00:01
So we have our x data and our y data, and i'm just going to put down the first few.
00:08
And we have, let me get over to here.
00:11
The first is 135 with 145.
00:15
And we need to give what their standard, we want to find their standardized residuals, which means we're going to need to have the observed y value, and then for the for these the observed y minus the predicted y and then divided by the standard error of the estimate and those will be our residual values and so we need to do a linear regression so when i do my linear regression i'm going to put into my software the lin reg t test and that will allow me to get that standard deviation the standard air and so we're doing that on the list one in list two and i find that that standard air comes out to be a twelve point four six two and we need those two two decimal three decimal places when we we find those values.
01:27
And by the way, the linear regression equation comes out to be 74 .04 plus 0 .3453 times x.
01:39
And we have a correlation coefficient of only 0 .566.
01:45
And we have an r -squared value of 0 .3205.
01:50
So it's not great.
01:52
But let's go through and find all of these observed values and i'm going to go into my into my my software and i'm going to put in i'm going to put into my list the regression equations of stat edit and i'm going to actually put the observed value into my list three so i'm going to squeeze in in my list three that observed that excuse me that predicted value.
02:22
So that is to go to variables, statistics, get to my eq, and i'm going to substitute list one in place of x.
02:30
So this value comes out to be 120 .066 and so on.
02:40
Now we'll get these values.
02:42
So i'm going to go to my list four and i will list these down for you, at least the first few.
02:49
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