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Aside from advertising costs, Echo included in his initial regression model the effect of customers' average satisfaction score on the monthly revenue of online stores. Thus, multiple regression analysis was performed. Software Output Coefficients: (Intercept) advertising.cost score Estimate -48389.7911 3.8371 14841.4866 Std. Error t value Pr(Plt) 28528.6469 -1.416 8.1652 8.7593 5.853 8.888812 *** 4653.9858 3.189 8.8829. Signif. codes: 8 8.881 8.81 0.05 0.1 Residual standard error: 33858 on 37 degrees of freedom Multiple R-squared: 8.7884, Adjusted R-squared: 8.6926 F-statistic: 44.94 on 2 and 37 DF, p-value: 1.255e-18 Q37. Given the results of the multiple linear regression analysis, the overall regression model is since the p-value is O significant, very small O significant, very large O not significant, very small O not significant, very large Q38. The monthly revenue of online stores is linearly dependent on O advertising cost only Osatisfaction score only O advertising cost and satisfaction score Oneither advertising cost nor satisfaction score

Aside from advertising costs, Echo included in his initial regression model the effect of customers' average satisfaction score on the monthly revenue of online stores. Thus, multiple regression analysis was performed. Software Output Coefficients: (Intercept) advertising.cost score Estimate -48389.7911 3.8371 14841.4866 Std. Error t value Pr(Plt) 28528.6469 -1.416 8.1652 8.7593 5.853 8.888812 *** 4653.9858 3.189 8.8829. Signif. codes: 8 8.881 8.81 0.05 0.1 Residual standard error: 33858 on 37 degrees of freedom Multiple R-squared: 8.7884, Adjusted R-squared: 8.6926 F-statistic: 44.94 on 2 and 37 DF, p-value: 1.255e-18 Q37. Given the results of the multiple linear regression analysis, the overall regression model is since the p-value is O significant, very small O significant, very large O not significant, very small O not significant, very large Q38. The monthly revenue of online stores is linearly dependent on O advertising cost only Osatisfaction score only O advertising cost and satisfaction score Oneither advertising cost nor satisfaction score

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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Kindly answer the following questions: Use 5% level of significance if needed

Q39. It can be said that, of the total variation in the monthly revenue of online stores is explained by the variation in the advertising costs and customers' satisfaction score. 16.52% O 44.94% 69.26% O70.84% Q40. A researcher wants to determine the factors that affect the number of sacks of rice a farmer usually buys. The explanatory variables include the amount (in kg) of rice harvested in the previous planting season and source of information about the seeds (classified as 0-fellow farmer, 1-seed grower, 2 - technician). There will be dummy variables to be constructed in the model. 02 O 0 O 3 01
Transcribed Image Text:Q39. It can be said that, of the total variation in the monthly revenue of online stores is explained by the variation in the advertising costs and customers' satisfaction score. 16.52% O 44.94% 69.26% O70.84% Q40. A researcher wants to determine the factors that affect the number of sacks of rice a farmer usually buys. The explanatory variables include the amount (in kg) of rice harvested in the previous planting season and source of information about the seeds (classified as 0-fellow farmer, 1-seed grower, 2 - technician). There will be dummy variables to be constructed in the model. 02 O 0 O 3 01
Aside from advertising costs, Echo included in his initial regression model the effect of customers' average satisfaction score on the monthly revenue of online stores. Thus, multiple regression analysis was performed. Software Output Coefficients: (Intercept) advertising.cost score --- Estimate -40389.7911 3.8371 14841.4066 Std. Errort value Pr(Plt) 28528.6469 -1.416 0.1652 8.7593 5.053 0.080012 *** 3.189 8.0829. 4653.9850 Signif. codes: 8 8.801 0.01 0.05 0.1 Residual standard error: 33858 on 37 degrees of freedom Multiple R-squared: 8.7884. Adjusted R-squared: 0.6926 F-statistic: 44.94 on 2 and 37 DF, p-value: 1.255e-18 Q37. Given the results of the multiple linear regression analysis, the overall regression model is since the p-value is O significant, very small Osignificant, very large O not significant, very small Onot significant, very large Q38. The monthly revenue of online stores is linearly dependent on O advertising cost only Osatisfaction score only O advertising cost and satisfaction score Oneither advertising cost nor satisfaction score
Transcribed Image Text:Aside from advertising costs, Echo included in his initial regression model the effect of customers' average satisfaction score on the monthly revenue of online stores. Thus, multiple regression analysis was performed. Software Output Coefficients: (Intercept) advertising.cost score --- Estimate -40389.7911 3.8371 14841.4066 Std. Errort value Pr(Plt) 28528.6469 -1.416 0.1652 8.7593 5.053 0.080012 *** 3.189 8.0829. 4653.9850 Signif. codes: 8 8.801 0.01 0.05 0.1 Residual standard error: 33858 on 37 degrees of freedom Multiple R-squared: 8.7884. Adjusted R-squared: 0.6926 F-statistic: 44.94 on 2 and 37 DF, p-value: 1.255e-18 Q37. Given the results of the multiple linear regression analysis, the overall regression model is since the p-value is O significant, very small Osignificant, very large O not significant, very small Onot significant, very large Q38. The monthly revenue of online stores is linearly dependent on O advertising cost only Osatisfaction score only O advertising cost and satisfaction score Oneither advertising cost nor satisfaction score
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