## Regression Analysis by ExampleThe essentials of regression analysis through practical applications Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgement. Regression Analysis by Example, Fourth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression. This new edition features the following enhancements: Chapter 12, Logistic Regression, is expanded to reflect the increased use of the logit models in statistical analysis A new chapter entitled Further Topics discusses advanced areas of regression analysis Reorganized, expanded, and upgraded exercises appear at the end of each chapter A fully integrated Web page provides data sets Numerous graphical displays highlight the significance of visual appeal Regression Analysis by Example, Fourth Edition is suitable for anyone with an understanding of elementary statistics. Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions of the techniques themselves, the required assumptions, and the evaluated success of each technique. The methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R. An Instructor's Manual presenting detailed solutions to all the problems in the book is available online from the Wiley editorial department. |

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Looks good. Realistically grapples with outliers, violations of assumptions, and other thorny issues.

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analyzed assumptions autocorrelation book’s Chapter classiﬁcation Coefﬁcient s.e. t-test collinearity conﬁdence interval Cor(Y correlation coefﬁcient corresponding data set deﬁned degrees of freedom deleted denote discussed distribution DOPROD Durbin-Watson statistic eigenvalues estimated regression coefﬁcients examine Figure ﬁrst ﬁt ﬁtted values full model given in Table graph Hadi heteroscedasticity index plot indicator variables inﬂuence ith observation least squares estimates linear model linear regression linear regression model linear relationship linearizable logistic regression mean square measures method multicollinearity multiple regression nonlinear null hypothesis obtained outliers Poisson regression predictor variables principal components problem reduced model regression analysis regression coefﬁcients regression equation regression output regression results residual plots response variable ridge regression ridge trace robust regression s.e. t-test p-value sample scatter plot signiﬁcant simple regression speciﬁc standard deviation standard error standardized residuals versus subset sum of squares t-test p-value Constant Variable Coefﬁcient s.e. variables X1 variance weight zero