Apply more sophisticated models with IBM SPSS Regression software’s wide range of nonlinear modeling procedures.
MLR: Predict categorical outcomes with more than two categories. Free of constraints such as yes or no answers, MLR allows you to model which factor predicts whether the customer buys product A, B or C.
Choose from four methods for selecting predictors: Forward entry, backward elimination, forward stepwise and backward stepwise
– Stepwise function in MLR: Save time and more easily find the best predictors for your data.
– Use Score an d Wald methods for a faster and more accurate conclusion for variable selection.
– Apply a highly scalable, high-performance algorithm to handle big data sets.
– 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 AIC and 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 can best predict your response variable.
NLR and CNLR: Get control over your model and your model expression. These procedures give you two methods for estimating parameters of nonlinear models.
Weighted least square regression (WLS): Give more weight to measurements within a series.
Probit analysis: 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.
Logistic Regression dialog box
Predict the presence or absence of a characteristic or outcome based on values of a set of predictor variables
Contingency Table for Hosmer-Lemeshow statistic