When the dependent variable is categorical it is often possible to show that the relationship between the dependent variable and the independent variables can be represented by using a logistic regression model. Using such a model the value of the dependent variable can be predicted from the values of the independent variables.
We review here binary logistic regression models where the dependent variable only takes one of two values. In Multinomial and Ordinal Logistic Regression we look at multinomial and ordinal logistic regression models where the dependent variable can take 2 or more values.
We also review a model similar to logistic regression called probit regression.
- Basic Concepts
- Finding Coefficients using Excel’s Solver
- Significance Testing of Logistic Regression Coefficients
- Testing Fit of the Logistic Regression Model
- Finding Coefficients using Newton’s Method
- Handling Categorical Coding
- Comparing Logistic Regression Models
- Hosmer-Lemeshow Test
- Classification Table
- ROC Curve
- Real Statistics Logistic Regression Functions
- Probit Regression