Practical Regression and Anova in R

The material in this text is old and has been superceded by Linear Models with R

This book is derived from material that I have taught in a class (STAT500) at the University of Michigan twenty years ago. This was a masters level course covering the following topics:Linear Models: Definition, fitting, inference, interpretation of results, meaning of regression coefficients, identifiablity, lack of fit, multicollinearity, ridge regression, principal components regression, partial least squares, regression splines, Gauss-Markov theorem, variable selection, diagnostics, transformations, influential observations, robust procedures, ANOVA and analysis of covariance, randomised block, factorial designs.

To take full advantage of the book, you will need to obtain a copy of R which may be obtained free of charge from the R web site. (There is also an accompanying package of data and R functions for the book.

Original Version: December 1999, Revised Versions: December 2000, July 2002 (final web version)