Analysis of covariance is a very useful but often misunderstood methodology for analyzing data where important characteristics of the experimental units are measured but not included as factors in the design. The authors of this book take a unique approach to the analysis of covariance. Looking at a set of regression models, one for each of the treatments or treatment combinations, analysts can use their knowledge of regression analysis and analysis of variance to help attack the problem. With a careful balance of theory and examples, this volume provides an invaluable guide to this strategy's techniques, theory, and application.

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George A. Milliken, Dallas E. Johnson

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