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?

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 …

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 …

Apr 15, 2016 · The word "regressed" is used instead of "dependent" because we want to emphasise that we are using a regression technique to represent this dependency between x …

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 …

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 …

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: …

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 …

Sep 23, 2020 · In a log-level regression, the independent variables have an additive effect on the log-transformed response and a multiplicative effect on the original untransformed response:

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...