Test Regression with a full-feature SPSS trial
IBM® SPSS® Regression enables you to predict categorical outcomes, create regression models, analyze model summaries and apply various nonlinear regression procedures to datasets when studying consumer buying habits, treatment responses, efficacy of diagnostic measures, credit risk analysis and other situations where ordinary regression and data analysis techniques are limiting or inappropriate.
Study consumer buying habits. Optimize marketing strategies and customer satisfaction.
Analyze dosage responses to improve care quality and achieve better patient outcomes.
Assess credit risks and outliers and enhance customer relationships through targeted offers.
Measure academic achievement tests and support institutional research.
Examine customer behavior to curate personalized offers.
Improve citizen services and safety. Assess tax payment compliance, minimize fraud and mitigate threats.
Predict the presence or absence of a characteristic or binary outcome based on values of a set of predictor variables.
Use the logit link function to model the dependence of a polytomous ordinal response on a set of predictors.
Classify subjects based on values of a set of predictor variables. This type of regression is like logistic regression, but it is more general because the dependent variable is not restricted to 2 categories.
Find a nonlinear model of the relationship between the dependent variable and a set of independent variables.
Use probit and logit response modeling to analyze the potency of responses to stimuli such as medicine doses, prices or incentives. This procedure measures the relationship between the strength of a stimulus and the proportion of cases exhibiting a certain response to the stimulus.
In the first stage, use instrumental variables that are uncorrelated with the error terms to compute the estimated values of one or more problematic predictors. In the second stage, use those computed values to estimate a linear regression model of the dependent variable.
Control the correlations between the predictor variables and error terms that can occur with time-based data. The weight estimation procedure tests a range of weight transformations and indicates which gives the best fit to the data.
The new linear elastic net extension procedure estimates regularized linear regression models for a dependent variable on one or more independent variables.
The new linear lasso extension estimates L1 loss in regularized linear regression models for a dependent variable on one or more independent variables.
The new linear ridge extension procedure estimates L2 or squared loss regularized linear regression models for a dependent variable on one or more independent variables.
Footnotes
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