00:01
So we're given 12 sets of data and we know what the weight of the vehicle is and what its gas mileage is.
00:08
And we're trying to see if there is a relationship here, a linear relationship.
00:14
So i graph the data, by the way, to begin with, because that's something i like to do always to start with.
00:20
And those could have been parts of your question, but i'm just going to show what i see on my screen.
00:28
And i just did a stat plot.
00:29
So my plot looks like this.
00:31
So there definitely looks like a linear association.
00:34
And this is the point up here that i'm going to later identify as the outlier of 27, 20, and 38.
00:41
So that's the one that i think is the most bothersome.
00:44
So you first of all wanted, and i believe it's part c, you wanted to find the least squares regression line.
00:50
And i'll give you more information than that.
00:52
I'm going to have stat and calculate.
00:53
I have my data in list one and list two in my calculator.
00:57
And i'm going to do a stat plot.
00:59
Excuse me, a linear regression between list one and list two.
01:03
And i'll give you that regression equation with four decimal place accuracy.
01:08
So the 43 .7591 is our y intercept and then minus.
01:16
So it has a negative association.
01:18
0 .058.
01:20
Excuse me, x.
01:22
And the r value for this is negative 0 .70198 in the r squared.
01:29
So this is the correlation, and this is the coefficient of determination, which we can list as a percent.
01:38
I'll give it as a decimal, but we can list it as a percent.
01:42
So we know that about 49 percent of the variation in the y variable, the gas knowledge, is explained by this linear relationship...