: (Y1) is the Company-Z daily sales along the period Pre-COVID-19 (November and December 2019); (Y2) is the same Company-Z daily sales but during the period Post-COVID-19 (April and May 2020); (X1) is the daily inventory holding cost during the period Pre-COVID-19 (November and December 2019); while (X2) is the same daily inventory holding cost but during the period Post-COVID-19 (April and May 2020). Accordingly, in your analysis, you will depend on two main random variables over two time series. The first random variable is the Company-Z daily sales, and the second random variable is the Company-Z daily inventory holding cost. Therefore, the two random variables will be chosen over two periods of time: the first is (Pre-COVID-19: November and December 2019), and the second is (Post-COVID-19: April and May 2020). Your task is to prepare comprehensive report to the company, fulfilling specific requirements outlined below in point. Answer that question: Discuss the appropriate statistical technique that can explain the variation in the daily sales because of the daily inventory holding cost Post-COVID-19 post COVID 19. Justify your answer. I [15 marks) Date Pre-COVID-19 Y1 X1 Date 1/Nov/2019 2/Nov/2019 3/Nov/2019 4/Nov/2019 5/Nov/2019 3226.7 3224.4 3228.1 3226.2 3226.6 8.5 8.2 7.3 8.0 8.0 1/Apr/2020 2/Apr/2020 3/Apr/2020 4/Apr/2020 5/Apr/2020 1714.4 1720.3 1710.7 1711.1 1709.6 13.1 9.8 13.6 14.3 14.3 Y2 Post-COVID-19 X2 6/Nov/2019 7/Nov/2019 8/Nov/2019 9/Nov/2019 10/Nov/2019 11/Nov/2019 3226.7 3228.8 3223.7 3225.0 3222.5 3223.8 7.9 7.6 8.4 8.4 8.3 7.9 6/Apr/20207/Apr/2020 8/Apr/2020 9/Apr/2020 10/Apr/2020 11/Apr/2020 1710.3 1702.6 1718.4 1712.3 1713.8 1718.5 14.7 17.8 11.1 10.8 11.7 7.3 12/Nov/2019 13/Nov/2019 14/Nov/2019 15/Nov/2019 16/Nov/2019 17/Nov/2019 3226.9 3224.9 3230.1 3225.6 3225.1 3225.7 7.7 8.0 7.8 7.9 7.7 7.8 12/Apr/2020 13/Apr/2020 14/Apr/2020 15/Apr/2020 16/Apr/2020 17/Apr/2020 1717.6 1710.5 1713.3 1713.5 1715.1 1718.0 11.0 15.3 14.0 11.0 10.9 10.3 18/Nov/2019 19/Nov/2019 20/Nov/2019 21/Nov/2019 22/Nov/2019 23/Nov/2019 3224.4 3224.9 3226.3 3228.1 3225.7 3224.4 8.4 7.8 7.7 7.4 8.1 8.2 18/Apr/2020 19/Apr/2020 20/Apr/2020 21/Apr/2020 22/Apr/2020 23/Apr/2020 1717.5 1716.1 1715.4 1715.8 1709.0 1710.8 10.9 9.7 12.2 11.2 13.7 13.2 24/Nov/2019 25/Nov/2019 26/Nov/2019 27/Nov/2019 28/Nov/2019 29/Nov/2019 3225.8 3225.7 3227.3 3226.2 3225.6 3226.8 8.1 7.9 8.2 7.8 8.4 7.9 24/Apr/2020 25/Apr/2020 26/Apr/2020 27/Apr/2020 28/Apr/2020 29/Apr/2020 1714.5 1714.4 1715.4 1717.9 1716.2 1716.5 10.5 12.0 12.7 10.2 12.3 11.6 30/Nov/2019 1/Dec/2019 2/Dec/2019 3/Dec/2019 4/Dec/2019 5/Dec/2019 3224.4 3228.5 3227.0 3224.5 3225.0 3227.7 8.1 7.7 7.9 8.0 8.0 