Applied Logistic RegressionWiley, 31. jul. 1989 - 328 sider Shows how to model a binary outcome variable from a linear regression analysis point of view. Develops the logistic regression model and describes its use in methods for modeling the relationship between a dichotomous outcome variable and a set of covariates. Following establishment of the model there is discussion of its interpretation. Several data sets are the source of the examples and the exercises, and a number of software packages are used to analyze data sets, including BMDP, EGRET, GLIM, SAS, and SYSTAT. |
Indhold
Introduction to the Logistic Regression Model | 1 |
The Multiple Logistic Regression Model | 25 |
Interpretation of the Coefficients of | 38 |
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analysis Appendix assess best subsets calculated Chapter chi-square coding computed confidence interval confounding constant covariance matrix 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 regression estimated odds ratio estimated probabilities estimated standard errors example fitted model fitted values given in Table goodness-of-fit independent variable interaction term likelihood function likelihood ratio test linear regression log odds log-likelihood 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 results of fitting risk factor sample scale shown in Table significance slope coefficients SMOKE ẞ₁ step stepwise stratum subjects test statistic univariate variance versus Wald statistics Wald test zero β₁