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 Revenue (Thousands of Dollars) Month Revenue (Thousands of Dollars) 321 10 821 542 11 833 540 12 850 581 13 862 641 14 858 700 15 861 698 16 873 710 17 879 799 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 0.921077041 0.848382915 0.83827511 63.80312729 17 ANOVA SS F df MS Regression 1 341,679.531863 341,679.531863 83.9334416 Residual 15 61,062.585784 4070.839052 Total 16 402,742.117647 P-value 14.43245622 3.34210E-10 28.93872549 3.158725486 9.161519612 1.56139E-07 Coefficients Standard Error Intercept 467.13970588 32.36730454 Month t Stat Step 2 of 3. Lising the model from the previous step predict the company's revenue for the 18th month Round to four decimal places if necessary
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 Revenue (Thousands of Dollars) Month Revenue (Thousands of Dollars) 321 10 821 542 11 833 540 12 850 581 13 862 641 14 858 700 15 861 698 16 873 710 17 879 799 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 0.921077041 0.848382915 0.83827511 63.80312729 17 ANOVA SS F df MS Regression 1 341,679.531863 341,679.531863 83.9334416 Residual 15 61,062.585784 4070.839052 Total 16 402,742.117647 P-value 14.43245622 3.34210E-10 28.93872549 3.158725486 9.161519612 1.56139E-07 Coefficients Standard Error Intercept 467.13970588 32.36730454 Month t Stat Step 2 of 3. Lising the model from the previous step predict the company's revenue for the 18th month Round to four decimal places if necessary
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.6: Regression And Median-fit Lines
Problem 6PPS
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Transcribed Image Text:Consider the following monthly revenue data for an up-and-coming cyber security company.
Month
1
2
3
4
5
6
Answer
7
8
9
Revenue (Thousands of Dollars) Month Revenue (Thousands of Dollars)
321
10
821
542
11
12
13
14
15
16
17
540
581
641
700
698
710
799
The summary output from a regression analysis of the data is also provided.
Sales Data
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
0.921077041
0.848382915
0.83827511
63.80312729
17
ANOVA
SS
F
df
MS
Regression 1 341,679.531863 341,679.531863 83.9334416
Residual 15 61,062.585784
4070.839052
Total 16 402,742.117647
833
850
862
858
861
873
879
Coefficients Standard Error
Intercept 467.13970588 32.36730454
Month
P-value
14.43245622 3.34210E-10
28.93872549 3.158725486 9.161519612 1.56139E-07
t Stat
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.
Tabl
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