7.9 30/Apr/2020 1/May/2020 2/May/2020 3/May/2020 4/May/2020 5/May/2020 1711.6 1717.5 1721.6 1713.2 1713.9 1709.1 15.3 11.8 6.4 12.4 12.8 14.4 6/Dec/2019 7/Dec/20198/Dec/20199/Dec/2019 10/Dec/2019 11/Dec/2019 3228.6 3228.4 3227.0 3227.0 3227.6 3226.8 7.8 7.5 7.9 8.1 7.6 7.6 6/May/2020 7/May/2020 8/May/2020 9/May/2020 10/May/2020 11/May/2020 1721.2 1720.1 1713.9 1712.7 1722.3 1719.7 9.5 9.6 9.5 12.0 8.6 11.1 12/Dec/2019 13/Dec/2019 14/Dec/2019 15/Dec/2019 16/Dec/2019 17/Dec/2019 3229.0 3224.0 3223.6 3228.0 3226.1 3226.5 7.7 7.9 8.0 7.6 8.1 8.0 12/May/2020 13/May/2020 14/May/2020 15/May/2020 16/May/2020 17/May/2020 1713.9 1711.8 1719.0 1720.8 1715.8 1717.4 14.4 12.8 8.0 9.9 13.7 8.1 I 18/Dec/2019 19/Dec/2019 20/Dec/2019 21/Dec/2019 22/Dec/2019 23/Dec/2019 3229.2 3223.5 3227.3 3225.5 3229.1 3225.6 7.6 8.7 7.8 7.7 8.3 18/May/2020 19/May/2020 20/May/2020 21/May/2020 22/May/2020 23/May/2020 1716.0 1710.9 1716.7 1716.4 1715.6 1716.6 11.0 14.8 10.2 11.0 11.2 12.6 7.8 8.2 24/Dec/2019 25/Dec/2019 26/Dec/2019 27/Dec/2019 28/Dec/2019 29/Dec/2019 3224.2 3226.5 3225.4 3224.1 3227.8 3226.7 8.1 8.1 8.4 7.6 7.9 24/May/2020 25/May/2020 26/May/2020 27/May/2020 28/May/2020 29/May/2020 1714.6 1719.1 1716.0 1713.4 1719.2 1718.5 11.7 10.5 10.1 12.9 10.7 8.0 30/Dec/2019 31/Dec/2019 3228.5 3225.4 7.6 7.8 30/May/2020 31/May/2020 1717.4 1720.9 10.2 10.1 : (Y1) is the Company-Z daily sales along the period Pre-COVID-19 (November and December 2019); (Y2) is the same Company-Z daily sales but during the period Post-COVID-19 (April and May 2020); (X1) is the daily inventory holding cost during the period Pre-COVID-19 (November and December 2019); while (X2) is the same daily inventory holding cost but during the period Post-COVID-19 (April and May 2020). Accordingly, in your analysis, you will depend on two main random variables over two time series. The first random variable is the Company-Z daily sales, and the second random variable is the Company-Z daily inventory holding cost. Therefore, the two random variables will be chosen over two periods of time: the first is (Pre-COVID-19: November and December 2019), and the second is (Post-COVID-19: April and May 2020). Your task is to prepare comprehensive report to the company, fulfilling specific requirements outlined below in point. Answer that question: Discuss the appropriate statistical technique that can explain the variation in the daily sales because of the daily inventory holding cost Post-COVID-19 post COVID 19. Justify your answer. I [15 marks) Date Pre-COVID-19 Y1 X1 Date 1/Nov/2019 2/Nov/2019 3/Nov/2019 4/Nov/2019 5/Nov/2019 3226.7 3224.4 3228.1 3226.2 3226.6 8.5 8.2 7.3 8.0 8.0 1/Apr/2020 2/Apr/2020 3/Apr/2020 