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 …
Why are regression problems called "regression" problems?
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."
regression - Trying to understand the fitted vs residual plot?
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 …
Regression with multiple dependent variables? - Cross Validated
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 …
regression - What does negative R-squared mean? - Cross Validated
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 …
correlation - What is the difference between linear regression on y ...
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 …
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 …
How to derive the standard error of linear regression coefficient
How to derive the standard error of linear regression coefficient Ask Question Asked 11 years, 5 months ago Modified 10 months ago
What happens when I include a squared variable in my regression ...
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. …
regression - Linear model with both additive and multiplicative …
Sep 23, 2020 · 0 You can use Linear Regression to model any linear/non-linear relationship using basis expansion (slides from Elements of Statistical Learning).