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 A. 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. 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 = 0.9, the double exponential smoothing model forecast for the next event is Ft + 1 = in . O C. 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=0.9, 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 F + 1 = |in. Develop a forecasting model for Event B. Select the correct choice below and fill in the answer box within your choice. (Round to three decimal places as needed.) O 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, p=0.9, 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 F + 1 = |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 i O C. 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 = 0.9, theDevelop a forecasting model for Event C. Select the correct choice below and fill in the answer box within your choice. (Round to three decimal places as needed.) O 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, =0.9, and y = 0.7, the Holt-Winters additive seasonality model forecast for the next event is Ft + 1 = in., and the Holt-Winters multiplicative seasonality model forecast for the next event is F + 1 = |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 . O C. 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 = 0.9, the double exponential smoothing model forecast for the next event is Ft + 1 = |in