Applied logistic regression analysis
Emphasizing the parallels between linear and logistic regression, Scott Menard explores logistic regression analysis and demonstrates its usefulness in analyzing dichotomous, polytomous nominal, and polytomous ordinal dependent variables. The book is aimed at readers with a background in bivariate and multiple linear regression.
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Summary Statistics for Evaluating the Logistic
Interpreting the Logistic Regression
An Introduction to Logistic Regression
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BELIEF4 bivariate BTBEL calculate categorical variable chi-square classification tables collinearity conditional mean conditional probabilities contingency tables DBETA degrees of freedom deleted delinquent friends dependent variable design variables deviance residual dichotomous dependent variable DIFDEV DRGTYPE EDF5 equal estimated ETHN ethnicity exposure to delinquent Figure frequency of marijuana Hosmer and Lemeshow independent indices of predictive interaction terms intercept lambda-p linear regression Log Likelihood log-likelihood logistic regression analysis logistic regression coefficients logistic regression model logit(PMRJ5 logit(y marginal distributions marijuana user nonlinear nonusers normal distribution null hypothesis number of errors observed value odds ratio one-unit change ordinal parameters plot PMRJ5 polytomous logistic regression predicted probabilities predicted values prediction model predictive efficiency predictors prevalence of marijuana proportion reference category regression equation relationship sample SAS PROC LOGISTIC Schwartz criterion SPSS LOGISTIC REGRESSION standard deviation standard errors standardized coefficients standardized logistic regression statistically significant stepwise Studentized residual sum of squares tau-p Unstandardized