Year Event A (in.) Event B (in.) Event C (in.) 1896 71.034 1147.625 249.791 1900 74.915 1418.595 282.868 1904 71.208 1546.479 289.443 1908 74.686 1609.615 294.256 1912 75.853 1779.694 299.397 1920 75.962 1758.933 281.303 1924 78.078 1816.947 293.285 1928 76.658 1863.458 304.937 1932 77.895 1946.929 300.202 1936 79.813 1987.813 316.864 1948 78.245 2078.043 307.247 1952 79.981 2167.086 298.302 1956 83.399 2218.161 307.658 1960 84.734 2330.362 319.331 1964 85.468 2401.885 317.598 1968 87.956 2551.049 350.364 1972 88.051 2535.344 324.723 1976 88.261 2656.896 327.935 1980 93.176 2623.703 336.225 1984 92.374 2622.541 336.555 1988 93.399 2710.081 343.576 1992 92.291 2564.028 334.777 1996 94.086 2732.187 334.782 2000 92.529 2728.896 336.475 2004 92.776 2751.482 338.007 2008 92.945 2709.373 328.137 2012 93.856 2687.268 326.966 2016 93.434 2691.586 329.316

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.) O in., and the Holt-Winters A. 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, B = 0.8, and y=0.3, the Holt-Winters additive seasonality model forecast for the next event is Ft+1 = multiplicative seasonality model forecast for the next event is F++ 1 = in. O B. 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 = 0.8, the double exponential smoothing model forecast for the next event is Ft+1 = in. O in., the three-period moving average forecast for the next event is in., and the four-period moving average forecast for C. 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 the next event is in. 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.) O in., and the Holt-Winters A. 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, B = 0.8, and y=0.3, the Holt-Winters additive seasonality model forecast for the next event is Ft+1 = multiplicative seasonality model forecast for the next event is F++ 1 = in. O B. 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 = 0.8, the double exponential smoothing model forecast for the next event is Ft+1 = in. O in., the three-period moving average forecast for the next event is in., and the four-period moving average forecast for C. 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 the next event is in