Just use NewX in your model instead of X. It will look something like: NewX = X – 20. Let’s say X is Age and the mean of Age in your sample 20. And all you do to get that is create a new version of X where you just subtract a constant from X. It means to re-scale X so that the mean or some other meaningful value = 0. Simply consider centering X.Ĭentering sounds fancy, but it’s not. When X never equals 0, but you want a meaningful intercept, it’s not hard to adjust things to get a meaningful intercept. In market research or data science, there is usually more interest in prediction, so the intercept is more important here. You do need the intercept to calculate predicted values. It’s not answering an actual research question. So whether the value of the intercept is meaningful or not, many times you’re just not interested in it. It doesn’t tell you anything about the relationship between X and Y. If so, and if X never = 0, there is no interest in the intercept. One is to understand the relationship between predictors and the response. In scientific research, the purpose of a regression model is one of two things. So while the intercept has a purpose, it’s not meaningful.īoth these scenarios are common in real data. You still need that intercept to give you unbiased estimates of the slope and to calculate accurate predicted values. If X never equals 0, then the intercept has no intrinsic meaning. In other words, it’s the mean of Y at one value of X. If X sometimes equals 0, the intercept is simply the expected value of Y at that value. Start with a very simple regression equation, with one predictor, X. So what does it really mean? Regression with One Predictor X But that definition isn’t always helpful. Here’s the definition: the intercept (often labeled the constant) is the expected value of Y when all X=0. Interpreting the Intercept in a regression model isn’t always as straightforward as it looks.
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