Month Revenue (Thousands 1 2 3 4 5 6 7 8 9 325 547 539 580 640 699 697 708 797 The summary output from a regression analysis of the data is also provided. Multiple R R Square Adjusted R Square Standard Error Month Regression Statistics Observations 10 11 12 13 14 15 16 17 18 Revenue (Thousands of Dollars) 819 0.93482589 0.873899444 0.86601816 59.72646232 18 ANOVA df SS MS Regression 1 395,546.939628 395,546.939628 Residual 16 57,076.004816 3567.250301 Total 17 452,622.944444 Coefficients Standard Error Intercept 470.50326797 29.37121675 Month 28.57275542 2.713437989 10.530093385 831 848 860 863 879 903 912 908 F 110.8828667 t Stat P-value 16.01919566 2.83992E-11 1.33255E-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.
Month Revenue (Thousands 1 2 3 4 5 6 7 8 9 325 547 539 580 640 699 697 708 797 The summary output from a regression analysis of the data is also provided. Multiple R R Square Adjusted R Square Standard Error Month Regression Statistics Observations 10 11 12 13 14 15 16 17 18 Revenue (Thousands of Dollars) 819 0.93482589 0.873899444 0.86601816 59.72646232 18 ANOVA df SS MS Regression 1 395,546.939628 395,546.939628 Residual 16 57,076.004816 3567.250301 Total 17 452,622.944444 Coefficients Standard Error Intercept 470.50326797 29.37121675 Month 28.57275542 2.713437989 10.530093385 831 848 860 863 879 903 912 908 F 110.8828667 t Stat P-value 16.01919566 2.83992E-11 1.33255E-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.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
100%
![Consider the following monthly revenue data for an up-and-coming technology company.
Month
1
2
3
4
5
6
7
8
9
Sales Data
Revenue (Thousands of Dollars)
325
547
539
580
640
699
697
708
797
The summary output from a regression analysis of the data is also provided.
Month Revenue (Thousands of Dollars)
10
819
11
831
12
848
13
860
14
863
15
879
16
903
17
912
18
908
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
0.93482589
0.873899444
0.86601816
59.72646232
18
ANOVA
df
SS
MS
F
Regression 1 395,546.939628 395,546.939628 110.8828667
Residual 16 57,076.004816
Total 17 452,622.944444
3567.250301
P-value
Coefficients Standard Error
t Stat
Intercept 470.50326797 29.37121675 16.01919566 2.83992E-11
Month 28.57275542 2.713437989 10.530093385 1.33255E-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.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F93d6464e-f155-4940-8921-7114e2ab97cc%2F40b07c7f-c015-44ee-b551-2e9567ab50f9%2Fr2dqd19_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Consider the following monthly revenue data for an up-and-coming technology company.
Month
1
2
3
4
5
6
7
8
9
Sales Data
Revenue (Thousands of Dollars)
325
547
539
580
640
699
697
708
797
The summary output from a regression analysis of the data is also provided.
Month Revenue (Thousands of Dollars)
10
819
11
831
12
848
13
860
14
863
15
879
16
903
17
912
18
908
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
0.93482589
0.873899444
0.86601816
59.72646232
18
ANOVA
df
SS
MS
F
Regression 1 395,546.939628 395,546.939628 110.8828667
Residual 16 57,076.004816
Total 17 452,622.944444
3567.250301
P-value
Coefficients Standard Error
t Stat
Intercept 470.50326797 29.37121675 16.01919566 2.83992E-11
Month 28.57275542 2.713437989 10.530093385 1.33255E-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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