Interpret the Slope and the Y-Intercept of the Least-Squares Regression $\hat { y } = \beta _ { 0 } + \beta _ { 1 } x$

Interpret the Slope and the y-intercept of the Least-Squares Regression Line
-A county real estate appraiser wants to develop a statistical model to predict the appraised value of houses in a section of the county called East Meadow. One of the many variables thought to be an important predictor of appraised value is the number of rooms in the house. Consequently, the appraiser decided to fit the simple linear regression model, $\hat { y } = \beta _ { 0 } + \beta _ { 1 } x$ , where $y =$ appraised value of the house (in \$thousands) and $x =$ number of rooms. Using data collected for a sample of $n = 74$ houses in East Meadow, the following results were obtained:
$$\begin{array} { l } \hat { y } = 74.80 + 22.75 x \\\mathrm {~s} \beta = 71.24 , t = 1.05 \text { (for testing } \beta _ { 0 } \text { ) } \\\mathrm { s } \beta = 2.63 , \mathrm { t } = 7.49 \text { (for testing } \beta _ { 1 } \text { ) } \\\mathrm { SSE } = 60,775 , \mathrm { MSE } = 841 , \mathrm {~s} = 29 , \mathrm { r } ^ { 2 } = .44\end{array}$$
Range of the x-values: $5 - 11$
Range of the $y$ -values: $160 - 300$
Give a practical interpretation of the estimate of the slope of the least squares line.

A) For each additional room in the house, we estimate the appraised value to increase $\$ 22,750$ .
B) For each additional room in the house, we estimate the appraised value to increase $\$ 74,800$ .
C) For each additional dollar of appraised value, we estimate the number of rooms in the house to increase by $22.75$ rooms.
D) For a house with 0 rooms, we estimate the appraised value to be $\$ 74,800$ .