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: How can I …

Feb 2, 2020 · Multivariable regression is any regression model where there is more than one explanatory variable. For this reason it is often simply known as "multiple regression". In the simple …

Aug 1, 2013 · Note that one perspective on the relationship between regression & correlation can be discerned from my answer here: What is the difference between doing linear regression on y with x …

Ridge regression is useful as a general shrinking of all coefficients together. It is shrinking to reduce the variance and over fitting. It relates to the prior believe that coefficient values shouldn't be too large …

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?

A quick question: Is "residual standard error" the same as "residual standard deviation"? Gelman and Hill (p.41, 2007) seem to use them interchangeably.

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 and dividin...

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 equivalent to …

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 the ...

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