00:01
Okay, so we're given a few means and stuff.
00:03
So first of all, we have the mean width, and at the beginning, we're going to use the width as our x.
00:10
So the mean width is 7 .586, with a standard deviation of 0 .873.
00:22
And then for the height, which is going to be our y for now, the mean is 14 .603, and a standard deviation of 1 .6 .3.
00:31
And the correlation coefficient is .8651.
00:35
Okay, so first of all, the slope of the regression equation.
00:40
One way that you can calculate slope is by taking r times the serendivation of the y's over serendivision of the x.
00:49
So we're going to go ahead and calculate that.
00:52
That'll be 0 .8651 times 1 .632 over over 0 .873, which gives us for our slope 1 .6, let's see, let's go, let's go 3 decimal points, so 1 .617.
01:17
Okay.
01:20
Now to write the equation of the best fit line, we also have to find the y intercept, which the y intercept can be computed by taking the y mean minus the slope times the x mean okay so i'm going to take 14 .603 minus 1 .617 times um 7 .586 and that gets me a y -y intercept of 2 .336 so now i can make my inters or my regression equation, y hat is equal to a plus b.
02:15
There you go.
02:20
What fraction of the variability in b and heights can be explained by the linear model of being height versus width.
02:26
Whenever we're asking for the percent of variability or the fraction of the variability, we're always asking for r squared.
02:36
Okay, so in this place, in this case, we're going to take our r of 0 .8651 and square it.
02:47
Okay, and that gives me 0 .7484.
03:01
So that's my r squared value...