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?

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 …

Hence, if the sum of squared errors is to be minimized, the constant must be chosen such that the mean of the errors is zero.) In a simple regression model, the constant represents the Y …

Would you suggest an alternative rule of thumb for minimum sample size for multiple regression? Alternatively, what alternative strategies would you suggest for determining minimum sample …

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 …

Well, it depends on the situation. In some cases, substantive theory indicates that there is a linear relationship between two variables (or a quadratic one, or whatever) and regression is used to …

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 …

I have a question related to multiple regression and interaction, inspired by this CV thread: Interaction term using centered variables hierarchical regression analysis? What variables …

Considering that all regression scenarios can be cast as a problem of solving a system of equations, when would there not be a closed-form solution? An ill-posed or sparse problem …

In answering this question John Christie suggested that the fit of logistic regression models should be assessed by evaluating the residuals. I'm familiar with how to interpret residuals in OLS, t...