 Hi, this is Dr. Don. We're going to spend a few minutes talking about the Lab 8 Personal Data Project. Now, this module, this lab is really one that is intended to be a bit unstructured because it's going to be your project, your choice of a dataset to use, your choice of the research question you're going to answer. And I would encourage you just to make sure you're familiar with all the resources we have as you're making your decision about what dataset to use and what question to try to answer. This is the main lab page that will give you the background there, again, going over some of the goals of the course, read data, work with data, communicate with data. And in this Lab 8, we're going to have two rehearses and then your report. Now, these are a bit unstructured again because it's your time. And we give you a few things we want you to do. In Rehears 1, we want you to pick your dataset and report on that, what the dataset is, where you got it, and then try to frame your research question and start thinking about the time of analysis. And again, you will be using the material that we've covered primarily in the last two modules, six and seven, because that's where we've got the most closely aligned examples of using data to answer questions. So you'll probably be looking at the datasets and then going to look at examples in modules six and seven. In Rehears 2, you just continue to work on it, hopefully by then you will have finalized pretty much the analysis that you're going to run. And again, we primarily want you to think about the downy infer process and the steps in it. And you know now, you know how to look at one of the examples in the Rehears and then insert a blank code chunk and copy the code in there and then edit it very carefully for your dataset and your variables of interest. And that's really the whole process. The only thing that's new this time is that we want you to present a report. And part of the goal of being data literate is being able to communicate with data. And a nice way to do that is a video report. We've got a simple tool you can use if you don't have one of your own, a free tool and instructions in the Rehearses on how to make that video. And there's an example in the Rehears that shows you what that final presentation will look like. So let's look a little bit more of the resources here. And we find them under this tab, Personal Data Project. We've got these additional resources. So I'm going to, I've already got that open. I'll click over there. And this is that particular set of resources. The review again about what we're talking about doing in this lab, a link to datasets. We've got some example code chunks for simple things you need like loading a data file and doing some basic charting in case you need to look at those. Some tips for some data rattling. Most of you won't need this unless you're going out and getting original data on the internet somewhere. You might have to wrangle a bit. There's some tips on that. The datasets that we provide are already wrangled. And so you won't have to do that. And then there's some more example problems for the Downey Infer Process Hypothesis Test and how to do confidence intervals, examples of that simple code. Again, most students will use the examples we have in our lab six and lab seven. And then finally is the presentation guide, the free version of Screencast-O-Matic, the how-to's, how to set it up. Very simple to use. Again, if you want to do it with PowerPoint and make the presentation right there within PowerPoint, you can do that too. Whatever tool you already have, you can use that. We just need to get this three to five minute presentation and either the actual recording or a link to it if you, you know, post it on the web somewhere. So let's keep going here and look at the datasets. And we've got a page there. And these are just the tip of the iceberg with the data that's available. These are sets that a lot of students will use. And in this first one here is the Texas Public Safety Personnel Employee Data, I should say. And I give you a bunch of example questions that you could ask and answer using the data in these. So one of the first things you'll do is start looking at these various datasets, exploring them. You go ahead and open them up in RStudio and look at the head and the tail and glimpse and inspect those datasets to get sets to get familiar with what's in there. And as you look at them and think of these questions, for example, you'll start hopefully remembering what you saw something something similar in either lab six or lab seven. For example, here's the difference in proportion. We're doing proportion sometimes we can think about two variables. And are they independent here? It would be sex of the person and whether they're managers or non managers. So there would be two variables tests for independence. So look at these. There's a bunch here on this page that you can look at. And we give you links to where you can get the data if it's if it's necessary for you to download somewhere. So you can look through these examples. Right down at the bottom is a great resource here. OpenEntro. OpenEntro remember is one of the packages that we can load into the library. And it's got a ton of datasets that are already cleaned and set up here. You go to the data sets there. This is the OpenEntro page here and scroll to the top. You can see they've got the CSV files already set up. And all you need to do is just click on the little location there, and it'll download the CSV file that you can then upload into your RStudio cloud workspace. If you want to use other types of data set there, you could, but we would most easiest to use as a CSV. And you can look at these and then if you want to get more information about it, you can click and it'll open up and it'll give you more detail, more detail even if you click there. And then it gives you the variables that are included in that particular data set. So you can do some of your inspection right there and OpenEntro. So that's basically what we've got to do in the Lab 8. I did want to mention in the example video of the Lab 8 report, the student use this data set, vote NSA, and you might recognize that format there. Again, it's looking to see if the party affiliation is related to or independent of whether or not they would approve NASA using master surveillance of phone behavior. So that's just kind of an example. Let's just finish up here by taking a brief look at the rehearses and remix pages. And again, this will just walk you through. It's tended to be a bit unstructured so you can structure what you submit, however it works best for you. Give you the basic steps again in the downy and fur process. And then this rehearse one, pick your data, do some exploratory data analysis, identify your variables, define and refine your research question and be thinking about the analysis you've got to do. And then if we go to rehearse two, we're just finishing up there. You know, polishing your analysis, doing that, those basic steps in the downy and fur process that you used in six and seven labs. And then we go over here to the final. And it just repeats what I've shown you here. We give you a basic template. But again, remember, you can put in the sections you want and insert additional code chunks as needed. As you, you know, copy those original code chunks out of the examples and paste them. So your final remix report, you know, would generally have these sections in it, but you may have a few additional sections that you insert in there, like your data visualizations and those kinds of things. So this is a quick overview, not as quick as I like. And again, there's the example report video that we talked about. And again, the how to zone how use the screen cast a magic if you want to use that. And that's it. Rehearse one, we want you to get that in my 30th, if you can, to kind of already pick your data set and your research question, research to what's, you know, you're finishing up, you get all your analysis done. And then in the remakes, you just do your report and submit the components. So I hope this helps.