The management team for the company in question 2 is concerned about seasonal sales trends for the business and would like to compare its regular regression forecast to a seasonally adjusted forecast. At the end of 2020, it has recorded the following actual sales: January 99,000 May 151,000 September 155,000 February 111,000 June 177,000 October 145,000 March 140,000 July 194,000 November 136,000 April 122,000 August 179,000 December 132,000 a. Based on the sales data for 2019 and 2020, develop a new linear regression equation for predicting long-term sales. Write the regression equation in the appropriate form. b. Using the regression equation, generate the regression sales forecast figures for each month of 2019 and 2020. c. Generate the regression sales forecast figures for each month of the following year (2021). d. Calculate the seasonal ratio for each month of 2019 and 2020 (round to 4 decimal places). e. Calculate the seasonal ratio for each month of 2021 by averaging the same respective months from the previous two years (round to 4 decimal places). f. Calculate the seasonal forecast for all 36 months.

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