Question

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=β0+β1x, 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.

y=74.80+17.79x

sβ=​71.24,

t=1.05

​(for testing β0​)     

sβ=​2.63,

t=7.49

​(for testing β1​)     

SSE=​60,775,

MSE=​841,

s=​29, r2=.44

Range of the​ x-values: 5​ - 11 Range of the​ y-values: 160​ - 300

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A. For each additional room in the​ house, the appraised value is estimated to increase $17​,790.

B.For each additional dollar of appraised​ value, the number of rooms in the house is estimated to increase by 17.79 rooms.

C. For each additional room in the​ house, the appraised value is estimated to increase​ $74,800.

D For a house with 0​ rooms, the appraised value is estimated to be​ $74,800.

Answer 1

here since from above given regression equation: slope= 17790

Correct interpretation of slope is option C:

C. For each additional room in the​ house, the appraised value is estimated to increase​ $74,800.