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 · When we say, to regress Y Y against X X, do we mean that X X is the independent variable and Y the dependent variable? i.e. Y = aX + b Y = a X + b.

I'm trying to fit a multiple linear regression model to my data with couple of input parameters, say 3.

Nov 17, 2023 · My question broadly is-- When is there little to no advantage of SEM over multiple regression, and when is this a distinction without much of a difference? Further, when is the added …

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 …

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 you can get 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 "correct" if the …

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 interpretation.

Jul 20, 2015 · 4 You can use logistic regression to build a perceptron. The logistic regression uses logistic function to build the output from a given inputs. Logistic function produces a smooth output …

Mar 3, 2014 · I want to know why logistic regression is called a linear model. It uses a sigmoid function, which is not linear. So why is logistic regression a linear model?