The following code produces confidence intervals in R using the normal distribution and confidence intervals using the t-distribution.

The code reproduces the figure 1 presented in this post.

Continue reading Confidence Intervals R Code Part 1The following code produces confidence intervals in R using the normal distribution and confidence intervals using the t-distribution.

The code reproduces the figure 1 presented in this post.

Continue reading Confidence Intervals R Code Part 1Julia presents various ways to carry out linear regressions. In this previous post, I explained how to run linear regression in Julia using the function *linreg().* Unfortunately, *linreg() *is deprecated and no longer exists in Julia v1.0.

In this post I will present how to use the native function of Julia to run OLS on the following model

Continue reading Linear Regression in Julia 1.0In this post, I am going to explain how to upgrade Debian 9 to Debian 10. You can enter the following eight steps in your terminal:

Continue reading Upgrade Debian 9 to 10In this post I am going to explain how to enable GUI root access on Debian 10. It is very similar to enabling Gui Root Login in Debian 8 and to enabling Gui Root Login in Debian 9. At this point I should warn you that using the root account is dangerous as you can ruin your whole system. Try to follow this guide exactly.

Continue reading How to Enable Gui Root Login in Debian 10In 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.

Continue reading Confidence Intervals in RIn Julia, one can generate random numbers that follow a Gamma distribution by using the Distribution package. Thereby one can use the `rand()`

function that draws random numbers and specify the Gamma distribution by using the `Gamma(a,b)`

command. The parameters a and b define the shape parameters of the Gamma distribution. This article provides a more generic overview of how to generate random numbers in Julia.

Continue reading Generate Gamma Distributed Numbers in Julia

How can one save objects in Julia? One easy way to do so it to use the `JLD`

package. The following examples demonstrates how to save data objects in Julia and how to load the once they are saved.