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Month 28.52012384 2.742253998 10.400248796 1.58675E-08 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.

Month 28.52012384 2.742253998 10.400248796 1.58675E-08 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
Problem 1P
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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) 315 535 533 574 628 659 697 709 789 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 Revenue (Thousands of Dollars) 10 819 11 827 12 843 13 855 14 849 15 858 16 870 17 901 18 913 Observations 0.933348483 0.871139392 0.863085604 60.36074188 18 ANOVA df MS SS Regression 1 394,091.071207 394,091.071207 Residual 16 58,294.706570 3643.419161 Total 17 452,385.777778 F 108.16517503 t Stat P-value Coefficients Standard Error 460.94771242 29.68313147 15.52894488 4.53893E-11 28.52012384 2.742253998 10.400248796 1.58675E-08 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) 315 535 533 574 628 659 697 709 789 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 Revenue (Thousands of Dollars) 10 819 11 827 12 843 13 855 14 849 15 858 16 870 17 901 18 913 Observations 0.933348483 0.871139392 0.863085604 60.36074188 18 ANOVA df MS SS Regression 1 394,091.071207 394,091.071207 Residual 16 58,294.706570 3643.419161 Total 17 452,385.777778 F 108.16517503 t Stat P-value Coefficients Standard Error 460.94771242 29.68313147 15.52894488 4.53893E-11 28.52012384 2.742253998 10.400248796 1.58675E-08 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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