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 total number of rooms in the house. Consequently the appraiser decided to fit the simple linear regression model, ^y=β0+β1x , where y= the appraised value of the house (in thousands of dollars) and x= the number of rooms. Using data collected for a sample of n = 74 houses in East Meadow, the following results were obtained:

Step-by-step explanation:

Hello!

The statistical model predicts the appraised value of houses in a section of the county East Meadow (Y) in relationship with the number of rooms of the house (X)

For a sample of n=64 houses the simple linear regression was estimated:

^Y= 74.80 + 24.93X

Range of X: 5 - 11

Range of Y: 160 - 300 ($ thousands of dollars)

Interpretation of the estimates of the y-intercept and the slope

y-intercept:

74.80 thousand dollars is the estimated average value of a house in a section of the county East Meadow when the house has zero rooms.

Slope:

24.93 is the modification of the estimated average value of a house in a section of the county East Meadow when the number of rooms increases on one.

I hope this helps!

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