Introduction to Regression Analysis

In general, the sections of each chapter cover the mathematical model, analysis of variance, point and interval estimation of variance components, hypothesis tests, and Bayesian estimation. Estimation methods include method of moments, maximum likelihood, restricted maximum likelihood, and Bayesian estimators. Comparisons of estimators are given for each case. Confidence intervals and hypothesis tests for variance components and functions of variance components are discussed thoroughly. Each chapter includes plenty of examples, most of which contain hand calculations. There are also numerous examples of software output, including SAS, SPSS, and BMDP output. The book also includes sample size calculations based on cost of sampling. The layout of this comprehensive book makes it easy to find exactly what you need. The writing is clear, giving extensive explanations of advanced topics. The authors make it clear in the Preface that they are providing comprehensive coverage of linear models with random effects. They do not, in general, provide proofs of theorems. There is also no material on methods other than the univariate models named in the chapter titles, such as sequential or nonparametric methods. However, plenty of references are given for the reader who wishes to delve into the theory more deeply. The Remarks section of each topic contains these references, as well as small details and historical information. In short, this book is an excellent addition to my modeling library. Volume II on unbalanced models has been published and looks to be as good a book as Volume I.

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