00:01
We're taking our x variable, which is the year since 1970, and we're comparing it to the dow stock index for each of those years.
00:12
And we're doing a linear model, and we're going to see how well that fits.
00:16
So we were given some information here.
00:22
And so we want to find the correlation.
00:31
The correlation is r, and r squared is 88 .3%.
00:36
So to find our correlation, all we do is take the square root of 88 .3%.
00:46
And when we do that, we get a correlation of 0 .94.
00:57
So that's about as perfect of correlation as you can get.
01:02
It's strong and positive.
01:05
Now our equation, year since, is always our slope, which in statistics is b.
01:12
And then our intercept, it says intercept, is a.
01:17
So that's our y intercept.
01:18
So when we write the equation, we write it as y hat is equal to negative 2485 .66 plus 354 .24, and i can't fit it on there, times x.
01:41
Where y hat is the predicted dow jones stock index, and x is the year since 1970.
01:55
So in the context, that means our y intercept means that if it is 1970, then we expect the dow jones stock index to be negative 2485 .66...