Not sure how to use these formulas, can some one give example.

this is the question I'm doing

Problem 15X 0 03- Forecasting OM X Forecasting simple moving X X V luce Bookshell OPERATX Module 2:0 wear.bolt.ca 02/e/content/642495/viewContent/4516615/View Averaging Methods Demand in previous n periods Moving Average Weighted :F, = Moving Average (Weight period w) xDemanderiod)! Weights Exponentia: F =F., +alA. - F.), Smoothing F=forecast a = smoothing A = actual constant 17 1642 De here to search shelf OPERATIX Bcrr Module 2: Ch 3 Problem 15 Part X BC Ch 003 - Forecasting - OPMT-11 X Forecasting - Sim Sks/9781259270185/cfi/1271/4/4@0.00:20.0 Torecasts umg seasonal relatives. Consider a specific part. Echlin makes brake calipers for a particular car model first introduced in 1986. Suppose it is now the end of 1992 and, using steps a to e above, Echlin has forecasted its own sales of this caliper for 1993. Mar Apr May Jun Year Echlin's Sales of Calipers 1990 1991 1992 1993 10,444 10,319 8,477 6,334 (forecast) a. Plot b. Desa c. Plot origi LOS 18. The fol quarterl would y terly sea 0.90, an then obs finally re Echlin also has quarterly sales of these calipers for the past three years. Quarter Q2 1990 2,370 2,058 2,778 3,238 10,444 1991 2,641 2,198 2,518 2,962 10,319 1992 2,281 1,814 2,127 2.255 8,477 Total LOS 19. A farmin terly grail 6), based tonnes): a. Use the 1990-92 quarterly data above to determine the quarterly seasonal relatives. b. Use your answer to part a and 1993 forecast sales (6,334 units) to forecast quarterly sales for 1993. c. Deseasonalize the data, fit an appropriate model to the deseasonalized data, project it four quarters ahead, and reseasonalize the projections to forecast quarterly demand in 1993. Contrast your results with part b. 104 16. A pharmacist has been monitoring sales of a certain over- Ye: Problem 15X 0 03- Forecasting OM X Forecasting simple moving X X V luce Bookshell OPERATX Module 2:0 wear.bolt.ca 02/e/content/642495/viewContent/4516615/View Averaging Methods Demand in previous n periods Moving Average Weighted :F, = Moving Average (Weight period w) xDemanderiod)! Weights Exponentia: F =F., +alA. - F.), Smoothing F=forecast a = smoothing A = actual constant 17 1642 De here to search shelf OPERATIX Bcrr Module 2: Ch 3 Problem 15 Part X BC Ch 003 - Forecasting - OPMT-11 X Forecasting - Sim Sks/9781259270185/cfi/1271/4/4@0.00:20.0 Torecasts umg seasonal relatives. Consider a specific part. Echlin makes brake calipers for a particular car model first introduced in 1986. Suppose it is now the end of 1992 and, using steps a to e above, Echlin has forecasted its own sales of this caliper for 1993. Mar Apr May Jun Year Echlin's Sales of Calipers 1990 1991 1992 1993 10,444 10,319 8,477 6,334 (forecast) a. Plot b. Desa c. Plot origi LOS 18. The fol quarterl would y terly sea 0.90, an then obs finally re Echlin also has quarterly sales of these calipers for the past three years. Quarter Q2 1990 2,370 2,058 2,778 3,238 10,444 1991 2,641 2,198 2,518 2,962 10,319 1992 2,281 1,814 2,127 2.255 8,477 Total LOS 19. A farmin terly grail 6), based tonnes): a. Use the 1990-92 quarterly data above to determine the quarterly seasonal relatives. b. Use your answer to part a and 1993 forecast sales (6,334 units) to forecast quarterly sales for 1993. c. Deseasonalize the data, fit an appropriate model to the deseasonalized data, project it four quarters ahead, and reseasonalize the projections to forecast quarterly demand in 1993. Contrast your results with part b. 104 16. A pharmacist has been monitoring sales of a certain over- Ye