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So you think you know about linear regression ... - Chris Stucchio

3,479 views · Published 30 July 2018 · 49:29 · Indexed 20 September 2026

Channel: Hasgeek TV · 2018 · Science & Technology

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Everyone has used linear regression. It’s boring, standard mathematics that we learned in Stats 101.

But how many of us really understand it at a deep level? One of the “rules” of linear regression is that your features must not exhibit multicollinearity. But where does this rule come from? What happens if we violate it? Many people suggest regularization or ridge regression as a solution, but why do these methods work? What are we actually doing?

In this talk I’ll discuss linear regression from the Bayesian perspective. This is a simple way to think about it which makes the answer to these questions quite transparent. It also provides an avenue to solve various harder problems (e.g. non-gaussian errors) that you might not have seen before.

As a running example I’ll consider predicting scores in fantasy sports, specifically the scores of a batter in Baseball or Cricket.

Chris Stucchio is a former physicist, high frequency trader and software developer. He’s currently the head of data science at Simpl. He’s been working in decision theory and bayesian optimization for the past 5 years, and has been teaching statistics to novices for much longer.

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