Fundamentals of Biostatistics

Fundamentals of Biostatistics [Print Replica] [Kindle Edition]

Author: Bernard Rosner | Language: English | ISBN: B00B6FAZVC | Format: PDF, EPUB

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Fundamentals of Biostatistics
Download books file now Fundamentals of Biostatistics [Print Replica] [Kindle Edition] for everyone book mediafire, rapishare, and mirror link Bernard Rosner's FUNDAMENTALS OF BIOSTATISTICS is a practical introduction to the methods, techniques, and computation of statistics with human subjects. It prepares students for their future courses and careers by introducing the statistical methods most often used in medical literature. Rosner minimizes the amount of mathematical formulation (algebra-based) while still giving complete explanations of all the important concepts. As in previous editions, a major strength of this book is that every new concept is developed systematically through completely worked out examples from current medical research problems.
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• File Size: 45477 KB
• Print Length: 888 pages
• Publisher: Cengage Learning; 7 edition (September 24, 2013)
• Sold by: Cengage Learning
• Language: English
• ASIN: B00B6FAZVC
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• Amazon Best Sellers Rank: #360,691 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
  • #32 in Kindle Store > Kindle eBooks > Nonfiction > Professional & Technical > Professional Science > Biological Sciences > Biostatistics
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This is the second year that I've taught introductory public health statistics out of Rosner's book.

Pros:

* Good sections on probability and fundamentals of inference that show enough of the derivations to allow the better students to appreciate the mathematical basis, without making that the emphasis of the text.
* Includes a brief discussion of Bayesian inference and simple examples. This constitutes a very small fraction of the book, but is typically absent from introductory biostatistics texts.
* As much emphasis on poisson and binomial models as normal models. This is critical for public health students!
* Great section on epidemiologic methods in the final chapters, including logistic regression.
* Good coverage of exact methods and a short chapter on classical non-parametrics.
* Includes some discussion of missing data analysis.
* Hundreds of homework problems to choose from at the end of each chapter, with solution sets available online to registered course instructors.

Cons:

* The two-tailed p-value is always described as 2 times the one-tailed p-value, which is fine for symmetric sampling distributions but not so good for binomial and poisson distributions. This is a big source of confusion in my class, as it's inconsistent with R.
* An antiquated emphasis on calculating critical values for hypothesis tests, rather than using p-values or confidence intervals.
* The chapter on two-sample t-tests instructs students to assume equal variances if a formal hypothesis test for equal variances does not result in rejection of the null. This is ill advised and makes the entire chapter needlessly complicated.

I grew frustrated with this book and sought out other ones, but in the end I returned to this book, because it is more comprehensive and contains more examples. These are easily its biggest strengths. But there are two huge, immensely frustrating downsides:

1. The language is downright incomprehensible. It is overly technical; it is entirely math-speak; he will use symbols and equations over words any day, including in a regular paragraph. When he wants to explain a concept, he often does so by deriving it mathematically through a series of half a dozen equations instead of just explaining in plain english what the statistical test does. There are times when I'm exasperated from trying to read two dozen mathematical equations trying to understand his point, only to look in another textbook and realize it was something I already knew and to laugh -- he had literally made something I already knew completely unrecognizable.

2. The author's habit of referring to previous examples and equations by their numbers only, for example "Equation 6.14", without reminding you what they were about. Sometimes he refers to an example or equation all the way back in the previous chapter like this, like you were supposed to just have memorized "Equation 6.14". It requires you to flip back and forth a whole lot.

For example in the first two sentences of section 7.8 ... well into chapter 7 ... he writes, "One limitation of the methods of interval estimation in Section 6.5 is that it is difficult to make direct statements such as Pr(c1 < ' < c2) = 1 ' '. Instead, we have made statements such as Equation 6.7." Of course, he doesn't remind you what Equation 6.7 is. You're just supposed to remember equation 6.7 and section 6.5, which were in the previous chapter.

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