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."

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 …

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.

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 …

Nov 30, 2022 · That applet and Excel are fitting a least squares line to these data, which is totally different: they aren't treating the first set as "predicted" values--they treat them as predictors. …

Jun 3, 2025 · I’m working with a dataset that includes 6 continuous predictor variables and 6 continuous response variables across 3 different years. I plan to run a total of 18 linear mixed …

Apr 15, 2016 · In its core, linear regression amounts to orthogonal projection of y y on (onto) X X, where y y is the n n -dimensional vector of observations of the dependent variable and X X is …

Apr 2, 2025 · I am working with a Generalized Additive Model for Location, Scale, and Shape (GAMLSS) and trying to determine the optimal $\lambda$ values for LASSO-penalized …

What kind of regression models are used for positive, non-count data?) Is it better generally to predict (say) the percentage instead of the ratio, and if so, why?

Reading about methods and results of statistical analysis, especially in epidemiology, I very often hear about adjustment or controlling of the models. How would you explain, to a non …