The cubic model seems to fit the data best among those three specification in Figure 9.1 of Stock and Watson (the left graph on page 19 of Lecture Slides 7). It might be worth considering the quadratic specification because the latter is also a polynomial specification and simpler than the cubic model. Choose the wrong statement. Oa. The adjusted Rés of the quadratic model and cubic model are 0.4762 and 0.4863, respectively. O b. You can estimate the quadratic regression function by the following R-code: > Im_robust(score4 - income + I(income^2), se_type = "HC1") O C. The estimated quadratic regression is strictly increasing in District Income for every point in the sample range of District Income. O d. According to the estimated quadratic model, the t-ratio to test Ho: the true model is linear, is -6.632. O e. The estimated intercept of the quadratic model is statistically significant at the 5% level.

The cubic model seems to fit the data best among those three specification in Figure 9.1 of Stock and Watson (the left graph on page 19 of Lecture Slides 7). It might be worth considering the quadratic specification because the latter is also a polynomial specification and simpler than the cubic model. Choose the wrong statement. Oa. The adjusted Rés of the quadratic model and cubic model are 0.4762 and 0.4863, respectively. O b. You can estimate the quadratic regression function by the following R-code: > Im_robust(score4 - income + I(income^2), se_type = "HC1") O C. The estimated quadratic regression is strictly increasing in District Income for every point in the sample range of District Income. O d. According to the estimated quadratic model, the t-ratio to test Ho: the true model is linear, is -6.632. O e. The estimated intercept of the quadratic model is statistically significant at the 5% level.

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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Concept explainers

Maximization

Have you ever seen a bridge in the shape of a parabola? Where is its maximum on the bridge? The maximum of that bridge is the highest point on that bridge. The process of finding the maximum of the bridge here is called the maximization.

Question

Example: Test Scores and Class Size sw Section 9.4
Test score
740
Test score
780
Linear regression
720
Linear-log regression
760
Linear-log regression
700
740
680
Cubic regression
720
Cubic regression
700
660
680
640
660
620
640
600
620
10
20
30
40
50
60
10
20
30
40
50
District income
District income
(thousands of dollars)
(thousands of dollars)
• = (MA, CA) for TestScore vs Income
(Left, Right)
They are similar: the relationship is steeper for low value of income
• Best functional forms differ (Cubic for MA, Linear-log for CA)

The cubic model seems to fit the data best among those three specification in Figure 9.1 of Stock and Watson (the left graph on page 19 of Lecture Slides 7). It might be worth considering the quadratic
specification because the latter is also a polynomial specification and simpler than the cubic model. Choose the wrong statement.
a.
The adjusted R's of the quadratic model and cubic model are 0.4762 and 0.4863, respectively.
Ob.
You can estimate the quadratic regression function by the following R-code:
> Im_robust(score4 - income + I(income^2), se_type = "HC1")
O C. The estimated quadratic regression is strictly increasing in District Income for every point in the sample range of District Income.
d.
According to the estimated quadratic model, the t-ratio to test
Ho: the true model is linear.
is -6.632.
O e. The estimated intercept of the quadratic model is statistically significant at the 5% level.

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