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 21, 2016 · Those words connote causality, but regression can work the other way round too (use Y to predict X). The independent/dependent variable language merely specifies how one …

Aug 1, 2013 · Regression is an analysis (estimation of parameters of a model and statistical test of their significance) of the adequacy of a particular functional relationship.

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?

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 …

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

None of the three plots show correlation (at least not linear correlation, which is the relevant meaning of 'correlation' in the sense in which it is being used in "the residuals and the fitted …

Is logistic regression better than multiple linear regression? The problem is that my dependent variable is not categorical, it's a scale variable. What can I do now? What other regression …

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

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 …