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."

With linear regression with no constraints, R2 must be positive (or zero) and equals the square of the correlation coefficient, r. A negative R2 is only possible with linear regression when either …

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 …

May 25, 2015 · Q&A for people interested in statistics, machine learning, data analysis, data mining, and data visualization

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.

The biggest challenge this presents from a purely practical point of view is that, when used in regression models where predictions are a key model output, transformations of the …

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 …

Here is my question, according to the book and Wikipedia, the standard error of β^1 β ^ 1 is

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 …

Where β∗ are the estimators from the regression run on the standardized variables and β^ is the same estimator converted back to the original scale, Sy is the sample standard deviation of …