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regression - What does it mean to regress a variable against another ...
As an example, the data is X = 1,...,100. The value of Y is plotted on the Y axis. The red line is the linear regression surface. Personally, I don't find the independent/dependent variable language to be that helpful. Those words connote causality, but regression can work the other way round too (use Y to predict X).

Why are regression problems called "regression" problems?
Origin of 'regression' The term "regression" was coined by Francis Galton in the 19th century to describe a biological phenomenon. The phenomenon was that the heights of descendants of tall ancestors tend to regress down towards a normal average (a phenomenon also known as regression toward the mean)(Galton, reprinted 1989).

regression - What methods to use in pre and post testing ... - Cross ...
Typically in a regression that requires an estimation of the change in scores, you would model the score as the response, with a dummy variable to toggle on/off the timing of the score (pre/post). This allows you to see what the conditional mean of the response is for each time.

regression - Linear model with both additive and multiplicative effects ...
$\begingroup$ in the top answer to the following unrelated question, a linear regression plot (second graph) shows a non-linear regression line shown in red. how can a linear regression model produce a non-linear curve for a regression line when we know that regression lines from a linear regression model can only be straight?

regression - What intuitively is "bias"? - Cross Validated
In regression we can get biased estimators of slopes by doing stepwise regression. A variable is more likely to be kept in a stepwise regression if the estimated slope is further from 0 and more likely to be dropped if it is closer to 0, so this is biased sampling and the slopes in the final model will tend to be further from 0 than the true slope.

correlation - What is the difference between linear regression on y ...
The insight that since Pearson's correlation is the same whether we do a regression of x against y, or y against x is a good one, we should get the same linear regression is a good one. It is only slightly incorrect, and we can use it to understand what is actually occurring.

regression - What are best practices in identifying interaction effects ...
$\begingroup$ @Brandon: This is standard model diagnostics and exploratory plotting skills. I would plot the residuals against one of the covariates I think might be a candidate for an interation, conditioned (in the ggplot2 or lattice way) on the values of the covariate I think is involved in the interaction.

regression - PanelOLS - R Squared - Cross Validated
I'm using the PanelOLS package in Python to estimate a fixed-effects model and have noticed that it provides four different R-squared values: rsquared_within, rsquared_between, rsquared_overall, and

regression - What is difference-in-differences? - Cross Validated
The regression model will inherently converge on the correct lift attributable to treatment when conditioning on group assignment and post-start period statuses. $\endgroup$ – jbuddy_13 Commented May 25, 2023 at 20:32

regression - Predictive model with half-normal distribution - Cross ...
From your plots its seem you have a half-normal distribution with mode at zero. If that is your model, this leads to a half-normal distribution with support $[0,\infty)$ independent of the one parameter $\sigma$, with density function $$ f(x; \sigma) = \frac{2}{\sqrt{2\pi}\sigma} e^{-\frac12 (\frac{x}{\sigma})^2}, \quad x \ge 0 $$ with $\sigma > 0$ and that half-normal is indeed an exponential ...