 8. 8. Boom. Alright, and then let's do our norm.dist equals norm.dist. We're picking up the mean, F4 in the keyboard, making it absolute, comma, standard deviation, and F4 in the keyboard, making it absolute, comma, hold on, first was the X. I have to pick up the X, I got ahead of myself. And then comma, then the mean, then the mean. So then the mean, and then F4 in the keyboard, making it absolute, comma, then the standard deviation, standard D, F4 in the keyboard, making it absolute, and comma, and then we want a zero or false closing up the brackets and enter. Let's percentize and decimalize, home tab, number group, percentize, decimalize, double click in the fill handle to bring it on down. So there we have it. Let's take the z-score, which is going to be equal to brackets, the three minus the mean, F4 in the keyboard to make it absolute, closing up the brackets, dividing by the standard D, F4 in the keyboard, and enter. Let's decimalize it, home tab, number group, decimalize, and double click to bring it down. So there we have it. Now if I, so if I looked at this bell curve kind of relationship, I can start to compare like the z-scores. So remember, I can't really compare the units themselves, but I can start to look at the z-score and I can say, okay, well this, this one, like this four, I don't see anyone, a four over here, but this 3.62 is similar to this 3.62, right? So if I like, huh, that's interesting, right? We can kind of see patterns happening here, 3.62, 3.62. And then I can be like, well this 2.8, is there, there's a 2.88 here. And so let's make this a different color and say, hmm, 2.88, 2.88. And if we looked at, if we looked at one foot, one foot, 1.41, I'm going to say 1.41, and compare that to the z-score over here, you're like, all right, 1.41 is 12 inches, right? So we kind of see that, that sounds 12 inches is one foot, z-score is the same. You see how you, we see a relationship happen, if there's, if I go to 24 inches, say I think I see a pattern here, 24 inches, that's 0.67, I would think that would be like two feet, 0.67. So you see, you see how these things are, you could say, you could start to see where the patterns are lined up because of the z-scores could kind of help you to line things up and say, okay, I think there's, there seems to be a relationship here. And that's in essence what the z-score is kind of doing. So what we'll do, we're going kind of long here. So I'm going to copy the formula on the z-score, and it's going to look something like this. So next time we'll, we'll consolidate, right now we've kind of come very, we're like, there's got to be a relationship between these data sets, right? We already know there's a relationship, but we can say, well, I noticed that it conforms to a bell curve. And I noticed that both the inches and the feet conform to a bell curve. And then when I plotted out the actual, like a bell curve based on the data for inches and feet, I can kind of see a relationship between the z-scores. And so now I could say, well, can I use those z-scores to kind of define the relationship mathematically? And here's the formula that we'll look into. We're going to sum up, in essence, the first z-score minus the second z-score over the n minus one. And the n minus one is the number of items minus one, which usually corresponds or ties into when we're using the sample data as to like the population data. And so, and so this will give us our formal kind of z-score calculation. And we'll do it a couple, we'll calculate that a couple different ways next time. I said formal z-score. It'll give us, we already calculated the z-scores, but the z-scores will be used within it to get the formal correlation calculation. And we'll do that next time.