4. The estimation of the model with quarterly car sales in the U.S. from 1975 to 1990 gives: Source | df MS Number of obs 64 Model Residual | .817286587 F( 2, 61) = Prob > F 12.21 = 0.0000 = 0.2859 .32720224 2 .16360112 R-squared Adj R-squared = 0.2625 Root MSE 61 .013398141 Total | 1.14448883 63 .018166489 .11575 lqnc | Coef. std. Err. P>|t| t [95% Conf. Interval] 1price lincome _cons | -.8280926 .1838504 -4.50 0.000 -1.195724 -.4604611 2.399991 .4860261 4.94 0.000 1.428121 3.37186 5.92543 .4843662 12.23 0.000 4.95688 6.89398 Based on the parameter estimates, what is the predicted effect of a 10% increase in price on the number of cars sold? What would be the effect of that price increase on the value of car sales?

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)
14th Edition
ISBN:9781305506381
Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris
Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris
Chapter4A: Problems In Applying The Linear Regression Model
Section: Chapter Questions
Problem 2E
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4. The estimation of the model with quarterly car sales in the U.S. from 1975 to 1990
gives:
Source |
df
MS
Number of obs =
64
F( 2,
Prob > F
61) =
12.21
Model
.32720224
2
.16360112
0.0000
Residual |
.817286587
61
.013398141
R-squared
Adj R-squared = 0.2625
Root MSE
0.2859
Total | 1.14448883
63
.018166489
.11575
lqne | cCoef.
t P>|t|
std. Err.
[95% Conf. Interval]
1price
lincome
-.4604611
3.37186
6.89398
-.8280926
.1838504
-4.50
0.000
-1.195724
2.399991
. 4860261
4.94
0.000
1.428121
_cons
5.92543
.4843662
12.23
0.000
4.95688
Based on the parameter estimates, what is the predicted effect of a 10% increase in
price on the number of cars sold? What would be the effect of that price increase on
the value of car sales?
Transcribed Image Text:4. The estimation of the model with quarterly car sales in the U.S. from 1975 to 1990 gives: Source | df MS Number of obs = 64 F( 2, Prob > F 61) = 12.21 Model .32720224 2 .16360112 0.0000 Residual | .817286587 61 .013398141 R-squared Adj R-squared = 0.2625 Root MSE 0.2859 Total | 1.14448883 63 .018166489 .11575 lqne | cCoef. t P>|t| std. Err. [95% Conf. Interval] 1price lincome -.4604611 3.37186 6.89398 -.8280926 .1838504 -4.50 0.000 -1.195724 2.399991 . 4860261 4.94 0.000 1.428121 _cons 5.92543 .4843662 12.23 0.000 4.95688 Based on the parameter estimates, what is the predicted effect of a 10% increase in price on the number of cars sold? What would be the effect of that price increase on the value of car sales?
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