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 6, 2025 · The coefficients of an OLS regression are just simple descriptive statistics; you can compute them on any data, w/o having to make any assumption whatsoever, just as you could …

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 …

Apr 24, 2020 · I have data containing several variables. I ran a regression model. Prior to running the model I have normalized the dependent variable Y and the independent variables X1 and …

Dec 14, 2014 · Modelling mortality rates using Poisson regression Ask Question Asked 10 years, 11 months ago Modified 9 years, 5 months ago

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: …

Jan 1, 2020 · Logistic regression is used when modelling the probability of an event happening, which will usually mean that the response variable is binary. The model provides odds ratios …

I find "log-linear regression" to be somewhat of a misnomer since the whole idea extends to much more than just plain regression (probably why wikipedia calls it log-linear analysis. Log-linear …

I've got some data (158 cases) which was derived from a Likert scale answer to 21 questionnaire items. I really want/need to perform a regression analysis to see which items on the …

Apr 7, 2020 · Are there other situations where inverse regression actually outperforms? Maybe there are other loss functions it does well with, or it's more robust to violations of assumptions, …