Exam 16: Time-Series Forecasting

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TABLE 16-4 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows. TABLE 16-4 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   -Referring to Table 16-4,exponential smoothing with a weight or smoothing constant of 0.2 will be used to smooth the wine sales.The value of E<sub>2</sub>,the smoothed value for 2006 is ________. -Referring to Table 16-4,exponential smoothing with a weight or smoothing constant of 0.2 will be used to smooth the wine sales.The value of E2,the smoothed value for 2006 is ________.

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TABLE 16-6 The president of a chain of department stores believes that her stores' total sales have been showing a linear trend since 1993.She uses Microsoft Excel to obtain the partial output below.The dependent variable is sales (in millions of dollars),while the independent variable is coded years,where 1993 is coded as 0,1994 is coded as 1,etc. SUMMARY OUTPUT Regression Statistics Multiple R 0.604 R Square 0.365 Adjusted R Square 0.316 Standard Error 4.800 Observations 17 Coefficients Intercept 31.2 Coded Year 0.78 -Referring to Table 16-6,the forecast for sales (in millions of dollars)in 2015 is ________.

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Which of the following statements about the method of exponential smoothing is not true?

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TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters,using quarterly data on its revenues during the 5-year period from 2008 to 2012.The following is the resulting regression equation: log10 TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters,using quarterly data on its revenues during the 5-year period from 2008 to 2012.The following is the resulting regression equation: log<sub>10</sub> <sub> </sub>   = 6.102 + 0.012 X - 0.129 Q<sub>1</sub> - 0.054 Q<sub>2</sub> + 0.098 Q<sub>3</sub> where   is the estimated number of contracts in a quarter X is the coded quarterly value with X = 0 in the first quarter of 2008 Q<sub>1</sub> is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise Q<sub>2</sub> is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise   is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise -Referring to Table 16-12,the best interpretation of the constant 6.102 in the regression equation is = 6.102 + 0.012 X - 0.129 Q1 - 0.054 Q2 + 0.098 Q3 where TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters,using quarterly data on its revenues during the 5-year period from 2008 to 2012.The following is the resulting regression equation: log<sub>10</sub> <sub> </sub>   = 6.102 + 0.012 X - 0.129 Q<sub>1</sub> - 0.054 Q<sub>2</sub> + 0.098 Q<sub>3</sub> where   is the estimated number of contracts in a quarter X is the coded quarterly value with X = 0 in the first quarter of 2008 Q<sub>1</sub> is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise Q<sub>2</sub> is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise   is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise -Referring to Table 16-12,the best interpretation of the constant 6.102 in the regression equation is is the estimated number of contracts in a quarter X is the coded quarterly value with X = 0 in the first quarter of 2008 Q1 is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise Q2 is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters,using quarterly data on its revenues during the 5-year period from 2008 to 2012.The following is the resulting regression equation: log<sub>10</sub> <sub> </sub>   = 6.102 + 0.012 X - 0.129 Q<sub>1</sub> - 0.054 Q<sub>2</sub> + 0.098 Q<sub>3</sub> where   is the estimated number of contracts in a quarter X is the coded quarterly value with X = 0 in the first quarter of 2008 Q<sub>1</sub> is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise Q<sub>2</sub> is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise   is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise -Referring to Table 16-12,the best interpretation of the constant 6.102 in the regression equation is is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise -Referring to Table 16-12,the best interpretation of the constant 6.102 in the regression equation is

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TABLE 16-4 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows. TABLE 16-4 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   -Referring to Table 16-4,a centered 3-year moving average is to be constructed for the wine sales.The moving average for 2009 is ________. -Referring to Table 16-4,a centered 3-year moving average is to be constructed for the wine sales.The moving average for 2009 is ________.

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TABLE 16-5 The number of passengers arriving at San Francisco on the Amtrak cross-country express on 6 successive Mondays were: 60,72,96,84,36,and 48. -Referring to Table 16-5,the number of arrivals will be exponentially smoothed with a smoothing constant of 0.1.The smoothed value for the second Monday will be ________.

