00:01
So for this problem, we are doing a hypothesis test essentially for goodness of fit or independence, effectively, where we are told that our alpha value is 0 .05.
00:13
We have n equals 100.
00:16
We have the data as shown there.
00:20
So one thing that'll note off the bat, i brought up, one second here, using a website called stat key, i was able to find the critical kai squared value.
00:33
It's with three degrees of freedom because we have four different categories, and it's always going to be degrees of freedom minus one here.
00:40
So we'd have that our critical kai squared value is 7 .815, as the sort of first part here, 7 .815.
00:50
Then we're asked to find the test statistic.
00:53
So the test statistic would be the kai, let's see here, the kye squared is going to be equal to the sum of the difference between the observed data point or the observed value for each category minus the expected value for each category divided by the expected value for each category.
01:14
In this case, since we have four categories and 100 total, we would have that the expected value for each one is going to be equal to 25 from having, well, not one second, here.
01:30
Pardon me...