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."

Oct 19, 2011 · LASSO regression is a type of regression analysis in which both variable selection and regulization occurs simultaneously. This method uses a penalty which affects they value …

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 …

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 …

Oct 3, 2021 · Although one can compute a single regression for all data points, if you include model assumptions such as i.i.d. normal errors, the model for all points combined can't be …

I see a similar constrained regression here: Constrained linear regression through a specified point but my requirement is slightly different. I need the coefficients to add up to 1. Specifically...

Mar 22, 2015 · I have problem with defining the unit of accuracy in a regression task. In classification tasks is easy to calculate sensitivity or specificity of classifier because output is …

Context - I'm performing OLS regression on a range of variables and am trying to develop the best explanatory functional form by producing a table containing the R-squared values …

Jun 13, 2016 · I usually hear about "ordinary least squares". Is that the most widely used algorithm used for linear regression? Are there reasons to use a different one?

Aug 24, 2021 · This is because any regression coefficients involving the original variable - whether it is the dependent or the independent variable - will have a percentage point change …