Make better predictions with powerful regression procedures

SPSS Regression Models^TM empowers you to apply more sophisticated models with its wide range of non-linear modelling procedures.

SPSS Regression Models has the procedures you need to move beyond basic data analysis. These procedures include:

• Multinomial Logistic Regression (MLR): predict categorical outcomes with more than two categories

• Binary Logistic Regression: easily classify your data into two groups

• Nonlinear Regression (NLR) and Constrained Nonlinear Regression (CNLR): estimate parameters of non-linear models

More statistics for data analysis

Expand SPSS® Base's capabilities for the data analysis stage in the analytical process. Using SPSS Regression Models^TM with SPSS Base gives you an even wider range of statistics so you can get the most accurate response for specific data types. It easily plugs into SPSS Base so you can seamlessly work in the SPSS environment.

Statistical highlights for SPSS Regression Models:

Multinomial Logistic Regression: classify people into two or more groups. When a dependent variable includes two or more categories, the Multinomial Logistic Regression procedure gives you what's needed to accurately predict group membership within key groups. For example, a telecommunications company can build a model to predict if a customer will order caller ID, voice mail, three-way calling or multiple options. If the model predicts the customer is likely to order caller ID, it can send direct mail emphasizing caller ID to that customer. This means it won't waste resources advertising products or services that aren't likely to interest its customers.

Binary Logistic Regression: group people with respect to their predicted action. If you need to build models in which the dependent variable is dichotomous (for example, buy or not buy, pay or default, graduate or not graduate). Or maybe you want to predict the probability of events, such as solicitation responses or program participation. For example, a utility company wants to know what predictors indicate failure to pay bills so it can create special bill payment plans for customers needing assistance. The Binary Logistic Regression procedure empowers you to select the predictive model for dichotomous dependent variables.

Binary Logistic Regression gives you depth and flexibility to specify models and to choose predictor order inclusion. You can use six types of forward- or backward-stepwise methods to select variables. This enables you to tell the procedure to find the best variables. Because you can work forward (the procedure selects the strongest variables until there are no more significant predictors in the dataset) or backwards (at each step, the procedure removes the least significant predictor in the dataset), you have the flexibility to select predictors the way you want to work. You can also set inclusion or exclusion criteria. The procedure produces a report telling you the action it took at each step to determine your variables.

Nonlinear Regression (NLR) and Unconstrained Nonlinear Regression (CNLR): estimate nonlinear equations. If you are you working with models that have nonlinear relationships, for example, if you are predicting coupon redemption as a function of time and number of coupons distributed, estimate nonlinear equations using one of two SPSS procedures: Nonlinear Regression (NLR) for unconstrained problems and Constrained Nonlinear Regression (CNLR) for both constrained and unconstrained problems. NLR enables you to estimate models with arbitrary relationships between independent and dependent variables using iterative estimation algorithms. While CNLR empowers you to:

• Use linear and nonlinear constraints on any combination of parameters

• Estimate parameters by minimizing any smooth loss function (objective function)

• Compute bootstrap estimates of parameter standard errors and correlations

SPSS Regression Models gives you:

• Multinomial Logistic Regression

• Binary Logistic Regression

• Unconstrained Nonlinear Regression (NLR)

• Constrained Nonlinear Regression (CNLR)

• Two-Stage Least Squares

• Probit Analysis

What's new in SPSS Regression Models?

The following feature was added to the Multinomial Logistic Regression procedure, which regresses a categorical dependent variable with more than two categories on a set of independent variables, in SPSS Regression Models:

• Specify the reference category in the dependent variables

This feature was added to the Binary Logistic Regression procedure, which regresses a dichotomous dependent variable on a set of independent variables:

• XML export of the model

SPSS Regression Models system requirements

• SPSS® Base 

• 1MB hard drive space

• Other system requirements vary according to platform