What it can do for your business
IBM SPSS Regression enables you to predict categorical outcomes and apply various nonlinear regression procedures. You can use these procedures for business and analysis projects where ordinary regression techniques are limiting or inappropriate. This includes studying consumer buying habits, responses to treatments or analyzing credit risk. The solution helps you expand the capabilities of SPSS Statistics for the data analysis stage of the analytical process.
This module is included in the SPSS Standard, Professional and Premium packages. It is available at an additional cost for the Base package.
Use more than two categories
Use multinomial logistic regression to free you from constraints such as yes/no answers.
Classify your data into two groups
Apply binary logistic regression to predict dichotomous variables such as buy or not buy and vote or not vote.
Gain more control over models
Use constrained and unconstrained nonlinear regression procedures for model control. For example, specify constraints on parameter estimates or get bootstrap estimates of standard errors.
- Binary logistic regression
- Logit response models
- Multinomial logistic regression
- Nonlinear regression
- Probit response analysis
- Two stage least squares
- Weighted least squares
- Quantile regression