 is in on the z-scores. So to calculate the bell, the curve, what I'm going to do is say we're going to take it four standard deviations and plot this out. So we'll have the x, or the low and the high, four standard deviations. So what does that mean? That means on the low h, we're going to say that the standard deviation was 1.9 times 4. And that's going to get us up to 7.6. I'm going to add that to, or subtract it to, to get the lower, the 67.99. So we're going to start at the low point at 60.39. And then on the high point, I'm going to say it's going to be 1.9 times 4 plus the middle point, 67.99, so 70.6. So when I look at the height, I'm not going to go down to 0 inches up to like 1,000 inches. That would be too much, too much, because all of the data is going to be between, for the most part, not all of it, but for practical purposes, all of it, is going to be between these two points. And then here, let's do the same here. The standard deviation is 11.66 times 4 minus the middle point, 127.08. That gives us the low point, which is negative. And you're not going to have negative weights, but we might start, well, no, it's not negative. It's not negative. It's going to be the 80 is the low point. And then the high point, we're going to say, it's negative the way I calculated it, but it's not negative because it's going to be that minus the standard deviation times 4. OK, so then let's do it. The other 11.66 times 4, and then plus the 127.08. And that's going to give us the 173 on the upper point for the weight in pounds. All right, so if we plot out the bell curve, which we focused on in a different section, but we'll plot that out here, so we'll do it. You've got each data point. And then the bell curve is going to be the norm.dist function, where we take the x, which is this, comma, the standard deviation, for the height is that. And then is it going to be cumulative or not? It's not going to be cumulative for this calculation. And that'll give us, for each height that we picked from 60, I started at 60, the low point, up to 76, I went to. Up to 76, it gives us our percentage, the percent likelihood that the data will be there. And so there we have it. And then we can calculate the z. So the z score is then same kind of calculation for the z score. I'm going to take each point, in this case, the 60. 60 minus the middle point, which is going to be the 60.39 divided by the standard. Sorry, that's not the middle. Let me do that again. 60 minus the middle point, which is 67.99, divided by the standard deviation, 1.9, means that it's over four standard deviations away, which makes sense, because four standard deviations away, as we just calculated, would be the 60.39, and we're at 60. So we're over four standard deviations away. That's what the z score is telling us. And we could do that same calculation for the z score all the way down. Now it's going to be in order. And it flips to be positive at the mean. So an exact zero would be at 67.99. So we don't have 0.99 here, but it's close to zero right there. And then so that's going to be that one. And then we could do the same thing for the weight, where we take each point. And we calculate the norm dot dist. This should be having n in front of it, norm dot dist. And that's going to be the x 79, the mean, which is going to be the mean of this data set, 127.08. And then it's going to be, and then the standard deviation is going to be the 11.66. And then it's not cumulative. And then we can calculate the z scores in the same fashion. Do that all the way down. And the z scores would be this one, of course, around 4.08.