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Master CDA and GLM applications
·
Module 9
Multinomial and Ordinal Logistic Regression
1
Understanding Multinomial Logistic Regression: When and How
Explain when multinomial logistic regression is appropriate and describe its structure, including the role of a reference category.
2
Interpreting Multinomial Logistic Regression Coefficients
Fit and interpret the coefficients from a multinomial logistic regression model.
3
Understanding and Applying Ordinal Logistic Regression
Explain when ordinal logistic regression is appropriate and describe the proportional odds (cumulative logit) model.
4
Interpreting Ordinal Logistic Regression Coefficients
Interpret the coefficients of an ordinal logistic regression model in terms of cumulative odds.
5
Testing the Proportional Odds Assumption
State and test the proportional odds assumption (e.g., using a Brant test).
6
Modeling Strategies for Violating Proportional Odds
Describe alternative modeling strategies for when the proportional odds assumption is violated.
Previous module
Binary Logistic Regression
Next module
Conditional Logistic Regression for Matched Data