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Consider the following monthly revenue data for an up-and-coming cyber security company. Month 1 2 3 4 5 6 7 8 9 Sales Data Revenue (Thousands of Dollars) 321 542 540 581 641 700 698 710 799 The summary output from a regression analysis of the data is also provided. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Month Revenue (Thousands of Dollars) 10 821 11 833 12 850 13 862 14 858 15 861 16 873 17 879 Observations 0.921077041 0.848382915 0.83827511 63.80312729 17 ANOVA SS F df MS Regression 1 341,679.531863 341,679.531863 83.9334416 Residual 15 61,062.585784 4070.839052 Total 16 402,742.117647 Coefficients Standard Error t Stat P-value Intercept 467.13970588 32.36730454 14.43245622 3.34210E-10 Month 28.93872549 3.158725486 9.161519612 1.56139E-07 Step 3 of 3: What percent of the variation in revenue is explained by the linear time trend model? Round to two decimal places, if necessary.

Consider the following monthly revenue data for an up-and-coming cyber security company. Month 1 2 3 4 5 6 7 8 9 Sales Data Revenue (Thousands of Dollars) 321 542 540 581 641 700 698 710 799 The summary output from a regression analysis of the data is also provided. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Month Revenue (Thousands of Dollars) 10 821 11 833 12 850 13 862 14 858 15 861 16 873 17 879 Observations 0.921077041 0.848382915 0.83827511 63.80312729 17 ANOVA SS F df MS Regression 1 341,679.531863 341,679.531863 83.9334416 Residual 15 61,062.585784 4070.839052 Total 16 402,742.117647 Coefficients Standard Error t Stat P-value Intercept 467.13970588 32.36730454 14.43245622 3.34210E-10 Month 28.93872549 3.158725486 9.161519612 1.56139E-07 Step 3 of 3: What percent of the variation in revenue is explained by the linear time trend model? Round to two decimal places, if necessary.

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
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Consider the following monthly revenue data for an up-and-coming cyber security company. Month 1 2 3 4 5 6 7 8 9 Revenue (Thousands of Dollars) 321 542 540 581 641 700 698 710 799 The summary output from a regression analysis of the data is also provided. Sales Data Intercept Month Regression Statistics Multiple R R Square Adjusted R Square Standard Error Month 10 11 12 13 14 15 16 17 Observations Revenue (Thousands of Dollars) 821 833 850 862 858 861 873 879 0.921077041 0.848382915 0.83827511 63.80312729 17 ANOVA df Regression 1 Residual 15 61,062.585784 4070.839052 Total 16 402,742.117647 MS F SS 341,679.531863 341,679.531863 83.9334416 P-value Coefficients Standard Error 467.13970588 32.36730454 14.43245622 3.34210E-10 28.93872549 3.158725486 9.161519612 1.56139E-07 t Stat Step 3 of 3: What percent of the variation in revenue is explained by the linear time trend model? Round to two decimal places, if necessary.
Transcribed Image Text:Consider the following monthly revenue data for an up-and-coming cyber security company. Month 1 2 3 4 5 6 7 8 9 Revenue (Thousands of Dollars) 321 542 540 581 641 700 698 710 799 The summary output from a regression analysis of the data is also provided. Sales Data Intercept Month Regression Statistics Multiple R R Square Adjusted R Square Standard Error Month 10 11 12 13 14 15 16 17 Observations Revenue (Thousands of Dollars) 821 833 850 862 858 861 873 879 0.921077041 0.848382915 0.83827511 63.80312729 17 ANOVA df Regression 1 Residual 15 61,062.585784 4070.839052 Total 16 402,742.117647 MS F SS 341,679.531863 341,679.531863 83.9334416 P-value Coefficients Standard Error 467.13970588 32.36730454 14.43245622 3.34210E-10 28.93872549 3.158725486 9.161519612 1.56139E-07 t Stat Step 3 of 3: What percent of the variation in revenue is explained by the linear time trend model? Round to two decimal places, if necessary.
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