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 …

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 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 …

Jun 5, 2012 · In some literature, I have read that a regression with multiple explanatory variables, if in different units, needed to be standardized. (Standardizing consists in subtracting the mean …

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 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 …

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 2, 2017 · Why are the Degrees of Freedom for multiple regression n - k - 1? For linear regression, why is it n - 2? [duplicate] Ask Question Asked 8 years, 2 months ago Modified 4 …

How to derive the standard error of linear regression coefficient Ask Question Asked 11 years, 5 months ago Modified 10 months ago

Mar 19, 2013 · 25 I start with my OLS regression: y = β0 +β1x1 +β2D + ε y = β 0 + β 1 x 1 + β 2 D + ε where D is a dummy variable, the estimates become different from zero with a low p-value. …