This tutorial covers center and variation of a sampling distribution. First off, we have the mean of the sampling distribution of the sample mean. Now I know it looks like it's a lot of words, but it's really not so bad.
First of all, we have a set of sample means. We create a sampling distribution of them. And then we find the mean of that.
Now the mean of the sampling distribution of the sample mean is the same as the mean of the underlying distribution. Sometimes it's called the grand mean. So here, the mean of our sampling distribution is the same as the mean of our underlying distribution, our population of x's.
Now, we also can talk about the standard deviation of the sampling distribution of the sample mean. And again, we have the set of sample means, we take the sampling distribution and create one for those sample means, and then we can find the standard deviation of that.
Now here, we have a little bit of a formula to use. The standard deviation of the underlying distribution, when we divide that by the square root of the sample size that gives us the standard deviation of the sampling distribution of the sample mean. We'll work through an example now.
Here is our example. We'll let x stand for the number of pounds the lawn chair can support before breaking. We sample 10 chairs at a time. And we find that the underlying mean is 250 and the standard deviation is 33.
So now in order to find the mean of the sampling distribution of the sample means, it's actually quite easy. It's just the same. So our mean of the sampling distribution of the sample mean is also going to be 250 pounds.
Now for the standard deviation of the sampling distribution of the sample mean, again, we've got a little bit of a formula to do. We need to take the underlying distributions standard deviation, 33, and we divide by the square root of the sample size divided by the square root of 10. When we do that, we get 10.44.
So that's all we have to do in order to find the mean of the sampling distribution of the sample mean and the standard deviation of the sampling distribution of the sample mean. This has been your tutorial on the center and variation of a sampling distribution.
