Practical guide to logistic regression

Statistical Models What Is a Statistical Model?  Basics of Logistic Regression Modeling  The Bernoulli Distribution  Methods of Estimation  SAS Code  Stata Code         Logistic Models: Single Predictor  Models with a Binary Predictor  Predictions, Probabilities, and Odds Ratios  Basic Model Statistics  Models with a Categorical Predictor  Models with a Continuous Predictor  Prediction  SAS Code  Stata Code         Logistic Models: Multiple Predictors  Selection and Interpretation of Predictors  Statistics in a Logistic Model  Information Criterion Tests  The Model Fitting Process: Adjusting Standard Errors  Risk Factors, Confounders, Effect Modifiers, and Interactions  SAS Code  Stata Code         Testing and Fitting a Logistic Model  Checking Logistic Model Fit  Classification Statistics  Hosmer-Lemeshow Statistic  Models with Unbalanced Data and Perfect Prediction  Exact Logistic Regression  Modeling Table Data  SAS Code Stata Code  Reference         Grouped Logistic Regression  The Binomial Probability Distribution Function  From Observation to Grouped Data  Identifying and Adjusting for Extra Dispersion  Modeling and Interpretation of Grouped Logistic Regression  Beta-Binomial Regression  SAS Code  Stata Code  References         Bayesian Logistic Regression  A Brief Overview of Bayesian Methodology  Examples: Bayesian Logistic Regression  SAS Code  Stata Code Concluding Comments  References