00:01
For part a, we just have to look at the table, where it says predictor, coefficient, sd coefficient.
00:06
This is going to help us build the linear regression model, because the linear regression is always y equals to, where it says coefficient, and look at where it says percentage, hs grads.
00:19
That percentage is our variables.
00:21
So 59 .66, which is the coefficient times x, minus the coefficient of the coefficient, which is 2 ,970.
00:33
The coefficient of the constant doesn't have any variable.
00:36
So this is a linear regression model.
00:38
This x represents the percentage of age as grads.
00:44
Now we can move on to part b, where it's asking us for what percentage of the variation in a store's monthly cell cannot be explained by its linear dependency.
00:55
We need to find our r -square value first.
00:58
Another squared is equal to the ssr divided by the ssr is the ssr is the sum of squares of regression.
01:14
Sst is the total sum of squares.
01:17
These values are given in the information of the problem.
01:21
Ssr is 7 ,333 ,350, 350.
01:28
Divided by the total sum of squares which is also given as 30 ,488 ,914.
01:43
We do these calculations, we get that the r square value is equal to 0 .240.
01:53
0 .0 .205, which is the same, by the way, as 24 .05%, this is the percent...