The appraisal of a warehouse can appear straightforward compared to other apprai
ID: 3223882 • Letter: T
Question
The appraisal of a warehouse can appear straightforward compared to other appraisal assignments. A warehouse appraisal involves comparing a building that is primarily an open shell to other such buildings. However, there are still a number of warehouse attributes that are plausibly related to appraised value. The article "Challenges in Appraising 'Simple' Warehouse Properties" (Donald Sonneman, The Appraisal Journal, April 2001, 174-178) gives the accompanying data on truss height (ft), which determines how high stored goods can be stacked, and sale price ($) per square foot. a. Is it the case that truss height and sale price are "deterministically" related mdash i.e., that sale price is determined completely and uniquely by truss height? b. Construct a scatterplot of the data. What does it suggest? c. Determine the equation of the least squares line. d. Give a point prediction of price when truss height is 27 ft, and calculate the corresponding residual. e. What percentage of observed variation in sale price can be attributed to the approximate linear relationship between truss height and price?Explanation / Answer
Answer:
a).
No, it is not determined completely and uniquely.
b).
There is positive relation between price and height.
c).
Regression Analysis
r²
0.963
n
19
r
0.981
k
1
Std. Error
1.416
Dep. Var.
price
ANOVA table
Source
SS
df
MS
F
p-value
Regression
890.3551
1
890.3551
444.11
1.27E-13
Residual
34.0814
17
2.0048
Total
924.4364
18
Regression output
confidence interval
variables
coefficients
std. error
t (df=17)
p-value
95% lower
95% upper
Intercept
23.7721
1.1135
21.350
1.03E-13
21.4229
26.1214
Height
0.9872
0.0468
21.074
1.27E-13
0.8883
1.0860
The regression line is
Price = 23.7721+0.9872*height
d).
when height =27, the predicted price =23.7721+0.9872*27 =50.4265
residual =48.07-50.4265 = -2.3565
e).
R square =0.963
96.3% of variation in price is explained by height.
Regression Analysis
r²
0.963
n
19
r
0.981
k
1
Std. Error
1.416
Dep. Var.
price
ANOVA table
Source
SS
df
MS
F
p-value
Regression
890.3551
1
890.3551
444.11
1.27E-13
Residual
34.0814
17
2.0048
Total
924.4364
18
Regression output
confidence interval
variables
coefficients
std. error
t (df=17)
p-value
95% lower
95% upper
Intercept
23.7721
1.1135
21.350
1.03E-13
21.4229
26.1214
Height
0.9872
0.0468
21.074
1.27E-13
0.8883
1.0860
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