I sent this question from yesterday but nobody answered it.

Pleasseee i need 13.2 answer

The fitted and observed values should be The discrepancy between them should be very small and randomly dirhund about In the next chapter we shall Produce forecasts for the next one cyde for all these methods using the work done in this chapter and show precise dhe Suecas are likely to be. Only time will tell his accurate they ar Tutorial 12: Time series analysis In Tinomial 12 you will construct and draw a graph of a time series in each question for a set of data, and in Tutorial 13 you will use the same series in order to produce short term forecasts using the same graphs. 12.1 The quarterly sales, r0,000) of a departmental store have been monitored for the past five years with the following re Total quarterlys (0,000) OF at GS 04 Year 2001 48 58 57 65 61 10 2002 2003 54 50 52 62 10 2004 52 64 2005 Por the given data as a time series leaving room for the four forecasts for 2006 le Calculate the three values missing in the table on the next page, where they are indicated by question marks For the trend Ine on the same graph as your observed values d) Calculate the seasonal averages for each of the four quarters Find the descasonalised data for the fire quarter of 2005 122 Aggi of louds has turned these figures for their parterly turnover 20,000 Tumover (0.000 You OF Q 03 04 2002 337 410 OF 374 2003 321 416 452 414 2004 335 421 447 300 2000 311 300 422 A) Calculate the dire vale mining in the table merkead, produced from the above data, which are indicated by question marks lo Ples the turnover figures and the trend figures on one graph leaving rosas r four firccaik Table Da 2001 01 G 00 17 15 04 2002 01 50 0261 0359 OF 55 2009 (12 0062 0009 OF 2004 8 52 64 00 0 O 73 2005 01 53 82.75 52.75 Q 45 63.25 03 00 OF 75 Table question 12.2 Da Sales (0000) Cyce average 2002 Q1337 G410 03438 OF 334 2001 91 321 0418 34075 366.76 367.25 393 25 40325 406.75 409.75 406.00 394.75 P 03462 0414 305 429 0447 04 369 2004 2006 381.50 376.25 311 02398 CS 422 que 12.1 Sales (3000) C 40 56 $7.00 17.50 14.25 18.75 10.50 40.00 40.25 40.25 40.30 60.50 61.00 $1:25 7 $2.50 Tand Fit Sentral 57250 -0.250 57375 +7.125 56.500 -6506 56.125 +1875 59 750 -6.750 60125 +7875 -0.000 -1.201 +0.406 -0.003 +0.450 -0.531 60250 -8250 +0.56 -0313 -0.201 -0.156 --$210 60.375 +1829 00.500 -1.500 00710 +8.250 41,125 -4.125 1 -237 62.375 62625-10375 -4700 42.750 63.000 +2000 7 +0.312 -1.150 +1.000 -684 +0.062 Trand Final Secord residual 347.750 50.250 -3917 487 366.500 -12.500 300250-250 300 250 17.750 400.000 $1,000 400.250 5.750 407.875-72.875 400 375 27.625 T . 385125-18.125 378.375-47375 0543 -4308 2833 13.375 -300 4.000 10 -8.500 2414 13.2 a) Using an additive model forecast the turnovers for 2005 Quarter 4, 2006 Quarter 1. 2006 Quarter 2, 2006 Quarter 3. Add them to your graph and check the continuity of the seasonal pattern. b) What is the maximum error likely in your forecasts? c) Obtain deseasonalised data for 2004 Quarter 4, 2005 Quarter 1, 2005 Quarter 2, 2005 Quarter 3. d) Is the model used appropriate for forecasting with this data? Explain your answer. The fitted and observed values should be The discrepancy between them should be very small and randomly dirhund about In the next chapter we shall Produce forecasts for the next one cyde for all these methods using the work done in this chapter and show precise dhe Suecas are likely to be. Only time will tell his accurate they ar Tutorial 12: Time series analysis In Tinomial 12 you will construct and draw a graph of a time series in each question for a set of data, and in Tutorial 13 you will use the same series in order to produce short term forecasts using the same graphs. 12.1 The quarterly sales, r0,000) of a departmental store have been monitored for the past five years with the following re Total quarterlys (0,000) OF at GS 04 Year 2001 48 58 57 65 61 10 2002 2003 54 50 52 62 10 2004 52 64 2005 Por the given data as a time series leaving room for the four forecasts for 2006 le Calculate the three values missing in the table on the next page, where they are indicated by question marks For the trend Ine on the same graph as your observed values d) Calculate the seasonal averages for each of the four quarters Find the descasonalised data for the fire quarter of 2005 122 Aggi of louds has turned these figures for their parterly turnover 20,000 Tumover (0.000 You OF Q 03 04 2002 337 410 OF 374 2003 321 416 452 414 2004 335 421 447 300 2000 311 300 422 A) Calculate the dire vale mining in the table merkead, produced from the above data, which are indicated by question marks lo Ples the turnover figures and the trend figures on one graph leaving rosas r four firccaik Table Da 2001 01 G 00 17 15 04 2002 01 50 0261 0359 OF 55 2009 (12 0062 0009 OF 2004 8 52 64 00 0 O 73 2005 01 53 82.75 52.75 Q 45 63.25 03 00 OF 75 Table question 12.2 Da Sales (0000) Cyce average 2002 Q1337 G410 03438 OF 334 2001 91 321 0418 34075 366.76 367.25 393 25 40325 406.75 409.75 406.00 394.75 P 03462 0414 305 429 0447 04 369 2004 2006 381.50 376.25 311 02398 CS 422 que 12.1 Sales (3000) C 40 56 $7.00 17.50 14.25 18.75 10.50 40.00 40.25 40.25 40.30 60.50 61.00 $1:25 7 $2.50 Tand Fit Sentral 57250 -0.250 57375 +7.125 56.500 -6506 56.125 +1875 59 750 -6.750 60125 +7875 -0.000 -1.201 +0.406 -0.003 +0.450 -0.531 60250 -8250 +0.56 -0313 -0.201 -0.156 --$210 60.375 +1829 00.500 -1.500 00710 +8.250 41,125 -4.125 1 -237 62.375 62625-10375 -4700 42.750 63.000 +2000 7 +0.312 -1.150 +1.000 -684 +0.062 Trand Final Secord residual 347.750 50.250 -3917 487 366.500 -12.500 300250-250 300 250 17.750 400.000 $1,000 400.250 5.750 407.875-72.875 400 375 27.625 T . 385125-18.125 378.375-47375 0543 -4308 2833 13.375 -300 4.000 10 -8.500 2414 13.2 a) Using an additive model forecast the turnovers for 2005 Quarter 4, 2006 Quarter 1. 2006 Quarter 2, 2006 Quarter 3. Add them to your graph and check the continuity of the seasonal pattern. b) What is the maximum error likely in your forecasts? c) Obtain deseasonalised data for 2004 Quarter 4, 2005 Quarter 1, 2005 Quarter 2, 2005 Quarter 3. d) Is the model used appropriate for forecasting with this data? Explain your