00:01
We are investigating the relationship between the treatment o .c.
00:06
And myocardial infarctions in a certain population.
00:10
So we're given the data that for 5 ,000 individuals who used o .c.
00:15
13 myocardial infarctions occurred, heart attacks, 13 heart attacks incurred.
00:20
And for 10 ,000 non -oc users, there were seven infarctions.
00:25
So our first task is to construct a two -way table to represent this data.
00:31
Now this is the observed data.
00:34
So we'll compare this to myocardial infarctions versus whether the oc was present.
00:40
And we'll say yes and no.
00:44
And then yes and no.
00:47
So the numbers we were given were that there were seven or there were 13, we'll start with a 13, 13 myocardial infarctions for people who used oc.
00:58
So there's a 13 there.
01:00
Yes for myocardial infarction, yes for oc.
01:03
We also know that there were seven mis of the 10 ,000 non -ocs.
01:09
So for yes, mis, there were seven in the non -oc category.
01:14
Now since we know there are 5 ,000 total people who were using oc, that gives us 4987, 4 ,987 individuals who were, did not experience a marocardial infarction with oc, and then similarly 9 ,993 individuals, did not have a myocardial infarction and we're not using oc.
01:41
So there is our observed data and now we need to make an x table for the expected data.
01:52
So that way we can calculate our kai squared value and determine whether there's a relationship, independence.
02:08
So to calculate the expected data for one of these four observations will still have yes, no, yes, no.
02:20
So all demonstrated here for this box, and then the rest of them follow a similar pattern.
02:25
The first thing we have to do is we need to calculate our totals.
02:33
Total, total.
02:37
So there were a total of 20 myocardial infarctions and a total of 14 ,980 individuals did not experience that.
02:49
There were 5 ,000 oc users and 10 ,000 non -oc users...