Tag Archives: R

What is the difference between using the t-distribution and the Normal distribution when constructing confidence intervals?

This blog post explains the difference between confidence intervals that use the t-distribution and confidence intervals that use the Normal distribution. Thereby, the post will not focus on the theoretical/mathematical differences of the two distributions, but rather compare the two types of confidence intervals using simulation studies. Furthermore, in case you are interested in replicating the presented results or simply play around with it yourself, I provide the R code to conduct the simulation exercises and to replicate the figures.

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Confidence Intervals in R

In this post, I will show how one can easily construct confidence intervals in R. Assume you have a vector of numbers and you want to construct a confidence interval around the mean of this vector. The subsequent R code shows one easy way to calculate the confidence interval around the mean of this vector. The following code loads a function that allows you to pass on the vector and returns the confidence intervals. Per default the function returns the 95% confidence interval. However, the parameter ‘conf_level’ allows you to specify the interval you want.

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Cluster Robust Standard Errors in Stargazer

In a previous post, we discussed how to obtain clustered standard errors in R. While the previous post described how one can easily calculate cluster robust standard errors in R, this post shows how one can include cluster robust standard errors in stargazer and create nice tables including clustered standard errors.

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