What’s in the SPSS Regression module?

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:

• Multinomial logistic regression: Predict categorical outcomes with more than two categories
• Binary logistic regression: Easily classify your data into two groups
• Nonlinear regression and constrained nonlinear regression (CNLR): Estimate parameters of nonlinear models
• Weighted least squares: Gives more weight to measurements within a series
• Two-stage least squares: Helps control for correlations between predictor variables and error terms
• Probit analysis: Evaluate the value of stimuli using a logit or probit transformation of the proportion responding
• Operating systems supported: Windows, Mac, Linux

Note, there are also some regression techniques in SPSS Base as standard. SPSS Base includes the Automatic Linear Models procedure as well as Linear Regression, Curve Estimation, Partial Least Squares and Ordinal regression.