datawithstata

Module 7: Choosing Between Logistic, Log-Binomial, Poisson, and Probit Models STATA playlist Open the full YouTube playlist This module teaches model choice as a practical decision rather than a theoretical contest. Your uploaded comparative notes distinguish logistic regression for odds ratios, log-binomial regression for direct risk ratios, Poisson regression with robust standard errors as a practical workaround, and probit regression as a latent-variable probability model. A particularly valuable part of your teaching notes is the real mistreatment example, where a log-binomial model was attempted but failed to converge. That example makes the teaching point clear: students must learn to balance interpretability, outcome prevalence, and whether the model actually behaves well in the data. Decision framework Logistic : stable and widely used when odds ratios are acceptable. Log-binomial : attractive for direct risk ratios, but convergence can be fragile. Poisson with...

Module 7: Choosing Between Logistic, Log-Binomial, Poisson, and Probit Models STATA playlist Open the full YouTube playlist This module teaches model choice as a practical decision rather than a theoretical contest. Your uploaded comparative notes distinguish logistic regression for odds ratios, log-binomial regression for direct risk ratios, Poisson regression with robust standard errors as a practical workaround, and probit regression as a latent-variable probability model. A particularly valuable part of your teaching notes is the real mistreatment example, where a log-binomial model was attempted but failed to converge. That example makes the teaching point clear: students must learn to balance interpretability, outcome prevalence, and whether the model actually behaves well in the data. Decision framework Logistic : stable and widely used when odds ratios are acceptable. Log-binomial : attractive for direct risk ratios, but convergence can be fragile. Poisson with...