## Applied logistic regressionFrom 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. |

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#### LibraryThing Review

User Review - bluetyson - LibraryThingA good book that certainly has practical application. It details the rise in use of this particular technique, and where it is applicable. Also details multiple varieties including multinomial and others. This is definitely a mathematics text that is worth the time to take a look at. Read full review

### Contents

Introduction to the Logistic Regression Model | 1 |

The Multiple Logistic Regression Model 2 5 | 25 |

Interpretation of the Coefficients of | 38 |

Copyright | |

18 other sections not shown

### Common terms and phrases

analysis assess best subsets calculated Chapter chi-square coding computed confidence interval confounder covariate patterns data set degrees of freedom denote design variables DETC deviance diagnostic statistics dichotomous variable distribution equation Error Coeff./SE estimated coefficients estimated logistic probabilities estimated logistic regression estimated odds ratio estimated probabilities estimated standard errors example fitted model fitted values given in Table goodness-of-fit independent variable interaction term leverage likelihood function likelihood ratio test linear regression log odds log-likelihood log-odds ratio logistic regression coefficients logistic regression model logistic regression software logit difference logit model low birth weight main effects matched design matrix maximum likelihood estimates method model containing multivariate model obtained outcome variable p-value parameters plot polytomous quartile regression program residual results of fitting risk factor sample saturated model scale shown in Table significance slope coefficients SMOKE step stepwise stratum subjects univariate variance versus Wald statistics Wald test weighted least squares zero