Applied Logistic Regression
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.
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The Multiple Logistic Regression Model
Interpretation of the Coefficients of
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additional analysis Appendix application approach assess associated assumption calculated Chapter coding computed conditional confounding consider constant continuous controls covariate patterns defined degrees of freedom denote dependent described design variables dichotomous difference discussed distribution effect equal equation estimated coefficients estimated standard errors examine example expected expression factor Figure follows function illustrate important independent variable interaction interpretation interval levels likelihood ratio test linear regression log-likelihood logistic regression model matched matrix maximum likelihood estimates mean measure method model containing multivariate normal noted observed obtained odds ratio outcome variable p-value package parameters plot possible present probability problem RACE reference risk sample scale selection shown significance similar situation slope SMOKE statistics step stratum subjects Table term univariate variance versus weight yields zero