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."

Consider the following figure from Faraway's Linear Models with R (2005, p. 59). The first plot seems to indicate that the residuals and the fitted values are uncorrelated, as they should be in a

In ANOVA/regression design, "covariate" just refers to factors/independent variables? I may have completely misunderstood this. Can anyone give a simple example of the term "covariate" …

Jan 15, 2016 · I have fish density data that I am trying to compare between several different collection techniques, the data has lots of zeros, and the histogram looks vaugley appropriate …

Jun 19, 2016 · A regression model is often used for extrapolation, i.e. predicting the response to an input which lies outside of the range of the values of the predictor variable used to fit the …

Oct 23, 2024 · With Cox survival regression models, what you get are estimated log-hazard differences or hazard ratios. Plots like those you show are log-hazard differences from some …

In simple terms, how would you explain (perhaps with simple examples) the difference between fixed effect, random effect in mixed effect models?

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 …

Nov 27, 2021 · I am trying to better understand the motivations and the applications behind Propensity Score Matching. I read the following that explains the motivations behind …

The Pearson correlation coefficient of x and y is the same, whether you compute pearson(x, y) or pearson(y, x). This suggests that doing a linear regression of y given x or x given y should be …