SPSS Regression enables you to predict categorical outcomes and apply a wide range of nonlinear regression procedures. Effective 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.

With SPSS Regression software, you can expand the capabilities of 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.

Use SPSS Regression Models for:

Multinomial logistic regression (MLR)
Predict categorical outcomes with more than two categories. Free of constraints such as yes/no answers, MLR allows you to model
which factor predicts if the customer buys product A, B, or C.


  • Stepwise function in MLR: Save time and easily find the best predictors for your data

    • Choose from four methods for choosing predictors: forward entry, backward elimination, forward stepwise, and backward stepwise.

    • Use Score and Wald methods for a faster and more accurate conclusion for variable selection .

  • Apply a highly scalable, high-performance algorithm to handle big datasets.

  • Save time by specifying the reference category in your outcome variable in the user interface. You no longer need to recode the dependent variable set up in the desired reference category.

  • Use Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to better assess model fit.

Binary logistic regression (BLR)
Predict dichotomous variables such as buy or not buy, vote or not vote. This procedure offers many stepwise methods to select the main and interaction effects that best predict your response variable.

Nonlinear regression (NLR) and constrained
nonlinear regression (CNLR)

Get control over your model and your model expression. These procedures give you two methods for estimating parameters of non-linear models.

Weighted least square regression (WLS)

Give more weight to measurements within a series.

Probit analysis (PROBIT)

Analyze potency of responses to stimuli, such as medicine doses, prices, or incentives. Probit evaluates the value of the stimuli using a logit or probit transformation of the proportion responding.