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 …

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 thing depends on …

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 …

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.

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

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 …

Are multiple and multivariate regression really different? What is a variate anyways?

I was wondering what difference and relation are between forecast and prediction? Especially in time series and regression? For example, am I correct that: In time series, forecasting seems …

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?

When running a multiple regression model in R, one of the outputs is a residual standard error of 0.0589 on 95,161 degrees of freedom. I know that the 95,161 degrees ...