An R Companion To Linear Statistical Models
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An R Companion To Linear Statistical Models
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Author : Christopher Hay-Jahans
language : en
Publisher: CRC Press
Release Date : 2011-10-19
An R Companion To Linear Statistical Models written by Christopher Hay-Jahans and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-10-19 with Mathematics categories.
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 Linear Statistical Models
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Author : Christopher Hay-Jahans
language : en
Publisher: CRC Press
Release Date : 2011-10-19
An R Companion To Linear Statistical Models written by Christopher Hay-Jahans and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-10-19 with Mathematics categories.
This work was prepared to serve as an R supplement for textbooks on Linear Statistical Models. It provides computational and coding details on the use of R that textbooks do not. Topics covered include simple and multiple linear regression models, models for one- and two-factor fixed-effects designs, covariance models, and models for randomized complete block designs. The text can serve as both a course supplement and a fairly detailed self-help resource. The development of grass-roots code alongside demonstrations of pre-packaged routines provides users with illustrations on how to develop their own programs with R.
Generalized Linear Models With Examples In R
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Author : Peter K. Dunn
language : en
Publisher: Springer
Release Date : 2018-11-10
Generalized Linear Models With Examples In R written by Peter K. Dunn and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-10 with Mathematics categories.
This textbook presents an introduction to generalized linear models, complete with real-world data sets and practice problems, making it applicable for both beginning and advanced students of applied statistics. Generalized linear models (GLMs) are powerful tools in applied statistics that extend the ideas of multiple linear regression and analysis of variance to include response variables that are not normally distributed. As such, GLMs can model a wide variety of data types including counts, proportions, and binary outcomes or positive quantities. The book is designed with the student in mind, making it suitable for self-study or a structured course. Beginning with an introduction to linear regression, the book also devotes time to advanced topics not typically included in introductory textbooks. It features chapter introductions and summaries, clear examples, and many practice problems, all carefully designed to balance theory and practice. The text also provides a working knowledge of applied statistical practice through the extensive use of R, which is integrated into the text. Other features include: • Advanced topics such as power variance functions, saddlepoint approximations, likelihood score tests, modified profile likelihood, small-dispersion asymptotics, and randomized quantile residuals • Nearly 100 data sets in the companion R package GLMsData • Examples that are cross-referenced to the companion data set, allowing readers to load the data and follow the analysis in their own R session
An R Companion To Applied Regression
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Author : John Fox
language : en
Publisher: SAGE Publications
Release Date : 2011
An R Companion To Applied Regression written by John Fox and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Social Science categories.
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.
Linear Regression Models
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Author : John P. Hoffmann
language : en
Publisher: CRC Press
Release Date : 2021-09-12
Linear Regression Models written by John P. Hoffmann and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-12 with Mathematics categories.
Research in social and behavioral sciences has benefited from linear regression models (LRMs) for decades to identify and understand the associations among a set of explanatory variables and an outcome variable. Linear Regression Models: Applications in R provides you with a comprehensive treatment of these models and indispensable guidance about how to estimate them using the R software environment. After furnishing some background material, the author explains how to estimate simple and multiple LRMs in R, including how to interpret their coefficients and understand their assumptions. Several chapters thoroughly describe these assumptions and explain how to determine whether they are satisfied and how to modify the regression model if they are not. The book also includes chapters on specifying the correct model, adjusting for measurement error, understanding the effects of influential observations, and using the model with multilevel data. The concluding chapter presents an alternative model—logistic regression—designed for binary or two-category outcome variables. The book includes appendices that discuss data management and missing data and provides simulations in R to test model assumptions. Features Furnishes a thorough introduction and detailed information about the linear regression model, including how to understand and interpret its results, test assumptions, and adapt the model when assumptions are not satisfied. Uses numerous graphs in R to illustrate the model’s results, assumptions, and other features. Does not assume a background in calculus or linear algebra, rather, an introductory statistics course and familiarity with elementary algebra are sufficient. Provides many examples using real-world datasets relevant to various academic disciplines. Fully integrates the R software environment in its numerous examples. The book is aimed primarily at advanced undergraduate and graduate students in social, behavioral, health sciences, and related disciplines, taking a first course in linear regression. It could also be used for self-study and would make an excellent reference for any researcher in these fields. The R code and detailed examples provided throughout the book equip the reader with an excellent set of tools for conducting research on numerous social and behavioral phenomena. John P. Hoffmann is a professor of sociology at Brigham Young University where he teaches research methods and applied statistics courses and conducts research on substance use and criminal behavior.
An R Companion To Applied Regression
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Author : John Fox
language : en
Publisher: SAGE Publications
Release Date : 2018-09-27
An R Companion To Applied Regression written by John Fox and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-27 with Social Science categories.
An R Companion to Applied Regression is a broad introduction to the R statistical computing environment in the context of applied regression analysis. John Fox and Sanford Weisberg provide a step-by-step guide to using the free statistical software R, an emphasis on integrating statistical computing in R with the practice of data analysis, coverage of generalized linear models, and substantial web-based support materials. The Third Edition has been reorganized and includes a new chapter on mixed-effects models, new and updated data sets, and a de-emphasis on statistical programming, while retaining a general introduction to basic R programming. The authors have substantially updated both the car and effects packages for R for this edition, introducing additional capabilities and making the software more consistent and easier to use. They also advocate an everyday data-analysis workflow that encourages reproducible research. To this end, they provide coverage of RStudio, an interactive development environment for R that allows readers to organize and document their work in a simple and intuitive fashion, and then easily share their results with others. Also included is coverage of R Markdown, showing how to create documents that mix R commands with explanatory text. "An R Companion to Applied Regression continues to provide the most comprehensive and user-friendly guide to estimating, interpreting, and presenting results from regression models in R." –Christopher Hare, University of California, Davis
Linear Regression Models
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Author : John P. Hoffmann
language : en
Publisher: CRC Press
Release Date : 2021-09-09
Linear Regression Models written by John P. Hoffmann and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-09 with Mathematics categories.
