Please attach Excel file so I can study formulas! Thank you

Demand Forecasting for the Inner-city Health Center Inner City Health Center is a federally funded health clinic that serves the needs of the inner-city poor. Currently the center is at the end of third-year operation and is preparing its staffing plan for the upcoming year. The federal government requires that the center prepare a budget request each year. The request is based largely on the forecast of the # of Patient Visit for specific services during the next year. The health center administrator has in the past tried using the last month's # of Patient Visit and has also tried using the average of all historical data to predict the next period's # of Patient Visit for the center. Neither of these two techniques has proven satisfactory due to complicated month to month data pattern. They are currently seeking outside helpers to forecast the # of patient visit for the upcoming January year 2016. The # of patient visit each month in the preceding three years (including the current year) is available In the following Table. Table. Emergency Service Demand for the Inner-city Health Center Month # of patient Visit Year Year Year 2013 2014 2015 Jan. 380 456 546 Feb. 388 533 602 Mar. 479 582 688 Apr. 501 573 680 May 574 649 732 June 565 732 854 July 546 636 812 Aug. 622 722 Sept. 452 574 725 Oct. 625 701 Nov. 451 571 717 Dec. 408 506 603 550 507 Assignment: use Ms. Excel Spreadsheet to do the following. (Suggestion: copy and paste the data to Excel spreadsheet and re-arrange data into One Dimension Array to make your job easier.) 1) Forecast a) Use a 4- Month Simple Moving Average Method to forecast the # of the emergency visit from May 2013 to January 2016. b) Use Linear Projection Forecast Method to forecast the # of the emergency visit from January 2013 to January 2016. c) Use Exponential Smoothing Forecast Method with a = 0.25 to forecast the # of the emergency visit from June 2013 to January 2016. Assume that initial forecast for May 2013 is 600. 2) Plot One (nice) Chart for Data Series over time: a) the historical data series, b) forecasts data series obtained in la), 1b) and lc). 3) Use either MAD, or MSE, or MAPE (you choose) forecast Error Measurement to determine which of the forecasts from la) 1b) or 1c) provides the best smallest) forecasting error for the given historical data set. (Please note: error comparison of different forecast methods should be done on a Consistent Base, that is, we should compare forecast results of different forecast methods based on forecast results of the Same Range of forecast Periods. 4) For the Exponential Smoothing forecast obtained in c), use Tracking Signal to monitor the forecast results and make conclusion on whether or not the forecasts are Biased, assuming C = 3, and -C = -3 are used as tracking signal control limits. 5) Use the same Forecast Error Measurement you used in question 3), find the best smoothing parameter a (i.e. the a that leads to the smallest forecast error) of Exponential Smoothing Forecast Method. 6) For the given historical data set, a) discuss the potential weakness when applying each method in part 1a), 1b) and 1c). b) then propose a Forecast Method that might be better than forecast methods 1a) - 1c). Use your Forecast Method to do the forecasts and then use the same forecast error measurement to compare it with the results from part la), lb) and lc). C) Demand Forecasting for the Inner-city Health Center Inner City Health Center is a federally funded health clinic that serves the needs of the inner-city poor. Currently the center is at the end of third-year operation and is preparing its staffing plan for the upcoming year. The federal government requires that the center prepare a budget request each year. The request is based largely on the forecast of the # of Patient Visit for specific services during the next year. The health center administrator has in the past tried using the last month's # of Patient Visit and has also tried using the average of all historical data to predict the next period's # of Patient Visit for the center. Neither of these two techniques has proven satisfactory due to complicated month to month data pattern. They are currently seeking outside helpers to forecast the # of patient visit for the upcoming January year 2016. The # of patient visit each month in the preceding three years (including the current year) is available In the following Table. Table. Emergency Service Demand for the Inner-city Health Center Month # of patient Visit Year Year Year 2013 2014 2015 Jan. 380 456 546 Feb. 388 533 602 Mar. 479 582 688 Apr. 501 573 680 May 574 649 732 June 565 732 854 July 546 636 812 Aug. 622 722 Sept. 452 574 725 Oct. 625 701 Nov. 451 571 717 Dec. 408 506 603 550 507 Assignment: use Ms. Excel Spreadsheet to do the following. (Suggestion: copy and paste the data to Excel spreadsheet and re-arrange data into One Dimension Array to make your job easier.) 1) Forecast a) Use a 4- Month Simple Moving Average Method to forecast the # of the emergency visit from May 2013 to January 2016. b) Use Linear Projection Forecast Method to forecast the # of the emergency visit from January 2013 to January 2016. c) Use Exponential Smoothing Forecast Method with a = 0.25 to forecast the # of the emergency visit from June 2013 to January 2016. Assume that initial forecast for May 2013 is 600. 2) Plot One (nice) Chart for Data Series over time: a) the historical data series, b) forecasts data series obtained in la), 1b) and lc). 3) Use either MAD, or MSE, or MAPE (you choose) forecast Error Measurement to determine which of the forecasts from la) 1b) or 1c) provides the best smallest) forecasting error for the given historical data set. (Please note: error comparison of different forecast methods should be done on a Consistent Base, that is, we should compare forecast results of different forecast methods based on forecast results of the Same Range of forecast Periods. 4) For the Exponential Smoothing forecast obtained in c), use Tracking Signal to monitor the forecast results and make conclusion on whether or not the forecasts are Biased, assuming C = 3, and -C = -3 are used as tracking signal control limits. 5) Use the same Forecast Error Measurement you used in question 3), find the best smoothing parameter a (i.e. the a that leads to the smallest forecast error) of Exponential Smoothing Forecast Method. 6) For the given historical data set, a) discuss the potential weakness when applying each method in part 1a), 1b) and 1c). b) then propose a Forecast Method that might be better than forecast methods 1a) - 1c). Use your Forecast Method to do the forecasts and then use the same forecast error measurement to compare it with the results from part la), lb) and lc). C)