00:01
Once again, welcome to a new problem.
00:04
And this time we're dealing with sampling distributions.
00:10
We're dealing with sampling distributions.
00:12
So think about a population.
00:16
And a population has a mean, which is the population, population mean.
00:24
And within the population mean, we have samples.
00:30
So we pick samples, and so x by 1, x by 2, x by 3, x by 4, x by 5, up until x by n.
00:43
So from this population, we're going to have a distribution.
00:49
And this distribution is the, oh, in fact, you know what, i just want to make sure i have the most appropriate distribution.
00:59
So this distribution is going to be a sampling distribution of sample sample mean and we're assuming it's a normal distribution.
01:22
We're assuming it's a normal distribution.
01:26
And we have different means for the samples up until the nth one.
01:35
So mu sub x bar is the same as x bar 1 plus x bar 2 up until x bar n, all over n.
01:47
So the mean of the sampling distribution of the sample distribution of the sample distribution of the sample means means, is equivalent to the population, population mean, and that's a mew.
02:22
And this is based on what we call the central limit theorem.
02:29
So we're looking at the central limit theorem.
02:32
The other aspect of the theorem is the standard deviation.
02:40
The standard deviation of the sampling distribution of sample of sample of sample means is equivalent to the standard error.
03:00
Again, this is based on the central limit theorem.
03:04
So these are the numbers you're looking at when it comes to the central limit theorem.
03:09
We do have a new problem right here.
03:14
And we're saying that a random sample, a random sample selected from a population, a random sample selected for a population with the mean of mu equals to 100, and the standard deviation is 10...