Logistic Regression: Model Interpretation and Comparison

This lecture covers the application of logistic regression in statistical modeling, focusing on interpreting model parameters and assessing model fit. Topics include the use of indicator variables for categorical predictors, the logit transformation, odds ratios, and deviance as a measure of model quality. The instructor explains how to estimate probabilities and odds for different scenarios, conduct inference tests for coefficients, and compare models using likelihood ratio tests. The lecture also delves into the importance of deviance in logistic regression and the significance of model comparison in statistical analysis.