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 enables you to expand the capabilities of SPSS Statistics for the data analysis stage of the analytical process.

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.

Key features

  • Weighted least squares
  • Probit analysis
  • Predictor selection
  • Two-stage least square regression

Product images

Weight Estimation variable selection
Weight Estimation variable selection
Weight Estimation options
Weight Estimation options
Probit Analysis options
Probit Analysis options
Probit Analysis variable selection
Probit Analysis variable selection
Logistic Regression variable selection
Logistic Regression variable selection

See how it works

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