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imagine I want to estimate a regression model (let's say OLS for simplicity's sake) using observational data. I include a number of controls that might confound the relationship. …

1: All variables are continuous variables (no dummy or categorical) Edit 2: An illustration of the problem has been added at the end of the post Problem My question can be illustrated with a simple regression … Thank you :) Multiple regression model always capture effect et ceteris paribus for each variable, meaning when every other variables are held constant. …

In the second step, I intend to run a regression analysis with several (control?) variables to see whether some of the effect stems from certain aspects of the event. … Question: Can I still run a meaningful regression even though I have a significant number of NA's or am I required to delete every single event with incomplete data? …

There are ways of handling errors in predictor variables, but they tend to (1) require that you be able to estimate the amount of variation/error a priori; (2) be a bit more complicated than standard regression …

Think about a regression to model the income of an individual i in a given year t. … We could now of course include a gender dummy in our regression - within each gender group there would be variation of the outcome. …

I noticed that in the "General Procedures" for Propensity Score mentioned in its Wikipedia entry, it begins: Run logistic regression: Dependent variable: Z = 1, if unit participated (i.e. is member of …

series, and statistically confirmed that time series with shorter time series length have smaller coefficient than the longer counterparts, all things being equal (this result has been achieved through regression …

It is also my understanding that this is because regression to the mean is at work, so that higher baseline scores will be associated with greater decreases and vice versa, independent of the treatment … My opinion is that using "change scores" seems not to really be doing anything about regression to the mean, whereas including the baseline score as a predictor allows baseline group differences to cancel …

If you aren't already familiar with multiple linear regression, this is a good time to learn it. … That makes regression in the log scale more likely to meet the assumptions of linear regression. …

In a regression model with multiple predictors, the word "control" is used to refer to the inclusion of other variables than the one relevant to a specific question (e.g., the effect of alcohol consumption … Partial regression coefficients are defined as the isolated association between a single X and Y when none of the other predictors are changing. …

I want to see if a change in an independent variable (T2-T1) predicts another variable at T2. For example, higher increases in cognitive impairment over 1 year for people with dementia predict lower q …

I'm working with some genetics data, performing linear regressions, and have been advised to control for population structure by performing principal components analysis. My model at the moment is of …

I am reading Data Analysis Using Regression and Hierarchical Models and am confused by the concept of ignorability. The description in the book seems to say different things. …

You wrote: I am getting different p-values for a variable in t-test and its coefficient in multiple linear regression so I am unsure which one to believe. … Should I believe the p-value from the t-test results or the multiple linear regression results? …

I've started using DAG to improve the construction of my regression models and I was wondering if it made any sense to interpret the estimates I get for variable I adjusted for in my model ? … If I build the following regression model : $ Y = \beta_1 X_1 + \beta_3 X_3 + \beta_4 X_4 + \beta_5 X_5$ the only estimate that makes sense is the one for X1, i.e $\beta_1$, right ? …