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...

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

arrow_forward

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 is964.9457

Month<br>Revenue (Thousands of Dollars)<br>Month<br>Revenue (Thousands of Dollars)<br>1<br>321<br>8.<br>710<br>2<br>542<br>9.<br>799<br>540<br>10<br>821<br>4<br>581<br>11<br>833<br>641<br>12<br>850<br>6.<br>700<br>13<br>862<br>7<br>698<br>14<br>858<br>nmary output from a regression analysis of the data is also provided.<br>Regression Statistics<br>Multiple R<br>0.941890566<br>R Square<br>0.887157839<br>Adjusted R Square<br>0.877754326<br>Standard Error<br>55.50745256<br>Observations<br>14<br>ANOVA<br>3.<br>

Extracted 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<br>df<br>SS<br>MS<br>F<br>Regression<br>1<br>290,678.786813 290,678.786813<br>94.34323112<br>Residual<br>12<br>36,972.927473<br>3081.077289<br>Total<br>13<br>327,651.714286<br>Coefficients<br>Standard Error<br>t Stat<br>P-value<br>Intercept<br>428.76923077<br>31.3349928<br>13.68339969<br>1.10591E-08<br>Month<br>35.74505495<br>3.68010827<br>9.71304438<br>4.90081E-07<br>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.<br>

Extracted 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.

Jun 08, 2022

SOLUTION.PDF

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...

Get Answer To This Question

Related Questions & Answers

Submit New Assignment