From the course: Machine Learning & AI Foundations: Linear Regression

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Dealing with multicollinearity: Manually combine IVs

Dealing with multicollinearity: Manually combine IVs - SPSS Tutorial

From the course: Machine Learning & AI Foundations: Linear Regression

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Dealing with multicollinearity: Manually combine IVs

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- [Instructor] I'd like to show you one of my favorite approaches of combating multicollinearity. It may come off as a bit informal. I probably would be less likely to do it in a very academic environment. But it's straightforward and it's practical. And although, maybe you might think of it as a bit primitive, it does the job, so I want to walk you through this. What we can do is we can identify two variables that we know that we have a problem with as a pair, like retail and restaurants, and just simply kind of reflect on what kind of variable they are. So in this case, retail and restaurants are both measured in acres. So, I'm just immediately thinking, why not add the two together and call it something like Commercial, Commercial acreage? Or, we could even call it just retail_restaurants. What I like about this is, it will be clear to everybody what I've done. It doesn't complicate the regression, but it certainly gets rid of the multicollinearity. Now, you might argue, aren't we…

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