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The Statistical Sleuth: A Course in Methods of Data Analysis (Book & CD), by Fred Ramsey, Daniel Schafer
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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.
- Sales Rank: #83370 in Books
- Brand: Brand: Cengage Learning
- Published on: 2002
- Original language: English
- Number of items: 1
- Dimensions: 1.32" h x 7.40" w x 9.48" l, 2.99 pounds
- Binding: Hardcover
- 768 pages
- Used Book in Good Condition
Review
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.
About the Author
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.
Most helpful customer reviews
25 of 28 people found the following review helpful.
Good concept, more care needed with datasets
By Ashtoreth
I have taught using this text 5 times now. I like the concept very much. My goals are to teach the students how to choose appropriate statistical tests and how to write up their results professionally. I very much like the concept of "project" exercises with real data sets.
Unfortunately, many of the datasets contain faulty data. I doubt that I have a full list of example, but Chapter 2, Problem 23 supposedly contains percent change in fatalities in states that retained and did not retain 55 mile per hour speed limits between 1995 and 1996. Unfortunately, the data reported have nothing in common with the real data. For instance, there were 87 fatalities in Alaska in 1995 and 81 in 1996 leading to a 7% decrease. The number used in the text is a 29% decrease. Other examples include Chapter 8, Problem 20 which is admittedly an approximation but the authors did not read the scale on the New York Times graphic correctly which places the supposed outlier rather differently than if the correct scale for the data is used. In Chapter 11, problem 24, there is an excessively heavy grasshopper mouse among other errant species weights.
The concept is great, a few data entry errors can be an effective learning tool, but too many blatantly incorrect datasets that lead to conclusions diametrically opposed to the real data seems sloppy to me.
18 of 20 people found the following review helpful.
Excellent resource for the non-specialist practitioner
By sweng
During my professional life I was several times in the situation of having to do statistical analysis of some data. I also saw other colleagues struggle with the same situation and it seems that for someone without a formal training in statistics and a lot of practical experience this is a strange mixture of technical know-how, guess-work and pure superstition.
This is the book I've been looking for for years now, to learn how to do the job with a reasonable understanding of the choices involved and the assumptions that are made when using this or that tool. It is a wonderfully practical and clear exposition of the methods that are likely to be used by a practitioner to answer practical questions with the help of data - and definitely helps to avoid the uneasy feeling of having to apply some tool without really knowing what is going on.
10 of 12 people found the following review helpful.
Excellent survey and introduction to statistics
By Alethephant
I have read more than 100 statistical texts on various subjects. This book is one of the best I've ever seen. It is extremely clear, well-organized, consistent in methodology and well-typeset. The use of well-documented case studies to illustrate every concept makes eclectic reading easy. The book also attempts to answer common trade-offs and philosophical points clearly. If I were teaching statistics, I'd use this text somewhere at the junior year level. I'd highly recommend it as a quick reference to anyone working with and familiar with statistics. It's very useful for a quick conceptual and practical overview.
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