The IBM SPSS Regression module contains a wide range of nonlinear regression models that augment the linear regression functionality in SPSS Base. Regression is a family of classical predictive techniques all of which involve fitting (or regressing) a line or curve to a series of observations in order to model effects or predict outcomes.
IBM SPSS Regression is often used in situations where the Linear Regression functionality in SPSS Statistics Base is either inappropriate or is too simplistic. Logistic Regression is a very widely-used technique for predicting categorical outcomes.
You can apply IBM SPSS Regression to many business and analysis projects where ordinary regression techniques are limiting or inappropriate: for example, studying consumer buying habits or responses to treatments, measuring academic achievement, and analyzing credit risks.
IBM SPSS Regression includes the following procedures: