This first half is for context I don't need help with this part, I need help with the next part!

Please help with the following part, not the parts before this as I know you cannot help with more than a few:

DigiSonic has been the leading supplier of Blu-Ray discs to the U.S. movie studios since 2006. For nearly a decade, the sales of Blu-Ray discs were high thanks to the popularity of the format. However, the demand for the Blu-Ray discs started to decrease last year, as customers started to watch movies from online platforms such as Amazon Instant Video, Google Play Movies and Netflix. DigiSonic is in critical need of a better forecasting method. They need your help to evaluate alternative forecasting methods and provide suggestions. The following table contains the past 12 months' sales data (Note: Month 1 refers to sales made 12 months ago, month 12 refers to sales made last month.) Month Month 1 Sales 1376 1269 1342 1298 1277 1183 Sales 969 992 852 3 9 4 10 839 5 873 11 12 6 791 a. Use a 3-month simple moving average to forecast sales. Compute the Mean Absolute Error (MAE) and the Mean Square Error (MSE) of the forecasts for months 4 to 12. (4pt) b. Using a 3-month weighted average method with weights of 0.5, 0.3 and 0.2 (which are the weights for the most recent, next most recent, and the third most recent month, respectively). Again, compute the MAE and MSE of the forecasts with the weighted moving average method. (3pt) c. Using MAE as the measure, compare the performance of the two forecasting methods (moving average, weighted moving average). Which forecasting method is better? (i) If you use MSE as the measure (instead of MAE) to compare the performance of the two forecasting methods (moving average, weighted moving average), which forecasting method is better? (3pt) DigiSonic would also like to explore a more sophisticated forecasting method. They want you to consider exponential smoothing. a. Using simple exponential smoothing, compute three sets of forecasts for the sales of DigiSonic's Blu-Ray discs, corresponding to a values of 0.2, 0.5 and 0.8. Assume that the forecast for month 1 is 1376. (8pt) b. To visually see the difference between forecasts and actual demand, plot the actual demand as well as the three simple exponential smoothing forecasts computed in the same graph. Identify which value of a produces the most responsive forecast? What is the implication of this finding? (4pt) c. Calculate the MAE and Mean Absolute Percentage Error (MAPE) using the forecast errors for each of these three sets of forecasts. Which a value seems to be most appropriate and explain why. (3pt)