00:01
For this problem, the approach that we'll take is to begin by constructing a regression equation for our data, predicting the disposable income based on, or actually predicting the yearly sales based on the disposable income.
00:12
So one thing that i need to note here is that i've actually, we need to swap around the labels for the variables.
00:21
It's not going to change too much mathematically, or anything mathematically here.
00:25
It's just that we are treating the disposable income as the independent variable and the yearly sales as the dependent variable.
00:31
So that being said, we can find the slope of our regression line by taking the sum of xi times yi minus 1 over n times the sum of x times the sum of y, divided by the sum of of xi squared minus 1 over n times the sum of x, or times the square of the sum of x.
01:07
So looking at the results from the table that i have above, we can see that we'll find the slope by taking sum of xi times yi is 180 .5.
01:18
Then we subtract from that 1 over 6 multiplied by sum of x is 42 and sum of y is 24.
01:26
Then we divide that by sum of x squared, which is 316 .5, minus 1 over 6 times 42, or excuse me, times 24 squared.
01:42
And as i said, we do need to swap around which one is x and which one was y.
01:47
So same thing for these squares...