Applied Logistic Regression: Course Outline
- Fitting simple and multiple Logistic Regression Models using MLE.
- Non-iterative weighted Least Squares and discriminant functional analysis methods.
- Polytomous and Continuous Independent Variables.
- Multivariate Case.
- Interaction and Confounding.
- Estimation of odds ratios in the presence of interaction.
- Model-building Strategies and Methods for Logistics Regression.
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