by Xin Yan (University of Missouri–Kansas City, USA) & Xiao Gang Su (University of Central Florida, USA)

Table of Contents (289k)
Preface (102k)
Chapter 1: Introduction (163k)

This volume presents in detail the fundamental theories of linear regression analysis and diagnosis, as well as the relevant statistical computing techniques so that readers are able to actually model the data using the methods and techniques described in the book. It covers the fundamental theories in linear regression analysis and is extremely useful for future research in this area. The examples of regression analysis using the Statistical Application System (SAS) are also included. This book is suitable for graduate students who are either majoring in statistics/biostatistics or using linear regression analysis substantially in their subject fields.

• Introduction
• Simple Linear Regression
• Multiple Linear Regression
• Detection of Outliers and Influential Observations in Multiple Linear Regression
• Model Selection
• Model Diagnostics
• Extensions of Least Squares
• Generalized Linear Models
• Bayesian Linear Regression