## 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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### Contents

1 Introduction to the Logistic Regression Model | 1 |

2 Multiple Logistic Regression | 31 |

3 Interpretation of the Fitted Logistic Regression Model | 47 |

4 ModelBuilding Strategies and Methods for Logistic Regression | 91 |

5 Assessing the Fit of the Model | 143 |

6 Application of Logistic Regression with Different Sampling Models | 203 |

### Common terms and phrases

95 percent confidence analysis assess benign breast disease best subsets binary Chapter clinical coded computed confidence interval constant continuous covariates correlation covariance matrix covariate patterns data set degrees-of-freedom denote design variables diagnostic statistics discussed distribution equation estimated coefficients estimated logistic probability estimated odds ratios estimated probability estimated standard errors example fitted model fitted values fractional polynomial goodness-of-fit Hosmer independent variable interaction interpretation interval estimates Lemeshow likelihood function likelihood ratio test linear regression log odds log-likelihood logistic model logistic regression model low birth weight main effects mammography maximum likelihood estimates methods model containing model fit model in Table NDRGTX observed obtained outcome variable p-value parameters plot population average model previous drug treatments RACE results of fitting sample Score test Section selection shown in Table significance slope coefficients SMOKE standard errors STATA step stepwise stratum subjects tion univariable variance versus Wald statistics Wald test zero