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What percent of the variation in revenue is explained by the linear time trend model

What percent of the variation in revenue is explained by the linear time trend model

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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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.
 
NOTE:
 
Step 1

The regression equation for relationship between company's revenue and month is given by

Revenue = 428.7692  + 35.7451Month

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Step 2

The predicted company's revenue for 15th month can be calculated as

Revenue = 428.7692  + (35.7451 × 15) = 964.9457

So the answer is 964.9457

Month Revenue (Thousands of Dollars) Month Revenue (Thousands of Dollars) 1 321 8. 710 2 542 9. 799 540 10 821 4 581 11 833 641 12 850 6. 700 13 862 7 698 14 858 nmary output from a regression analysis of the data is also provided. Regression Statistics Multiple R 0.941890566 R Square 0.887157839 Adjusted R Square 0.877754326 Standard Error 55.50745256 Observations 14 ANOVA 3.
Transcribed Image Text:Month Revenue (Thousands of Dollars) Month Revenue (Thousands of Dollars) 1 321 8. 710 2 542 9. 799 540 10 821 4 581 11 833 641 12 850 6. 700 13 862 7 698 14 858 nmary output from a regression analysis of the data is also provided. Regression Statistics Multiple R 0.941890566 R Square 0.887157839 Adjusted R Square 0.877754326 Standard Error 55.50745256 Observations 14 ANOVA 3.
ANOVA df SS MS F Regression 1 290,678.786813 290,678.786813 94.34323112 Residual 12 36,972.927473 3081.077289 Total 13 327,651.714286 Coefficients Standard Error t Stat P-value Intercept 428.76923077 31.3349928 13.68339969 1.10591E-08 Month 35.74505495 3.68010827 9.71304438 4.90081E-07 Step 2 of 3: Using the model from the previous step, predict the company's revenue for the 15th month. Round to four decimal places, if necessary.
Transcribed Image Text:ANOVA df SS MS F Regression 1 290,678.786813 290,678.786813 94.34323112 Residual 12 36,972.927473 3081.077289 Total 13 327,651.714286 Coefficients Standard Error t Stat P-value Intercept 428.76923077 31.3349928 13.68339969 1.10591E-08 Month 35.74505495 3.68010827 9.71304438 4.90081E-07 Step 2 of 3: Using the model from the previous step, predict the company's revenue for the 15th month. Round to four decimal places, if necessary.
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