BookReviewLogisticRegression.docx - Book review of “Logistic regression a self-learning text” 3rd edition David G Kleinbaum and Mitchell Klein

BookReviewLogisticRegression.docx - Book review of...

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Book review of “Logistic regression: a self-learning text” 3 rd edition David G. Kleinbaum and Mitchell Klein Springer Table of Contents Introduction to Logistic regression Important special cases of the logistic model Computing the odds ration in logistic regression Maximum likelihood techniques: an overview Statistical techniques using maximum likelihood techniques Modelling strategy guidelines Modelling strategy for assessing interaction and confounding Additional modelling strategy issues Assessing goodness of fit for logistic regression Assessing discriminatory performance of a binary logistic model: ROC curves Analysis of matched data using logistic regression Polytomous logistic regression Ordinal logistic regression Logistic regression for correlated data: GEE GEE examples Other approaches for analysis of correlated data Readership Researchers; undergraduate students; graduate students The third edition of this book continues the tradition of the authors of a two-column book that really does act as a self-learning text. The left-hand column is like a collection of Powerpoint
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Unformatted text preview: slides, including generic-style computer output and diagrams to visualise the relationship between concepts. Each chapter contains about 10 exercises, some routine calculation and some asking for explanation of particular points. Answers are provided immediately. Tests consist of about 20 multiple choice questions and about 15 longer questions that mirror the exercises. There are fewer multiple choice questions as the chapters progress, and answers are given at the back of the book. The reference list includes abut 40 items and has been updated to include publications up to 2008. The authors’ website ( ) provides links to the data sets that are used for illustration throughout the book. Alice Richardson: [email protected] Faculty of Information Sciences and Engineering University of Canberra ACT 2601, Australia...
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