Week 6

Multinomial Regression

Materials for meeting on

February 27, 2023

Before Class

Articles

  • https://bookdown.org/chua/ber642_advanced_regression/multinomial-logistic-regression.html#introduction-to-multinomial-logistic-regression

  • McNulty, K. (2021). Handbook of Regression Modeling in People Analytics: With Examples in R and Python. Multinomial Regression.

    • Describe the difference between a stratified versus a multinomial approach to modeling an outcome with more than two nominal categories.

    • Describe how you would interpret the odds ratio of an input variable for a given category in a stratified modeling approach.

    • Describe what is meant by the ‘reference’ of a multinomial logistic regression model with at least three nominal outcome categories.

    • Describe how you would interpret the odds ratio of an input variable for a given category in a multinomial modeling approach.

    • Given a multinomial logistic regression model with outcome categories A, B, C and D and reference category A, describe two ways to determine the coefficients of a multinomial logistic regression model with reference category C.

    • Describe a process for safely simplifying a multinomial logistic regression model by removing input variables.