A Course in Categorical Data Analysis
Developed from long experience in teaching categorical analysis to a multidisciplinary mix of undergraduate and graduate students, A Course in Categorical Data Analysis presents the easiest, most straightforward ways of extracting real-life conclusions from contingency tables. The author uses a Fisherian approach to categorical data analysis and incorporates numerous examples and real data sets. Although he offers S-PLUS routines through the Interest, readers do not need full knowledge of a statistical software package.
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TwobyTwo Contingency Tables
Simpsons Paradox and 23 Tables
The Madison Drug and Alcohol Abuse Study
8 other sections not shown
2x2 tables alcohol intake B2 test binomial distribution cell frequencies chi-squared colour column variables conclusions confidence interval Consider contingency table corresponding data in Table degrees of freedom denotes the number density Diagnosis No Yes discrimination equal estimated standard errors example explanatory variables Fisher's exact test freedom significance probability gender gives LRS hypertension independence of rows individuals interaction effects investigate log-measure-of-association logistic regression lurking variable maximum likelihood estimates measure of association methodology multinomial distribution Negative Predictive Value normal test statistics obesity parameters patients percentages Poisson distribution positive association Positive Predictive Value possess probability mass function procedure product binomial model prog2 progl Property r x s random sampling random variable replacement residual deviance row totals rows and columns screening Serum Cholesterol Simpson's paradox single multinomial model Splus commands standard normal distribution statistically significant substance abusers subtables three-directional approach unconditional cell probabilities zero