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 …

Also, for OLS regression, R^2 is the squared correlation between the predicted and the observed values. Hence, it must be non-negative. For simple OLS regression with one predictor, this is …

What statistical tests or rules of thumb can be used as a basis for excluding outliers in linear regression analysis? Are there any special considerations for multilinear regression?

Jan 6, 2021 · Three linear machine learning algorithms: Linear Regression, Logistic Regression and Linear Discriminant Analysis. Five nonlinear algorithms: Classification and Regression …

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 …

I have a problem where I need to standardize the variables run the (ridge regression) to calculate the ridge estimates of the betas. I then need to convert these back to the original variables scale.

Dec 17, 2024 · This kind of regression seems to be much more difficult. I've read several sources, but the calculus for general quantile regression is going over my head. My question is this: …

There ain’t no difference between multiple regression and multivariate regression in that, they both constitute a system with 2 or more independent variables and 1 or more dependent …

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

Sep 23, 2020 · In a log-level regression, the independent variables have an additive effect on the log-transformed response and a multiplicative effect on the original untransformed response: