June 2019 Member Training: A Predictive Modeling Primer: Regression and Beyond

Predicting future outcomes, the next steps in a process, or the best choice(s) from an array of possibilities are all essential needs in many fields. The predictive model is used as a decision making tool in advertising and marketing, meteorology, economics, insurance, health care, engineering, and would probably be useful in your work too!

Join Elaine Eisenbeisz as she presents the rationale and risks of predictive modeling via supervised learning techniques. Elaine will also provide an overview of some of the many available modeling techniques including:

• Linear regression
• Logistic regression
• Linear discriminant analysis
• K-Nearest Neighbors
• Resampling methods (Cross-Validation, Bootstrap)
• Subset selection
• Shrinkage methods (Ridge regression, Lasso regression)
• Tree-Based methods (Decision trees, Bagging, Random Forests, Boosting)

Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

Date and Time

Wednesday, June 26, 2019
1pm – 2:30pm (US EDT) (In a different time zone?)

About the Instructor

Elaine Eisenbeisz is a private practice statistician and owner of Omega Statistics, a statistical consulting firm based in Southern California. Elaine has over 30 years of experience in creating data and information solutions. She designs methodology and analyzes data for studies in the clinical, and biotechnology fields. Additionally, Elaine and Omega Statistics are the go-to resource for ABD students who require assistance with dissertation methodology and analysis.

Throughout her tenure as a private practice statistician, Elaine has published work with researchers and colleagues in peer-reviewed journals. Fitting of her eclectic tastes, her current interests include statistical genetics and psychometric survey development.

Elaine earned her B.S. in Statistics at UC Riverside and her Master’s Certification in Applied Statistics from Texas A&M. She is currently finishing her graduate studies at Rochester Institute of Technology. Elaine is a member in good standing with the American Statistical Association and a member of the Mensa High IQ Society.

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Tagged as: bagging, boosting, Bootstrap, cross-validation, decision trees, discriminant analysis, K-Nearest Neighbors, lasso, Linear Regression, Logistic Regression, predictive models, random forests, Regression, Resampling Techniques, ridge regression, shrinkage methods, subset selection, supervised learning, tree-based methods