The following data represent the Coca-Cola's revenues (in $ hundred millions) during 2019-2022. Quarter 2019 2020 2021 2022 Oct-Dec Jan-Mar Apr-Jun Jul-Sep 44 56 55 49 45 53 54 49 41 54 53 48 45 57 58 52 (i) Plot the series in a line graph. Do you find any seasonal pattern? Calculate the seasonal index and interpret. (ii) Deseasonalized the data and fit a linear trend to the deseasonalized series. (iii)Obtain the deseasonalized forecasts and seasonal forecasts for 2023 revenues. In which quarter, the seasonal forecast has the most?

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(b) The following data represent the Coca-Cola's revenues (in $ hundred millions) during 2019-2022. Quarter 2019 2020 2021 2022 Oct-Dec Jan-Mar Apr-Jun Jul-Sep 44 56 55 49 45 53 54 49 41 54 53 48 45 57 58 52 (i) Plot the series in a line graph. Do you find any seasonal pattern? Calculate the seasonal index and interpret. (ii) Deseasonalized the data and fit a linear trend to the deseasonalized series. (iii)Obtain the deseasonalized forecasts and seasonal forecasts for 2023 revenues. In which quarter, the seasonal forecast has the most? The least? Is this logical? (iv) To estimate a regression model using seasonal binaries, set up a data set. If the estimated regression using seasonal binaries is given by Revenues = 48.31+ 0.12Time – 5.39 Qtr 1+ 5.74 Qtr 2+ 5.62 Qtr 3 Find the forecast the revenues for each quarter in 2023 and compare the forecasts in (iii).
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