IBM SPSS Regression (formerly called SPSS Regression) enables you to apply more sophisticated models to your data using its wide range of nonlinear regression models. You can apply IBM SPSS Regression, to many disciplines, including:
IBM SPSS Regression includes these procedures:
IBM SPSS Regression is available in English, Japanese, French, German, Italian, Spanish, Chinese, Polish, Korean, and Russian. Contact your local office to find out more.
Bootstrapping in IBM SPSS Statistics
Expand IBM SPSS Statistics Base's capabilities for the data analysis stage in the analytical process. Using IBM SPSS Regression with IBM SPSS Statistics Base gives you an even wider range of statistics so you can get the most accurate response for specific data types. You can seamlessly work in the IBM SPSS Statistics environment.
IBM SPSS Regression includes additional diagnostics for use when developing a classification table.
Multinomial logistic regression (MLR): Regress a categorical dependent variable with more than two categories on a set of independent variables. This procedure helps you 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.
You can also use stepwise functionality, including forward entry, backward elimination, forward stepwise or backward stepwise, to find the best predictor from dozens of possible predictors. If you have a large number of predictors, Score and Wald methods can help you more quickly reach results. You can access your model fit using Akaike information criterion (AIC) and Bayesian information criterion (BIC; also called Schwarz Bayesian criterion, or SBC).
The multinomial logistic regression procedure predicts a categorical outcome such as "primary reason for Web use." The categories in this example are: a) work only, b) shopping only, c) both working and shopping, and d) neither (reference category). From the results above, we can see that search engine use was a better predictor of "shopping only" than print media use.
Binary logistic regression: Group people with respect to their predicted action. Use this procedure 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). You can also use binary logistic regression 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. This procedure enables you to select the predictive model for dichotomous dependent variables.
With binary logistic regression, you can select variables using six types of stepwise methods, including forward (the procedure selects the strongest variables until there are no more significant predictors in the dataset) and backward (at each step, the procedure removes the least significant predictor in the dataset) methods. 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 constrained 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 IBM SPSS Statistics 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 enables you to:
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