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Applied logistic regression
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1990
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Quantitative MethodsEngineeringMachine LearningLogistic Regression ModelsPredictive AnalyticsStatistical FoundationLogistic RegressionBiostatisticsStatistical InferenceRegression AnalysisApplied Logistic RegressionPublic HealthStatistical ModelingStatisticsMedical StatisticQuantitative ManagementLogistic Regression Model
The book expands logistic regression from biostatistics and epidemiology to data mining and machine learning, guiding readers through dichotomous data modeling across diverse fields. It introduces new topics and expands existing material, supported by numerous real‑world examples and freely available datasets online. The revised edition is widely praised as an accessible, well‑written, comprehensive guide to logistic regression, updated with recent advances and software tools.
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 Statistician In 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. Ample new topics and expanded discussions of existing material are accompanied by a wealth of real-world examples-with extensive data sets available over the Internet.