\f. Question 12 The following table shows quarterly demand (in thousand units) for a product over 5 years. Year 1 Year 2 Year 3 Year 4 Year 5 Quarter 1 54 71 64 63 70 Quarter 2 115 100 106 104 109 Quarter 3 179 173 156 172 175 Quarter 4 117 111 121 121 110 PART A Assuming there is no trend, calculate the seasonal index (S.I.) for each quarter. Report final answers accurate to 4 decimal places. Do not round intermediate calculations. S.I. for Quarter 1 = S.I. for Quarter 2 = S.I. for Quarter 3 = S.I. for Quarter 4 = PART B Suppose projected total demand for the product above is 471,200 units in Year 7. Forecast demand for the four quarters of Year 7. Report final answers as integers accurate to the nearest thousand units. Quarter 1 forecast = thousands Quarter 2 forecast = thousands Quarter 3 forecast = thousands Quarter 4 forecast = thousands. Question 6 A small company gathered sales data over the last 7 months as follows: Month Sales January 249 February 210 March 204 April 201 May 207 June 255 July 285 August Download CSV Do not round answers. a) What is the 3-month moving average forecast for August? b) What is the 2-month weighted moving average forecast for August using weights 3 and 2? Assign higher weight to the most recent period. c) Given that the exponentially smoothed forecast for February is 249, what is the simple exponential smoothing forecast for March with a = 0.2?* Question 8 Score on last try: 0 of 2 pts. See Details for more. > Next question You can retry this question below The following table shows actual sales and 3-period moving average forecasts. Determine the missing actual demands, A2 and Ac. Time Actual Forecast At 195 2 A2 3 192 4 204 189 5 174 192 6 A6 190 7 150 185 8 167 A2 = 198 X A6 = 185 X. Question 9 Below is a transportation company's records of actual on-time delivery indexes over the last 7 periods along with some 3-period weighted moving average forecasts. Compute the mean squared error MSE. Do not round results. Period Actual Forecast 1 103 2 111 3 113 4 102 111 5 118 107 6 113 111 7 93 114 Mean Squared Error =