Applied Logistic Regression: Course Outline

• Fitting simple and multiple Logistic Regression Models using MLE.
• Non-iterative weighted Least Squares and discriminant functional analysis methods.
• Dichotomous.
• 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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