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TABLE 16-10 Business closures in Laramie,Wyoming from 2007 to 2012 were: TABLE 16-10 Business closures in Laramie,Wyoming from 2007 to 2012 were:   Microsoft Excel was used to fit both first-order and second-order autoregressive models,resulting in the following partial outputs: SUMMARY OUTPUT - 2nd Order Model Coefficients Intercept -5.77 X Variable 1 0.80 X Variable 2 1.14 SUMMARY OUTPUT - 1st Order Model Coefficients Intercept -4.16 X Variable 1 1.59 -Referring to Table 16-10,the fitted values for the first-order autoregressive model are ________,________,________,________,and ________. Microsoft Excel was used to fit both first-order and second-order autoregressive models,resulting in the following partial outputs: SUMMARY OUTPUT - 2nd Order Model Coefficients Intercept -5.77 X Variable 1 0.80 X Variable 2 1.14 SUMMARY OUTPUT - 1st Order Model Coefficients Intercept -4.16 X Variable 1 1.59 -Referring to Table 16-10,the fitted values for the first-order autoregressive model are ________,________,________,________,and ________.

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Which of the following statements about moving averages is not true?

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TABLE 16-14 A contractor developed a multiplicative time-series model to forecast the number of contracts in future quarters,using quarterly data on number of contracts during the 3-year period from 2010 to 2012.The following is the resulting regression equation: ln TABLE 16-14 A contractor developed a multiplicative time-series model to forecast the number of contracts in future quarters,using quarterly data on number of contracts during the 3-year period from 2010 to 2012.The following is the resulting regression equation: ln   = 3.37 + 0.117 X - 0.083 Q<sub>1</sub> + 1.28 Q<sub>2</sub> + 0.617 Q<sub>3</sub> where   is the estimated number of contracts in a quarter X is the coded quarterly value with X = 0 in the first quarter of 2010 Q<sub>1</sub> is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise Q<sub>2</sub> is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise Q<sub>3</sub> is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise -Referring to Table 16-14,using the regression equation,which of the following values is the best forecast for the number of contracts in the second quarter of 2014? = 3.37 + 0.117 X - 0.083 Q1 + 1.28 Q2 + 0.617 Q3 where TABLE 16-14 A contractor developed a multiplicative time-series model to forecast the number of contracts in future quarters,using quarterly data on number of contracts during the 3-year period from 2010 to 2012.The following is the resulting regression equation: ln   = 3.37 + 0.117 X - 0.083 Q<sub>1</sub> + 1.28 Q<sub>2</sub> + 0.617 Q<sub>3</sub> where   is the estimated number of contracts in a quarter X is the coded quarterly value with X = 0 in the first quarter of 2010 Q<sub>1</sub> is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise Q<sub>2</sub> is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise Q<sub>3</sub> is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise -Referring to Table 16-14,using the regression equation,which of the following values is the best forecast for the number of contracts in the second quarter of 2014? is the estimated number of contracts in a quarter X is the coded quarterly value with X = 0 in the first quarter of 2010 Q1 is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise Q2 is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise Q3 is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise -Referring to Table 16-14,using the regression equation,which of the following values is the best forecast for the number of contracts in the second quarter of 2014?

