Rstat Release 1.2 New Feature Update and Customer Case Study
Thursday, March 18, 2010 @ 11:00 a.m. - 12:00 p.m Eastern Time
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Presented by Michael Meagher, Blue Leaf Environment
Come hear how Blue Leaf Environment is using Rstat to predict survival of fish as they migrate up and down the rivers of Washington State! Michael Meagher will talk about how Blue Leaf Environmental uses acoustic tags and hydrophones to monitor fish as they swim down river and pass through dams.
Mr. Meagher has used FOCUS and WebFOCUS for thirty years to summarize and analyze environmental data for a wide variety of studies including hazardous waste siting, smokestack emissions for garbage incineration, the effects of the Exxon Valdez oil spill and monitoring fish movement around and through hydroelectric dams.
Presented by Ali Rahim and Dan Carotenuto, Information Builders
WebFOCUS RStat is a data mining and predictive modeling workbench embedded in WebFOCUS Developer Studio. RStat supports common statistical and data mining tasks and the development of predictive models that can be deployed as scoring applications on any WebFOCUS platform. RStat enables data miners and Business Intelligence developers to collaborate with the same tools to access, manipulate and transform data, develop predictive models, and incorporate this scoring functionality into operational BI applications.
In this session, many enhancements in the most recent release of 1.2 will be discussed:
* Survival Analysis models and scoring routines (COXPH Risk and Parametric Time) – Allows Time-to-Event analysis, whether Success or Failure.
* Scoring routines for Neural Network model with comprehensive output – Enables users to compile NNET models into WebFOCUS functions for creation of applications.
* 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.
* Indicators to Regression models ANOVA table to show significance – Enables users to determine the variables that are significant to the model.
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