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Quarter Revenue 2014Q1 1050 2014Q2 1005 2014Q3 1169 2014Q4 1420 2015Q1 1566 2015Q2 1824 2015Q3 2085 2015Q4 2405 2016Q1 2566 2016Q2 2886 2016Q3 3231 2016Q4 3536 2017Q1 3661 2017Q2 4100 2017Q3 4584 2017Q4 5113 2018Q1 5442 2018Q2 6105 2018Q3 6679 2018Q4 7430 2019Q1 7696 2019Q2 8381 2019Q3 8995 2019Q4 9954 2020Q1 10219 2020Q2 10808 2020Q3 11601 2020Q4 12742 2021Q1 13503 2021Q2 14809 2021Q3 16110 2021Q4 17780 In R language, Create a time series plot of the Amazon Web Services revenue data. 1) Use linear regression first to model the trend in the time series. After conducting a linear regression to model the trend in the time series, what is the slope coefficient for the trend variable? 2) Calculate accuracy measures for predicted values based on the regression in (1).What is the MAPE based on the results of the linear regression you just conducted? (Round to two decimal places) 3) Based on the pattern in the time series data, choose a regression approach to use to model the trend, and conduct that regression analysis. Based on the pattern in this time series data, what type of regression discussed in this module would be most appropriate to calculate forecasts based on this time series? (a. Quadratic regression, b. logistic regression, c. seasonal regression, d. linear regression) 5) Calculate accuracy measures for predicted values based on the regression in (3). What is the MAPE based on the results of the regression you just conducted? (Round to two decimal places) 6) Forecast Q1, Q2, Q3, and Q4 revenues for Amazon Web Services for 2022 using the results of the second regression analysis. Based on results from the second regression analysis, what is the forecasted revenue for Amazon Web Services for Quarter 3 of 2022? (Round to two decimal places)

Quarter Revenue 2014Q1 1050 2014Q2 1005 2014Q3 1169 2014Q4 1420 2015Q1 1566 2015Q2 1824 2015Q3 2085 2015Q4 2405 2016Q1 2566 2016Q2 2886 2016Q3 3231 2016Q4 3536 2017Q1 3661 2017Q2 4100 2017Q3 4584 2017Q4 5113 2018Q1 5442 2018Q2 6105 2018Q3 6679 2018Q4 7430 2019Q1 7696 2019Q2 8381 2019Q3 8995 2019Q4 9954 2020Q1 10219 2020Q2 10808 2020Q3 11601 2020Q4 12742 2021Q1 13503 2021Q2 14809 2021Q3 16110 2021Q4 17780 In R language, Create a time series plot of the Amazon Web Services revenue data. 1) Use linear regression first to model the trend in the time series. After conducting a linear regression to model the trend in the time series, what is the slope coefficient for the trend variable? 2) Calculate accuracy measures for predicted values based on the regression in (1).What is the MAPE based on the results of the linear regression you just conducted? (Round to two decimal places) 3) Based on the pattern in the time series data, choose a regression approach to use to model the trend, and conduct that regression analysis. Based on the pattern in this time series data, what type of regression discussed in this module would be most appropriate to calculate forecasts based on this time series? (a. Quadratic regression, b. logistic regression, c. seasonal regression, d. linear regression) 5) Calculate accuracy measures for predicted values based on the regression in (3). What is the MAPE based on the results of the regression you just conducted? (Round to two decimal places) 6) Forecast Q1, Q2, Q3, and Q4 revenues for Amazon Web Services for 2022 using the results of the second regression analysis. Based on results from the second regression analysis, what is the forecasted revenue for Amazon Web Services for Quarter 3 of 2022? (Round to two decimal places)

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Question
Quarter Revenue
2014Q1 1050
2014Q2 1005
2014Q3 1169
2014Q4 1420
2015Q1 1566
2015Q2 1824
2015Q3 2085
2015Q4 2405
2016Q1 2566
2016Q2 2886
2016Q3 3231
2016Q4 3536
2017Q1 3661
2017Q2 4100
2017Q3 4584
2017Q4 5113
2018Q1 5442
2018Q2 6105
2018Q3 6679
2018Q4 7430
2019Q1 7696
2019Q2 8381
2019Q3 8995
2019Q4 9954
2020Q1 10219
2020Q2 10808
2020Q3 11601
2020Q4 12742
2021Q1 13503
2021Q2 14809
2021Q3 16110
2021Q4 17780

In R language,

Create a time series plot of the Amazon Web Services revenue data.
1) Use linear regression first to model the trend in the time series. After conducting a linear regression to model the trend in the time
series, what is the slope coefficient for the trend variable?
2) Calculate accuracy measures for predicted values based on the regression in (1).What is the MAPE based on the results of the linear regression you just conducted? (Round to two decimal places)
3) Based on the pattern in the time series data, choose a regression approach to use to model the trend, and conduct that regression analysis. Based on the pattern in this time series data, what type of regression discussed in this module would be most appropriate to calculate forecasts based on this time series? (a. Quadratic regression, b. logistic regression, c. seasonal regression, d. linear regression)
5) Calculate accuracy measures for predicted values based on the regression in (3). What is the MAPE based on the results of the regression you just conducted? (Round to two decimal places)
6) Forecast Q1, Q2, Q3, and Q4 revenues for Amazon Web Services for 2022 using the results of the second regression analysis. Based on results from the second regression analysis, what is the forecasted revenue for Amazon Web Services for Quarter 3 of 2022? (Round to two decimal places)

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