Note: Adjusted R-Squared

Ah! Ok. Now it all makes sense.

In the previous blog post I wasn’t quite sure about throwing out a specific independent variable just because it was slightly above the Significance Level. Intuitively, I would have kept it.

Quick recap: We were predicting a startup’s profit based on admin expenses, R&D expenses, marketing expenses and location. Admin expenses and location were dismissed unquestionably, whereas marketing was a bit of a struggle.

But, following procedure, marketing had to go.

A bit later, the instructors explained R-Squared and Adjusted R-Squared, the math and the meaning for the model. It represents “Goodness of fit” as a number, more precisely: the closer R-Squared to 1, the better your model is fitted.
# I won’t go into detail here, but Adjusted R-Squared is actually the parameter that is more suitable.

By looking at the Adjusted R-Squared numbers of our MLR model, the best model (with ARSq closest to 1) seemed to be the one including both, R&D and marketing, even though marketing was slightly above the SL (that we set ourselves, btw).

Interpreting Coefficients
Sign (has to positive)
Magnitude (comparing magnitude in units of the independent variables)

Note: Of course the example data and the numbers are made up, still good to do some thinking and not just copy-paste.

 

Very happy about this. Let’s move on.

 

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