Applied Regression Analysis, Part 766An outstanding introduction to the fundamentals of regression analysis-updated and expanded The methods of regression analysis are the most widely used statistical tools for discovering the relationships among variables. This classic text, with its emphasis on clear, thorough presentation of concepts and applications, offers a complete, easily accessible introduction to the fundamentals of regression analysis. Assuming only a basic knowledge of elementary statistics, Applied Regression Analysis, Third Edition focuses on the fitting and checking of both linear and nonlinear regression models, using small and large data sets, with pocket calculators or computers. This Third Edition features separate chapters on multicollinearity, generalized linear models, mixture ingredients, geometry of regression, robust regression, and resampling procedures. Extensive support materials include sets of carefully designed exercises with full or partial solutions and a series of true/false questions with answers. All data sets used in both the text and the exercises can be found on the companion disk at the back of the book. For analysts, researchers, and students in university, industrial, and government courses on regression, this text is an excellent introduction to the subject and an efficient means of learning how to use a valuable analytical tool. It will also prove an invaluable reference resource for applied scientists and statisticians. |
Contents
CHAPTER | 1 |
THE EXAMINATION OF Residuals | 3 |
THE MATRIX APPROACH TO LINEAR REGRESSION | 126 |
Copyright | |
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Common terms and phrases
actually additional alternative analysis of variance apply appropriate approximate assume b₁ B₂ calculations called Chapter coefficients column compared confidence correct correlation corresponding defined degrees of freedom dependent determine deviation discussed distribution effect elements equal equation error estimate examine example F-test fact Figure follows function give given independent indicated interval involved lack of fit least squares limits linear matrix mean method needed nonlinear normal Note observations obtain occur original orthogonal overall parameters partial plot possible practical prediction predictor variables problem procedure pure error regression repeat residuals response ridge runs selected shown shows significant situations solution Source space specific ẞo standard statistic straight line sum of squares Suppose Table transformation true usually variation vector write X₁ Y₁ zero