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) and x = number of rooms. Using data collected for a sample of
houses in East Meadow, the following results were obtained:
= 74.80 + 17.80x
s
β = 71.24, t = 1.05 (for testing β
0)
s
β = 2.63, t = 7.49 (for testing β
1)
SSE = 60,775, MSE = 841, s = 29, r
2 = .44
Range of the x-values: 5 - 11
Range of the y-values: 160 - 300
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 dollar of appraised value, we estimate the number of rooms in the house to increase by 17.80 rooms.
◦ For each additional room in the house, we estimate the appraised value to increase $17,800.
◦ For each additional room in the house, we estimate the appraised value to increase $74,800.
Cutaway Model of Earth
Model of a Neutron Star’s Interior
The Biopsychosocial Model
What is real and what is an illusion?
A model for RecA-mediated strand exchange
How does PCA reduce the dimension of a statistical problem? How do you select the reduced ...