Consider the following monthly revenue data for an up-and-coming technology company. Sales Data Consider the following monthly revenue data for an up-and-coming technology company. Sales

Consider the following monthly revenue data for an up-and-coming technology company.

Consider the following monthly revenue data for an up-and-coming technology company.

Sales Data

Month	Revenue (Thousands of Dollars)	Month	Revenue (Thousands of Dollars)
1

315

11

827

2

535

12

843

3

533

13

855

4

574

14

849

5

628

15

858

6

659

16

870

7

697

17

901

8

709

18

912

9

789

19

915

10

819

20

921

The summary output from a regression analysis of the data is also provided.

Regression Statistics

Multiple R	0.927968374

R Square	0.861125304

Adjusted R Square	0.853410043

Standard Error	62.99950902

Observations	20

Regression	1

442,986.063534

442,986.063534

111.61324471

Residual	18

71,440.886466

3968.938137

Total	19

514,426.950000

Stat	P-value
Intercept	479.44736842

29.26525366

16.3828195

2.92189E-12

Month	25.80977444

2.443016174

10.564716973

3.80986E-09

Step 1 of 3 :

Write the estimated regression equation using the least squares estimates for b0

and b1. Round to four decimal places, if necessary.

Step 2 of 3: Using the model from the previous step, predict the company's revenue for the 18th month. Round to four decimal places, if necessary.

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.

315

11

827

2

535

12

843

3

533

13

855

4

574

14

849

5

628

15

858

6

659

16

870

7

697

17

901

8

709

18

912

9

789

19

915

10

819

20

921

The summary output from a regression analysis of the data is also provided.

Regression Statistics

Multiple R	0.927968374

R Square	0.861125304

Adjusted R Square	0.853410043

Standard Error	62.99950902

Observations	20

Regression	1

442,986.063534

442,986.063534

111.61324471

Residual	18

71,440.886466

3968.938137

Total	19

514,426.950000

Stat	P-value
Intercept	479.44736842

29.26525366

16.3828195

2.92189E-12

Month	25.80977444

2.443016174

10.564716973

3.80986E-09

Step 1 of 3 :

Write the estimated regression equation using the least squares estimates for b0

and b1. Round to four decimal places, if necessary.

Step 2 of 3: Using the model from the previous step, predict the company's revenue for the 18th month. Round to four decimal places, if necessary.

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.