Linear regression with a continuous predictor is set up to measure the constant relationship between that predictor and a continuous outcome.
This relationship is measured in the expected change in the outcome for each one-unit change in the predictor.
One big assumption in this kind of model, though, is that this rate of change is the same for every value of the predictor. It’s an assumption we need to question, though, because it’s not a good approach for a lot of relationships.
Segmented regression allows you to generate different slopes and/or intercepts for different segments of values of the continuous predictor. This can provide you with a wealth of information that a non-segmented regression cannot.
In this webinar, we will cover the how’s, why’s, and when’s of creating segmented regressions. We’ll also discuss how to determine when the segmented regression adds value and whether it is a statistical improvement over the non-segmented regression.
Date and Time
Wednesday, April 12, 2017
3 pm – 4:30 pm (US EDT)
(In a different time zone?)
About the Instructor
Jeff Meyer is a statistical consultant, instructor and writer for The Analysis Factor.
Jeff has an MBA from the Thunderbird School of Global Management and an MPA with a focus on policy from NYU Wagner School of Public Service.
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