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TABLE 16-13 Given below is the monthly time-series data for U.S.retail sales of building materials over a specific year. TABLE 16-13 Given below is the monthly time-series data for U.S.retail sales of building materials over a specific year.   The results of the linear trend,quadratic trend,exponential trend,first-order autoregressive,second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the 1<sup>st</sup> month is 0: Linear trend model:   Quadratic trend model:   Exponential trend model:   First-order autoregressive:   Second-order autoregressive:   Third-order autoregressive:   Below is the residual plot of the various models:   -Referring to Table 16-13,what is your forecast for the 13<sup>th</sup> month using the linear-trend model? The results of the linear trend,quadratic trend,exponential trend,first-order autoregressive,second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the 1st month is 0: Linear trend model: TABLE 16-13 Given below is the monthly time-series data for U.S.retail sales of building materials over a specific year.   The results of the linear trend,quadratic trend,exponential trend,first-order autoregressive,second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the 1<sup>st</sup> month is 0: Linear trend model:   Quadratic trend model:   Exponential trend model:   First-order autoregressive:   Second-order autoregressive:   Third-order autoregressive:   Below is the residual plot of the various models:   -Referring to Table 16-13,what is your forecast for the 13<sup>th</sup> month using the linear-trend model? Quadratic trend model: TABLE 16-13 Given below is the monthly time-series data for U.S.retail sales of building materials over a specific year.   The results of the linear trend,quadratic trend,exponential trend,first-order autoregressive,second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the 1<sup>st</sup> month is 0: Linear trend model:   Quadratic trend model:   Exponential trend model:   First-order autoregressive:   Second-order autoregressive:   Third-order autoregressive:   Below is the residual plot of the various models:   -Referring to Table 16-13,what is your forecast for the 13<sup>th</sup> month using the linear-trend model? Exponential trend model: TABLE 16-13 Given below is the monthly time-series data for U.S.retail sales of building materials over a specific year.   The results of the linear trend,quadratic trend,exponential trend,first-order autoregressive,second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the 1<sup>st</sup> month is 0: Linear trend model:   Quadratic trend model:   Exponential trend model:   First-order autoregressive:   Second-order autoregressive:   Third-order autoregressive:   Below is the residual plot of the various models:   -Referring to Table 16-13,what is your forecast for the 13<sup>th</sup> month using the linear-trend model? First-order autoregressive: TABLE 16-13 Given below is the monthly time-series data for U.S.retail sales of building materials over a specific year.   The results of the linear trend,quadratic trend,exponential trend,first-order autoregressive,second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the 1<sup>st</sup> month is 0: Linear trend model:   Quadratic trend model:   Exponential trend model:   First-order autoregressive:   Second-order autoregressive:   Third-order autoregressive:   Below is the residual plot of the various models:   -Referring to Table 16-13,what is your forecast for the 13<sup>th</sup> month using the linear-trend model? Second-order autoregressive: TABLE 16-13 Given below is the monthly time-series data for U.S.retail sales of building materials over a specific year.   The results of the linear trend,quadratic trend,exponential trend,first-order autoregressive,second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the 1<sup>st</sup> month is 0: Linear trend model:   Quadratic trend model:   Exponential trend model:   First-order autoregressive:   Second-order autoregressive:   Third-order autoregressive:   Below is the residual plot of the various models:   -Referring to Table 16-13,what is your forecast for the 13<sup>th</sup> month using the linear-trend model? Third-order autoregressive: TABLE 16-13 Given below is the monthly time-series data for U.S.retail sales of building materials over a specific year.   The results of the linear trend,quadratic trend,exponential trend,first-order autoregressive,second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the 1<sup>st</sup> month is 0: Linear trend model:   Quadratic trend model:   Exponential trend model:   First-order autoregressive:   Second-order autoregressive:   Third-order autoregressive:   Below is the residual plot of the various models:   -Referring to Table 16-13,what is your forecast for the 13<sup>th</sup> month using the linear-trend model? Below is the residual plot of the various models: TABLE 16-13 Given below is the monthly time-series data for U.S.retail sales of building materials over a specific year.   The results of the linear trend,quadratic trend,exponential trend,first-order autoregressive,second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the 1<sup>st</sup> month is 0: Linear trend model:   Quadratic trend model:   Exponential trend model:   First-order autoregressive:   Second-order autoregressive:   Third-order autoregressive:   Below is the residual plot of the various models:   -Referring to Table 16-13,what is your forecast for the 13<sup>th</sup> month using the linear-trend model? -Referring to Table 16-13,what is your forecast for the 13th month using the linear-trend model?

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TABLE 16-4 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows. TABLE 16-4 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   -Referring to Table 16-4,a centered 3-year moving average is to be constructed for the wine sales.The moving average for 2006 is ________. -Referring to Table 16-4,a centered 3-year moving average is to be constructed for the wine sales.The moving average for 2006 is ________.

