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Statistical Modeling: A Fresh Approach Paperback – July 14, 2009
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About the Author
Daniel Kaplan is DeWitt Wallace Professor of Mathematics and Computer Science at Macalester College where he teaches statistics, applied mathematics, and scientific computing. He earned a B.A. in physics from Swarthmore College and a Ph.D. in biomedical physics from Harvard University. He is a winner of Macalester's Excellence in Teaching award. Other books by the author: Understanding Nonlinear Dynamics Introduction to Scientific Computation and Programming
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This book, as the title suggest, focusses on a modeling/regression approach but still includes an appropriate discussion on null hypothesis testing and anova. This discussion comes near the end of the book once the modeling approach is clearly understood. It could easily be used as a first statistics book (which is how I would use it), but it could also follow a more traditional null hypothesis based first course.
The writing style is clear and easily followed, with just enough explanation to understand why something is being done, but without the mathematical notation that scares so many students away. A geometrical approach is taken to explain some of the more theoretical aspects. It's also just long enough to be useful, without intimidating students with an enormous number of pages. Chapters are generally short and there is a nice logical flow as you work through the book. The practical examples use R, are well thought out, and great care is taken to explain the output.
Probably not the book for an undergraduate statistician, but for other disciplines that need a good understanding of not only statistical practice, but also statistical thinking, I have yet to come across anything better. Indeed I am so impressed with this book that I felt compelled to write my first ever Amazon review.