 Statistics and Excel. Standard deviation and variance for a population. Calories data. Get ready, taking a deep breath, holding it in for 10 seconds, looking forward to a smooth soothing Excel. Here we are in Excel. If you don't have access to this workbook, that's okay because we'll basically build this from a blank worksheet. But if you do have access, three tabs down below. Example, practice blank. Example, in essence, answer key. Practice tab, having pre-formatted cells so you can get right to the heart of the practice problem. Blank tab, blank worksheet, but with just our data in it so we can practice formatting the cells within Excel as we work through the practice problem. If you don't have access to this data, it's a pretty long data set so it would be difficult to simply type it in there. You can look at online resources for sample data sets such as Kaggle.com, K-A-G-G-L-E.com. Let's go to our example tab to get an idea of what we are doing. We're going to be looking at calories data, doing a similar kind of process that we have done in prior presentations. Just working with different data sets now and that we'll be doing our statistical calculations using mainly Excel functions. Then we'll do our histogram of the data and then we'll break out in more detail focusing in on the standard deviation and the variance of this data set. So let's go to the blank tab to the right. I'm going to remove the Kaggle. I'm going to format the entire sheet like we do pretty much every time. Putting my cursor on the triangle, right-clicking on those selected cells and formatting the cells. Let's go to the currency. Then negative numbers, we're going to make them red and bracketed. I don't want any dollar sign. We don't need any decimals. Let's remove those as well and okay. So let's embold the whole thing, home tab or I will. You don't have to. Fonts group, bold it. I think that might make it easier for the screencast. Holding control, scrolling in a bit so we can see a little bit more detail. I'm currently at the 265 on the zoom in. Let's put a table. Now note, by the way, that when I formatted the entire worksheet, it messed up the date field. So the date field is still there, but now I'm just going to reformat the date field to be a date. So I'm going to select column A and then go to the home tab, numbers group, dropping it down on the numbers. We want the short date and that should convert it back. Hold on. That's normal date. I want the short date and I'm going to make this a little bit larger. There it is. Okay. So let's hold control and scroll down just a bit. So now I'm currently at 220 on the zoom in. All right. Now I'm going to put my cursor in the data and we're going to go to the insert tab up top, the tables, and then put a table around this data, the dancing ants doing their magic dance around the table, creating a table from it, allowing us then to sort the data by date, which is what it's currently sorted in, or if we wanted to, by the calorie count, which we can go from lowest to highest. So we have the lowest calories here on these dates where we didn't have any calories. We were like starved to death or just we didn't want to get out of bed those days or something. And then we have the highest calorie counts from Z to a. All right, taking that data. Let's make a skinny C column. That's what happens when you don't eat the calories. You get skinny and see. So you see what happens. You get skinny. So in any case, then let's do our normal calculations here. So we're going to, we're going to have the mean or average, average calculation. And that's going to give us the truth about the calorie count for us. Even if it's mean, even if it's mean to do it, the truth is the facts, just the facts here. So then we're going to take the, let's take the minimum. Let's take the Q1. Let's take the median, median, which you could be called Q2. Let's take Q3. Let's take the max and then we'll do the standard deviation, which is going to be for the population, we're going to say, and that's represented by a sigma. I won't put the sigma in here, but then we've got the variance for the pop population, not your dad, but for a population versus a sample because we're working with population stats for the most part at this time. All right, let's do it. Let's do our calculations. I'm going to do this quickly because we've seen it in the past. We're just doing it with this data set, which is a fairly long data set here. We'll do a little bit longer data set next time, but you can see we have a pretty large sample and we can do our same calculations and the functions obviously quite helpful to get to the calculations quickly, but it still can be useful to break out in like a table format what is actually happening because that's another way to sort the data, another viewpoint at the data that could give us more insight. So let's say this is going to be equal to the average and I'm just going to say tab now to pick up the formula. So there it is. And then I'm just going to put my drop down arrow on the data, the dancing ants around the data and enter. So there's our average. I'm going to do these fairly quick equals the men, the smallest number tab to get the function. My cursor is already in the section for the arrow drop down and there it is. By the way, if I want to look at the men, I can sort from lowest to highest and there's those zero days, those zero days when we had no calories at all, I was starving, but whatever. And then solve for the cause man, quartile, let's do the quartiles and