## Applied Logistic Regression Analysis, Volume 106; Volume 2002The focus in this - More detailed consideration of grouped as opposed to case-wise data throughout the book
- Updated discussion of the properties and appropriate use of goodness of fit measures, R-square analogues, and indices of predictive efficiency
- Discussion of the misuse of odds ratios to represent risk ratios, and of over-dispersion and under-dispersion for grouped data
Updated coverage of unordered and ordered polytomous logistic regression models. |

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BELIEF4 bivariate BTBEL calculate categorical variable coefficient of determination collinearity conditional mean conditional probabilities contingency table covariate pattern DBETA degrees of freedom deleted delinquent friends dent variable dependent variable design variables deviance residual dichotomous dependent variable EDF5 effect equal estimated ETHN exposure to delinquent Figure frequency of marijuana goodness-of-fit Hosmer and Lemeshow included independent vari 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 fits 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 proportion reference category 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 unstandardized

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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.