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Intercept Month Coefficients Standard Error t Stat 460.94771242 29.68313147 15.52894488 28.52012384 2.742253998 10.400248796 P-value 4.53893E-11 1.58675E-08 Step 2 of 3: Using the model from the previous step, predict the company's revenue for the 19th month. Round to four decimal places, if nece

Intercept Month Coefficients Standard Error t Stat 460.94771242 29.68313147 15.52894488 28.52012384 2.742253998 10.400248796 P-value 4.53893E-11 1.58675E-08 Step 2 of 3: Using the model from the previous step, predict the company's revenue for the 19th month. Round to four decimal places, if nece

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 Sales Data Month Revenue (Thousands of Dollars) 315 10 535 11 533 12 574 13 628 14 659 15 697 16 709 17 789 18 The summary output from a regression analysis of the data is also provided. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations Revenue (Thousands of Dollars) 819 827 843 855 849 858 870 901 913 0.933348483 0.871139392 0.863085604 60.36074188 18 ANOVA df SS MS 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 Intercept 460.94771242 29.68313147 15.52894488 4.53893E-11 Month 28.52012384 2.742253998 10.400248796 1.58675E-08 Step 2 of 3: Using the model from the previous step, predict the company's revenue for the 19th month. Round to four 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 Sales Data Month Revenue (Thousands of Dollars) 315 10 535 11 533 12 574 13 628 14 659 15 697 16 709 17 789 18 The summary output from a regression analysis of the data is also provided. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations Revenue (Thousands of Dollars) 819 827 843 855 849 858 870 901 913 0.933348483 0.871139392 0.863085604 60.36074188 18 ANOVA df SS MS 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 Intercept 460.94771242 29.68313147 15.52894488 4.53893E-11 Month 28.52012384 2.742253998 10.400248796 1.58675E-08 Step 2 of 3: Using the model from the previous step, predict the company's revenue for the 19th month. Round to four decimal places, if necessary.
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