4 The data, in the next Company. Use minitab to able , represent Historical Monthly Usage of certain material by the represent 1. Plot the data and check for stationarity using autocorelation and Partial Autocorlation. 2. Estimate two different ARIMA models using the Box-Jenkins approach and find theb the AIC, BIC and the principle of parsimony model using 3. Do model checking using the ACF and PACF of residuals, residuals Histogram 4. Forecast the 5. Compare the Forecasts obtained in 4 with forecasts obtained by, Single Exponential Smoothing Method Residuals, Residuals vs Fits. Normality plot of monthly consumption for year 2007 using the best model identified in 2. SES)Double Exponential Smoothing Method (DES), Additive or Multiplicative decomposition methods. Use MAD and MAPD for the comparison. Year onth Jan0.00 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 200 39.73 20.57 53.51 47.25 36.30 47.68 31.51 80.66 144.49 144.68 67.43 64.9 0.00 34.68 37.03 49.78 35.38 60.47 93.02 160.80 83.67 81.54 83.75 6422 61.15 0.00 | 3373 5042 53921 57531 90.23 6294 6089- 34 | 12.17 102.701205 5670 50.87 | 46.71 | 44.4857.08 | 27.34 | 50.41 | 15.00 | 80.19 I 8374 | 11499 | 11928 | 6538 | 10346 42.88 31.54 32.83 39.10 0.00 0.00 0.00 105.811 8120-9835 | 111.791.57 7111.46 67.88 3638 41.24 53.26 0.00 0.00 163.16 76.60 80.13 15281 11653 86.09 98.86 66.51 60.23 65.30 4431 Feb Mar Apr 0.00 78.66 62.56 8994 87.44 113.83 11235 88.08 1400 Jul Aug Sep 27.39 37.34 51.04 43.69 32.51 24188392 104.10 18567 14200 12636 91.83 8931 Oct 31.33 44.33 147.95 40.95 82.16 8417 116.41 105.10 85 98 109 6 L97A 1164as 42.79 47.86 48.34 55.91 0.00 80.90 53.76 79.03 90.99 150.01 130.99 93.86 - 32.87 48.16 38.13 55.33 8324 138.33 7721 7681 8763 9423 68.61 131.90 100.76 29.34 79.83 54.98 49.44 129.51 54.92 49.68 68.42 69.61 119.57 123.28 220.56 108.62 Dec 4 The data, in the next Company. Use minitab to able , represent Historical Monthly Usage of certain material by the represent 1. Plot the data and check for stationarity using autocorelation and Partial Autocorlation. 2. Estimate two different ARIMA models using the Box-Jenkins approach and find theb the AIC, BIC and the principle of parsimony model using 3. Do model checking using the ACF and PACF of residuals, residuals Histogram 4. Forecast the 5. Compare the Forecasts obtained in 4 with forecasts obtained by, Single Exponential Smoothing Method Residuals, Residuals vs Fits. Normality plot of monthly consumption for year 2007 using the best model identified in 2. SES)Double Exponential Smoothing Method (DES), Additive or Multiplicative decomposition methods. Use MAD and MAPD for the comparison. Year onth Jan0.00 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 200 39.73 20.57 53.51 47.25 36.30 47.68 31.51 80.66 144.49 144.68 67.43 64.9 0.00 34.68 37.03 49.78 35.38 60.47 93.02 160.80 83.67 81.54 83.75 6422 61.15 0.00 | 3373 5042 53921 57531 90.23 6294 6089- 34 | 12.17 102.701205 5670 50.87 | 46.71 | 44.4857.08 | 27.34 | 50.41 | 15.00 | 80.19 I 8374 | 11499 | 11928 | 6538 | 10346 42.88 31.54 32.83 39.10 0.00 0.00 0.00 105.811 8120-9835 | 111.791.57 7111.46 67.88 3638 41.24 53.26 0.00 0.00 163.16 76.60 80.13 15281 11653 86.09 98.86 66.51 60.23 65.30 4431 Feb Mar Apr 0.00 78.66 62.56 8994 87.44 113.83 11235 88.08 1400 Jul Aug Sep 27.39 37.34 51.04 43.69 32.51 24188392 104.10 18567 14200 12636 91.83 8931 Oct 31.33 44.33 147.95 40.95 82.16 8417 116.41 105.10 85 98 109 6 L97A 1164as 42.79 47.86 48.34 55.91 0.00 80.90 53.76 79.03 90.99 150.01 130.99 93.86 - 32.87 48.16 38.13 55.33 8324 138.33 7721 7681 8763 9423 68.61 131.90 100.76 29.34 79.83 54.98 49.44 129.51 54.92 49.68 68.42 69.61 119.57 123.28 220.56 108.62 Dec