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.Continue reading What is the difference between using the t-distribution and the Normal distribution when constructing confidence intervals?
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. Furthermore, if you do not have many observations, you may want to use Student’s t-distribution instead of the Normal distribution. The Student’s t-distribution has wider tales when the number of observations is low and gives a you more conservative estimates of your confidence interval. In case you want to use Student’s t-distribution you case set the parameter ‘distribution’, i.e. distribution=”normal”.Continue reading Confidence Intervals in R
This post is part of the series on the omitted variable bias and provides a simulation exercise that illustrates how omitting a relevant variable from your regression model biases the coefficients. The R code will be provided at the end. Continue reading Omitted Variable Bias: An Example
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.
In a previous post, we discussed how to obtain robust standard errors in R. While the previous post described how one can easily calculate robust standard errors in R, this post shows how one can include robust standard errors in stargazer and create nice tables including robust standard errors.
A Venn diagram (also sometimes also called primary diagram or set diagram) is a diagram that depicts all possible logical relations between a collection of sets. Certain subjects, such as omitted variable bias, can be best be explained by using a Venn diagram. This post shows how to construct a Venn diagram in R.
What are the commands for the most important mathematical operations in Julia and R? The following table translates the most common Julia commands into R language.