 point, which, okay, that's not nice. So this is going to be equal to the average. Let's take the average of this. And then so there's the middle point, the mean of our data set. And then we're going to take the difference, differ, difference. And this is going to be equal to the calories that day, versus the middle point, the average, and we're over on these days. But of course, we're under on the days way down here. Because that is by definition, the middle point, and you would assume that we would be hovering somewhere around the middle point. Otherwise, we would get amazingly large or amazingly skinny, you would think, if we were on one side or the other for an extended period of time. So anyways, then we're going to take that squared, we're going to square that. So now we've done this bit. And we're going to square them, and then we will sum them up getting the numerator. So this equals the data point squared is a shift six, the carrot to the power of two, to the power of two. And there we have it. It's not quite as powerful as grayscale to the power of grayscale, whatever that means. But still, it's pretty effective. And then down below, let's put a total column and let's total this stuff up. I'm in my table. So I've got my table design up top in the table style options. Let's give ourselves a total column. And then over here on the calories, let's go ahead and just take the average, I'm going to recalculate the average just because we can. And then here, let's do a count. So I can count them, meaning 12345 on the line items 457 line items, which is a pretty fair amount of data, but so easy to do and work with in Excel, because of the functionality that Excel provides us, right? If we sum it up, it should still add up to zero, because we're taking the difference of every data point from the mean, the mean being in essence, that middle point. And then let's make this column a little bit wider. So I can so I can see what the number is. We have a huge number because we squared everything, which got rid of the negative numbers, but still is now it's all squared. So let's take that and complete our variance calculation and the standard deviation. So the squared difference from the mean is basically what we have here, or the numerator, in essence, of our formula for the variance formula. And then if we if we divide that this thing so I can put a divide by the count, which is n in our formula, which we calculated here, the number of line items 457 equals the 457. Let's put an underline under that font group under line. And then let's take the variance variance. And just so we know the symbol is a sigma squared oftentimes represented as let's go to the insert tab, go into our Greek lettering so we can be cool with the Greek stuff and Greek and Coptic. And so then we have I'm just I have it in my favorites down here, but it's right there too. And then insert. And then okay, and then I like to hit enter, and then go back into it, then put a two, then hold down shift and select the two where you could just select just the two, right click, and then format the cell and make it a subscript so I can get that squared notation looking nice like that, then we'll do the division problem. This equals this number divided by this number, the square difference of the mean divided by the number that count gives us the variance. Then we want the standard deviation, standard deviation of the pop elation population, population of data. And we're going to say that this is going to be then the letter then would be sigma. Let's go to the insert tab, symbols, and add a sigma, because that's the cool thing to do if you add those little symbols, people really think you know what you're talking about, tell you what, it's all you need to do and people are people will will say that your stuff is good man. So this is going to be the square root square root of that. And so there's the 815 if we add a couple decimals, home tab, number decimal lies in it with a couple decimals. There we have it. That's not really a word. Some people get mad that I use it. But I like to I think it should be a word. And it will be at some point, due to our use of it. So font group if I hit the dropdown here, let's make this blue and bordered. So there we have it. So this just another kind of example of us getting our calculation with a fairly long and different data set than the salaries. And remember that which a lot of times what you would be doing with different data sets is basically being able to compare, you know, the variance and the standard deviation of this data set possibly to related data sets. You know, if this was one population versus another population, and those will give you some ideas about the spread some concepts that we'll get into in more detail in future presentations. But that's the 814 59, which we calculated over here as well. The 815 59, the 814 59. And this was the for the sample for the sample data.