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The intercept of a regression will be the predicted y-value when x is equal to zero (the general idea is the same for multiple regression, but obviously there are more x-variables to consider). … Other than that, and maybe a few other use cases, centering or scaling your variables is just to facilitate the interpretability of your final regression equation. …

For example, if you want to predict the probability that someone answers any given call, then you have a logistic regression. … If you just want to predict the call response rate, then you might consider a gamma regression. …

Assuming a generic linear regression in R, you would specify something like this: fit <- brm(out | me(out, error) ~ x1, data = dat) Just to briefly overview that code, the real work is the … This is sometimes called a variance regression or a distributional model, but it's basically a joint estimation of a linear model for the outcome and the residual variance term. …

If you don't want to do prediction, then maybe framing things as a regression is not needed. …

In the book Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, Cohen et al. point out that ordinary least squares regression on bounded, discrete data can lead to biased standard … Now, the other side of this is the relative robustness of most regression methods. …

Generally speaking, there are few cases where we actually are about whether a regression model is significant or not. … By and large, it's not hard to get a significant regression model. Now, to speak to reporting on trends. I assume this means whether a result trends toward significant or not. …

) and generalized regression (linear regression assuming a different kind of distribution). … This package lets you specify your normal linear regression using lm() and it tests all the assumptions for linear regression, including the assumption of the linear link function (a significant result …

Structural equation modeling and linear regression are related, but different, analyses. … An assumption of linear regression models, like those used for the PROCESS macro, is that all variables are measured without error. …

In my field, we usually just refer to this as hierarchical regression. The basic idea would be that you have some model predicting a specific outcome. …