So as usual this file is going to be available to you. You can upload it to Julia box or if you have Julia installed on your local system you're going to be able to use this file and please read this introduction. But it basically covers what we talked about in the first video in this honors section. So let's move along and start talking about just adding packages. Now, a Julia box as it that stands at the moment has a lot of involved packages but when you go to the front page in the dashboard you would know at least that it is for Julia's version 0.6. So not everything is installed for 1.0 and at least some of these packages that we are going to use here to do our statistical analysis requires some form of installation. And the way that I want you to go about it is, as it's stated here in the start of adding packages. So using Pkg and semicolon if you want to, you don't have to put that there. And then so that's the whole package management system, we're going to use that. And then pkg.add as we used to do for 0.6 and before. And then you can just add the package name. So you can do that right here in the notebook inside of Julia box. Of course, if you're got Julia installed on your local system, you can do that in the report. Or if you have Julia Pro installed, 1.0.1.1 as we stand with this recording, you can just do it in Juno itself when you start the report inside of Juno. But here in the notebook inside of Julia box online, this is what you should do. So you're going to say, using pkg, the package management system and then pkg.add. So let's just briefly talk about these packages that we are going to use for this other section. But these are quite commonly used when we talk about statistical analysis. So using, as per usual, just to import as packages, distribution. Distribution is a package that allows us to create random variables along certain distributions. Very well known distributions and when you look at the distributions package, obviously quite a few of them, both the suite and continuous distributions. And you can create these random data point values according to the distribution that you choose. Stats base allows for many statistical, common statistical functions. Things that we might want to use every day when we do statistical analysis. CSV for comma-separated values, that is the package that we're going to use to both import spreadsheet files. But in this module, I'm going to show you really how to export your data frame that you've created, the simulated data that you've put inside of a data frame, how to export that as a spreadsheet file which you can then open inside of Excel or other spreadsheet software. Data frames, very commonly used, very nice package that allows us to manipulate data once we import data or we take data that we simulated, that we've created. And we put inside of data frame that allows for very easy manipulation of the data and that is a really a workhorse of data analysis. Hypothesis tests, that is a very nice package that includes many of the commonly used parametric and non parametrical statistical tests. So that's where we're actually going to use, to do, for instance, our t-test, or other common statistical tests. StatPlots is a subplot of the plots package, and that is going to allow us just to do very, very nice plots, plus a really, really full presentation, or for publication very nice package to use. GLM, generalized linear models, and that is what we're going to use just to do our linear regression building and generalized linear models. In this module, we're going to have a look at creating some models through the use of linear regression. And then our back into step plots what we are going to use for this is, pyplot. So we just use pyplot and you see the lower case but when we import the package, when we you install the package, remember the two uppercase P's but then just pyplot. At the moment, at least using this browser on this operating system that I'm working on now, GR, which is the default, does not work so well. And it really is going through different phases. Sometimes something works, and sometimes it doesn't. Pyplot works at the moment, and that's what we're going to use. So I'm going to hit Shift and Enter, and then I'll see you in the next video.