00:01
I have a real estate agent who wants to be able to predict the monthly rental cost apartments based on the size of the apartment.
00:09
So here's the data and it tells us that the rent is the y.
00:13
That's what we want to predict based on the size.
00:17
This can be your x variable.
00:20
That's important for the work we're going to do.
00:22
So we want to, in size they give us the coefficients for the regression, which is nice.
00:29
Determine the coefficient of determination r squared.
00:33
So for that, i use the function corral equals corral equals.
00:43
Let's hear about that.
00:45
Correl.
00:46
And then you put in your x list and your y list.
00:51
And then out pops the correlation coefficient.
00:53
And then to get r squared, you square that value.
00:55
So 0 .833 is the correlation coefficient.
00:59
Square it.
01:00
You get the coefficient of determination, which is 0 .695.
01:06
We run the three decimals.
01:11
And this stuff, a and b, this is just, i did this just to verify that we, these are in fact the coefficients of the regression.
01:17
So as you see that, that's all that is.
01:20
How do we interpret this r squared? well, let's see.
01:24
The way we interpret this is we say this number is the percent of the variation in y that can be explained by the variation in x.
01:33
So we said x, the size rent is y.
01:36
So measures the proportion of variation in monthly rent.
01:40
That's we're looking for that's our y variable.
01:43
It's one of these two.
01:44
That can be explained.
01:45
It can be explained.
01:47
That's what we're looking for.
01:48
So it's b.
01:52
Now we're going to determine the standard error of the estimate...