plz help its one question

Canton Supplies, Inc., is a service firm that employs approximately 100 people. Because of the necessity of meeting monthly cash obligations, the chief financial officer wants to develop a forecast of monthly cash requirements. Because of a recent change in equipment and operating policy, only the past seven months of data are considered relevant. The change in operations has had a great impact on cash flow. What forecasting model do you recommend? Use the Moving Average and exponential Smoothing Excel templates or other Excel tools to help you answer this question. Cash Required Cash Required Month ($1,000) Month ($1,000) 1 186 5 227 2 211 6 237 3 217 7 198 4 265 Find the best number of months to use in a moving average forecast based on MSE. Do not round intermediate calculations. Round your answers to two decimal places. Number of months MSE 2 3 4 The 4-month moving average model is the best. Find the best single exponential smoothing model by evaluating the MSE from 0.1 to 0.9, in increments of 0.1. Do not round intermediate calculations. Round your answers to two decimal places. Alpha MSE 0.1 0.2 0.3 0.4 > 0.5 The 4-month moving average : model is the best. Find the best single exponential smoothing model by evaluating the MSE from 0.1 to 0.9, in increments of 0.1. Do not round intermediate calculations. Round your answers to two decimal places. Alpha MSE 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 The model based on a smoothing constant of 0.7 is the best Build the linear regression model and calculate the MSE. Do not round intermediate calculations. Round your answers to two decimal places The regression model is Cash Required (Month). The MSE IS How does the best single exponential smoothing model compare with the best moving average model and the linear regression model? The linear regression model has the lowest MSE. andheel pa WY B6 fe 0.2 F 1 G M K C N 0 1 Exponential Smoothing Forecasting 2 Cengage Learning Not for commercial use. 4 Enter data only in yellow cells. The template is designed for up to 20 observation 5 Smoothing constant 0.21 7 Time Period Error Error 2 9 Exponential Smoothing Forecast 10 11 45.00 9.00 50.20 -13 84 12 13 14 28.07 200 15 19.54 2025.00 81.00 252004 191.55 788.04 381.91 69.99 1467.44 3096.44 2968.40 548 18 16 8.37 17 9 3831 10 55.65 100 Observation Forecast 172 172.00 213 172.00 190 181.00 233 182.80 179 192,84 162 190,07 204 184 46 188.37 225 186.69 250 19435 151 205.40 218 19459 #N/A #N/A AN/A AN/A AN/A AN/A AN/A AN/A 13 199.27 19 11 54.48 $ 20 12 23.41 13 AN/A 18.20 SINS 14 711 116 15 16 #N/A #N/A #N/A #N/A AN/A IN/A #N/A #N/A 17 AN/A AN/A AN/A AN/A #N/A WN/A NN/A 11 28 19 20 Next period forecast Time periode 29 MSE 1285 28 12 Formatting Office Update To keep up-to-date with security updates, foes, and improvements, choose Check for Updates B6 0 F G H 1 1 M N 1 Moving Average Forecasting 2 e learning Not for commercial use 3 Anterdut only in yellow cells. The templates designed for up to 70 observations and 2.1.4 5 Pertod in moving average, k (2.3. or 4) 7 # Time Period Error Error 9 Moving Average Forecast 2 10 11 12 93 14 15 5 Observation Forecast 122 212 190 WNIA 191.00 120 167 200 67 204 10111 10 18167 225 182.00 250 201.00 151 21333 21 IN/A 1600.00 117170 1995.11 160.44 2.78 1849.00 220900 16 . 9 10 11 12 308 67 AN/A 4000 3433 1867 1267 167 4100 4700 -5733 911 NN/A AN/A AN/A #N/A UN/A #N/A UN/A WN/A 13 14 N/A WNIA 23 15 16 AN/A AN/A #N/A 17.11 AN/A #N/A RN/A AN/A #N/A IN/A N/A N/A 25 26 18 IN/A 19 Tube 25 20 MN/A N/A 206,33 25 Next period forecast Time period 13 30 MSE 145733 31 32 33