.365
31.2
35.1
35.88

Question 7
4 / 4 points
A real estate builder wishes to determine how house size (House) is influenced by family income
(Income), family size (Size), and education of the head of household (School). House size is measured in
hundreds of square feet, income is measured in thousands of dollars, and education is in years. The
builder randomly selected 50 families and ran the multiple regression. The business literature involving
human capital shows that education influences an individual’s annual income. Combined, these may
influence family size. With this in mind, what should the real estate builder be particularly concerned with
when analyzing the multiple regression model?

Question 8
4 / 4 points
In multiple regression, the __________ procedure permits variables to enter and leave the model at
different stages of its development.

Question 9
4 / 4 points
A regression diagnostic tool used to study the possible effects of collinearity is

Question 10
4 / 4 points
The Variance Inflationary Factor (VIF) measures the
correlation of the
X
variables with the
Y
variable.
correlation of the
X
variables with each other.
contribution of each
X
variable with the
Y
variable after all other
X
variables are
included in the model.
standard deviation of the slope.

Question 11
4 / 4 points
TABLE 15-3
In Hawaii, condemnation proceedings are under way to enable private citizens to own the property
that their homes are built on. Until recently, only estates were permitted to own land, and
homeowners leased the land from the estate. In order to comply with the new law, a large Hawaiian
estate wants to use regression analysis to estimate the fair market value of the land. The following
model was fit to data collected for n = 20 properties, 10 of which are located near a cove.
where Y = Sale price of property in thousands of dollars
X1 = Size of property in thousands of square feet
X2 = 1 if property located near cove, 0 if not
Using the data collected for the 20 properties, the following partial output obtained from Microsoft
Excel is shown:
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.985
R Square 0.970
Standard Error 9.5
Observations 20
ANOVA
df SS MS F Signif F
Regression 5 28324 5664 62.2 0.0001
Residual 14 1279 91
Total 19 29063
Coeff StdError t Stat P-value
Intercept - 32.1 35.7 – 0.90 0.3834

Size 12.2 5.9 2.05 0.0594
Cove – 104.3 53.5 – 1.95 0.0715
Size*Cove 17.0 8.5 1.99 0.0661
SizeSq – 0.3 0.2 – 1.28 0.2204
SizeSq*Cove – 0.3 0.3 – 1.13 0.2749
Referring to Table 15-3, is the overall model statistically adequate at a 0.05 level of significance for
predicting sale price (
Y
)?