[下载]Apllied Linear Regression (3rd Edt 2005)

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收藏 2008-12-15

线性回归的最经典入门教材，不多介绍了作者信息：SANFORD WEISBERG University of Minnesota School of Statistics Minneapolis, MinnesotaA JOHN WILEY & SONS, INC., PUBLICATIONWhat’s New in this Edition Many of the examples and homework data sets from the second edition have been kept, although some have been updated. The fuel consumption data, for example, now uses 2001 values rather than 1974 values. Most of the derivations are the same as in the second edition, although the order of presentation is somewhat different. To keep the length of the book nearly unchanged, methods that failed to gain general usage have been deleted, as have the separate chapters on prediction and missing data. These latter two topics have been integrated into the remaining text.The continuing theme of the second edition was the need for diagnostic methods, in which fitted models are analyzed for deficiencies, through analysis of residuals and influence. This emphasis was unusual when the second edition was published and important quantities like Studentized residuals and Cook’s distance were not readily available in the commercial software of the time.Times have changed, and so has the emphasis of this book. This edition stresses graphical methods including looking at data both before and after fitting models. This is reflected immediately in the new Chapter 1, which introduces the key idea of looking at data with scatterplots and the somewhat less universal tool of scatterplot matrices. Most analyses and homework problems start with drawing graphs. We tailor analyses to correspond to what we see in the graphs, and this additional step can make modeling easier and fitted models reflect the data more closely. Remarkably, this also lessens the need for diagnostic methods.The emphasis on graphs leads to several additional methods and procedures that were not included in the second edition. The use of smoothers to help summarize a scatterplot is introduced early, although only a little of the theory of smoothing is presented (in Appendix A.5). Transformations of predictors and the response are stressed, and relatively unfamiliar methods based both on smoothing and on generalization of the Box–Cox method are presented in Chapter 7.Another new topic included in the book is computationally intensive methods and simulation. The key example of this is the bootstrap, in Section 4.6, which can be used to make inferences about fitted models in small samples. A somewhat different computationally intensive method is used in an example in Chapter 10, which is a completely rewritten chapter on variable selection.The book concludes with two expanded chapters on nonlinear and logistic regression, both of which are generalizations of the linear regression model. I have included these chapters to provide instructors and students with enough information for basic usage of these models and to take advantage of the intuition gained about them from an in-depth study of the linear regression model. Each of these can be treated at book-length, and appropriate references are given.