00:01
You have a list of the number of false alarms for each of the months of january all the way up through december.
00:11
And that total comes out to be 408.
00:15
And so the january is 28, and then you have all the rest of these down here.
00:24
So if we take that 408 and divide that by the 12 categories, we get, 34 so each of these expected values these are the observed values and the expected values each of these is 34 and they will add up to 408 so our null hypotheses they have said he thinks that the false alarms are all equally distributed so that the proportion of false alarms is equal to one twelfth for each month and alternately not all monthly proportions, monthly proportions equal 112th.
01:21
So we will be doing a kye squared test, and you needed to know your kai squared statistic, and i'm going to use my software for that, using the stat and tests, and a kai squared goodness of fit test.
01:36
So this is the kai squared goodness of fit.
01:38
Went and we know we take 28 minus 34 squared divided by 34 for all 12 of those cells.
01:46
Our degrees of freedom will be 11 due to the fact that we have 12 categories less one.
01:53
And so that test statistic comes out to be, oops, and let me quick get our, my goodness, a fit test coming out.
02:04
I have my observed enlist one.
02:05
I have my expected enlist two.
02:07
And we have 11 degrees of freedom...