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."

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 thing depends on …

Aug 1, 2013 · In particular one piece of information a linear regression gives you that a correlation does not is the intercept, the value on the predicted variable when the predictor is 0. In short - …

Then this simplified version can be visually shown as a simple regression as this: I'm confused on this in spite of going through appropriate material on this topic. Can someone please explain to …

Also, for OLS regression, R^2 is the squared correlation between the predicted and the observed values. Hence, it must be non-negative. For simple OLS regression with one predictor, this is …

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 …

Nov 8, 2018 · Some site claims linear regression means the continuous value output. If I have an MLP with hidden layers, and its output is continuous value (ex: house price), then is it called …

What statistical tests or rules of thumb can be used as a basis for excluding outliers in linear regression analysis? Are there any special considerations for multilinear regression?

Are multiple and multivariate regression really different? What is a variate anyways?

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