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Statistical Tools for Epidemiologic Research, by Steve Selvin
Ebook Free Statistical Tools for Epidemiologic Research, by Steve Selvin
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In this innovative new book, Steve Selvin provides readers with a clear understanding of intermediate biostatistical methods without advanced mathematics or statistical theory (for example, no Bayesian statistics, no causal inference, no linear algebra and only a slight hint of calculus). This text answers the important question: After a typical first-year course in statistical methods, what next?
Statistical Tools for Epidemiologic Research thoroughly explains not just how statistical data analysis works, but how the analysis is accomplished. From the basic foundation laid in the introduction, chapters gradually increase in sophistication with particular emphasis on regression techniques (logistic, Poisson, conditional logistic and log-linear) and then beyond to useful techniques that are not typically discussed in an applied context. Intuitive explanations richly supported with numerous examples produce an accessible presentation for readers interested in the analysis of data relevant to epidemiologic or medical research.
- Sales Rank: #2610712 in Books
- Published on: 2011-01-14
- Original language: English
- Number of items: 1
- Dimensions: 6.50" h x 1.60" w x 9.40" l, 1.76 pounds
- Binding: Hardcover
- 512 pages
About the Author
Steve Selvin, PhD, is Professor and Head of Biostatistics at the School of Public Health, University of California, Berkeley.
Most helpful customer reviews
1 of 1 people found the following review helpful.
Book does not have problems for students
By Sarah Schwartz
The book is clearly written, and it uses both Stata and R (R code available on the text's website), but I didn't see any problems to assign to students in the book.
Currently I use statistics textbooks for teaching epidemiologic methods and data analysis that have terrific datasets and problems that come with them. The texts of the books that I use is only so-so, and not totally adapted for teaching epidemiology, so I wanted to consider a statistics book for epidemiology students. I sat down to start doing the problems in the book so that I could evaluate whether to switch to this book. I pulled out a pen and paper and my computer and skimmed the first chapter and liked it, but there were no problems to do. Thinking that maybe that was just the first chapter, I did the same with skimming the second chapter, and again, no problems, and skimming the rest, no problems. If I continued to do the examples in the book itself, perhaps I could think of extensions that work as problem sets, but I would hate to put so much work into the textbook and count on finding problems that are natural extension of the text: that is the author's job, and much easier for the author to do than for me because the author knows the examples really well already.
I really appreciate that the author published the Stata output and R code on the website. Some of the R code needs debugging because it refers to objects that aren't there. I don't want to put too much time into this book before deciding that I should just stick with what I have, but it seems that I will end up doing just that.
0 of 3 people found the following review helpful.
Deep math
By Tony
I took a course at the Johns Hopkins Bloomberg School of Public Health which recommended this book for the course. Thankfully, I got the Kindle version of the book and was able to follow along with the instructor who was madly writing extreme math formulas on a blackboard. The book is very heavy, and unless you are into logistic regression and other advanced stats, this book will be way too complex to understand.
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