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 R 2 must be positive (or zero) and equals the square of the correlation coefficient, r r. A negative R2 R 2 is only possible with linear …

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.

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 …

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 …

Feb 1, 2019 · I'm struggling to grasp the concept of bias in the context of linear regression analysis. What is the mathematical definition of bias? What exactly is biased and why/how? …

Nov 3, 2017 · In linear regression, we often get multiple R and R squared. What are the differences between them?

Nov 24, 2015 · For the top set of points, the red ones, the regression line is the best possible regression line that also passes through the origin. It just happens that that regression line is …

Nov 5, 2020 · I am quite confused in these terminologies (especially but not limited to regression) I do understand what Variance and Standard Deviation means, they measure the dispersion / …