Using the accompanying table, determine the most appropriate forecasting technique for the accompanying data about the Dow Jones Industrial Average yearly closing value and implement the model Click here to view the table LOADING Click here to view the data LOADING What is the most appropriate forecasting technique A Double exponential smoothing Your answer is correct B Holt Winters additive seasonality models with trend C Simple moving average D Holt Winters multiplicative seasonality models with trend E Simple exponential smoothing F Holt Winters multiplicative seasonality models without trend or multiple regression G Holt Winters additive seasonality models without trend or multiple regression Use the model forecast the closing value for 2017 Use alphaequals 0 90 9, betaequals 0 40 4, and gammaequals 0 80 8, as needed F2017equals (Round to two decimal places as needed ) Year Close 1983 1258 64 1984 1211 57 1985 1546 67 1986 1895 95 1987 1938 83 1988 2168 57 1989 2753 20 1990 2633 66 1991 3168 83 1992 3301 11 1993 3754 09 1994 3834 44 1995 5117 12 1996 6448 27 1997 7908 25 1998 9181 43 1999 11497 12 2000 10786 85 2001 10021 50 2002 8341 63 2003 10453 92 2004 10783 01 2005 10717 50 2006 12463 15 2007 13264 82 2008 8776 39 2009 10428 05 2010 11557 50 2011 12217 60 2012 13104 10

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Question: Using the accompanying table, determine the most appropriate forecasting technique for the accompanying data about the Dow Jones Industrial Average yearly closing value and implement

Using the accompanying table, determine the most appropriate forecasting technique for the accompanying data about the Dow Jones Industrial Average yearly closing value and implement the model.

Click here to view the table.

Click here to view the data.

What is the most appropriate forecasting technique?

Double exponential smoothing

Your answer is correct.

Holt-Winters additive seasonality models with trend

Simple moving average

Holt-Winters multiplicative seasonality models with trend

Simple exponential smoothing

Holt-Winters multiplicative seasonality models without trend or multiple regression

Holt-Winters additive seasonality models without trend or multiple regression

Use the model forecast the closing value for

2017.

Use

alphaequals=0.90.9,

betaequals=0.40.4,

and

gammaequals=0.80.8,

as needed.

F2017equals ?

(Round to two decimal places as needed.)

Year Close 1983 1258.64 1984 1211.57 1985 1546.67 1986 1895.95 1987 1938.83 1988 2168.57 1989 2753.20 1990 2633.66 1991 3168.83 1992 3301.11 1993 3754.09 1994 3834.44 1995 5117.12 1996 6448.27 1997 7908.25 1998 9181.43 1999 11497.12 2000 10786.85 2001 10021.50 2002 8341.63 2003 10453.92 2004 10783.01 2005 10717.50 2006 12463.15 2007 13264.82 2008 8776.39 2009 10428.05 2010 11557.50 2011 12217.60 2012 13104.10

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