In a recent article, we reviewed the impact of removing the intercept from a regression model when the predictor variable is categorical. This month we’re going to talk about removing the intercept when the predictor variable is continuous.
Spoiler alert: You should never remove the intercept when a predictor variable is continuous.
Here’s why. (more…)
I just wanted to follow up on my last post about Regression without Intercepts.
Regression through the Origin means that you purposely drop the intercept from the model. When X=0, Y must = 0.
The thing to be careful about in choosing any regression model is that it fit the data well. Pretty much the only time that a regression through the origin will fit better than a model with an intercept is if the point X=0, Y=0 is required by the data.
Yes, leaving out the intercept will increase your df by 1, since you’re not estimating one parameter. But unless your sample size is really, really small, it won’t matter. So it really has no advantages.
A recent question on the Talkstats forum asked about dropping the intercept in a linear regression model since it makes the predictor’s coefficient stronger and more significant. Dropping the intercept in a regression model forces the regression line to go through the origin–the y intercept must be 0.
The problem with dropping the intercept is if the slope is steeper just because you’re forcing the line through the origin, not because it fits the data better. If the intercept really should be something else, you’re creating that steepness artificially. A more significant model isn’t better if it’s inaccurate.