Do we always need the intercept term in a regression model?

The shortest answer: never, unless you are sure that your linear approximation of the data generating process (linear regression model) either by some theoretical or any other reasons is forced to go through the origin.

Can intercept be insignificant?

So, a highly significant intercept in your model is generally not a problem. By the same token, if the intercept is not significant you usually would not want to remove it from the model because by doing this you are creating a model that says that the response function must be zero when the predictors are all zero.

What is the intercept in a regression model?

The intercept (sometimes called the “constant”) in a regression model represents the mean value of the response variable when all of the predictor variables in the model are equal to zero. This tutorial explains how to interpret the intercept value in both simple linear regression and multiple linear regression models.

When to use regression and when to use linear regression?

The variance for the estimators will be an important indicator. When the auxiliary variable x is linearly related to y but does not pass through the origin, a linear regression estimator would be appropriate. This does not mean that the regression estimate cannot be used when the intercept is close to zero.

What if there is no constant term in regression equation?

These results don’t hold in the case where there is no constant term in the regression equation. Instead of fitting a line through the mean values, we need to instead fit the line through the origin. Since Thus, for regression without a constant term, we still have SST = SSReg + SSRes and dfT = dfReg + dfRes where

Should I use multiple regression or multiple regression estimates?

The two estimates, regression and ratio may be quite close in such cases and you can choose the one you want to use. In addition, if multiple auxiliary variables have a linear relationship with y, multiple regression estimates may be appropriate.