Consider the table below which represents the number of active credit card accounts issued by a small credit union in a particular customer segment from 1994 to 2017 and a relevant forecast method. Time Credit cards A B er abs sq abs% 1994 13260 13260 60 1995 13310 13319 60 10 10 97 0.0007 1996 13383 13379 60 13379 4 4 20 0.0003 1997 13427 13438 59 13439 12 12 149 0.0009 1998 13455 13493 58 13497 42 42 1743 0.0031 1999 13520 13548 57 13551 31 31 962 0.0023 2000 13663 13611 59 13605 -58 58 3360 0.0042 2001 13717 13675 60 13670 47 47 2220 0.0034 2002 13753 13737 61 13735 -17 17 296 0.0013 2003 13792 13798 61 13798 6 6 37 0.0004 2004 13858 13858 61 13858 1 1 0.0000 2005 13915 13919 61 13919 4 4 13 0.0003 2006 13966 13978 60 13979 13 13 177 0.0010 2007 14017 14036 60 14038 21 21 450 0.0015 2008 13945 14081 55 14096 151 151 22852 0.0108 2009 14008 14123 51 14136 128 128 16382 0.0091 2010 14076 14164 48 14174 98 98 9658 0.0070 2011 14122 14204 46 14213 90 90 8128 0.0064 2012 14164 14241 43 14249 85 85 T303 0.0060 2013 14194 14275 40 14284 90 90 8052 0.0063 2014 14238 14307 38 14315 77 77 5971 0.0054 2015 14310 14342 37 14345 35 35 1210 0.0024 2016 14339 14375 36 14379 40 40 1606 0.0028 2017 14340 14403 34 14411 70 70 4943 0.0049 2018 14437 МАЕ MSE МАРЕ 49 4158 RMSE D Based on the information provided in the table, which of the following statements is correct? [Press CTRL and "-" to see the whole table by reducing the font size. Press CTRL and "+" to restore the font size] Select one: O a. The model provided is not a suitable forecast method for the data as it doesn't capture seasonal components of the time series b. The model provided is a suitable forecast model for the time series without any changes required c. This model has an adjustable smoothing parameter for level as it is an ARRSES model d. The model theoretically captures the systematic components of the time series but can likely be improved by changing the two relevant smoothing parameters e. The model assumes a constant trend for the time series

Consider the table below which represents the number of active credit card accounts issued by a small credit union in a particular customer segment from 1994 to 2017 and a relevant forecast method. Time Credit cards A B er abs sq abs% 1994 13260 13260 60 1995 13310 13319 60 10 10 97 0.0007 1996 13383 13379 60 13379 4 4 20 0.0003 1997 13427 13438 59 13439 12 12 149 0.0009 1998 13455 13493 58 13497 42 42 1743 0.0031 1999 13520 13548 57 13551 31 31 962 0.0023 2000 13663 13611 59 13605 -58 58 3360 0.0042 2001 13717 13675 60 13670 47 47 2220 0.0034 2002 13753 13737 61 13735 -17 17 296 0.0013 2003 13792 13798 61 13798 6 6 37 0.0004 2004 13858 13858 61 13858 1 1 0.0000 2005 13915 13919 61 13919 4 4 13 0.0003 2006 13966 13978 60 13979 13 13 177 0.0010 2007 14017 14036 60 14038 21 21 450 0.0015 2008 13945 14081 55 14096 151 151 22852 0.0108 2009 14008 14123 51 14136 128 128 16382 0.0091 2010 14076 14164 48 14174 98 98 9658 0.0070 2011 14122 14204 46 14213 90 90 8128 0.0064 2012 14164 14241 43 14249 85 85 T303 0.0060 2013 14194 14275 40 14284 90 90 8052 0.0063 2014 14238 14307 38 14315 77 77 5971 0.0054 2015 14310 14342 37 14345 35 35 1210 0.0024 2016 14339 14375 36 14379 40 40 1606 0.0028 2017 14340 14403 34 14411 70 70 4943 0.0049 2018 14437 МАЕ MSE МАРЕ 49 4158 RMSE D Based on the information provided in the table, which of the following statements is correct? [Press CTRL and "-" to see the whole table by reducing the font size. Press CTRL and "+" to restore the font size] Select one: O a. The model provided is not a suitable forecast method for the data as it doesn't capture seasonal components of the time series b. The model provided is a suitable forecast model for the time series without any changes required c. This model has an adjustable smoothing parameter for level as it is an ARRSES model d. The model theoretically captures the systematic components of the time series but can likely be improved by changing the two relevant smoothing parameters e. The model assumes a constant trend for the time series

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.6: Moving Averages Models

Problem 22P: The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six...

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