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, y=Po+B1x, 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 accompanying results were obtained. Give a practical interpretation of the estimate of the slope of the least squares line. i Click the icon to view the results. A. For each additional dollar of appraised value, the number of rooms in the house is estimated to increase by 22.71 rooms.
B. For a house with 0 rooms, the appraised value is estimated to be $74,800.
C. For each additional room in the house, the appraised value is estimated to increase $22,710
D. For each additional room in the house, the appraised value is estimated to increase $74,800.