Introduction to GLM in R: Binary, Multinomial, and Ordinal Logistic Regression (Part 2) скачать в хорошем качестве

Introduction to GLM in R: Binary, Multinomial, and Ordinal Logistic Regression (Part 2)

These videos provide a tutorial on estimating models for categorical dependent variables in R. Generalized linear models (GLM), as the name implies, generalize ordinary linear regression. Common applications include binary (two categories), multinomial (3+ unordered categories) and ordinal regression (3+ ordered categories). Part 1 focuses on binary logistic regression, from model estimation to evaluating fit (e.g., likelihood ratio test, Hosmer-Lemeshow goodness-of-fit) and classification (e.g., area under the curve). Part 2 continues with a focus on multinomial and ordinal logistic regression. Data & Script File: https://drive.google.com/drive/folder... Website: https://westdesignanalysis.com/ Refs/Recs: Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (3rd ed.). John Wiley & Sons. Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society, 135(3), 370-384.