STATISTICAL SLEUTH is an innovative treatment of general statistical methods, taking full advantage of the computer, both as a computational and an analytical tool. The material is independent of any specific software package. In "The American Statistician" (February 2000, Vol. 54, No. 1), George Cobb commented, "What is new and different about Ramsey and Schafer's book, what makes it a 'larger contribution, ' is that it gives much more prominence to modeling and interpretation of the sort that goes beyond the routine ...
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STATISTICAL SLEUTH is an innovative treatment of general statistical methods, taking full advantage of the computer, both as a computational and an analytical tool. The material is independent of any specific software package. In "The American Statistician" (February 2000, Vol. 54, No. 1), George Cobb commented, "What is new and different about Ramsey and Schafer's book, what makes it a 'larger contribution, ' is that it gives much more prominence to modeling and interpretation of the sort that goes beyond the routine patterns." His students did "substantially better" on term papers based on the analysis of data. In the book, the focus is on a serious analysis of real case studies; on strategies and tools of modern statistical data analysis; on the interplay of statistics and scientific learning; and on the communication of results. With interesting examples, real data, and a variety of exercise types (conceptual, computational, and data problems), the authors get students excited about statistics. Benefits: Real-world case studies introduce each method. Problems are those graduate researchers typically encounter, with regression as the principal featured tool. Generalized linear models, repeated measures, and serial correlation are included. Strong, early coverage (Chapters 1-4) of the fundamentals of drawing sound inferences (introduced by means of two-sample problems). Excellent discussion of design issues (as they apply to case studies) throughout, with two experimental design chapters at the end of the book. NEW! Updated data problems to cover current events. NEW! New section on Akaik's and the Bayes Information Criteria for Model Selection in Chapter 12. NEW! New discussion of Bayesian Inference in Chapter 21.
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