Question 18
4 / 4 points
The coefficient of multiple determination
r
2
Y
.12
measures the variation around the predicted regression equation.
measures the proportion of variation in
Y
that is explained by
X
1
and
X
measures the proportion of variation in
Y
that is explained by
X
1
holding
X
2
constant.
will have the same sign as
b
1
2
.
.

Question 19
4 / 4 points
The variation attributable to factors other than the relationship between the independent variables and the
explained variable in a regression analysis is represented by

Question 20
4 / 4 points
TABLE 14-3
An economist is interested to see how consumption for an economy (in $ billions) is influenced by gross
domestic product ($ billions) and aggregate price (consumer price index). The Microsoft Excel output of this
regression is partially reproduced below.
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.991
R Square
0.982
Adjusted R Square 0.976
Standard Error
0.299
Observations
10
ANOVA
df
SS
MS
F
Signif F
Regsion
2
33.4163
16.7082
186.325
0.0001
Resdual
7
0.6277
0.0897
Total
9
34.0440

Coeff
StdError
t Stat
P-value
Intcept
– 0.0861
0.5674
– 0.152
0.8837
GDP
0.7654
0.0574
13.340
0.0001
Price
– 0.0006
0.0028
– 0.219
0.8330
Referring to Table 14-3, when the economist used a simple linear regression model with consumption as
the dependent variable and GDP as the independent variable, he obtained an
r
2
value of 0.971. What
additional percentage of the total variation of consumption has been explained by including aggregate
prices in the multiple regression? In other words, the economist was explaining 97.1%, how much has
that percentage or R-square increased after adding "price" as a second independent variable?

Question 21
4 / 4 points
TABLE 14-3
An economist is interested to see how consumption for an economy (in $ billions) is influenced by gross
domestic product ($ billions) and aggregate price (consumer price index). The Microsoft Excel output of this
regression is partially reproduced below.
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.991
R Square
0.982
Adjusted R Square 0.976
Standard Error
0.299
Observations
10
ANOVA
df
SS
MS
F
Signif F
Regsion
2
33.4163
16.7082
186.325
0.0001
Resdual
7
0.6277
0.0897
Total
9
34.0440
Coeff
StdError
t Stat
P-value
Intcept
– 0.0861
0.5674
– 0.152
0.8837
GDP
0.7654
0.0574
13.340
0.0001
Price
– 0.0006
0.0028
– 0.219
0.8330
1.
Referring to Table 14-3, the
p
-value for GDP is