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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)equalsbeta 0plusbeta 1​x, where y equals the appraised value of the house​ (in thousands of​ dollars) and x equals the number of rooms. What set of hypotheses would you test to determine whether the appraised value is positively linearly related to number of​ rooms?

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Answer:

Step-by-step explanation:

The slope represents how much y is predicted to change on average for each 1 unit in x.

y = appraised value of a house in East Meadow County

x = # of rooms

1 unit of x = 1 additional room

1 unit of y = $1,000

slope = 19.79

Answer: Each additional room in a house in East Meadow County is predicted to increase the appraised value by 19.79 x $1,000 = $19,790, on average.

Note: Since we only sampled homes in East Meadow County, our regression equation is only good to predict appraised values in that county. So, our interpretation must include that fact.

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