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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.
Fred Ramsey received his undergraduate degree from the University of Oregon (1961) and graduate degrees from Iowa State University (1963, 1964). He completed post-doctorate work at Johns Hopkins University. He has been on the faculty of the Department of Statistics at Oregon State University since 1966, with leaves for teaching and research positions at the University of Copenhagen, Denmark (1972-1973); Murdoch University, Perth, Western Australia (1997-1978); the University of Wollongong, NSW, Australia (1985-1986); and Oregon Health Sciences University in Portland, Oregon (1990-1991). His principal research interest is applications of statistics to wildlife problems.
Daniel Schafer holds an undergraduate degree in Mathematics from Pomona College (1978) and graduate degrees in Statistics from the University of Chicago (1981, 1982). He is currently a professor of statistics at Oregon State University. His hobby is wildlife photography.
1. Drawing Statistical Conclusions. 2. Inference Using t-Distributions. 3. A Closer Look at Assumptions. 4. Alternatives to the t-Tools. 5. Comparisons among Several Samples. 6. Linear Combinations and Multiple Comparisons of Means. 7. Simple Linear Regression: A Model for the Mean. 8. A Closer Look at Assumptions for Simple Linear Regression. 9. Multiple Regression. 10. Inferential Tools for Multiple Regression. 11. Model Checking and Refinement. 12. Strategies for Variable Selection. 13. The Analysis of Variance for Two-Way Classifications. 14. Multifactor Studies Without Replication. 15. Adjustment for Serial Correlation. 16. Repeated Measures and Other Multivariate Responses. 17. Exploratory Tools for Summarizing Multivariate Responses. 18. Comparisons of Proportions or Odds. 19. More Tools for Tables of Counts. 20. Logistics Regression for Binary Response Variables. 21. Logistic Regression for Binomial Counts. 22. Log-Linear Regression for Poisson Counts. 23. Elements of Research Design. 24. Factorial Treatment Arrangements and Blocking Designs. Appendix A. Tables. Appendix B. References. Bibliography. Index.