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Here is the article that motivated this question: Does impatience make us fat? I liked this article, and it nicely demonstrates the concept of “controlling for other variables” (IQ, career, income, a …

However, in general, you do not need to center or standardize your data for multiple regression. …

A more common approach is to include the variables you want to control for in a regression model. … Now then, onto the regression model. Type the following: lm(outcome~exposure) Did you get an Intercept = 2.0 and an exposure = 0.6766? …

The general form of ZIP regression models incorporating covariates is due to Lambert (1992). … Regression Modeling with Actuarial and Financial Applications Cambridge University Press, 2011 Dalrymple, M. L.; Hudson, I. L. & Ford, R. P. K. …

We want to build a regression model that predicts $Y$, and we are especially interested in its relationship with $X_1$. There are two basic possibilities. … If we fit a regression model that ignored $X_2$, we would get the solid black regression line. …

I mean, a regression model can be used essentially a causal model, since we are essentially assuming that one variable is the cause and another is the effect (hence correlation is different approach from … regression modelling) and testing whether this causal relationship explains the observed patterns. …

If there is a correlation between the fixed effects and $D_{it}$ then estimating this regression via OLS will be biased given that the fixed effects are not controlled for. … Then you would regress $$Y_{it} = \beta_1 \gamma_s + \beta_2 \lambda_t + \rho T_{it} + \epsilon_{it}$$ where $\gamma_s$ is again a dummy for the control group and $\lambda_t$ are time dummies. …

Below is a simulated example using prediction from a logistic regression model to classify. The cutoff is varied to see what cutoff gives the "best" classifier under each of these three measures. … In this example the data comes from a logistic regression model with three predictors (see R code below plot). …

As I see it, there are two related reasons to consider matching instead of regression. … Part 1: Regression You decide to run a regression of the outcome on the treatment and confounders as a way to control for confounding by these variables because that is what linear regression is supposed …

In general, in an observational study because the above-mentioned assumptions do not generally hold, we either partition our sample accordingly or we control for difference through "regression-like" techniques …

Offsets can be used in any regression model, but they are much more common when working with count data for your response variable. … (See also this excellent CV thread: When to use an offset in a Poisson regression?) When used correctly with count data, this will let you model rates instead of counts. …

’re all familiar with observational studies that attempt to establish a causal link between a nonrandomized predictor X and an outcome by including every imaginable potential confounder in a multiple regression … It is difficult for a single model (multiple regression) to adequately adjust for covariates and simultaneously model the predictor-outcome relationship. …

You can get great $R^2$ by putting the same variable on the left and right hand side of a regression, but this huge $R^2$ regression would almost certainly be useless. … Adding everything as a regressor is known as a "kitchen sink" regression. …

Just to give a (famous) example : https://www.nature.com/articles/nm0601_673 https://doi.org/10.1023/A:1012487302797 All these methods have regression variants for continuous data as well. …

Yes, you can overfit logistic regression models. … It is indeed possible to overfit a logistic regression model. …