1. RStat: Release 1.2 Ali-Zain Rahim, Strategic Product Manager March 18, 2010

2. Agenda:  Differentiators and Benefits  Review 1.2 Enhancements  Survival Analysis demo - Child welfare  Questions

3. RStat: Differentiators & Benefits  Based on R-Project  Open Source  Maintained by world wide consortium of universities, scientists, government funded research organizations, statisticians.  Over 2000 packages  RStat is a GUI to R  Intuitive guided approach to modeling  Simple model evaluation  Intended both for business analysts and advanced modelers  Single BI and Predictive Modeling Environment  Re-use metadata and queries  Perform data manipulation and sampling  Build scoring applications  Unique Deployment Method for Scoring Solutions  Scoring models are built directly into WF metadata  Deployment on any platform and operating system - Windows, Unix, Linux, Z/OS, and i Series.

4. RStat 1.2 Enhancements:  New Modeling Technique:  Survival Analysis:  Two Techniques – Cox Regression and Parametric Time Regression  Cox Regression – risk scoring routine  Parametric regression – time scoring routine  What Survival Does and when to use  Survival analysis encompasses a wide variety of methods for analyzing the timing of events with censored data (Censoring: Nearly every sample contains some cases that do not experience an event)  How to study the causes of  Births and Deaths  Marriages and Divorces  Arrests and Convictions  Job Changes and Promotions  Bankruptcies and Mergers  Wars and Revolutions  Residence Changes  Consumer Purchases  Adoption of Innovations  Hospitalizations.

5. RStat 1.2 Enhancements – cont’d  New Scoring Routines:  Neural Network model with comprehensive output – Enables users to compile NNET models into WebFOCUS functions for creation of applications.  Transformation capabilities for scoring routines – Allows for data manipulation within the RStat tool. Some methods are: Imputation, Scaling, and Remapping  Enhanced statistical output:  Indicators to Regression models ANOVA table to show significance – Enables users to determine the variables that are significant to the model.  Performance and Usability optimization  Auto sampling for faster visualization of large data sets in the KMeans model – Enables more optimized and efficient resource usage to display Cluster model statistics and data plots.

6.  Performance and Usability optimization  Model optimization – Allows only the variables used to create the model to be included in the exported C file. [In RStat 1.1 all variables selected by the user were included in the model]  Enhanced Log functionality – Allows users to create R-scripts for use with other applications, such as a Dialogue Manager application.  Process Cancellation capability – Allows users to cancel a long running process from within RStat.  Special characters functionality – Enables efficient handling of data with special characters.  Timestamp within the RConsole and Log Textview – Enables users to view and match the log with any errors received, thereby allowing for easier troubleshooting. RStat 1.2 Enhancements – cont’d

7. Copyright 2007, Information Builders. Slide 7

8. Demo: Child Welfare Use Case To identify the children who will stay in Child Welfare programs, and at what age will the children leave the programs – a time to event analysis

9. Foster Care Analytical Framework: Background and Optimization Goals  Half a million children in foster care  Managed by county departments and the private agencies who train families  It is a team effort to find a child a permanent home  Severe consequence of bad foster care:  Youth who leave the system are more likely to be homeless, incarcerated, unemployed, and unskilled.  Foster Care Analytical Framework: Goals & Benefits :  Provide better understanding of the factors that contribute to better foster care to all parties involved in the process  Provide standardized analytic and reporting system  Match children with better foster parents  Optimize child foster care duration

10. Survival Analysis – Child Welfare

12. Survival Analysis – Child Welfare (cont’d)

22. Copyright 2007, Information Builders. Slide 22

23. Thank you! "..if you are serious about statistics as a career, you need to become familiar with R because it is the most powerful and flexible language available, and may become the lingua franca of statistical programming in the near future.“ Source: "Statistics in a Nutshell" by Sarah Boslaugh published by O'Reilly