Improve the accuracy of predictions with advanced regression procedures


IBM SPSS Regression enables you to predict categorical outcomes and apply various nonlinear regression procedures. You can apply the procedures to business and analysis projects where ordinary regression techniques are limiting or inappropriate — such as studying consumer buying habits, responses to treatments or analyzing credit risk. The software allows you to expand the capabilities of SPSS Statistics for the data analysis stage in the analytical process.

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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.


Explore detailed information about this product, its uses and how it can help your business.

See how it works

See how powerful regression techniques can help you discover hidden relationships in your data.

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IBM SPSS Regression - Apply more sophisticated models to your data

Use the range of nonlinear modeling procedures in IBM® SPSS® Regression to apply more sophisticated models to your data, whether you work in business, academia or government.

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Features spotlight

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


Quickly develop reliable forecasts and predict trends using time-series data

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Streamlines the data preparation process so that you can get ready for analysis faster and reach more accurate conclusions

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Summarize IBM SPSS Statistics data in different styles for different audiences

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Estimate the sampling distribution of an estimator by resampling with replacement from the original sample

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