 So let's take a somewhat closer look at these measures of variation, and again this goes back to the concept of a deviation from a center, and the idea is that we use these deviations from the center to define two important quantities, the obedient and the mean, but we can actually use these deviations from the center in a further sense. So again, we might take a look at this notion of a mean deviation, and again the key property of a mean is that if I imagine a quantity to be distributed equally among end recipients, the mean is going to be the amount that each recipient receives if the quantity is going to be shared equally, and this leads to the notion of a mean deviation, and we have two of them because we have two primary types of deviations. We might take a look at the mean of the absolute deviations, and then we might take a look at the mean of the squared deviations. So for example, let's consider a set of data values that look something like that. Let's see if we can find the median and the mean absolute deviation. So find the median, so I want to put those data values in order. So let's see, I have a 5, I have an 8, I have a 14, I have a 6, I'll put the 6 right here, I have a 3, I'll put the 3 down here, I have a 9, that goes up here someplace, another 5 goes right there. So let's see, there's an odd number of values again. The median is going to be the one right here in the middle, the 6, there's 3 values to the right, there's 3 values to the left, 6 is going to be my median. Next thing I want to do is I want to find the mean absolute deviation, and for that I need to find the absolute deviations. So again what I'm looking at is the difference between the data values and the median. So that's 3 minus 6, 5 minus 6, again 6 minus 6, 8 minus 6, 9 minus 6, and so on. So I find the sum of the absolute deviations, and I want to find the mean of those values. So I'm going to add them together, and I'm going to divide by the number 1, 2, 3, 4, 5, 6, 7. So there's 7 data values, my mean absolute deviation, when I sum it and round it 2.57. Well I can also do the same thing for the mean and the mean square deviation. So again remember the mean minimizes the sum of the square deviations, and so the mean square deviation is going to be some indication of how small that value can be. So I'll add the numbers together, divide by 7, and my mean 7.14, and then I'll find the sum of the square deviation. So again that's my data value minus the mean. So that's 5 minus the mean squared, 8 minus the mean squared, 14 minus the mean squared, and so on down the line. So there's my square deviations all look like that, and then I want to find the mean of these 7 values. So I'm going to add the values together, add all of these together, and since there's 7 of them I'm going to then divide by 7 to find the mean squared deviation, which will work out to be 11.27.