Poisson distributed data is intrinsically integer-valued, which makes sense for count data. Ordinary Least Squares (OLS, which you call "linear regression") assumes that true values are …

This was in discussions of interpreting logistic regression coefficients, but Poisson regression is similar if you use an offset of time at risk to get rates. You add first all the coefficients …

Mar 29, 2023 · 3 I am trying to perform an apriori power analysis to estimate sample size for a Poisson regression model. The background is that a RCT is proposed to compare the rate of …

From Poisson regression, relative risks can be reported, which some have argued are easier to interpret compared with odds ratios, especially for frequent outcomes, and especially by …

A Poisson regression is a regression where the outcome variable consists of non-negative integers, and it is sensible to assume that the variance and mean of the model are the same. …

In Poisson regression, I need to compute the deviance, in order to do that I need to compute the log-likelihood function. It seems not difficult because I have the estimated model and my data …

When to use robust standard errors in Poisson regression? Ask Question Asked 13 years, 1 month ago Modified 12 years, 2 months ago

Jul 23, 2022 · Trying to understand how log-transforming a count variable changes the regression equation. The reason I ask is because I see a lot of researchers log-transform their data so …

So now, I'm trying a regression with Poisson Errors. With a model with all significant variables, I get: Null deviance: 12593.2 on 53 degrees of freedom Residual deviance: 1161.3 on 37 …

Apr 24, 2023 · I have created a Poisson regression model with robust error variance (https://academic.oup.com/aje/article/159/7/702/71883) to calculate relative risks. This is the …