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Sample Size Determination and Power
J**M
Great coverage of the topic
Great review of the topics covered. Comprehensive in that the topics are explored from several angles and the author comments on many of the considerations that come up in practice. My background is engineering, and I have taken several graduate level stats courses, but the topic of sample size always seems to get put on the back burner. I purchased the book to fill in gaps in my own understanding, and the book was perfect for that. The only improvement I would suggest is to have more examples or problems to work out at the end of the chapters. If I could give this 4.9 stars, I would. This might not be a great book for someone who is new to stats.
A**R
Very useful, whenever someone ask me about sample size
Very useful, whenever someone ask me about sample size, now, I have a place to start and it great to have it in the computer
T**S
Recommended for applied statisticians
"How large should my sample be?" is a question that is often put to applied statisticians. This book is a valuable resource for answering this question. Of course there are web sites that will do the calculation, but, in my experience, they often do not give me much understanding of the theory that underpins the calculation. In this book, Ryan blends theory, practical advice, a guide to software and an overview of the research literature - he has lots of references. There are exercises for each chapter too - with answers to selected exercises. Right now I am looking for information on sample size problems for repeated measures designs: Ryan has some helpful pages on this not-so-standard topic. Also, Ryan writes well. I recommend this book to any applied or industrial statistician: it will be a good addition to your professional library. Finally, I like the size of the book (374 pages) - good for reading on the train. "Sample size determination and power" should be in every university library.
A**R
New sights on sample size topics- like non-inferirity design
I could not find anything new and detailed. New sights on sample size topics- like non-inferirity design, simulation, interim analysis required the industry applications were completely missed.
A**R
Much below expectations
Much below expectations. No formula give for many situations.
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