You can get the slides if you missed Part 1 of
"The Evolution of Regression Modeling" webinar series,
and you can still join for Parts 2, 3, & 4 - Space is Limited!

www1.gotomeeting.com/register/500959705

Download (optional) a free evaluation of the SPM software suite v7.0 (used in the hands-on components of the webinar). As a webinar participant you will qualify for a 60-Day Evaluation of the software at no charge

Course Outline:
Overcoming Linear Regression Limitations
Regression is one of the most popular modeling methods, but the classical approach has significant problems. This webinar series addresses these problems.

• Are you working with larger datasets?
• Is your data challenging?
• Does your data include missing values, nonlinear relationships, local patterns and interactions?

This webinar series is for you! We will cover improvements to conventional and logistic regression, and will include a discussion of classical, regularized, and nonlinear regression, as well as modern ensemble and data mining approaches. This series will be of value to any classically trained statistician or modeler.

Part 2: March 15, 10-11am PT, 1-2 pm ET
Hands-on demonstration of concepts discussed in Part 1 (Classical Regression, Logistic Regression, Regularized Regression: GPS Generalized Path Seeker, Nonlinear Regression: MARS Regression Splines)

• Step-by-step demonstration
• Datasets and software available for download
• Instructions for reproducing demo at your leisure
• For the dedicated student: apply these methods to your own data (optional)

Part 3: March 29, 10-11am PT, 1-2pm ET - Regression methods discussed
*Part 1 is a recommended pre-requisite

• Nonlinear Ensemble Approaches: TreeNet Gradient Boosting; Random Forests; Gradient Boosting incorporating RF
• Ensemble Post-Processing: ISLE; RuleLearner

Part 4: April 12, 10-11am PT, 1-2pm ET - Hands-on demonstration of concepts discussed in Part 3

• Step-by-step demonstration
• Datasets and software available for download
• Instructions for reproducing demo at your leisure
• For the dedicated student: apply these methods to your own data (optional)