Research in social and behavioral sciences has benefited from linear regression models (LRMs) for decades to identify and understand the associations among a set of explanatory variables and an outcome variable. Linear Regression Models: Applications in R provides you with a comprehensive treatment of these models and indispensable guidance about how to estimate them using the R software environment. After furnishing some background material, the author explains how to estimate simple and multiple LRMs in R, including how to interpret their coefficients and understand their assumptions. Several chapters thoroughly describe these assumptions and explain how to determine whether they are satisfied and how to modify the regression model if they are not. The book also includes chapters on specifying the correct model, adjusting for measurement error, understanding the effects of influential observations, and using the model with multilevel data. The concluding chapter presents an alternative model—logistic regression—designed for binary or two-category outcome variables. The book includes appendices that discuss data management and missing data and provides simulations in R to test model assumptions. Features Furnishes a thorough introduction and detailed information about the linear regression model, including how to understand and interpret its results, test assumptions, and adapt the model when assumptions are not satisfied. Uses numerous graphs in R to illustrate the model’s results, assumptions, and other features. Does not assume a background in calculus or linear algebra, rather, an introductory statistics course and familiarity with elementary algebra are sufficient. Provides many examples using real-world datasets relevant to various academic disciplines. Fully integrates the R software environment in its numerous examples. The book is aimed primarily at advanced undergraduate and graduate students in social, behavioral, health sciences, and related disciplines, taking a first course in linear regression. It could also be used for self-study and would make an excellent reference for any researcher in these fields. The R code and detailed examples provided throughout the book equip the reader with an excellent set of tools for conducting research on numerous social and behavioral phenomena. John P. Hoffmann is a professor of sociology at Brigham Young University where he teaches research methods and applied statistics courses and conducts research on substance use and criminal behavior.
Linear Models With R Second Edition
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Author : Julian J. Faraway
language : en
Publisher: CRC Press
Release Date : 2014-07-01
Linear Models With R Second Edition written by Julian J. Faraway and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-01 with Mathematics categories.
A Hands-On Way to Learning Data Analysis Part of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the first edition. New to the Second Edition Reorganized material on interpreting linear models, which distinguishes the main applications of prediction and explanation and introduces elementary notions of causality Additional topics, including QR decomposition, splines, additive models, Lasso, multiple imputation, and false discovery rates Extensive use of the ggplot2 graphics package in addition to base graphics Like its widely praised, best-selling predecessor, this edition combines statistics and R to seamlessly give a coherent exposition of the practice of linear modeling. The text offers up-to-date insight on essential data analysis topics, from estimation, inference, and prediction to missing data, factorial models, and block designs. Numerous examples illustrate how to apply the different methods using R.
Regression Analysis And Linear Models
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Author : Richard B. Darlington
language : en
Publisher: Guilford Publications
Release Date : 2016-09-27
Regression Analysis And Linear Models written by Richard B. Darlington and has been published by Guilford Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-27 with Social Science categories.
Ephasizing conceptual understanding over mathematics, this user-friendly text introduces linear regression analysis to students and researchers across the social, behavioral, consumer, and health sciences. Coverage includes model construction and estimation, quantification and measurement of multivariate and partial associations, statistical control, group comparisons, moderation analysis, mediation and path analysis, and regression diagnostics, among other important topics. Engaging worked-through examples demonstrate each technique, accompanied by helpful advice and cautions. The use of SPSS, SAS, and STATA is emphasized, with an appendix on regression analysis using R. The companion website (www.afhayes.com) provides datasets for the book's examples as well as the RLM macro for SPSS and SAS. Pedagogical Features: *Chapters include SPSS, SAS, or STATA code pertinent to the analyses described, with each distinctively formatted for easy identification. *An appendix documents the RLM macro, which facilitates computations for estimating and probing interactions, dominance analysis, heteroscedasticity-consistent standard errors, and linear spline regression, among other analyses. *Students are guided to practice what they learn in each chapter using datasets provided online. *Addresses topics not usually covered, such as ways to measure a variable?s importance, coding systems for representing categorical variables, causation, and myths about testing interaction.
An R Companion To Political Analysis
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Author : Philip H. Pollock III
language : en
Publisher: CQ Press
Release Date : 2017-03-31
An R Companion To Political Analysis written by Philip H. Pollock III and has been published by CQ Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-31 with Political Science categories.
Teach your students to conduct political research using R, the open source programming language and software environment for statistical computing and graphics. An R Companion to Political Analysis by Philip H. Pollock III and Barry C. Edwards offers the same easy-to-use and effective style as the best-selling SPSS and Stata Companions. The all-new Second Edition includes new and revised exercises and datasets showing students how to analyze research-quality data to learn descriptive statistics, data transformations, bivariate analysis (cross-tabulations and mean comparisons), controlled comparisons, statistical inference, linear correlation and regression, dummy variables and interaction effects, and logistic regression. The clear explanation and instruction is accompanied by annotated and labeled screen shots and end-of-chapter exercises to help students apply what they have learned.