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This book is an introduction to regression analysis for upper division and graduate students in science, engineering, social science and medicine. The emphasis is on the classical linear regression diagnostics, ridge and logistic regression are treated as well. In contrast to other books at this level, the theoretical foundation of the subject is presented in some detail based on extensive use of matrix algebra. throughout the text, model building and evaluation are emphasized and illustrated with many numerical examples.
Preface
Introduction
Some Basic Results in Probability and Statistics
Simple Linear Regression
Random Vectors and Matrix Algebra
Multiple Regression
Residuals, Diagnostics and Transformations
Further Applications of Regression Techniques
Selection of a Regression Model
Multicollinearity: Diagnosis and Remedies
Appendix
Bibliography
Index