ABSTRACT

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.

part I|2 pages

I Background

chapter 1|20 pages

Getting Started

chapter 2|12 pages

Working with Numbers

chapter 3|24 pages

Working with Data Structures

chapter 4|24 pages

Basic Plotting Functions

chapter 5|16 pages

Automating Flow in Programs

part II|2 pages

II Linear Regression Models

chapter 6|32 pages

Simple Linear Regression

chapter 7|34 pages

Simple Remedies for Simple Regression

chapter 8|36 pages

Multiple Linear Regression

chapter 10|18 pages

Simple Remedies for Multiple Regression

part III|2 pages

III Linear Models with Fixed-Effects Factors

chapter 11|32 pages

One-Factor Models

chapter 12|18 pages

One-Factor Models with Covariates

chapter 13|18 pages

One-Factor Models with a Blocking Variable

chapter 14|16 pages

Two-Factor Models

chapter 15|6 pages

Simple Remedies for Fixed-Effects Models