In the previous lesson, we dealt with basic topics of logistic regression. Recall that logistic regression is a special type of regression where the probability of "success" is modeled through a set of predictors. The predictors may be categorical, nominal or ordinal, or continuous. Logit or log odds of success is assumed to be a linear function of the predictors and such a model is fitted. This lesson deals with continuous covariates and model diagnostics for logistic regression.
- Objective 7.1
Explain the role that logistic regression model residuals play in assessing the model fit and use them to measure evidence against the model.
- Objective 7.2
Assess the relative importance of multiple predictors when fitting a logistic regression model
- Objective 7.3
Explain what overdispersion means in the logistic regression model and account for it when making conclusions.
- Objective 7.4
Interpret sensitivity and specificity in the context of logistic regression and use them to assess the predictive power of the model.