I was just wondering why regression problems are called "regression" problems. What is the story behind the name? One definition for regression: "Relapse to a less perfect or developed state."

It is expected that if a model is perfect, the unexplained variation in y should be 0 and thereby the intercept should be zero. In a regression model where the intercept is negative implies that the model …

Dec 21, 2016 · Those words connote causality, but regression can work the other way round too (use Y to predict X). The independent/dependent variable language merely specifies how one thing depends …

Aug 1, 2013 · Note that one perspective on the relationship between regression & correlation can be discerned from my answer here: What is the difference between doing linear regression on y with x …

Feb 2, 2020 · Multivariable regression is any regression model where there is more than one explanatory variable. For this reason it is often simply known as "multiple regression". In the simple …

What statistical tests or rules of thumb can be used as a basis for excluding outliers in linear regression analysis? Are there any special considerations for multilinear regression?

Jun 5, 2012 · In some literature, I have read that a regression with multiple explanatory variables, if in different units, needed to be standardized. (Standardizing consists in subtracting the mean and dividin...

A quick question: Is "residual standard error" the same as "residual standard deviation"? Gelman and Hill (p.41, 2007) seem to use them interchangeably.

Ridge regression is useful as a general shrinking of all coefficients together. It is shrinking to reduce the variance and over fitting. It relates to the prior believe that coefficient values shouldn't be too large …

Dec 23, 2016 · A good residual vs fitted plot has three characteristics: The residuals "bounce randomly" around the 0 line. This suggests that the assumption that the relationship is linear is reasonable. The …