From the course: R Essential Training Part 2: Modeling Data

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Predicting outcomes with quantile regression

Predicting outcomes with quantile regression

From the course: R Essential Training Part 2: Modeling Data

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Predicting outcomes with quantile regression

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- [Instructor] One of the worst things you can have in data analysis is an outlier and when you're doing a uni-varied analysis looking at things like the mean, you know that a single outlier can arbitrarily distort or destroy the mean so that it doesn't tell you anything close to what you want. When it comes to associations like regression, outliers can be even worse depending on the nature of what's going on. And methods like least squares linear regression, that's the standard regression, can get thrown off hugely. Fortunately, there are ways to deal with this with robust methods. Robust means not as easily influenced by outliers and one of the best ways is with quantile regression. Think of that as analogy to the median as opposed to the mean but applied to the association of variables in regression. To show you how this works, I'm going to load a few packages including one called QuantReg for quantile regression.…

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