IBM SPSS Statistics Features
SPSS Statistics is loaded with powerful analytic techniques and time-saving capabilities to help you quickly and easily find new insights in your data.
Here’s a look at the newest features and enhancements designed to help you:
Discover causal relationships in time series data
Uncover hidden causal relationships among large numbers of time series using the Temporal Causal Modeling (TCM) technique. SPSS Statistics enables you to feed many time series into TCM to find out which series are causally related, and can automatically determine the best predictors for each target series.
Integrate, explore and model location and time data
SPSS Statistics includes geospatial analytics capabilities to help you explore the relationship between data elements that are tied to a geographic location.
Choose from a wider range of R programming options
Develop and test R programs using a full-featured, integrated R development environment within SPSS Statistics. You can also write R functions that use SPSS Statistics functionality with command syntax from within R, and return results to R.
Enhance categorical analysis outcomes
Use a wider range of categorical principal component analysis (CATPCA) capabilities, including:
Create next generation web output
SPSS Statistics web reports have been completely redesigned, with more interactivity and functionality and web server support.
Bulk load data for faster performance
SPSS Statistics writes the data to a text data file, and then the bulk loader script writes the text data back to the database, providing superior performance when handling large datasets.
In addition, SPSS Statistics: