IBM SPSS Statistics is an integrated family of products that addresses the entire analytical process, from planning to data collection to analysis, reporting and deployment. With more than a dozen fully integrated modules to choose from, you can find the specialized capabilities you need to increase revenue, outperform competitors, conduct research and make better decisions.
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:
Gain deeper predictive insights from large and complex datasets.
Reveal relationships and trends hidden in geospatial data.
Speed deployment and return on investment.
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.
Discover trends over time and space - Use the Spatio-Temporal Prediction (STP) technique to fit linear models for measurements taken over time at locations in 2D and 3D space, so you can predict how those areas may change over time.
Create association rules that incorporate geospatial attributes - Find associations between spatial and non-spatial attributes using the Generalized Spatial Association Rule (GSAR). It uses historical data such as location, type of event and the time an event happened to describe the occurrences of events, such as crimes or disease outbreaks.
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:
Non-parametric bootstrapping for more stable estimates
Clustering of cases in addition to variables
New rotation options for better convergence
An easier way to use continuous variables
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:
Enables users of Stata 13 to import, read and write Stata 9-13 files within SPSS Statistics.
Supports enterprise users who need to access the software with their employee identification badges and badge readers.