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regression - When is R squared negative? - Cross Validated 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 regression when either the intercept or the slope are constrained so that the "best-fit" line (given the constraint) fits worse than a horizontal line
Regression with multiple dependent variables? - Cross Validated 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 doesn't seem like it
regression - What does negative R-squared mean? - Cross Validated 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 worse than using a horizontal line, and hence gives a negative R-Squared Undefined R-Squared
How should outliers be dealt with in linear regression analysis? 8 I've published a method for identifying outliers in nonlinear regression, and it can be also used when fitting a linear model HJ Motulsky and RE Brown Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate BMC Bioinformatics 2006, 7:123
regression - Converting standardized betas back to original variables . . . Where β∗ β ∗ are the estimators from the regression run on the standardized variables and β^ β ^ is the same estimator converted back to the original scale, Sy S y is the sample standard deviation of the regressand, and Sx S x is the sample standard deviation Unfortunately the book doesn't cover the analogous result for multiple