Summarize and standardize scale variables using the descriptives procedure. Study relationships between scale and categorical variables using procedures such as means, summarize, and OLAP cubes.

Model the value of a dependent variable based on its relationship with predictor variables using procedures such as linear, ordinal or partial least square regression.

Use advanced techniques to streamline the data preparation stage – delivering faster analysis and accurate conclusions.

Measure how variables are related to each other using procedures like bivariate correlations or partial correlations.

Reveal natural groupings or clusters within a data set that would otherwise not be apparent using exploratory tools such as two-step, hierarchical or k-means cluster analysis procedures.

Derive robust estimates of standard errors and confidence intervals for estimates including mean, median, correlation coefficient and regression coefficient.

With Chart Builder you can build drag and drop chart types from a predefined gallery onto the canvas. You can also use tools such as ROC analysis to assess the accuracy of model predictions by plotting sensitivity vs (1-specificity) of a classification test.

Save the results of your analysis in multiple formats including HTML, text, Word, RTF, Excel, PowerPoint (97 or later), and PDF. Quickly export charts to one of the supported graphics formats.