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 |
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Common terms and phrases
analysis of variance ANOVA Source df b₁ B₁X B₁X₁ B₂ calculations Chapter column confidence interval confidence limits correct corresponding degrees of freedom distribution dummy variables examine example F-test F-value Figure fit the model fitted equation fitted model fitted values follows given lack of fit least squares estimates linear method nonlinear normal equations Note observations obtain orthogonal polynomials overall parameters Partial F-test predictor variables problem procedure pure error regression analysis regression equation residual mean square residual sum ridge regression second-order Section selected shown significant Source df SS SS MS F ẞ₁ ẞo standard deviation statistic stepwise straight line subsets sum of squares Suppose Technometrics transformation true mean value usually variance table variance-covariance matrix variation vector weighted least squares X₁ X₂ Y₁ Y₂ Z₁ Z₂ zero Σ Χ σ²