An R Companion to Linear Statistical Models

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.

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.