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

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Dealing with multicollinearity: Factor analysis/PCA

Dealing with multicollinearity: Factor analysis/PCA - SPSS Tutorial

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

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Dealing with multicollinearity: Factor analysis/PCA

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- [Instructor] We know that the waste data set has multicollinearity present. One way of combating that is factor analysis, principal components analysis. It has both strengths and weaknesses. Let's take a quick look. So we'll go to Analyze, Dimension Reduction, Factor and just the fact that this in a folder called Dimension Reduction gives us a clue to what's going on. What's going to happen is, we're going to give Factor Analysis our five independent variables. And what we're trying to do is extract variables from these five. It's going to be fewer than five. That, together, represent as much variance of the original five as possible. And we're going to be able to use these new factors as a substitute in our regression model. Let's give it a try. Now Factor Analysis is a big subject and I can't hope to cover Factor Analysis as a stand alone topic in this video. Simply, how it can help us deal with our multicollinearity problem in regression. So I'm going to go to extraction and…

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