Improve the accuracy of predictions with advanced regression procedures

IBM® SPSS® Regression software enables you to predict categorical outcomes and apply a range of nonlinear regression procedures. You can apply the procedures to business and analysis projects where ordinary regression techniques are limiting or inappropriate—such as studying consumer buying habits, responses to treatments or analyzing credit risk.

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

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.

Binomial regression parameter estimates

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.

Multinomial regression parameter estimates

Multinomial regression classification

The classification table shows the practical results of using the multinomial logistic regression model. For each case, the predicted response category is chosen by selecting the category with the highest model-predicted probability. Cells on the diagonal are correct predictions. Cells off the diagonal are incorrect predictions.

Multinomial regression classification

Nonlinear regression parameter estimates

The parameter estimates table summarizes the model-estimated value of each parameter. Parameters in a nonlinear regression model usually do not have the same interpretation as linear regression coefficients, and often vary from model to model. In this example, b1 represents the maximum possible sales, even if infinite advertising money were available. Its small standard error with respect to the value of the estimate suggests that you can be confident in the estimate. b2 is the difference between maximum possible sales and sales when no advertising money is spent. Its standard error is large and confidence interval is wide compared to the value of the estimate, so there is some uncertainty here. b3 controls the rate at which the maximum is reached, the so-called "rate constant". Like b2, there is some uncertainty in the estimate.

Nonlinear regression parameter estimates

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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.

Binomial regression parameter estimates

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.

Multinomial regression parameter estimates

Multinomial regression classification

The classification table shows the practical results of using the multinomial logistic regression model. For each case, the predicted response category is chosen by selecting the category with the highest model-predicted probability. Cells on the diagonal are correct predictions. Cells off the diagonal are incorrect predictions.

Multinomial regression classification

Nonlinear regression parameter estimates

The parameter estimates table summarizes the model-estimated value of each parameter. Parameters in a nonlinear regression model usually do not have the same interpretation as linear regression coefficients, and often vary from model to model. In this example, b1 represents the maximum possible sales, even if infinite advertising money were available. Its small standard error with respect to the value of the estimate suggests that you can be confident in the estimate. b2 is the difference between maximum possible sales and sales when no advertising money is spent. Its standard error is large and confidence interval is wide compared to the value of the estimate, so there is some uncertainty here. b3 controls the rate at which the maximum is reached, the so-called "rate constant". Like b2, there is some uncertainty in the estimate.

Nonlinear regression parameter estimates
Next

Predict categorical outcomes

Easily classify your data

Estimate parameters of nonlinear models

Meet statistical assumptions

Evaluate the value of stimuli

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Improve the accuracy of predictions with advanced regression procedures

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