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 + 19.72x
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 = 0.44
Range of the x-values: 5 - 11
Range of the y-values: 160 - 300
Give a practical interpretation of the estimate of the y-intercept of the least squares line.
◦ For each additional room in the house, we estimate the appraised value to increase $19,720.
◦ We estimate the base appraised value for any house to be $74,800.
◦ There is no practical interpretation, since a house with 0 rooms is nonsensical.
◦ For each additional room in the house, we estimate the appraised value to increase $74,800.
Much time and effort occurs between the development of a research idea and the statistical analysis
brain model
Masters and Johnson’s Four-Phase Model of the Sexual Response Cycle
A Model of Phonological Dyslexia
The sliding-filament model
A polygenic model for a threshold trait