The following data are the monthly salaries y and the
grade point averages x for students who obtained a
bachelor's degree in business administration.
GPA | Monthly Salary ($) |
2.6 | 3600 |
3.4 | 3900 |
3.6 | 4300 |
3.2 | 3800 |
3.5 | 4200 |
2.9 | 3900 |
The estimated regression equation for these data is = 2090.5 + 581.1x and MSE = 21,284.
Round your answer to two decimal places.
a. Develop a point estimate of the starting
salary for a student with a GPA of 3.0 (to 1 decimal).
$
b. Develop a 95% confidence interval for the
mean starting salary for all students with a 3.0 GPA (to 2
decimals).
$ ( , )
c. Develop a 95% prediction interval for Ryan
Dailey, a student with a GPA of 3.0 (to 2 decimals).
$ ( , )
a. Develop a point estimate of the starting salary for a student with a GPA of 3.0 (to 1 decimal).
= 2090.5 + 581.1*3 = 3833.8
$
b. Develop a 95% confidence interval for the mean starting salary for all students with a 3.0 GPA (to 2 decimals).
predict(model, data.frame(x=3) ,interval = "confidence")
fit lwr
upr
1 3833.784 3643.485 4024.082
(3643.49,4024,08)
c. Develop a 95% prediction interval for Ryan Dailey, a student with a GPA of 3.0 (to 2 decimals).
predict(model, data.frame(x=3) ,interval = "prediction")
fit lwr
upr
1 3833.784 3386.254 4281.313
=(3386.25,4281.31)
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