A County Real Estate Appraiser Wants to Develop a Statistical $$E ( y ) = \beta _ { 0 } + \beta 1 ^ { x }$$

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: $$E ( y ) = \beta _ { 0 } + \beta 1 ^ { x }$$
where $y =$ appraised value of the house (in thousands of dollars) and $x =$ number of rooms. Using data collected for a sample of $n = 84$ houses in East Meadow, the following results were obtained:
$$\hat { y } = 84.80 + 19.72 x$$
What are the properties of the least squares line, $y = 84.80 + 19.72 x ?$

A) Average error of prediction is 0 , and $S S E$ is minimum.
B) It will always be a statistically useful predictor of $y$ .
C) All 84 of the sample $y$ -values fall on the line.
D) It is normal, mean 0 , constant variance, and independent.