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?

Also, for OLS regression, R^2 is the squared correlation between the predicted and the observed values. Hence, it must be non-negative. For simple OLS regression with one predictor, this is …

The Pearson correlation coefficient of x and y is the same, whether you compute pearson(x, y) or pearson(y, x). This suggests that doing a linear regression of y given x or x given y should be …

There ain’t no difference between multiple regression and multivariate regression in that, they both constitute a system with 2 or more independent variables and 1 or more dependent …

For multiple regression, you have some theory to suggest a minimum sample size. If you are going to be using ordinary least squares, then one of the assumptions you require is that the …

Collinearity in regression: a geometric explanation and implications The first picture below shows a normal regression situation with two predictors (we'll speek of linear regression).

For your first question, I don't think that a linear regression model assumes that your dependent and independent variables have to be normal. However, there is an assumption about the …

Considering that all regression scenarios can be cast as a problem of solving a system of equations, when would there not be a closed-form solution? An ill-posed or sparse problem …

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."

When running a multiple regression model in R, one of the outputs is a residual standard error of 0.0589 on 95,161 degrees of freedom. I know that the 95,161 degrees ...