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TABLE 16-5 The number of passengers arriving at San Francisco on the Amtrak cross-country express on 6 successive Mondays were: 60,72,96,84,36,and 48. -Referring to Table 16-5,the number of arrivals will be smoothed with a 3-term moving average.The last smoothed value will be ________.

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TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters,using quarterly data on its revenues during the 5-year period from 2008 to 2012.The following is the resulting regression equation: log10 TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters,using quarterly data on its revenues during the 5-year period from 2008 to 2012.The following is the resulting regression equation: log<sub>10</sub> <sub> </sub>   = 6.102 + 0.012 X - 0.129 Q<sub>1</sub> - 0.054 Q<sub>2</sub> + 0.098 Q<sub>3</sub> where   is the estimated number of contracts in a quarter X is the coded quarterly value with X = 0 in the first quarter of 2008 Q<sub>1</sub> is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise Q<sub>2</sub> is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise   is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise -Referring to Table 16-12,the estimated quarterly compound growth rate in revenues is around = 6.102 + 0.012 X - 0.129 Q1 - 0.054 Q2 + 0.098 Q3 where TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters,using quarterly data on its revenues during the 5-year period from 2008 to 2012.The following is the resulting regression equation: log<sub>10</sub> <sub> </sub>   = 6.102 + 0.012 X - 0.129 Q<sub>1</sub> - 0.054 Q<sub>2</sub> + 0.098 Q<sub>3</sub> where   is the estimated number of contracts in a quarter X is the coded quarterly value with X = 0 in the first quarter of 2008 Q<sub>1</sub> is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise Q<sub>2</sub> is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise   is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise -Referring to Table 16-12,the estimated quarterly compound growth rate in revenues is around is the estimated number of contracts in a quarter X is the coded quarterly value with X = 0 in the first quarter of 2008 Q1 is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise Q2 is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters,using quarterly data on its revenues during the 5-year period from 2008 to 2012.The following is the resulting regression equation: log<sub>10</sub> <sub> </sub>   = 6.102 + 0.012 X - 0.129 Q<sub>1</sub> - 0.054 Q<sub>2</sub> + 0.098 Q<sub>3</sub> where   is the estimated number of contracts in a quarter X is the coded quarterly value with X = 0 in the first quarter of 2008 Q<sub>1</sub> is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise Q<sub>2</sub> is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise   is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise -Referring to Table 16-12,the estimated quarterly compound growth rate in revenues is around is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise -Referring to Table 16-12,the estimated quarterly compound growth rate in revenues is around

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When a time series appears to be increasing at an increasing rate,such that percentage difference from value to value is constant,the appropriate model to fit is the

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Which of the following terms describes the overall long-term tendency of a time series?

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To assess the adequacy of a forecasting model,one measure that is often used is

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TABLE 16-4 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows. TABLE 16-4 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   -Referring to Table 16-4,a centered 3-year moving average is to be constructed for the wine sales.The result of this process will lead to a total of ________ moving averages. -Referring to Table 16-4,a centered 3-year moving average is to be constructed for the wine sales.The result of this process will lead to a total of ________ moving averages.

