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Stat Labs: Mathematical Statistics Through Applications Review
I took my introductory mathematical statistics course from Brad Efron and Charles Stein at Stanford. It gave me a strong foundation and understanding of decision theory, hypothesis testing and the power of exponential families of distributions. Although I understood the Neyman - Pearson theory of hypothesis testing and confidence intervals, I was not completely comfortable applying what I had learn when confronted with real problems. Students even at the best universities in the US still need consulting experience before they are comfortable with the tools they learn.
That is why this book so intrigued me. It approaches the theory and methods from applications first. Each chapter poses an interesting real problem and then progresses to a solution, introducing only the necessary tools. Many important statistical tools and topics are covered this way. It is particularly good for students interested in biostatistics as many of the applications fall in that area. In fact the subject of maternal smoking and infant health is treated in Chapter 1 and revisited in Chapter 10 with additional data to consider.
It is designed for an advanced undergraduate course for statistics majors and has been successfully implemented by the authors at Berkeley. I think this can really work and make mathematical statistics interesting. However, the student should not think that he or she can come out of this class and jump right into consulting. The biggest problem in consulting is working with the client to pose a well formulated problems that address their questions. In this text the authors have already done that long and difficult task for us and we are left in a position to learn the subject matter and the statistical tools needed for the solution.
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