4/Apr/2020 5/Apr/2020 1714.4 1720.3 1710.7 1711.1 1709.6 13.1 9.8 13.6 14.3 14.3 Y2 Post-COVID-19 X2 6/Nov/2019 7/Nov/2019 8/Nov/2019 9/Nov/2019 10/Nov/2019 11/Nov/2019 3226.7 3228.8 3223.7 3225.0 3222.5 3223.8 7.9 7.6 8.4 8.4 8.3 7.9 6/Apr/20207/Apr/2020 8/Apr/2020 9/Apr/2020 10/Apr/2020 11/Apr/2020 1710.3 1702.6 1718.4 1712.3 1713.8 1718.5 14.7 17.8 11.1 10.8 11.7 7.3 12/Nov/2019 13/Nov/2019 14/Nov/2019 15/Nov/2019 16/Nov/2019 17/Nov/2019 3226.9 3224.9 3230.1 3225.6 3225.1 3225.7 7.7 8.0 7.8 7.9 7.7 7.8 12/Apr/2020 13/Apr/2020 14/Apr/2020 15/Apr/2020 16/Apr/2020 17/Apr/2020 1717.6 1710.5 1713.3 1713.5 1715.1 1718.0 11.0 15.3 14.0 11.0 10.9 10.3 18/Nov/2019 19/Nov/2019 20/Nov/2019 21/Nov/2019 22/Nov/2019 23/Nov/2019 3224.4 3224.9 3226.3 3228.1 3225.7 3224.4 8.4 7.8 7.7 7.4 8.1 8.2 18/Apr/2020 19/Apr/2020 20/Apr/2020 21/Apr/2020 22/Apr/2020 23/Apr/2020 1717.5 1716.1 1715.4 1715.8 1709.0 1710.8 10.9 9.7 12.2 11.2 13.7 13.2 24/Nov/2019 25/Nov/2019 26/Nov/2019 27/Nov/2019 28/Nov/2019 29/Nov/2019 3225.8 3225.7 3227.3 3226.2 3225.6 3226.8 8.1 7.9 8.2 7.8 8.4 7.9 24/Apr/2020 25/Apr/2020 26/Apr/2020 27/Apr/2020 28/Apr/2020 29/Apr/2020 1714.5 1714.4 1715.4 1717.9 1716.2 1716.5 10.5 12.0 12.7 10.2 12.3 11.6 30/Nov/2019 1/Dec/2019 2/Dec/2019 3/Dec/2019 4/Dec/2019 5/Dec/2019 3224.4 3228.5 3227.0 3224.5 3225.0 3227.7 8.1 7.7 7.9 8.0 8.0 7.9 30/Apr/2020 1/May/2020 2/May/2020 3/May/2020 4/May/2020 5/May/2020 1711.6 1717.5 1721.6 1713.2 1713.9 1709.1 15.3 11.8 6.4 12.4 12.8 14.4 6/Dec/2019 7/Dec/20198/Dec/20199/Dec/2019 10/Dec/2019 11/Dec/2019 3228.6 3228.4 3227.0 3227.0 3227.6 3226.8 7.8 7.5 7.9 8.1 7.6 7.6 6/May/2020 7/May/2020 8/May/2020 9/May/2020 10/May/2020 11/May/2020 1721.2 1720.1 1713.9 1712.7 1722.3 1719.7 9.5 9.6 9.5 12.0 8.6 11.1 12/Dec/2019 13/Dec/2019 14/Dec/2019 15/Dec/2019 16/Dec/2019 17/Dec/2019 3229.0 3224.0 3223.6 3228.0 3226.1 3226.5 7.7 7.9 8.0 7.6 8.1 8.0 12/May/2020 13/May/2020 14/May/2020 15/May/2020 16/May/2020 17/May/2020 1713.9 1711.8 1719.0 1720.8 1715.8 1717.4 14.4 12.8 8.0 9.9 13.7 8.1 I 18/Dec/2019 19/Dec/2019 20/Dec/2019 21/Dec/2019 22/Dec/2019 23/Dec/2019 3229.2 3223.5 3227.3 3225.5 3229.1 3225.6 7.6 8.7 7.8 7.7 8.3 18/May/2020 19/May/2020 20/May/2020 21/May/2020 22/May/2020 23/May/2020 1716.0 1710.9 1716.7 1716.4 1715.6 1716.6 11.0 14.8 10.2 11.0 11.2 12.6 7.8 8.2 24/Dec/2019 25/Dec/2019 26/Dec/2019 27/Dec/2019 28/Dec/2019 29/Dec/2019 3224.2 3226.5 3225.4 3224.1 3227.8 3226.7 8.1 8.1 8.4 7.6 7.9 24/May/2020 25/May/2020 26/May/2020 27/May/2020 28/May/2020 29/May/2020 1714.6 1719.1 1716.0 1713.4 1719.2 1718.5 11.7 10.5 10.1 12.9 10.7 8.0 30/Dec/2019 31/Dec/2019 3228.5 3225.4 7.6 7.8 30/May/2020 31/May/2020 1717.4 1720.9 10.2 10.1