Primer of Applied Regression & Analysis of Variance Review

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(More customer reviews)Like all advanced stats books, this one has mathematical rigor and plenty of examples. But unlike the others, this one is written from the point of view of a biologist. You won't just learn the math, you'll learn how to make sense of the results. The title is a bit misleading. This is not a "primer" of statistics. But once you've learned the basic principles of statistics, this is THE book to learn about various kinds of ANOVAS and regressions.

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Applicable for all statistics courses or practical use, teaches how to understand more advanced multivariate statistical methods, as well as how to use available software packages to get correct results. Study problems and examples culled from biomedical research illustrate key points. New to this edition: broadened coverage of ANOVA (traditional analysis of variance), the addition of ANCOVA (analysis of Co-Variance); updated treatment of available statistics software; 2 new chapters (Analysis of Variance Extensions and Mixing Regression and ANOVA: ANCOVA). (20010101)

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Survival Analysis for Epidemiologic and Medical Research (Practical Guides to Biostatistics and Epidemiology) Review

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(More customer reviews)Ah, the shudder when a professor assigns his own book. But never fear, Steve Selvin, recipient of numerous teaching awards, is not publishing a book to line the faded jeans pocket, or to swell his balding curly head. Rather, it's just a practical way of printing his lecture notes and homeworks. Furthermore, its rare that a top research university professor has time to write textbooks, so the burden falls to the mediocre. Selvin somehow manages to find the time and save us from so many other books that screw up statistics.

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This practical guide shows why the analytic methods work and how to effectively analyze and interpret epidemiologic and medical survival data with the help of modern computer systems. The introduction presents a review of a variety of statistical methods that are not only key elements of survival analysis but are also central to statistical analysis in general. Techniques such as statistical tests, transformations, confidence intervals, and analytic modeling are presented in the context of survival data but are, in fact, statistical tools that apply to understanding the analysis of many kinds of data. Similarly, discussions of such statistical concepts such as bias, confounding, independence, and interaction are presented in the context of survival analysis as well as the basic components of a broad range of applications.

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