The image shows the estimation of the model with quarterly car sales in the U.S. from 1975 to 1990.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? Number of obs =F( 2,Prob > FSourcedfMS6461) =12.21Model.327202242.163601120.0000R-aquaredAdj R-squared -Root MSEResidual.81728658761.0133981410.28590.2625Total|1.1444888363.018166489.115751gne |Coef.Std. Err.tP>|t|[954 Conf. Interval)lpricelincome-.8280926.1838504-4.500.000-1.195724-.46046112.399991.48602614.940.0001.4281213.37186cons5.92543.484366212.230.0004.956886.89398

Regression Analysis: Regression analysis is used to estimate the Linear relationship between two or…

Answered: The image shows the estimation of the…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The image shows the estimation of the model with quarterly car sales in the U.S. from 1975 to 1990.

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?

Number of obs =
F( 2,
Prob > F
Source
df
MS
64
61) =
12.21
Model
.32720224
2
.16360112
0.0000
R-aquared
Adj R-squared -
Root MSE
Residual
.817286587
61
.013398141
0.2859
0.2625
Total|
1.14448883
63
.018166489
.11575
1gne |
Coef.
Std. Err.
t
P>|t|
[954 Conf. Interval)
lprice
lincome
-.8280926
.1838504
-4.50
0.000
-1.195724
-.4604611
2.399991
.4860261
4.94
0.000
1.428121
3.37186
cons
5.92543
.4843662
12.23
0.000
4.95688
6.89398

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