What does it mean to hold a variable constant in regression?

It means that when you look at the effect of one variable in the model, you are holding constant all of the other predictors in the model. Or “ceteris paribus,” as the Romans would’ve said. In other words, you can isolate the role of one variable from all of the others in the model.

Why we add a constant value in while doing regression?

Most regression models include a constant term (i.e., an “intercept”), since this ensures that the model will be unbiased, in other words, the mean of the residuals will be zero. It does not depend on the true value of the intercept, it can be zero or not.

What happens when you hold a variable constant?

It mean you only change one variable while other variable do not change (remain unchanged). This assumption will let you to observe the effect of the only variable changes on the dependent variable of the model(regression).

How do you control a variable in a regression?

If you want to control for the effects of some variables on some dependent variable, you just include them into the model. Say, you make a regression with a dependent variable y and independent variable x. You think that z has also influence on y too and you want to control for this influence.

Do I need to standardize variables for linear regression?

In regression analysis, you need to standardize the independent variables when your model contains polynomial terms to model curvature or interaction terms. When your model includes these types of terms, you are at risk of producing misleading results and missing statistically significant terms.

Do you standardize the dependent variable?

You should standardize the variables when your regression model contains polynomial terms or interaction terms. While these types of terms can provide extremely important information about the relationship between the response and predictor variables, they also produce excessive amounts of multicollinearity.