The new enhancements in IBM SPSS Statistics v21ensure that the most advanced analytics techniques are available to a broader group of business users, statisticians, analysts and researchers.Making it easier to access and manage big data, set up and perform analyses, and share results across the organization, IBM SPSS Statistics now includes:
�Simulation Modeling� Using Monte Carlo simulation techniques, users can now build better models and assess risk when inputs are uncertain.
�Advanced Techniques for Big Data�Quickly understand large and complex datasets using advanced statistical procedures to provide high accuracy and drive quality decision making.
�Improved Integration�Deploy analytics faster with seamlessaccess to common data types and external programming languages, including Java and IBM Cognos business intelligence.
Monte Carlo Simulation
The new simulation modeling feature is designed to account for uncertainty in data inputs, such as determining how weather conditions affect energy consumption, how costs of materials (e.g., steel prices) affect profitability of a construction project, or to better understand risks around investment planning.
By using Monte Carlo simulation, theunknown inputs and historical distributions are used to create confidence intervalsand visualizations(see graphic) to help make the best decision.
For example, energy and utilities organizations run simulations on potential weather temperatures, compared against historical weather temperatures, to then determine how much energy it would likely need to generate for an 85 degree day on August 31. This process can be repeated many times (typically thousands or tens of thousands of times), resulting in a distribution of outcomes so users can make the best decision.
Unlike other software packages, IBM SPSS Statistics doesn�tforce users to start from scratch, but allows them to leverage existing predictive models and existing data as the starting points for simulation.
Watch a short demofor an overview of Monte Carlo simulation.
Advanced Techniques forBig Data
IBM SPSS Statistics now makes working with big data easier, more scalable and ensures optimal performance when working with multiple predictors. By introducing a data file comparison tool, users now have the ability to compare datasets or data files to identify any discrepancies and ensure that the data values and records are compatible.
Users can now compare files for better quality control. For example, users can now find discrepancies between data sets that contain responses by the same respondents to a survey, but entered by two different people.
Also, IBM SPSS Statistics now allows operations like sorts and aggregations to be pushed back to the database, where they can be performed faster. Temporary files created by analytical procedures can be distributed across multiple disks, and large files can be compressed to save disk space when sorting, improving performance and speeding up analysis.
For example, users can run multiple analytical jobs at the same time while continuing to work on their desktops at other tasks. Users can also continue to run server jobs while disconnected from the server without sacrificing the quality of their analysis or output, then reconnect to access their completed jobs.
With IBM SPSS Statistics, users can now use a Java� plug-in to call IBM SPSS Statistics functionality from a Java application and have IBM SPSS Statistics output appear in the Java application.
Finally, IBM SPSS Statistics now provides the ability to easily import IBM Cognos business intelligence data for analysis. Users can now read custom data with or without filters, and import predefined reports from IBM Cognos directly into IBM SPSS Statistics.
Click herefor other new features.
For more information:
�Downloada fully-functional trial version of IBM SPSS Statistics v21
�Review the e-Catalogfor a complete list of IBM SPSS Statistics modules.
�Register for the on-demand webinarto see all the new IBM SPSS Statistics features and functionality.
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