00:01
So in this question, we are given this excel table that has the list of schools and basically has two x variables, one which is the recruiter score and the tuition and fees of these schools in thousands of dollars.
00:19
And the y variable or the dependent variable, which is the salary and the bonus.
00:26
So the salary and the bonus that the students receive is a function of the recruiter score of the school and the tuition and fees.
00:37
So in the first part, we are asked to develop a regression equation that can be used to predict the salary using just the recruiter assessment score.
00:53
So for this equation, now we have all of the data there.
00:58
So we have all the recruiters scores over here.
01:01
And we have all the salaries in column d.
01:06
So let's use the stats tool in excel to get our regression equation.
01:15
So we have our stats tool pack and we're going to use linear regression.
01:19
So our input range here, the y range goes from.
01:24
D1 all the way up to d21, which is over here.
01:32
And our x range, so let's change that, so it goes from d1 to d21, and our x range is the recruiter's score, so it goes from v1 to d21.
01:54
So our input x range is b1.
01:59
Let's make sure we get that.
02:05
So b21.
02:07
We want the labels to be there.
02:09
We set the confidence level to be 99%.
02:12
And we start our output at a23.
02:17
So we start our output here.
02:21
We keep everything else this way and then we click ok, which should give us our linear regression equation, of the salary in terms of the recruiter's score.
02:36
So let's look here.
02:39
So here, so let's make sure our input range is correct.
02:52
Okay, so checking, yes, our input range is correct.
02:58
Now we get the summary output, which will give us our equation.
03:03
So if we go here, we see that the intercept is minus 20 .02 and our coefficient for the recruiter score is 33 .70.
03:14
So we're going to take these values.
03:16
We're going to write our equation...