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."

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 …

For a simple logistic regression model like this one, there is only one covariate (Area here) and the intercept (also sometimes called the 'constant'). If you had a multiple logistic regression, …

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?

81 What is the main difference between maximum likelihood estimation (MLE) vs. least squares estimaton (LSE) ? Why can't we use MLE for predicting y y values in linear regression and …

Sep 2, 2015 · What is the problem? What do you want to do? Will the model be used for prediction in the future, and avoid measuring those 8 variables will save money? If not, I …

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 …

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 …

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 …

Application to Multiple Regression This geometric process has a direct multiple regression interpretation, because columns of numbers act exactly like geometric vectors. They have all …