Nov 14, 2010 · Is it possible to have a (multiple) regression equation with two or more dependent variables? Sure, you could run two separate regression equations, one for each DV, but that …

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."

Dec 4, 2014 · 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 …

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 5, 2023 · Linear regression can use the same kernels used in SVR, and SVR can also use the linear kernel. Given only the coefficients from such models, it would be impossible to …

Consider the following figure from Faraway's Linear Models with R (2005, p. 59). The first plot seems to indicate that the residuals and the fitted values are uncorrelated, as they should be in a

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 …

Jan 27, 2025 · It appears that isotonic regression is a popular method to calibrate models. I understand that isotonic guarantees a monotonically increasing or decreasing fit. However, if …

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?

Sep 2, 2024 · When would one use a negative binomial regression and when would one use Poisson regression with respect to the mean and variance?