Name: Applied logistic regression
ISBN: 9780471356325

Applied logistic regression

From the reviews of the First Edition."An interesting, useful, and well-written book on logistic regression models... Hosmer and Lemeshow have used very little mathematics, have presented difficult concepts heuristically and through illustrative examples, and have included references."--Choice"Well written, clearly organized, and comprehensive... the authors carefully walk the reader through the estimation of interpretation of coefficients from a wide variety of logistic regression models... their careful explication of the quantitative re-expression of coefficients from these various models is excellent."--Contemporary Sociology"An extremely well-written book that will certainly prove an invaluable acquisition to the practicing statistician who finds other literature on analysis of discrete data hard to follow or heavily theoretical."--The StatisticianIn this revised and updated edition of their popular book, David Hosmer and Stanley Lemeshow continue to provide an amazingly accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets. Hosmer and Lemeshow extend the discussion from biostatistics and epidemiology to cutting-edge applications in data mining and machine learning, guiding readers step-by-step through the use of modeling techniques for dichotomous data in diverse fields.

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olooneyOct 2018

Some of the best textbooks in statistics and machine learning:

Applied -------

Hosmer et al., Applied Logistic Regression. An exhaustive guide to the perils and pitfalls of logistic regression. Logistic regression is the power tool of interpretable statistical models, but if you don't understand it, it will take your foot off (concretely, your inferences will be wrong and your peers will laugh at you.) This book is essential. Graduate level, or perhaps advanced undergraduate, intended for STEM and social science grad students.

https://www.amazon.com/Applied-Logistic-Regression-Probabili...

Peter Christen's Data Matching. Record Linkage is a relatively niche concept, so Christen's book has no right to be as good as it is. But it covers every relevant topic in a clear, even-handed way. If you are working on a record linkage system, then there's nothing in this book you can afford not to know. Undergraduate level, but intended for industry practitioners.

https://www.amazon.com/Data-Matching-Techniques-Data-Centric...

Max Kuhn's Applied Predictive Modeling. Even if you don't use R, this is an incredibly good introduction to how predictive modeling is done in practice. Early undergraduate level.

http://appliedpredictivemodeling.com/

Theoretical -----------

The Elements of Statistical Learning. Probably the single most respected book in machine learning. Exhaustive and essential. Advanced undergraduate level.

https://www.amazon.com/Elements-Statistical-Learning-Predict...

Kevin Murphy's Machine Learning: A Probabilistic Perspective. Covers lots of the same ground as Elements but is a little easier. Undergraduate level.

https://www.amazon.com/Machine-Learning-Probabilistic-Perspe...

Taboga's Lectures on Probability Theory and Mathematical Statistics. Has the distinction of being available for free in web-friendly format at https://www.statlect.com/ . Undergraduate level for math majors.

https://www.amazon.com/Lectures-Probability-Theory-Mathemati...

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singingfishJul 2018

Hosmer, Applied Logistic Regression.

Lots of applied stats problems are based on categorical data, and this book really helped me to understand the theory better.

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