Regression Modeling and
Many Correlated Predictors
High Dimensional Data Analysis in Practice
Instructor: Jay Magidson, Statistical Innovations Inc.
Dates: April 15, 2011 - May 13, 2011
Please note: There is no "live" element to the course (required login times). You can access course materials and discussion board at your own pace based upon your schedule!
Recent advances in analysis of high dimensional data now allow reliable regression models to be developed even when the number of predictors exceeds the number of cases! In this course we begin by reviewing problems and limitations with traditional linear and logistic regression. Our applications-oriented presentation provides insight into how the new approaches work through examples and by providing an overview of the relevant theory, supplemented by the supporting equations. We use real and simulated data sets to illustrate the different approaches.
Note: Participants need not license a copy of the CORExpress program. All participants will have free access to the demo version of CORExpress, which allows unrestricted analyses of all course datasets. In addition, users may access R programs for high dimensional data analysis to perform additional optional exercises.
Learn more about CORExpress
Course Outline: See www.statisticalinnovations.com/services/course2.html
Who Should Attend:
Marketing, biomedical and other researchers who want to improve their understanding of regression model development in the presence of many correlated predictors.
Prerequisites: Familiarity with linear and logistic regression analysis at an applied level.
What you will learn
- How to develop reliable models, even in the presence of extreme multicolinearity and when # predictors >> number of sample observations
- Why many popular variable selection techniques are suboptimal
- About a new powerful step-down variable reduction technique in CORExpressTM
- About free and commercially available software for handing high dimensional data
Send all questions to Will Barker, firstname.lastname@example.org