| Simple Linear Regression |
Used when both the dependent variable and the independent variable are continuous variables and there is a linear relationship between them. |
| Multiple Linear Regression |
Used when the dependent variable is continuous and we have more than one independent variable in a linear model equation. |
| Simple Logistic Regression |
Used when the dependent variable is binary (dichotomous outcome) and we have a single continuous or categorical independent variable. |
| Multivariate Logistic Regression |
When the dependent variable is a binary variable and we have more than one independent variable in a relation. The independent variables can be continuous variables or categorical variables. It is an extension of Logistic Regression. |
| Multinomial Regression |
When the dependent variable has more than two categories and we have more than one independent variable in a relation. |
| Ordinal Regression |
When the dependent variable is an ordinal variable and we have more than one independent variable in a relation. |
| Poisson Regression |
When the dependent variable is a count variable and we have more than one independent variable in a relation. |
| Probit Regression |
An alternative approach to logistic regression models using normal cumulative distributions, normally applied when handling binary dependent variables. |
| Nonlinear Regression |
Used when the analytical correlation pattern parameters run in curves or clusters that cannot be properly mapped with straight linear parameters. |
