Providing an introduction to modern regression analysis, this text emphasises understanding concepts rather than computational details or mathematical proofs. The frequent discussions of computing with SAS allows emphasis on whole concepts rather than conceptual details. This text includes topics unique for a book at this level, such as a chapter on a geometric interpretation of least square analysis, coverage of reparametrization, regression diagnostics including Gabriel's biplots and the analysis of unbalanced data. The text uses matrix notation throughout and includes extensive discussions on the application of regression models to analysis of variance problems, the limitations of stepwise regression procedures and of analyzing highly colinear data. This book should be of interest to degree and diploma students taking courses in statistics.

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