1. Introduction & overview
1.1 Overview: Categorical data & graphics
1.2 Discrete distributions
1.3 Testing association
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berkeley-freq.sas
sexfun-cmh.sas
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sexfun-chisq.R []
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2. Two-way and n-way tables
2.1 2 x 2 tables
2.2 Two-way tables
2.3 Observer agreement
2.4 Correspondence analysis
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berk-4fold.sas
vision-sieve.sas
msdiag-agree.sas
mental-ca.sas
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berk-4fold.R []
vision-sieve.R []
msdiag-agree.R []
mental-ca.R []
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3. Mosaic displays & loglinear models
3.1 n-way tables: Models & graphs
3.2 Mosaics software
3.3 Structured tables
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berkeley-glm.sas
titanic-loglin.sas
mentgen2.sas
vision-quasi.sas
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berkeley-glm.R []
titanic-loglin.R []
mental-glm.R []
vision-quasi.R []
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4. Logit models & logistic regression
4.1 Logit models
4.2 Logistic regression models
4.3 Effect plots
4.4 Influence & diagnostic plots
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berkeley-logit.sas
arthritis-logistic.sas
cowles-logistic.sas
Arrests-logistic.sas
cowles-effect.sas
Arrests-effect.sas
berkeley-diag.sas
berkeley-diag-ods.sas
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berkeley-logit.R []
arthritis-logistic.R []
cowles-logistic.R []
Arrests-logistic.R []
cowles-effect.R []
Arrests-effects.R []
berkeley-diag.R []
arthritis-diag.R []
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5. Polytomous response models
5.1 Proportional odds models
5.2 Nested dichotomies
5.3 Generalized logits
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arthritis-propodds.sas
arthritis-propodds-ods.sas
wlf-nested.sas
wlf-glogit.sas
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arthritis-propodds.R []
wlf-nested.R []
wlf-glogit.R []
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