Includes bibliographical references (p. 341-345) and index.
Note continued: 13.5.2.Without a control treatment
13.6.Tukey's Nonadditivity Test
13.7.For the Curious
13.7.1.Bonferroni's pairwise comparisons
13.7.2.Generating data to play with
14.2.Exploratory Data Analysis
14.3.Model Construction and Fit
14.5.Pairwise Comparisons of Treatment Effects
14.5.1.With a control treatment
14.5.2.Without a control treatment
14.6.What if Interaction Effects Are Significant?
14.7.Data with Exactly One Observation per Cell
14.8.Two-Factor Models with Covariates
14.9.For the Curious: Scheffe's F-Tests
15.Simple Remedies for Fixed-Effects Models
15.2.Issues with the Error Assumptions
15.4.Issues Specific to Covariates
15.4.2.Transformations of covariates
15.4.3.Blocking as an alternative to covariates
15.5.For the Curious.
"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. "-- Provided by publisher.
"Preface This work (referred to as Companion from here on) targets two primary audiences: Those who are familiar with the theory and applications of linear statistical models and wish to learn how to use R or supplement their abilities with R through unfamiliar ideas that might appear in this Companion; and those who are enrolled in a course on linear statistical models for which R is the computational platform to be used. About the Content and Scope While applications of several pre-packaged functions for complex computational procedures are demonstrated in this Companion, the focus is on programming with applications to methods used for linear regression and designed experiments with up to two fixed-effects factors, including blocking variables and covariates. The intent in compiling this Companion has been to provide as comprehensive a coverage of these topics as possible, subject to the constraint on the Companion's length. The reader should be aware that much of the programming code presented in this Companion is at a fairly basic level and, hence, is not necessarily very elegant in style. The purpose for this is mainly pedagogical; to match instructions provided in the code as closely as possible to computational steps that might appear in a variety of texts on the subject. Discussion on statistical theory is limited to only that which is necessary for computations; common "rules of thumb" used in interpreting graphs and computational output are provided. An effort has been made to direct the reader to resources in the literature where the scope of the Companion is exceeded, where a theoretical refresher might be useful, or where a deeper discussion may be desired. The bibliography lists a reasonable starting point for further references at a variety of levels"-- Provided by publisher.