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Webcast: The Transactional Dilemma: Understanding Regression with Attribute Data

Smita Skrivanek — Principal Statistician at MoreSteam.com

Logistic regression is a technique for modeling and predicting categorical data. Although it is a standard technique in biomedical research, where researchers routinely deal with the relative risk of the outcome in one group compared with another group, most people outside of this field fail to use it when they should — probably due to lack of training or stats-averseness.

However, there is no dearth of attribute processes in the transactional arena! Master Black Belts and Black Belts would do well to understand the mechanisms and principles associated with binary logistic regression (as they do differ in many respects from continuous variable multiple regression) and how they extend to multinomial and ordinal variables.

Join Smita Skrivanek, Principal Statistician at MoreSteam.com, in this recorded Webcast as she discusses how to use and teach the concepts of binary logistic regression.

In this session, the following key points will be covered:

Smita Skrivanek — Principal Statistician at MoreSteam.com

Smita Skrivanek is MoreSteam's Senior Statistician and a regular contributor to our Webcast series. Smita has been with MoreSteam for over 10 years, building curriculum, coaching, reviewing projects, and assisting students with questions on advanced statistics. Currently, she heads the research and development operation for EngineRoom software, including its patented hypothesis testing and DOE wizard elements. Smita previously taught college-level Statistics courses.

Smita has a Masters in Statistics from The Ohio State University and an MBA from Indiana University Kelley School of Business.