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mylen [45]
3 years ago
10

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?
Mathematics
1 answer:
andre [41]3 years ago
7 0

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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lisov135 [29]
Hi there!

In order to solve, you can use substitution. This means that you use one equation and solve for one variable, then use that one equation and plug it into the other equation. Here's how we'd do it:

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