00:01
For this problem, we are told that the number of pounds of steam used per month by a chemical plant is thought to be related to the average ambient temperature for that month.
00:08
We're given the temperature and the usage per 1 ,000.
00:12
And for part a, we're asked, assuming that a simple linear regression model is appropriate, fit the regression model relating steam usage to the average temperature.
00:20
And we're asked, what is the estimate of sigma squared.
00:23
So, in this case, what we can do is i've set up in excel.
00:29
So when i use the formula that i'm going to use here, we'll have an m value or our slope and our b or intercept.
00:37
So i'm going to use the lin -est function.
00:41
We select our known y's, then we select our known x's.
00:46
We want b to be calculated normally, so we say true.
00:51
We don't need additional regression statistics.
00:53
Actually, let me just check here.
00:56
Because if this does return a sigma right off the bat, then that would be rad.
01:04
Okay, so we get standard error values for the coefficients.
01:08
Regression sum of squares, residual sum of squares.
01:11
Okay, standard error for the y estimate.
01:15
That is going to be pretty much what we want.
01:18
So actually, let's do this with getting the extra things.
01:23
So that's going to be down there, sort of third row.
01:26
Let's see here...