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Do I need to fit them all into 1 multiple regression? …

Regression without FEs: lm(return ~ esg_score + education, data = df) Do I manage to control for: firm fixed effects industry by year fixed effects country by year fixed effects By adding them to the … regression as follows? …

’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. …

I have a theoretical question in regards to confidence intervals for simple regression. Say we a set of a data (x_i,y_i). …

I am a little confused as to which model specifications to use for my question. I have number of technological failures (positive count variable) as dependent variable but I am supposed to control f …

I'm planning on using a Poisson regression, but I am not sure the best way to control for heterogeneity. …

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 …

only have so many year's worth of data post-recession and I want to investigate including more datapoints, which would mean using pre-recession, and mid-recession (2008 - 2009 Q2) data in my multiple regression …

The goal of the analysis is to find a correlation between birth context and stature, while controlling for age. Each person has an ID number, age, stature, and birth context. That last part is what …

The short version of my question is: How is it possible to control for a confounding variable that has a Dirichlet distribution? Suppose I have electoral and census data for a large set of cities. In …

(In terms of regression, ANOVA framework). In above example, does choosing male and female randomly constitutes control? …

I'm wondering if it's sufficient to just "plug in" two variables you're interested in and go, or if you need to control for potential omitted variables like you do in multivariate regression studies. …

Essentially, I have two collinear variables which could be seen as either random or as fixed effects, a dependent variable I'm fitting the model to, and a variable that's assuredly a random effect. D …

I'm facing a dilemma in a pre/post cohort matching analysis for a healthcare intervention: Matching on the pre-treatment outcome $Y_0$ (a continuous variable) will likely lead to regression to the mean … This protects us from both regression to the mean bias and confounder bias. …

Question 1: Say I am running an OLS regression with tweets as independent variable and stockRet as dependent. …