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 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 …

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 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 …

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 …

Aug 1, 2013 · the standardised regression coefficient is the same as Pearson's correlation coefficient The square of Pearson's correlation coefficient is the same as the R2 R 2 in simple …

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 …

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 …

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 / …

Here is the summary of the results in the abstract: Although outcome transformations bias point estimates, violations of the normality assumption in linear regression analyses do not. The …