![]() ![]() And then we divide it, and then we divide it by the We take the absolute value, because once again, Then we have the absolute deviation of four from three, from the mean. How far is it from three? It's fairly easy toĬalculate in this case. Taking two minus three, taking the absolute value, that's just saying its absolute deviation. Then we have another two, so we find that absoluteĭeviation from three. So we take each of the data points and we figure out, what's its absoluteĭeviation from the mean? So we take the first two. Mean Absolute Deviation of this first data set. So what does that mean? (chuckles) I'm using the word "mean," using it a little bit too much. How much do the deviate from the mean, but the absolute of it? So each of these points at two, they are one away from the mean. Much do each of these points, their distance, so absolute deviation. And all we're talking about, we're gonna figure out how Mean Absolute Deviation, or if you just use the acronym, MAD, mad, for Mean Absolute Deviation. And what we're about to calculate, this is called Mean Absolute Deviation. To overuse the word "mean." So we wanna figure out, on average, how far each of theseĭata points from the mean. ![]() And one of the more straightforward ways to think about variability is, well, on average, how farĪre each of the data points from the mean? That might sound a little complicated, but we're gonna figure out what that means in a second, (chortles) not That's an interesting question that we ask ourselves in statistics. Points are on average further away from the mean than these data points are. How do they look different? Well, we've talked about notions of variability or variation. Measure of that central point which we use as the mean, well, it looks the same, but ![]() When we measure it by the mean, the central point, or And we calculated that the mean is three. Let's say this is zero, one, two, three, four, five, six, and I'll go one more, seven. And let's visualize it, to see if we can see a difference. But there's something about this data set that feels a little bitĭifferent about this. We have different numbers, but we have the same mean. And this is two plus six is eight, plus four is 12, 12 divided by four. The mean, the mean over here is going to be equal to one plus one plus six plus four, all of that over, we still So, one four and anotherįour, right over there. If this is zero, one, two, three, four, and five. I'll do a little bit of a dot plot here so we can see all of the values. We can visualize this a little bit on a number line. This is gonna be 12 over four, which is equal to three. Two plus two is four, plus four is eight, plus four is 12. So we have one, two, three, four numbers. And then we're gonna divide by the number of numbers that we have. That's gonna be two plus two plus four plus four. We know how to do that is by finding the mean. "Is there a number that can give me "a measure of center ofĮach of these data sets?" And one of the ways that ![]() And then, in the otherĭata set, I have a one. The first data set, I have two, another two, a four, and a four. Let's say that I've got two different data sets. ![]()
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