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) = β0 + β1x, where y = appraised value of the house (in thousands of dollars) and x = number of rooms. Using data collected for a sample of n = 74 houses in East Meadow, the following results were obtained: = 74.80 + 19.84 x Give a practical interpretation of the estimate of the slope of the least squares line. For a house with 0 rooms, we estimate the appraised value to be $74,800. For each additional room in the house, we estimate the appraised value to increase $74,800. For each additional room in the house, we estimate the appraised value to increase $19,840. For each additional dollar of appraised value, we estimate the number of rooms in the house to increase by 19.84.
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) = β0 + β1x, where y = appraised value of the house (in thousands of dollars) and x = number of rooms. Using data collected for a sample of n = 74 houses in East Meadow, the following results were obtained: = 74.80 + 19.84 x Give a practical interpretation of the estimate of the slope of the least squares line. For a house with 0 rooms, we estimate the appraised value to be $74,800. For each additional room in the house, we estimate the appraised value to increase $74,800. For each additional room in the house, we estimate the appraised value to increase $19,840. For each additional dollar of appraised value, we estimate the number of rooms in the house to increase by 19.84.
Calculus For The Life Sciences
2nd Edition
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Chapter1: Functions
Section1.CR: Chapter 1 Review
Problem 86CR
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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:
where y = appraised value of the house (in thousands of dollars) and x = number of rooms. Using data collected for a sample of n = 74 houses in East Meadow, the following results were obtained:
= 74.80 + 19.84 x
Give a practical interpretation of the estimate of the slope of the least squares line.
E(y) = β0 + β1x,
where y = appraised value of the house (in thousands of dollars) and x = number of rooms. Using data collected for a sample of n = 74 houses in East Meadow, the following results were obtained:
= 74.80 + 19.84 x
Give a practical interpretation of the estimate of the slope of the least squares line.
|
For a house with 0 rooms, we estimate the appraised value to be $74,800.
|
||
|
For each additional room in the house, we estimate the appraised value to increase $74,800.
|
||
|
For each additional room in the house, we estimate the appraised value to increase $19,840.
|
||
|
For each additional dollar of appraised value, we estimate the number of rooms in the house to increase by 19.84.
|
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