Categorical data analysis using the SAS system
Discusses hypothesis testing strategies for the assessment of association in contingency tables and sets of contingency tables. Also discusses various modeling strategies available for describing the nature of the association between a categorical outcome measure and a set of explanatory variables.
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Alternative Hypothesis DF ANOVA Table categorical data CATMOD procedure cell Chapter chi-square distribution Coefficient computed contains contingency table contrast CONTROLLING covariance matrix data set degrees of freedom DF Chi-Square Prob DF Value Prob displayed in Output drug explanatory variables females Fisher's Exact Test Frequency gender GENMOD goodness-of-fit statistics Hypothesis DF Value input interaction levels linear logistic regression loglinear model main effects model males Mantel-Haenszel Statistics marginal Maximum Likelihood Estimates Mean Scores Differ model matrix MODEL statement Nonzero Correlation null hypothesis observations odds ratio option outcome parameter estimates patients Phi Coefficient placebo Prob INTERCEPT PROC FREQ PROC LOGISTIC repeated measures RESIDUAL response functions response variable Row Mean Scores sample SAS System saturated model score statistic significant specify Square Prob standard Statistic Alternative Hypothesis subjects SUMMARY STATISTICS Table Scores TABLE Source DF Total treatment VARIANCE TABLE Wald weight count weighted least squares Yes Yes