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

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Hierarchical regression: Interpreting the output

Hierarchical regression: Interpreting the output - SPSS Tutorial

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

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Hierarchical regression: Interpreting the output

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- [Instructor] Let's start working through the output. Okay, the first thing that you'll notice is that we have the variables entered removed shows four models. Because we're indeed building the model step by step. And then as we scroll down you'll see that the model summary has a lot more going on than historically we've had to worry about because we've got the four models. And keep your eye on that R square change. The little subtitles beneath the print out is gonna help you keep track. So remember that we started with education and work experience, and that all by itself had an R squared of 45% of variance explained. But then we made a big jump when we added in the second block. That is the collection of our job category. So all by itself job category in its dummy coded form increased R squared by more than 30%. Then we discover that even though we've removed education, we've removed work experience, we've removed job category, the additional R squared attributable to sex of…

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