The following time series shows the number of units of a particular product sold over the past six months. Compute the MSE for the 4-month moving average. Month Units Sold 1 8 2 3 3 4 4 5 5 12 6 10 7
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The following time series shows the number of units of a particular product sold over the past six months. Compute the MSE for the 4-month moving average. Month Units Sold 1 8 2 3 3 4 4 5 5 12 6 10 7
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- The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six months of observations for validation purposes, use the method of moving averages with a carefully chosen span to forecast U.S. retail sales in the next year. Comment on the performance of your model. What makes this time series more challenging to forecast?From the following time series data of sale project the sales for the next three years. Year 2001 2002 2003 2004 2005 2006 2007 Sales (*000 units) 80 90 92 83 94 99 92DFC Company has recorded the past years sales for the company: Year(t) Sales(x) (in Million Pesos) 2011(1) 2012(2) 2013(3) 2014(4) 2015(5) 2016(6) 2017(7) 2018(8) 2019(9) 2020(10) 219 224 268 272 253 284 254 278 282 298 Use the naïve model. Compute for MAE and MSE Use a three period moving average. Compute for the MAE and MSE Use the simple exponential smoothing to make a forecasting table. Compute the MAE and MSE of the forecasts. Alpha = 0.1 Use the least square method to make the forecasting table. Compute the MAE and MSE *** Use excel or r programming
- 2. What is the Mean Absolute Deviation (MAD)? 3. Use the following set of data to calculate the Mean Absolute Deviation (MAD) for the following set of data. Month Actual (At) Forecast (Ft) Forecast Error Absolute (Deviation) Forecast Eror January February 45 45 42 50 March 34 45 April May 48 40 38 45 MAD =The following table gives the number of pints of type A blood used at Damascus Hospital in the past 6 weeks: a) The forecasted demand for the week of October 12 using a 3-week moving average = Week Of August 31 September 7 September 14 September 21 September 28 October 5 pints (round your response to two decimal places). Pints Used 345 389 408 381 371 374The following table gives the number of pints of type A blood used at Damascus Hospital in the past 6 weeks: Week Of August 31 September 7 September 14 September 21 September 28 October 5 Pints Used 345 389 412 383 366 371 a) The forecasted demand for the week of October 12 using a 3-week moving average decimal places). pints (round your response to two
- Obtain estimates of daily relatives for the number of customers at a restaurant for the evening meal, given the following data. a. Use the centered moving average method. (Hint: Use a seven-day moving average.)b. Use the SA method.Day Number Served1 802 753 784 955 1306 1367 408 829 7710 8011 9412 13113 13714 4215 8416 7817 8318 9619 13520 14021 4422 8723 8224 8825 9926 14427 14428 48In the Atlanta area, the number of daily calls for the repair of Speedy copy machines has been recorded as follows: October Calls 1 92 2 127 3 106 4 165 5 125 6 111 7 178 8 97 Prepare a three-period moving-average forecast for periods 4-8. What is the error on each day? Prepare a three-period weighted moving average forecast for periods 4-8 using weights of w1=.5, w2=.3, w3=.2 Which of the two forecasts is better? (Use CFE to calculate the better method)The following data are for calculator sales in units at an electronics store over the past nine weeks: Week 1 2 3 4 5 Obtain the error measures. (Enter your responses rounded to two decimal places.) CFE MSE Sales Find the coefficient of determination (²). The coefficient of determination r² = 0. (Enter your response rounded to two decimal places.) 44459 46 51 58 Use trend projection with regression to forecast sales for weeks 10-13. What are the error measures (CFE, MSE, 6, MAD, and MAPE) for this forecasting procedure? How about r²? Obtain the trend projection with regression forecast for weeks 10-13. (Enter your responses rounded to two decimal places.) Forecast, Ft Period 10 11 12 13 Week 6 69809 7 Sales 54 63 53 61 MAD U MAPE %
- Determine the Forecast Error for the data below using the Mean Absolute Deviation (MAD) formula. Month Demand Forecast 16 205 210 17 197 175 18 230 210 19 240 228 20 252 220The following table shows the three-period moving average and five-period moving average for monthly sales of Budget Furniture's during 2019. Moving averages of Budget Furniture's Time period Months Sales Three-period moving average (rounded off to Five-period moving average four decimals) R'millions 1 Jan 7 5.0000 6.2 February 5.6667 6.6 March 5 7.0000 B 4 April 8.3333 8.2 May 7 9.3333 8.4 June 8.3333 9.6 7 July 12 8.6667 9.6 August 4 A 9.2 September 10 10.6667 10 October 13 10.6667 11 November 9 12 December 10 The seasonal index for the month of February in 2019 is: LO3. Quarterly sales of a car dealer exhibit an almost constant mean over time, but sales fluctuate depending on the quarter of the year (exhibits seasonality). Quarter Q1 - Spring Q2 - Summer Q3 - Fall Q4 - Winter Mean Year 1 32 74 53 22 45.25 Year 2 30 71 63 15 44.75 Year 3 35 83 45 19 45.5 (a) Using three years' worth of quarterly data that is provided, develop a set of seasonal factors that could be used to forecast car sales (b) Forecast car sales in each of the four quarters (Q1-Q4) of the following year (Year 4), using these seasonal factors.