Interpreting the Intercept in a Regression Model (repost)

The following is not a Stats Make Me Cry original, but rather something I came across and found very interesting. If you are interested in the topic, please read the preview and follow the link that follows to the original site.

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The intercept (often labeled the constant) is the expected mean value of Y when all X=0.

Start with a regression equation with one predictor, X.

If X sometimes = 0, the intercept is simply the expected mean value of Y at that value.

If X never = 0, then the intercept has no intrinsic meaning. In scientific research, the purpose of a regression model is to understand the relationship between predictors and the response. If so, and if X never = 0, there is no interest in the intercept. It doesn’t tell you anything about the relationship between X and Y.

You do need it to calculate predicted values, though. In market research, there is usually more interest in prediction, so the intercept is more important here...

Read the rest of Karen's article here...

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