Plane Answers to Complex Questions
This textbook provides a wide-ranging introduction to the use and theory of linear models for analyzing data. The author's
emphasis is on providing a unified treatment of linear models, including analysis of variance models and regression models,
based on projections, orthogonality, and other vector space ideas. Every chapter comes with numerous exercises and examples.
Advanced Linear Modeling: Statistical Learning and Dependent Data
Now in its third edition, this companion volume to the
author's Plane Answers to Complex Questions: The Theory of Linear Models uses three fundamental concepts from standard linear model theory - best prediction, projections,
and Mahalanobis distance - to extend standard linear modeling into the realms of Statistical Learning and Dependent Data.
This new edition features a wealth of new and revised content. In Statistical Learning it delves into nonparametric regression, penalized estimation (regularization), reproducing kernel Hilbert spaces, the kernel trick, and support vector machines. For Dependent Data it uses linear model theory to examine general linear models, linear mixed models, time series, spatial data, (generalized) multivariate linear models, discrimination, and dimension reduction. While numerous references to Plane Answers are made throughout the volume, Advanced Linear Modeling can be used on its own given a solid background in linear model theory. Accompanying R code for the analyses is available.
Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data
This book presents a comprehensive examination of basic statistical
methods and their applications. It focuses on addressing for unbalanced data the same issues
that the previous edition addressed for balanced data.
Analysis of Variance, Design, and Regression, First Edition
This book presents a comprehensive examination of basic statistical
methods and their applications. It focuses primarily on the analysis of variance
and regression but also includes discussions of basic ideas in experimental
design and of count data.
Log-Linear Models and Logistic Regression
This book examines statistical models for frequency data. The
primary focus is on log-linear models for contingency tables, but in this second
edition, greater emphasis has been placed on logistic regression. Topics such
as logistic discrimination and generalized linear models are also explored. Most of the book is aimed at second year Master's students. Three late chapters
explore the theory using matrix and vector ideas. The final chapter introduces Bayesian logistic regression.
(R code and new chapters on Exact Conditional Tests and Correspondence Analysis are available.)
Bayesian Ideas and Data Analysis
This book by Ronald Christensen, Wesley Johnson, Adam Branscum, and Timothy E. Hanson presents statistical tools to address scientific questions. It highlights foundational issues in statistics,
the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing data.
The first five chapters of the book contain core material that spans basic Bayesian ideas, calculations, and inference including modeling one and two sample data from traditional sampling models.
The text then covers Monte Carlo methods such as Markov chain Monte Carlo (McMC) simulation.
After discussing linear structures in regression, it presents binomial regression, normal regression, analysis of variance, and Poisson regression before extending these methods to handle correlated data.
The authors also examine survival analysis and binary diagnostic testing. The last chapter on nonparametric inference explores density estimation and flexible regression modeling of mean functions.
Features:
Books that will probably never get finished/published:
Topics in Experimental Design
This book contains new material on screening designs, old but unpublished work on "powers of primes" designs, and material removed from newer versions of
Analysis of Variance, Design, and Regression, Plane Answers to Complex Questions, and
Advanced Linear Modeling. R Code for the book is available.
Statistical Learning
Course notes for Statistical Learning.
Statistical Inference
Course notes for Advanced Inference.
Industrial Statistics
Course notes for Industrial Statistics.