Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data, Second Edition presents linear structures for modeling data with an emphasis on how to incorporate specific ideas (hypotheses) about the structure of the data into a linear model for the data. The book carefully analyzes small data sets by using tools that are easily scaled to big data. The tools also apply to small relevant data sets that are extracted from big data.

New to the Second Edition

  • Reorganized to focus on unbalanced data
  • Reworked balanced analyses using methods for unbalanced data
  • Introductions to nonparametric and lasso regression
  • Introductions to general additive and generalized additive models
  • Examination of homologous factors
  • Unbalanced split plot analyses
  • Extensions to generalized linear models
  • R, Minitab®, and SAS code on the author’s website

The text can be used in a variety of courses, including a yearlong graduate course on regression and ANOVA or a data analysis course for upper-division statistics students and graduate students from other fields. It places a strong emphasis on interpreting the range of computer output encountered when dealing with unbalanced data.

chapter 1|26 pages


chapter 2|30 pages

One Sample

chapter 3|30 pages

General Statistical Inference

chapter 4|21 pages

Two Samples

chapter 5|23 pages

Contingency Tables

chapter 6|23 pages

Simple Linear Regression

chapter 7|21 pages

Model Checking

chapter 8|25 pages

Lack of Fit and Nonparametric Regression

chapter 9|30 pages

Multiple Regression: Introduction

chapter 10|20 pages

Diagnostics and Variable Selection

chapter 11|21 pages

Multiple Regression: Matrix Formulation

chapter 12|45 pages


chapter 13|11 pages

Multiple Comparision Methods

chapter 14|25 pages


chapter 15|18 pages

ACOVA and Interactions

chapter 16|17 pages

Multifactor Structures

chapter 17|24 pages

Basic Experimental Designs

chapter 18|18 pages

Factorial Treatments

chapter 19|42 pages

Dependent Data

chapter 20|31 pages

Logistic Regression: Predicting Counts

chapter 21|24 pages

Log-Linear Models: Describing Count Data

chapter 23|17 pages

Nonlinear Regression