Homework: Homework 1 - Foreca... Question 6, 9.5.29 Part 1 of 3 HW Score: 5%, 5 of 100 points O Points: 0 of 15 Save The

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Homework: Homework 1 - Foreca... Question 6, 9.5.29 Part 1 of 3 HW Score: 5%, 5 of 100 points O Points: 0 of 15 Save The

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Homework Homework 1 Foreca Question 6 9 5 29 Part 1 Of 3 Hw Score 5 5 Of 100 Points O Points 0 Of 15 Save The 1
Homework Homework 1 Foreca Question 6 9 5 29 Part 1 Of 3 Hw Score 5 5 Of 100 Points O Points 0 Of 15 Save The 1 (75.03 KiB) Viewed 45 times
Homework Homework 1 Foreca Question 6 9 5 29 Part 1 Of 3 Hw Score 5 5 Of 100 Points O Points 0 Of 15 Save The 2
Homework Homework 1 Foreca Question 6 9 5 29 Part 1 Of 3 Hw Score 5 5 Of 100 Points O Points 0 Of 15 Save The 2 (52.58 KiB) Viewed 45 times
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Homework: Homework 1 - Foreca... Question 6, 9.5.29 Part 1 of 3 HW Score: 5%, 5 of 100 points O Points: 0 of 15 Save The accompanying data provide the winning distances for three separate competitions in a long-running international sporting event. Develop forecasting models for each of the events. Click the icon to view the winning distance data. Develop a forecasting model for Event A. Select the correct choice below and fill in the answer box within your choice. (Round to three decimal places as needed.) A. It is appropriate to include all of the data, and there is a clear linear trend, but seasonality is not present, so a double exponential smoothing model may be the best option. For a = 0.2 and B=0.5, the double exponential smoothing model forecast for the next event is F.+ 4 = in. O B. It is not appropriate to include all the data, so a moving average model may be the best option. The two-period moving average forecast for the next event is in.. the three-period moving average forecast for the next event is in., and the four-period moving average forecast for the next event is in. OC. It is appropriate to include all of the data, seasonality is present, and there is a clear trend, so a Holt-Winters model may be the best option. For a = 0.2, p=0.5, and y= 0.7, the Holt-Winters additive seasonality model forecast for the next event is F. +1 = in., and the Holt-Winters multiplicative seasonality model forecast for the next event is Ft+1= in

Winning Distances X 5% Da De ct ial a ti ta ef or ent Year 1896 1900 1904 1908 1912 1920 1924 1928 1932 1936 1948 1952 1956 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 Onn Event A (in.) 71.233 74.723 70.683 74.636 75.656 76.497 77.618 76.039 77.853 79.727 77.859 80.697 83.384 84.938 85.093 88.598 88.059 88.433 92.676 92.775 93.771 92.419 94.391 92.867 0166 Event B (in.) 1147.223 1418.564 1546.406 1610.174 1779.755 1759.281 1817.018 1862.571 1947.107 1987.699 2078.258 2166.339 2218.778 2330.389 2401.604 2550.754 2534.761 2656.865 2623.415 2622.273 2709.993 2563.881 2731.924 2727.854 Event C (in.) 249.685 282.843 288.595 294.195 299.384 281.797 293.776 304.535 300.704 317.128 308.279 298.702 308.754 320.212 317.061 350.658 324.068 327.965 336.452 335.987 344.108 333.891 335.349 337,409 320 470 2, b Jals Aכר רFלר Print Done

2004 2008 2012 2016 93.156 92.485 93.493 93.528 2752.234 2708.859 2687.375 2691.082 338.478 327.863 327.968 329.748 Print Done
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