Math Problem Solver

The table provided below represents the absolute deviations for each forecast prepared by the analyst. Absolute Deviations for Forecast 1 (a = 0,2) Absolute Deviations for Forecast 2 (a = 0,5) Absolute Deviations for Forecast 3 (a = 0,8) Year Sales, (R millions) Sales, (R millions) Sales, (R millions) Sales, (R millions) 2013 13 - - - 2014 19 - - - 2015 24 9.8 8 6.2 2016 30 13.84 10 7.24 2017 40 21.07 15 11.45 2018 45 N 12.5 7.29 2019 28 0.49 10.75 15.54 2020 17 10.61 Q 14.11 2021 20 5.49 5.19 0.18 2022 22 2.39 0.59 S 2023 36 12.09 13.70 14.41QUESTION 24 (4 Marks) Using the forecasts for the 2015 – 2023 period, calculate the mean absolute deviation (MAD) for Forecast 1, Forecast 2 and Forecast 3. A. The MAD for Forecast 1, Forecast 2, and Forecast 3 are 10.85 million, 8.72 million, and 9.27 million, respectively. B. The MAD for Forecast 1, Forecast 2, and Forecast 3 are 10.85 million, 10.23 million and 8.72 million, respectively. C. The MAD for Forecast 1, Forecast 2, and Forecast 3 are 10.23 million, 8.72 million, and 9.27 respectively. D. The MAD for Forecast 1, Forecast 2, and Forecast 3 are 10.23 million, 9.27 million, and 10.85 million, respectively. QUESTION 25 (4 Marks) Based on the mean absolute deviation (MAD) calculated for Forecast 1, Forecast 2, and Forecast 3, which of the following statements is correct? A. The analyst’s choice of a high smoothing constant (a = 0.8) was informed by the need for the forecast to be more responsive to recent changes, which is useful as the data set in Table D shows significant fluctuations. B. The analyst’s choice of a moderate smoothing constant (a = 0.5) ensured that a balanced forecast to recent fluctuations was obtained, which is beneficial for a very stable data set in Table D. C. The mean absolute deviation (MAD) for Forecast 1, Forecast 2, and Forecast 3 are not significantly different from each other; thus, indicating that the choice of the smoothing constant did not significantly alter forecast performance. D. The analyst’s choice of a low smoothing constant (a = 0.2) ensured that a smoother and less reactive forecast to short-term fluctuations was obtained, which is beneficial for the relatively stable data set in Table D.