00:01
Once again, welcome to a new problem.
00:04
This time we're dealing with hypothesis testing.
00:09
We're dealing with hypothesis testing.
00:12
And of course, when it comes to hypothesis testing, we have two samples t test.
00:23
We have the two sample t test.
00:25
And when it comes to the two sample t test, and when it comes to the two sample t test, these are independent.
00:32
Independent samples and these independent samples have equal variances.
00:40
So we have independent samples with equal variances and of course what's going to happen is that the test statistic, the test statistic for independent samples with equal variances is the same as x bar 1 minus x minus mu1 minus mu2 all over the standard error.
01:10
And of course, the standard error has its own components.
01:14
It has the pooled variance.
01:17
Remember the variances are equal, so you can pull them together.
01:22
And then it also has the reciprocal of the sample sizes.
01:26
The pooled variance formula is the same as n1.
01:32
Minus 1, which is a sample, the degrees of freedom for the first sample, multiplied by the variance of the first sample, plus the degrees of freedom of the second sample, multiplied by the variance of the second sample.
01:48
And then you want to divide by the degrees of freedom of the entire test.
01:53
So the two tests you're running have, they do have their own degrees of freedom.
01:59
Just remember that xxb1 is the sample 1 mean and x bar 2 is the sample 2 mean.
02:14
And of course, the mules, mu1 is the population one mean, and mu2 is population one mean, and mu2 is population one mean.
02:33
Two mean.
02:35
So we have these two populations.
02:37
We do have the sample size for the first group.
02:45
Sample one size, we want to call it that.
02:51
And then we also have the sample size for the second group.
02:55
So sample size.
02:58
We have the two samples.
03:00
And then this happens to be the pooled variance.
03:07
We also have the pool variance.
03:10
And this is our test statistic if we're running what we call an independent sample t test for two groups with equal variances.
03:23
So we have two groups with equal variances.
03:25
We have a new problem.
03:27
And in this particular problem, it just so happens that we're given two samples, sample one in sample 2 and they have equal variances from normal populations.
03:41
We can see the numbers for these two samples that you're seeing right here.
03:46
And the mean of the first population represents the cfa designation.
03:53
So the population mean for the cfa designation.
03:57
And the mean for the second sample represents the population mean of mbas or subjects who have an mbs.
04:04
The first thing we're going to do is to determine the hypothesis that the mean for the second population, that's mu2, is larger than the mean of the first population, that's mu1.
04:21
So in terms of hypothesis testing, the now hypothesis is that the two population means are the same, and the alternative hypothesis is that the two population means are different.
04:34
The next step of the problem is to determine the test statistics...