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TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters,using quarterly data on its revenues during the 5-year period from 2008 to 2012.The following is the resulting regression equation: log10 TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters,using quarterly data on its revenues during the 5-year period from 2008 to 2012.The following is the resulting regression equation: log<sub>10</sub> <sub> </sub>   = 6.102 + 0.012 X - 0.129 Q<sub>1</sub> - 0.054 Q<sub>2</sub> + 0.098 Q<sub>3</sub> where   is the estimated number of contracts in a quarter X is the coded quarterly value with X = 0 in the first quarter of 2008 Q<sub>1</sub> is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise Q<sub>2</sub> is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise   is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise -Referring to Table 16-12,using the regression equation,what is the forecast for the revenues in the fourth quarter of 2014? = 6.102 + 0.012 X - 0.129 Q1 - 0.054 Q2 + 0.098 Q3 where TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters,using quarterly data on its revenues during the 5-year period from 2008 to 2012.The following is the resulting regression equation: log<sub>10</sub> <sub> </sub>   = 6.102 + 0.012 X - 0.129 Q<sub>1</sub> - 0.054 Q<sub>2</sub> + 0.098 Q<sub>3</sub> where   is the estimated number of contracts in a quarter X is the coded quarterly value with X = 0 in the first quarter of 2008 Q<sub>1</sub> is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise Q<sub>2</sub> is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise   is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise -Referring to Table 16-12,using the regression equation,what is the forecast for the revenues in the fourth quarter of 2014? is the estimated number of contracts in a quarter X is the coded quarterly value with X = 0 in the first quarter of 2008 Q1 is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise Q2 is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters,using quarterly data on its revenues during the 5-year period from 2008 to 2012.The following is the resulting regression equation: log<sub>10</sub> <sub> </sub>   = 6.102 + 0.012 X - 0.129 Q<sub>1</sub> - 0.054 Q<sub>2</sub> + 0.098 Q<sub>3</sub> where   is the estimated number of contracts in a quarter X is the coded quarterly value with X = 0 in the first quarter of 2008 Q<sub>1</sub> is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise Q<sub>2</sub> is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise   is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise -Referring to Table 16-12,using the regression equation,what is the forecast for the revenues in the fourth quarter of 2014? is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise -Referring to Table 16-12,using the regression equation,what is the forecast for the revenues in the fourth quarter of 2014?

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True or False: A trend is a persistent pattern in annual time-series data that has to be followed for several years.

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TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters,using quarterly data on its revenues during the 5-year period from 2008 to 2012.The following is the resulting regression equation: log10 TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters,using quarterly data on its revenues during the 5-year period from 2008 to 2012.The following is the resulting regression equation: log<sub>10</sub> <sub> </sub>   = 6.102 + 0.012 X - 0.129 Q<sub>1</sub> - 0.054 Q<sub>2</sub> + 0.098 Q<sub>3</sub> where   is the estimated number of contracts in a quarter X is the coded quarterly value with X = 0 in the first quarter of 2008 Q<sub>1</sub> is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise Q<sub>2</sub> is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise   is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise -Referring to Table 16-12,to obtain a fitted value for the fourth quarter of 2009 using the model,which of the following sets of values should be used in the regression equation? = 6.102 + 0.012 X - 0.129 Q1 - 0.054 Q2 + 0.098 Q3 where TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters,using quarterly data on its revenues during the 5-year period from 2008 to 2012.The following is the resulting regression equation: log<sub>10</sub> <sub> </sub>   = 6.102 + 0.012 X - 0.129 Q<sub>1</sub> - 0.054 Q<sub>2</sub> + 0.098 Q<sub>3</sub> where   is the estimated number of contracts in a quarter X is the coded quarterly value with X = 0 in the first quarter of 2008 Q<sub>1</sub> is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise Q<sub>2</sub> is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise   is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise -Referring to Table 16-12,to obtain a fitted value for the fourth quarter of 2009 using the model,which of the following sets of values should be used in the regression equation? is the estimated number of contracts in a quarter X is the coded quarterly value with X = 0 in the first quarter of 2008 Q1 is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise Q2 is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters,using quarterly data on its revenues during the 5-year period from 2008 to 2012.The following is the resulting regression equation: log<sub>10</sub> <sub> </sub>   = 6.102 + 0.012 X - 0.129 Q<sub>1</sub> - 0.054 Q<sub>2</sub> + 0.098 Q<sub>3</sub> where   is the estimated number of contracts in a quarter X is the coded quarterly value with X = 0 in the first quarter of 2008 Q<sub>1</sub> is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise Q<sub>2</sub> is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise   is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise -Referring to Table 16-12,to obtain a fitted value for the fourth quarter of 2009 using the model,which of the following sets of values should be used in the regression equation? is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise -Referring to Table 16-12,to obtain a fitted value for the fourth quarter of 2009 using the model,which of the following sets of values should be used in the regression equation?

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