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Dependent Variable: SALES Method: Least Squares Date: 09/04/20 Time: 22:23 Heteroskedasticity Test. White Null hypothesis: Homoskedasticity 0.4936 Sample: 175 Included observations: 75 F-statistic Obs*R-squared Scaled explained s 0.888641 Prob. F(5,69) 4.537390 Prob. Chi-Square(5) 4.546183 Prob. Chi-Square(5) 0.4749 0.4737 Variable Coefficient Std. Error t-Statistic Prob. 6.351638 Test Equation: Dependent Variable: RESID'2 Method: Least Squares Date: 09/05/20 Time: 10:55 118.9136 18.72172 0.0000 PRICE ADVERT -7.907854 1.095993 -7.215241 0.0000 1.862584 0.683195 2.726283 0.0080 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic 0.448258 Mean dependent var 0.432932 SD. dependent var 4.886124 Akalke info criterion 1718.943 Schwarz criterion 77.37467 Sample: 175 Induded observations: 75 6.488537 6.049854 6.142553 Variable Coefficient Std. Error 1-Statistic Prob. -223.8695 Hannan-Quinn criter. 6.086868 -0.938179 -1.133925 0.725819 1.068521 0.116646 0.3514 0 2608 0.4704 29.24786 Durbin-Watson stat 2.183037 -583.6848 622.1463 Prob(F-statistic) -21.44581 6.372200 0.000000 PRICE'2 PRICE'ADVERT 18.91290 8.779323 PRICE 232.2527 217.3591 7.058356 0.2890 ADVERT2 0.823326 0 9075 ADVERT -46.19697 56.24529 -0.821348 0.4143 What can you conclude from the above statistical results? Select one: O a. The regression shows the evidence of autocorrelation O b. The regression shows the evidence of no heteroskedasticity Oc. The regression shows the evidence of non-normality O d. The regression shows the evidence of heteroskedasticity

Dependent Variable: SALES Method: Least Squares Date: 09/04/20 Time: 22:23 Heteroskedasticity Test. White Null hypothesis: Homoskedasticity 0.4936 Sample: 175 Included observations: 75 F-statistic Obs*R-squared Scaled explained s 0.888641 Prob. F(5,69) 4.537390 Prob. Chi-Square(5) 4.546183 Prob. Chi-Square(5) 0.4749 0.4737 Variable Coefficient Std. Error t-Statistic Prob. 6.351638 Test Equation: Dependent Variable: RESID'2 Method: Least Squares Date: 09/05/20 Time: 10:55 118.9136 18.72172 0.0000 PRICE ADVERT -7.907854 1.095993 -7.215241 0.0000 1.862584 0.683195 2.726283 0.0080 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic 0.448258 Mean dependent var 0.432932 SD. dependent var 4.886124 Akalke info criterion 1718.943 Schwarz criterion 77.37467 Sample: 175 Induded observations: 75 6.488537 6.049854 6.142553 Variable Coefficient Std. Error 1-Statistic Prob. -223.8695 Hannan-Quinn criter. 6.086868 -0.938179 -1.133925 0.725819 1.068521 0.116646 0.3514 0 2608 0.4704 29.24786 Durbin-Watson stat 2.183037 -583.6848 622.1463 Prob(F-statistic) -21.44581 6.372200 0.000000 PRICE'2 PRICE'ADVERT 18.91290 8.779323 PRICE 232.2527 217.3591 7.058356 0.2890 ADVERT2 0.823326 0 9075 ADVERT -46.19697 56.24529 -0.821348 0.4143 What can you conclude from the above statistical results? Select one: O a. The regression shows the evidence of autocorrelation O b. The regression shows the evidence of no heteroskedasticity Oc. The regression shows the evidence of non-normality O d. The regression shows the evidence of heteroskedasticity

MATLAB: An Introduction with Applications
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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Dependent Variable: SALES Method: Least Squares Date: 09/04/20 Time: 22:23 Heteroskedasticity Test White Null hypothesis: Homoskedasticity 0.4936 Sample: 175 Included observations: 75 F-statistic Obs*R-squared Scaled explained s 0.888641 Prob. F(5,69) 4.537390 Prob. Chi-Square(5) 4.546183 Prob. Chi-Square(5) 0.4749 0.4737 Variable Coefficient Std. Error t-Statistic Prob. 6.351638 Test Equation: Dependent Variable: RESID'2 Method: Least Squares Date: 09/05/20 Time: 10:55 118.9136 18.72172 0.0000 -7.215241 2.726283 PRICE -7.907854 1.095993 0.683195 0.0000 ADVERT 1.862584 0.0080 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic 0.448258 Mean dependent var 0.432932 SD. dependent var 4.886124 Akalke info criterion 1718.943 Schwarz criterion 77.37467 Sample: 175 Induded observations: 75 6.488537 6.049854 6.142553 Variable Coefficient Std. Error 1-Statistic Prob. -223.8695 Hannan-Quinn criter. 6.086868 -0.938179 -1.133925 0.725819 1.068521 0.116646 0.3514 0.2608 0.4704 0.2890 0 9075 29.24786 Durbin-Watson stat 2.183037 -583.6848 622.1463 Prob(F-statistic) 0.000000 PRICE'2 PRICE"ADVERT -21.44581 6.372200 18.91290 8.779323 PRICE 232.2527 217.3591 0.823326 7.058356 56.24529 ADVERT2 ADVERT -46.19697 -0.821348 0.4143 What can you conclude from the above statistical results? Select one: O a. The regression shows the evidence of autocorrelation O b. The regression shows the evidence of no heteroskedasticity Oc. The regression shows the evidence of non-normality O d. The regression shows the evidence of heteroskedasticity
Transcribed Image Text:Dependent Variable: SALES Method: Least Squares Date: 09/04/20 Time: 22:23 Heteroskedasticity Test White Null hypothesis: Homoskedasticity 0.4936 Sample: 175 Included observations: 75 F-statistic Obs*R-squared Scaled explained s 0.888641 Prob. F(5,69) 4.537390 Prob. Chi-Square(5) 4.546183 Prob. Chi-Square(5) 0.4749 0.4737 Variable Coefficient Std. Error t-Statistic Prob. 6.351638 Test Equation: Dependent Variable: RESID'2 Method: Least Squares Date: 09/05/20 Time: 10:55 118.9136 18.72172 0.0000 -7.215241 2.726283 PRICE -7.907854 1.095993 0.683195 0.0000 ADVERT 1.862584 0.0080 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic 0.448258 Mean dependent var 0.432932 SD. dependent var 4.886124 Akalke info criterion 1718.943 Schwarz criterion 77.37467 Sample: 175 Induded observations: 75 6.488537 6.049854 6.142553 Variable Coefficient Std. Error 1-Statistic Prob. -223.8695 Hannan-Quinn criter. 6.086868 -0.938179 -1.133925 0.725819 1.068521 0.116646 0.3514 0.2608 0.4704 0.2890 0 9075 29.24786 Durbin-Watson stat 2.183037 -583.6848 622.1463 Prob(F-statistic) 0.000000 PRICE'2 PRICE"ADVERT -21.44581 6.372200 18.91290 8.779323 PRICE 232.2527 217.3591 0.823326 7.058356 56.24529 ADVERT2 ADVERT -46.19697 -0.821348 0.4143 What can you conclude from the above statistical results? Select one: O a. The regression shows the evidence of autocorrelation O b. The regression shows the evidence of no heteroskedasticity Oc. The regression shows the evidence of non-normality O d. The regression shows the evidence of heteroskedasticity
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