 it comes out pretty close to 2.75, right? And so that's what we need. We need that mean in order to plot the Poisson curve. So now that we have our data, we can calculate the mean and we can plot our curve. Now another indication that we have a Poisson situation is that the mean is going to be equivalent oftentimes or roughly equivalent to the variance. So to calculate the variance, let's take our data. Remember what we did is we basically take all of our data and we subtract it from the midpoint, the mean, right? So let's just do that real quick. I'm going to add some cells to do that. I'm going to put my cursor on F to G, right click, and insert. And then I'm going to say, now this is going to be the mean. And then I can say, OK, now this equals that 2.73. I'm going to say F4 on the keyboard and copy that down. And then I'll just subtract every point from the mean difference, call it the difference. So this equals this minus that. And then I'll copy that down, boom. And then if I scroll down to the bottom, we can then say, OK, all the way down. I can just hit Control-Shift down. All right, so then we have our totals. And so this I can do a count here if I want equals count. Just to put something here, count. And I should come up to 1,000. And this is going to be the sum. I'm going to say alt equals to the sum. It's going to be 0 because these are showing the amounts that are above and below the mean. I'm going to add another column, putting my cursor on column H, right click, and insert. And this is going to be the squared amount squared. So now I'm going to take this carrot to the second power or squared, and double click, copy that down. And then if I go down to the bottom, we're going to say, all right, let's sum this up, which is alt equals. So now we've got that amount. So this is going to be the squared. This is the squared sum of difference, let's say, which is equaling that. And then we're going to divide by the count, which we represent the formula as n, which equals 1,000. So now I can divide this out. And this is going to be the variance. And this is going to be the variance sigma squared equals this divided by this. I'm going to add some decimals. And you could see that comes out to 2.55, which is fairly close to the mean. And so that's going to be the variance. And if I take the standard deviation, you'll recall that we take equals this, or I'm sorry, the square root sq square root of that. And that's going to be the standard deviation. But right now we're kind of looking at that variance, because if that equals the mean, that's an indication that we're in this Poisson distribution situation. Now, if I wanted to calculate that variance this way, I could do it with a function. I can say equals var variance of the population, double-clicking that. And then take this whole column of our data, Control-Shift-Down, Shift-Up so I don't pick up the total, Enter. And so I added a couple decimals, 2.55. Here's the variance s equals variance of a sample. And then again, I'm going to put my cursor on here, Control-Shift-Down, Shift-Up so I don't pick up the total, and then add a couple decimals. So the idea here, so if I looked at this data set, these are going to be the indications that I have a Poisson distribution kind of situation. I could say, OK, yeah, it looks like the curve looks kind of like a Poisson distribution. It's slightly skewed to the right. If I calculate my variance and the mean, those are roughly equal. And in a perfect Poisson relationship, those would be equal. So the closer those two are together, the more I'm going, OK, maybe I can use a Poisson distribution. And if I do that, then I can plot an actual smooth curve instead of a jagged data curve. And so that's what we'll do next time.