Applied Regression Analysis, Part 766
Offers a complete introduction to the fundamentals, emphasizing an understanding of concepts & the application of methods. Focuses on the creation of mathematical models using large amounts of data & high-speed computing equipment, screening of independent variables, methods for creating new variables, & problems with nonlinear models. Revised & updated to include changes in the field through 1980, with new exercises & worked examples.
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The Matrix Approach to Linear Regression
The Examination of Residuals
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analysis of variance ANOVA Source df calculations Chapter Coefficients and Confidence column confidence interval Confidence Limits confidence region contours correlation coefficient corresponding degrees of freedom deviation of residuals distribution dummy variables estimate of a2 examine example experimental F Total F-value Figure fit the model fitted equation fitted model follows function given lack of fit least squares estimates linear regression method nonlinear model normal equations Note obtained orthogonal polynomials Overall F parameters Partial F-test plot prediction equation predictor variables problem procedure pure error regression analysis regression equation regression model residual sum Response variable ridge regression sample second-order Section selected shown significant solution Source df SS Square of Partials SS MS F Standard deviation statistic stepwise straight line subsets sum of squares Technometrics Term in Prediction tion Total corrected transformation usually variance table vector weighted least squares zero