00:01
Given question have observed frequencies.
00:06
That is, here our n is 1 ,137.
00:12
Here variables are categorical and we have a counts data, so we can use a kai square test in this situation to test association.
00:25
Here our null hypothesis is there is no association between party and opinion of using the full body x -ray.
00:50
Opinion of using the full body x -ray.
00:56
And the alternative hypothesis is, h .a.
01:01
Is there is an association.
01:04
There is an association between party and opinion of using full body x -ray.
01:15
For finding kai square test statistic, we have to calculate the expected frequencies.
01:22
Expected frequency can be calculated using the equation row total into column total divided by the whole total.
01:34
That is first row total is 914 into first column total is 318 divided by the whole total is 1137.
01:47
We get 255 .63.
01:53
Next is first row total is 914 into first column total is 369 divided by the whole total is 1 ,10069.
02:04
37 we get 296 .63 so all values can be calculated like this we get the expected frequency table is all values can be calculated like this we get the expected frequency table is this so next we have to find the kye square test statistic the equation for finding the kye square test statistic is summation oh i means observative frequency minus expected frequency the whole square divided by expected frequency that is first observed frequency is 264 minus first expected frequency is 255 .63 the whole square divided by expected frequency is 255 .63 plus etc all values can be calculator like this and final value is observed frequency is 22 minus expected is 20 .97 the whole square divided by the expected frequency is 20 .97 that is we get 4 .366 as our kai square test statistic then next we have to find the p value here our degrees of freedom is column minus 1 into number of column minus 1 into number of row minus 1...