The Dean of the Business School at a Small Florida $$E ( y ) = \beta _ { 0 } + \beta _ { 1 } x$$

The dean of the Business School at a small Florida college wishes to determine whether the grade-point average (GPA) of a graduating student can be used to predict the graduateʹs starting
Salary. More specifically, the dean wants to know whether higher GPAs lead to higher starting
Salaries. Records for 23 of last yearʹs Business School graduates are selected at random, and data
On GPA (x) and starting salary (y, in $thousands) for each graduate were used to fit the model $$E ( y ) = \beta _ { 0 } + \beta _ { 1 } x$$
The results of the simple linear regression are provided below.
$\begin{array} { l l } \hat { y } = 4.25 + 2.75 x , & S S x y = 5.15 , S S x x = 1.87 \\ & S S y y = 15.17 , S S E = 1.0075 \\ \text { Range of the } x \text {-values: } & 2.23 - 3.85 \\ \text { Range of the } y \text {-values: } & 9.3 - 15.6 \end{array}$
Range of the $y$ -values: $\quad 9.3 - 15.6$
Suppose a $95 \%$ prediction interval for $y$ when $x = 3.00$ is $( 16,21 )$ . Interpret the interval.

A) We are $95 \%$ confident that the starting salary of a Business School graduate will increase between $\$ 16,000$ and $\$ 21,000$ for every 3 -point increase in GPA.
B) We are $95 \%$ confident that the mean starting salary of all Business School graduates with GPAs of $3.00$ will fall between $\$ 16,000$ and $\$ 21,000$ .
C) We are $95 \%$ confident that the starting salary of a Business School graduate will fall between $\$ 16,000$ and $\$ 21,000$ .
D) We are $95 \%$ confident that the starting salary of a Business School graduate with a GPA of $3.00$ will fall between $\$ 16,000$ and $\$ 21,000$ .