Applied Logistic Regression AnalysisThe focus in this Second Edition is again on logistic regression models for individual level data, but aggregate or grouped data are also considered. The book includes detailed discussions of goodness of fit, indices of predictive efficiency, and standardized logistic regression coefficients, and examples using SAS and SPSS are included.
Updated coverage of unordered and ordered polytomous logistic regression models. |
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BELIEF4 BTBEL calculate categorical variable collinearity conditional mean conditional probabilities contingency table covariate pattern DBETA degrees of freedom deleted delinquent friends dent variable dependent vari dependent variable design variables deviance residual dichotomous dependent variable EDF5 estimated ETHN exposure to delinquent Figure frequency of marijuana goodness-of-fit Hosmer and Lemeshow independent indices of predictive Intercept linear regression log likelihood logistic regres logistic regression analysis logistic regression coefficients logistic regression model logit(Y males marijuana user measures Model Fitting Nagelkerke nonlinear nonusers normal distribution null hypothesis number of errors observed value odds ratio Omnibus Tests output parameters Pearson plot PMRJ5 Predicted Probabilities predicted values prediction model predictive efficiency predictors prevalence of marijuana reference category regression analysis regression equation relationship sample SAS PROC LOGISTIC sion SPSS LOGISTIC REGRESSION SPSS NOMREG standard deviation standard errors standardized coefficients statistically significant stepwise Studentized residual sum of squares tic regression
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Page 110 - SAS/STAT User's Guide, Version 6. 4th Ed. Vols. 1 and 2. Cary, NC: SAS Institute, Inc.: 1989.