessing their validity. We shall see that deciding what to plot and how each plot should be interpreted will be a major challenge. In order to overcome this challenge we shall need to understand the mathematical properties of the fitted regression models and associated diagnostic procedures. As such this will be an area of focus throughout the book. In particular, we shall carefully study the properties of resi- als in order to understand when patterns in residual plots provide direct information about model misspecification and when they do not. The regression output and plots that appear throughout the book have been gen- ated using R. The output from R that appears in this book has been edited in minor ways. On the book web site you will find the R code used in each example in the text.

Applied Regression Modeling

Applied Regression Modeling Author Iain Pardoe
ISBN-10 9781118345047
Release 2013-01-07
Pages 346
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Praise for the First Edition "The attention to detail is impressive. The book is very well written and the author is extremely careful with his descriptions . . . the examples are wonderful." —The American Statistician Fully revised to reflect the latest methodologies and emerging applications, Applied Regression Modeling, Second Edition continues to highlight the benefits of statistical methods, specifically regression analysis and modeling, for understanding, analyzing, and interpreting multivariate data in business, science, and social science applications. The author utilizes a bounty of real-life examples, case studies, illustrations, and graphics to introduce readers to the world of regression analysis using various software packages, including R, SPSS, Minitab, SAS, JMP, and S-PLUS. In a clear and careful writing style, the book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, and Bayesian modeling. In addition, the Second Edition features clarification and expansion of challenging topics, such as: Transformations, indicator variables, and interaction Testing model assumptions Nonconstant variance Autocorrelation Variable selection methods Model building and graphical interpretation Throughout the book, datasets and examples have been updated and additional problems are included at the end of each chapter, allowing readers to test their comprehension of the presented material. In addition, a related website features the book's datasets, presentation slides, detailed statistical software instructions, and learning resources including additional problems and instructional videos. With an intuitive approach that is not heavy on mathematical detail, Applied Regression Modeling, Second Edition is an excellent book for courses on statistical regression analysis at the upper-undergraduate and graduate level. The book also serves as a valuable resource for professionals and researchers who utilize statistical methods for decision-making in their everyday work.

Extending the Linear Model with R

Extending the Linear Model with R Author Julian J. Faraway
ISBN-10 0203492285
Release 2016-02-10
Pages 312
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Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the data described in the book is available at Statisticians need to be familiar with a broad range of ideas and techniques. This book provides a well-stocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught.

Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences

Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences Author Brian S. Everitt
ISBN-10 9781439807705
Release 2009-09-28
Pages 320
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Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences shows students how to apply statistical methods to behavioral science data in a sensible manner. Assuming some familiarity with introductory statistics, the book analyzes a host of real-world data to provide useful answers to real-life issues. The author begins by exploring the types and design of behavioral studies. He also explains how models are used in the analysis of data. After describing graphical methods, such as scatterplot matrices, the text covers simple linear regression, locally weighted regression, multiple linear regression, regression diagnostics, the equivalence of regression and ANOVA, the generalized linear model, and logistic regression. The author then discusses aspects of survival analysis, linear mixed effects models for longitudinal data, and the analysis of multivariate data. He also shows how to carry out principal components, factor, and cluster analyses. The final chapter presents approaches to analyzing multivariate observations from several different populations. Through real-life applications of statistical methodology, this book elucidates the implications of behavioral science studies for statistical analysis. It equips behavioral science students with enough statistical tools to help them succeed later on in their careers. Solutions to the problems as well as all R code and data sets for the examples are available at

Introductory Statistics with R

Introductory Statistics with R Author Peter Dalgaard
ISBN-10 9780387790541
Release 2008-06-27
Pages 364
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This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.

Applied Regression Analysis

Applied Regression Analysis Author Norman R. Draper
ISBN-10 9781118625682
Release 2014-08-25
Pages 736
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An outstanding introduction to the fundamentals of regression analysis-updated and expanded The methods of regression analysis are the most widely used statistical tools for discovering the relationships among variables. This classic text, with its emphasis on clear, thorough presentation of concepts and applications, offers a complete, easily accessible introduction to the fundamentals of regression analysis. Assuming only a basic knowledge of elementary statistics, Applied Regression Analysis, Third Edition focuses on the fitting and checking of both linear and nonlinear regression models, using small and large data sets, with pocket calculators or computers. This Third Edition features separate chapters on multicollinearity, generalized linear models, mixture ingredients, geometry of regression, robust regression, and resampling procedures. Extensive support materials include sets of carefully designed exercises with full or partial solutions and a series of true/false questions with answers. All data sets used in both the text and the exercises can be found on the companion disk at the back of the book. For analysts, researchers, and students in university, industrial, and government courses on regression, this text is an excellent introduction to the subject and an efficient means of learning how to use a valuable analytical tool. It will also prove an invaluable reference resource for applied scientists and statisticians.

