Includes bibliographical references (pages 635-646) and indexes

Review of Simple Regression -- Introduction to Matrices -- Multiple Regression in Matrix Notation -- Analysis of Variance and Quadratic Forms -- Case Study: Five Independent Variables -- Geometry of Least Squares -- Model Development: Variable Selection -- Polynomial Regression -- Class Variables in Regression -- Problem Areas in Least Squares -- Regression Diagnostics -- Transformation of Variables -- Collinearity -- Case Study: Collinearity Problems -- Models Nonlinear in the Parameters -- Case Study: Response Curve Modeling -- Analysis of Unbalanced Data -- Mixed Effects Models -- Case Study: Analysis of Unbalanced Data -- Appendix: Tables

"Aimed at the scientist who wishes to gain a working knowledge of regression analysis, the basic purpose of this volume is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is neither a cookbook nor a theoretical book. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching applied regression courses to graduate students in the sciences. Applied Regression Analysis serves as an excellent text for a service course on regression for nonstatisticians and also as a reference for researchers. It also provides a bridge between a two-semester introduction to statistical methods and a theoretical linear models course."--Jacket