16. The following results are from data where the dependent variable is the selling price of used cars, the independent variables are similar to those in the above regression along with some additional variables. The data were split into 2 samples and the following regression results were obtained from the split data. SUMMARY OUTPUT Regression Statistics Multiple R 0.846 R Square 0.715 Adjusted R Square 0.653 Standard Error 872.9 Observations 49 ANOVA df SS MS F Significance F Regression 9 76575468 8508385 11.2 1.03933E-08 Residual 40 30479336 761983 Total 49 107054804 Coefficients Standard Error t Stat P-value Intercept 17270.81 2684.71 6.43 0.00 MSRP_Amt 0.35 0.06 5.55 0.00 Manual 384.14 309.31 1.24 0.22 Convertible -814.13 375.34 -2.17 0.04 VIN8_6Cylinder -637.40 452.57 -1.41 0.17 Mileage_Sale -64.41 14.82 -4.35 0.00 ModelYearAge_Months -276.45 41.09 -6.73 0.00 CondReptAmt -0.50 0.49 -1.02 0.31 RepairAmt 1.52 0.70 2.17 0.04 InventoryAge_Days -7.91 10.17 -0.78 0.44 SUMMARY OUTPUT Regression Statistics Multiple R 0.724 R Square 0.524 Adjusted R Square 0.423 Standard Error 1622.0 Observations 52 ANOVA df SS MS F Significance F Regression 9 116147955 12905328 4.90 0.000104741 Residual 40 105247338 2631183 Total 49 221395293 Coefficients Standard Error t Stat P-value Intercept 13992.20 3169.78 4.41 0.00 MSRP_Amt 0.19 0.07 2.72 0.01 Manual 629.14 459.84 1.37 0.18 Convertible -920.90 1060.78 -0.87 0.39 VIN8_6Cylinder -1251.81 752.26 -1.66 0.10 Mileage_Sale -49.71 13.24 -3.75 0.00 ModelYearAge_Months -153.04 55.28 -2.77 0.01 CondReptAmt -0.49 0.32 -1.53 0.13 RepairAmt -1.09 1.09 -0.99 0.33 InventoryAge_Days -19.88 7.84 -2.54 0.01 If we wanted to test for heteroscedasticity, what would be the null and alternative hypothesis?

16. The following results are from data where the dependent variable is the selling price of used cars, the independent variables are similar to those in the above regression along with some additional variables. The data were split into 2 samples and the following regression results were obtained from the split data. SUMMARY OUTPUT Regression Statistics Multiple R 0.846 R Square 0.715 Adjusted R Square 0.653 Standard Error 872.9 Observations 49 ANOVA df SS MS F Significance F Regression 9 76575468 8508385 11.2 1.03933E-08 Residual 40 30479336 761983 Total 49 107054804 Coefficients Standard Error t Stat P-value Intercept 17270.81 2684.71 6.43 0.00 MSRP_Amt 0.35 0.06 5.55 0.00 Manual 384.14 309.31 1.24 0.22 Convertible -814.13 375.34 -2.17 0.04 VIN8_6Cylinder -637.40 452.57 -1.41 0.17 Mileage_Sale -64.41 14.82 -4.35 0.00 ModelYearAge_Months -276.45 41.09 -6.73 0.00 CondReptAmt -0.50 0.49 -1.02 0.31 RepairAmt 1.52 0.70 2.17 0.04 InventoryAge_Days -7.91 10.17 -0.78 0.44 SUMMARY OUTPUT Regression Statistics Multiple R 0.724 R Square 0.524 Adjusted R Square 0.423 Standard Error 1622.0 Observations 52 ANOVA df SS MS F Significance F Regression 9 116147955 12905328 4.90 0.000104741 Residual 40 105247338 2631183 Total 49 221395293 Coefficients Standard Error t Stat P-value Intercept 13992.20 3169.78 4.41 0.00 MSRP_Amt 0.19 0.07 2.72 0.01 Manual 629.14 459.84 1.37 0.18 Convertible -920.90 1060.78 -0.87 0.39 VIN8_6Cylinder -1251.81 752.26 -1.66 0.10 Mileage_Sale -49.71 13.24 -3.75 0.00 ModelYearAge_Months -153.04 55.28 -2.77 0.01 CondReptAmt -0.49 0.32 -1.53 0.13 RepairAmt -1.09 1.09 -0.99 0.33 InventoryAge_Days -19.88 7.84 -2.54 0.01 If we wanted to test for heteroscedasticity, what would be the null and alternative hypothesis?

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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SUMMARY OUTPUT

Regression Statistics

Multiple R	0.846
R Square	0.715
Adjusted R Square	0.653
Standard Error	872.9
Observations	49

ANOVA
	df	SS	MS	F	Significance F
Regression	9	76575468	8508385	11.2	1.03933E-08
Residual	40	30479336	761983
Total	49	107054804

	Coefficients	Standard Error	t Stat	P-value
Intercept	17270.81	2684.71	6.43	0.00
MSRP_Amt	0.35	0.06	5.55	0.00
Manual	384.14	309.31	1.24	0.22
Convertible	-814.13	375.34	-2.17	0.04
VIN8_6Cylinder	-637.40	452.57	-1.41	0.17
Mileage_Sale	-64.41	14.82	-4.35	0.00
ModelYearAge_Months	-276.45	41.09	-6.73	0.00
CondReptAmt	-0.50	0.49	-1.02	0.31
RepairAmt	1.52	0.70	2.17	0.04
InventoryAge_Days	-7.91	10.17	-0.78	0.44

SUMMARY OUTPUT

Regression Statistics

Multiple R	0.724
R Square	0.524
Adjusted R Square	0.423
Standard Error	1622.0
Observations	52

ANOVA
	df	SS	MS	F	Significance F
Regression	9	116147955	12905328	4.90	0.000104741
Residual	40	105247338	2631183
Total	49	221395293

	Coefficients	Standard Error	t Stat	P-value
Intercept	13992.20	3169.78	4.41	0.00
MSRP_Amt	0.19	0.07	2.72	0.01
Manual	629.14	459.84	1.37	0.18
Convertible	-920.90	1060.78	-0.87	0.39
VIN8_6Cylinder	-1251.81	752.26	-1.66	0.10
Mileage_Sale	-49.71	13.24	-3.75	0.00
ModelYearAge_Months	-153.04	55.28	-2.77	0.01
CondReptAmt	-0.49	0.32	-1.53	0.13
RepairAmt	-1.09	1.09	-0.99	0.33
InventoryAge_Days	-19.88	7.84	-2.54	0.01

If we wanted to test for heteroscedasticity, what would be the null and alternative hypothesis?

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