An R and S Plus Companion to Applied Regression

An R and S Plus Companion to Applied Regression Author John Fox
ISBN-10 0761922806
Release 2002-06-05
Pages 312
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"This book fits right into a needed niche: rigorous enough to give full explanation of the power of the S language, yet accessible enough to assign to social science graduate students without fear of intimidation. It is a tremendous balance of applied statistical "firepower" and thoughtful explanation. It meets all of the important mechanical needs: each example is given in detail, code and data are freely available, and the nuances of models are given rather than just the bare essentials. It also meets some important theoretical needs: linear models, categorical data analysis, an introduction to applying GLMs, a discussion of model diagnostics, and useful instructions on writing customized functions. " —JEFF GILL, University of Florida, Gainesville

Linear Statistical Models

Linear Statistical Models Author James H. Stapleton
ISBN-10 9780470317761
Release 2009-09-25
Pages 472
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Linear Statistical Models Developed and refined over a period of twenty years, the material in this book offers an especially lucid presentation of linear statistical models. These models lead to what is usually called "multiple regression" or "analysis of variance" methodology, which, in turn, opens up a wide range of applications to the physical, biological, and social sciences, as well as to business, agriculture, and engineering. Unlike similar books on this topic, Linear Statistical Models emphasizes the geometry of vector spaces because of the intuitive insights this approach brings to an understanding of the theory. While the focus is on theory, examples of applications, using the SAS and S-Plus packages, are included. Prerequisites include some familiarity with linear algebra, and probability and statistics at the postcalculus level. Major topics covered include: * Methods of study of random vectors, including the multivariate normal, chi-square, t and F distributions, central and noncentral * The linear model and the basic theory of regression analysis and the analysis of variance * Multiple regression methods, including transformations, analysis of residuals, and asymptotic theory for regression analysis. Separate sections are devoted to robust methods and to the bootstrap. * Simultaneous confidence intervals: Bonferroni, Scheffe, Tukey, and Bechhofer * Analysis of variance, with two- and three-way analysis of variance * Random component models, nested designs, and balanced incomplete block designs * Analysis of frequency data through log-linear models, with emphasis on vector space viewpoint. This chapter alone is sufficient for a course on the analysis of frequency data.

Introduction to WinBUGS for Ecologists

Introduction to WinBUGS for Ecologists Author Marc Kery
ISBN-10 0123786061
Release 2010-07-19
Pages 320
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Introduction to WinBUGS for Ecologists introduces applied Bayesian modeling to ecologists using the highly acclaimed, free WinBUGS software. It offers an understanding of statistical models as abstract representations of the various processes that give rise to a data set. Such an understanding is basic to the development of inference models tailored to specific sampling and ecological scenarios. The book begins by presenting the advantages of a Bayesian approach to statistics and introducing the WinBUGS software. It reviews the four most common statistical distributions: the normal, the uniform, the binomial, and the Poisson. It describes the two different kinds of analysis of variance (ANOVA): one-way and two- or multiway. It looks at the general linear model, or ANCOVA, in R and WinBUGS. It introduces generalized linear model (GLM), i.e., the extension of the normal linear model to allow error distributions other than the normal. The GLM is then extended contain additional sources of random variation to become a generalized linear mixed model (GLMM) for a Poisson example and for a binomial example. The final two chapters showcase two fairly novel and nonstandard versions of a GLMM. The first is the site-occupancy model for species distributions; the second is the binomial (or N-) mixture model for estimation and modeling of abundance. Introduction to the essential theories of key models used by ecologists Complete juxtaposition of classical analyses in R and Bayesian analysis of the same models in WinBUGS Provides every detail of R and WinBUGS code required to conduct all analyses Companion Web Appendix that contains all code contained in the book and additional material (including more code and solutions to exercises)