IBM SPSS Regression

With IBM® SPSS® Regression software, you can expand the capabilities of IBM SPSS Statistics Base for the data analysis stage
in the analytical process.

  • Predict categorical outcomes with more than two categories using multinomial logistic regression (MLR).
  • Easily classify your data into groups using binary logistic regression.
  • Estimate parameters of nonlinear models using nonlinear regression (NLR) and constrained nonlinear regression (CNLR).
  • Meet statistical assumptions using weighted least squares and two-stage least squares. 
  • Evaluate the value of stimuli using probit analysis.

SPSS Regression Screenshots


 
Binomial regression parameter estimates

The parameter estimates table summarizes the effect of each predictor. The ratio of the coefficient to its standard error, squared, equals the Wald statistic.
If the significance level of the Wald statistic is small (less than 0.05) then the parameter is useful to the model.
The predictors and coefficient values shown in the last step are used by the procedure to make predictions.

 


Multinomial regression parameter estimates
The parameter estimates table summarizes the effect of each predictor.
The ratio of the coefficient to its standard error, squared, equals the Wald statistic.
If the significance level of the Wald statistic is small (less than 0.05) then the parameter is different from 0.
Parameters with significant negative coefficients decrease the likelihood of that response category with respect to the reference category.
Parameters with positive coefficients increase the likelihood of that response category.