Categorical Logistic Regression Interpretation. Generalized linear model logistic regression linear regression binary responses categorical factors. The explanatory variables may be continuous or with dummy variables discrete.
For example lets say you want to figure out if smoking more cigarettes increases. One possible way to interpret them is to get back to the definition of a logistic. Probability expXb1 expXb Where Xb is the linear predictor.
What is being assessed by the test of the intercept is whether that probability is 50.
Logistic regression fits. Then we assess the logistic regression model and consider issues such as factor assumptions separation and fitting the model. That is it is a model that is used to predict the probabilities of the different possible outcomes of a categorically distributed dependent variable given a set of independent variables which may be real. We conclude with other types of logistic regression.
