An R companion to linear statistical models
 Responsibility
 Christopher HayJahans.
 Language
 English.
 Imprint
 Boca Raton, FL : CRC Press, c2012.
 Physical description
 xvii, 354 p. : ill ; 25 cm.
Access
Available online
 marc.crcnetbase.com CRCnetBASE
Math & Statistics Library

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QA279 .H39 2012

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QA279 .H39 2012
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Creators/Contributors
 Author/Creator
 HayJahans, Christopher.
Contents/Summary
 Bibliography
 Includes bibliographical references (p. 341345) and index.
 Contents

 Background Getting Started Introduction Starting up R Searching for Help Managing Objects in the Workspace Installing and Loading Packages from CRAN Attaching R Objects Saving Graphics Images from R Viewing and Saving Session History Citing R and Packages from CRAN The R Script Editor Working with Numbers Introduction Elementary Operators and Functions Sequences of Numbers Common Probability Distributions User Defined Functions Working with Data Structures Introduction Naming and Initializing Data Structures Classifications of Data within Data Structures Basics with Univariate Data Basics with Multivariate Data Descriptive Statistics For the Curious Basic Plotting Functions Introduction The Graphics Window Boxplots Histograms Density Histograms and Normal Curves Stripcharts QQ Normal Probability Plots HalfNormal Plots TimeSeries Plots Scatterplots Matrix Scatterplots Bells and Whistles For the Curious Automating Flow in Programs Introduction Logical Variables, Operators, and Statements Conditional Statements Loops Programming Examples Some Programming Tips Linear Regression Models Simple Linear Regression Introduction Exploratory Data Analysis Model Construction and Fit Diagnostics Estimating Regression Parameters Confidence Intervals for the Mean Response Prediction Intervals for New Observations For the Curious Simple Remedies for Simple Regression Introduction Improving Fit Normalizing Transformations Variance Stabilizing Transformations Polynomial Regression Piecewise Defined Models Introducing Categorical Variables For the Curious Multiple Linear Regression Introduction Exploratory Data Analysis Model Construction and Fit Diagnostics Estimating Regression Parameters Confidence Intervals for the Mean Response Prediction Intervals for New Observations For the Curious Additional Diagnostics for Multiple Regression Introduction Detection of Structural Violations Diagnosing Multicollinearity Variable Selection Model Selection Criteria For the Curious Simple Remedies for Multiple Regression Introduction Improving Fit Normalizing Transformations Variance Stabilizing Transformations Polynomial Regression Adding New Explanatory Variables What if None of the Simple Remedies Help? For the Curious: BoxTidwell Revisited Linear Models with FixedEffects Factors OneFactor Models Introduction Exploratory Data Analysis Model Construction and Fit Diagnostics Pairwise Comparisons of Treatment Effects Testing General Contrasts Alternative Variable Coding Schemes For the Curious OneFactor Models with Covariates Introduction Exploratory Data Analysis Model Construction and Fit Diagnostics Pairwise Comparisons of Treatment Effects Models with Two or More Covariates For the Curious OneFactor Models with a Blocking Variable Introduction Exploratory Data Analysis Model Construction and Fit Diagnostics Pairwise Comparisons of Treatment Effects Tukey's Nonadditivity Test For the Curious TwoFactor Models Introduction Exploratory Data Analysis Model Construction and Fit Diagnostics Pairwise Comparisons of Treatment Effects What if Interaction Effects Are Significant? Data with Exactly One Observation per Cell TwoFactor Models with Covariates For the Curious: Scheffe's FTests Simple Remedies for FixedEffects Models Introduction Issues with the Error Assumptions Missing Variables Issues Specific to Covariates For the Curious Bibliography Index.
 (source: Nielsen Book Data)
 Publisher's Summary
 Focusing on userdeveloped 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 Rbased course on regression and analysis of variance. For those who have never used R, the book begins with a selfcontained 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 fixedeffects factors, including blocking variables and covariates. It also demonstrates applications of several prepackaged functions for complex computational procedures.
(source: Nielsen Book Data)
Subjects
Bibliographic information
 Publication date
 2012
 ISBN
 9781439873656 (hardback)
 1439873658 (hardback)