The insight that since Pearson's correlation is the same whether we do a regression of x against y, or y against x is a good one, we should get the same linear regression is a good one. It is …

Origin of 'regression' The term "regression" was coined by Francis Galton in the 19th century to describe a biological phenomenon. The phenomenon was that the heights of descendants of …

As an example, the data is X = 1,...,100. The value of Y is plotted on the Y axis. The red line is the linear regression surface. Personally, I don't find the independent/dependent variable …

In regression we can get biased estimators of slopes by doing stepwise regression. A variable is more likely to be kept in a stepwise regression if the estimated slope is further from 0 and …

I have a Java code that performs a linear regression on a set of data using the Gauss-Jordan elimination. It calculates a linear, quadratic and cubic functions using the least squares …

Regression results are typically estimated based upon parametric Student's t distribution parameters and typically regression, especially from poorly matched to the data regression …

Oct 27, 2011 · $\begingroup$ @FrankHarrell a little simulation can prove that the correlation between the p-values (presuming you're talking about the F-test or equivalent) and the AIC is …

Various texts on regression will tell you that you should never include an interaction term without the base effects --- that is not correct. One circumstance where it is appropriate to include an …

Oct 5, 2011 · A better alternative is often quantile regression. I wrote about this for NESUG recently. That paper is here. One exception to the above is when the categories are …

$\begingroup$ Readers here may also want to look at these closely related threads: How to interpret logarithmically transformed coefficients in linear regression, & when-and-why-to-take …