In regression analysis, we look at the correlations between one or more input variables, or factors, and a response. We might look at how baking time and temperature relate to the hardness of a piece of plastic, or how educational levels and the region of one’s birth relate to annual income. The number of potential factors you might include in a regression model is limited only by your imagination... and your capacity to actually gather the data you imagine.

But before throwing data about every potential predictor under the sun into your regression model, remember a thing called multicollinearity. With regression, as with so many things in life, there comes a point when adding more is not better. In fact, sometimes not only does adding more factors to a regression model fail to make things clearer, it actually makes things harder to understand.

Why should I care?

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