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

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Stepwise regression: Interpreting the output

Stepwise regression: Interpreting the output - SPSS Tutorial

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

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Stepwise regression: Interpreting the output

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- [Instructor] Okay, we're going to start working through the step wise output. Now, remember that step wise is inherently exploratory. But, one of the things that you're uncovering is which variables were entered and which variables were left out. So, we see that engine size was entered first, so according to the step wise, that's the most important or most significant variable. Then we have weight, peak rpm, width, stroke, comp ratio. Now, take a look at a variable like width. Now, obviously, that may really be the width of the car, but it may really be size. So, it could be that it happened to pick up width here, but, on a slightly different data set, it might've picked up length. Why am I pausing to mention this? Well, when academics talk about how step wise has issues or limitations, they're usually referring to the fact that it can be shown that step wise is not as stable as we would like to consistently produce the same variable list. So, if the primary reason that you're doing…

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