Deck 16: Time-Series Analysis and Index Numbers

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

A) It can be used to smooth a series.
B) It gives equal weight to all values in the computation.
C) It is simpler than the method of exponential smoothing.
D) It gives greater weight to more recent data.
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Question
Which of the following is not an advantage of exponential smoothing?

A) It enables us to perform one-period ahead forecasting.
B) It enables us to perform more than one-period ahead forecasting.
C) It enables us to smooth out seasonal components.
D) It enables us to smooth out cyclical components.
Question
Which of the following statements about the method of exponential smoothing is not true?

A) It gives greater weight to more recent data.
B) It can be used for forecasting.
C) It uses all earlier observations in each smoothing calculation.
D) It gives greater weight to the earlier observations in the series.
Question
To assess the adequacy of a forecasting model, one measure that is often used is

A) quadratic trend analysis.
B) the MAD.
C) exponential smoothing.
D) moving averages.
Question
The annual multiplicative time-series model does not possess ________ component.

A) a trend
B) a cyclical
C) an irregular
D) a seasonal
Question
A model that can be used to make predictions about long-term future values of a time series is

A) linear trend.
B) quadratic trend.
C) exponential trend.
D) All of the above.
Question
The overall upward or downward pattern of the data in an annual time series will be contained in the ________ component.

A) trend
B) cyclical
C) irregular
D) seasonal
Question
When a time series appears to be increasing at an increasing rate, such that percentage difference from observation to observation is constant, the appropriate model to fit is the

A) linear trend.
B) quadratic trend.
C) exponential trend.
D) None of the above.
Question
The fairly regular fluctuations that occur within each year would be contained in the ________ component.

A) trend
B) cyclical
C) irregular
D) seasonal
Question
The method of moving averages is used

A) to plot a series.
B) to exponentiate a series.
C) to smooth a series.
D) in regression analysis.
Question
TABLE 16-1
The number of cases of chardonnay wine sold by a Paso Robles winery in an 8-year period follows.
<strong>TABLE 16-1 The number of cases of chardonnay wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-1, does there appear to be a relationship between year and the number of cases of wine sold?</strong> A) No, there appears to be no relationship between the year and the number of cases of wine sold by the vintner. B) Yes, there appears to be a slight negative linear relationship between the year and the number of cases of wine sold by the vintner. C) Yes, there appears to be a slight positive relationship between the year and the number of cases of wine sold by the vintner. D) Yes, there appears to be a negative nonlinear relationship between the year and the number of cases of wine sold by the vintner. <div style=padding-top: 35px>
Referring to Table 16-1, does there appear to be a relationship between year and the number of cases of wine sold?

A) No, there appears to be no relationship between the year and the number of cases of wine sold by the vintner.
B) Yes, there appears to be a slight negative linear relationship between the year and the number of cases of wine sold by the vintner.
C) Yes, there appears to be a slight positive relationship between the year and the number of cases of wine sold by the vintner.
D) Yes, there appears to be a negative nonlinear relationship between the year and the number of cases of wine sold by the vintner.
Question
When using the exponentially weighted moving average for purposes of forecasting rather than smoothing,

A) the previous smoothed value becomes the forecast.
B) the current smoothed value becomes the forecast.
C) the next smoothed value becomes the forecast.
D) None of the above.
Question
You need to decide whether you should invest in a particular stock. You would like to invest if the price is likely to rise in the long run. You have data on the daily average price of this stock over the past 12 months. Your best action is to

A) compute moving averages.
B) perform exponential smoothing.
C) estimate a least square trend model.
D) compute the MAD statistic.
Question
Which of the following methods should not be used for short-term forecasts into the future?

A) exponential smoothing
B) moving averages
C) linear trend model
D) autoregressive modeling
Question
The effect of an unpredictable, rare event will be contained in the ________ component.

A) trend
B) cyclical
C) irregular
D) seasonal
Question
In selecting an appropriate forecasting model, the following approaches are suggested:

A) Perform a residual analysis.
B) Measure the size of the forecasting error.
C) Use the principle of parsimony.
D) All of the above.
Question
After estimating a trend model for annual time-series data, you obtain the following residual plot against time: <strong>After estimating a trend model for annual time-series data, you obtain the following residual plot against time:   The problem with your model is that:</strong> A) The cyclical component has not been accounted for. B) The seasonal component has not been accounted for. C) The trend component has not been accounted for. D) The irregular component has not been accounted for. <div style=padding-top: 35px> The problem with your model is that:

A) The cyclical component has not been accounted for.
B) The seasonal component has not been accounted for.
C) The trend component has not been accounted for.
D) The irregular component has not been accounted for.
Question
The method of least squares is used on time-series data for

A) eliminating irregular movements.
B) deseasonalizing the data.
C) obtaining the trend equation.
D) exponentially smoothing a series.
Question
Based on the following scatter plot, which of the time-series components is not present in this quarterly time series? <strong>Based on the following scatter plot, which of the time-series components is not present in this quarterly time series?  </strong> A) trend B) seasonal C) cyclical D) irregular <div style=padding-top: 35px>

A) trend
B) seasonal
C) cyclical
D) irregular
Question
TABLE 16-1
The number of cases of chardonnay wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-1 The number of cases of chardonnay wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-1, set up a scatter diagram (i.e., a time-series plot) with year on the horizontal X-axis.<div style=padding-top: 35px>
Referring to Table 16-1, set up a scatter diagram (i.e., a time-series plot) with year on the horizontal X-axis.
Question
The following is the list of MAD statistics for each of the models you have estimated from time-series data: <strong>The following is the list of MAD statistics for each of the models you have estimated from time-series data:   Based on the MAD criterion, the most appropriate model is</strong> A) linear trend. B) quadratic trend. C) exponential trend. D) AR(2). <div style=padding-top: 35px> Based on the MAD criterion, the most appropriate model is

A) linear trend.
B) quadratic trend.
C) exponential trend.
D) AR(2).
Question
TABLE 16-3
The following table contains the number of complaints received in a department store for the first 6 months of last year.
<strong>TABLE 16-3 The following table contains the number of complaints received in a department store for the first 6 months of last year.   Referring to Table 16-3, suppose the last two smoothed values are 81 and 96 (Note: they are not). What would you forecast as the value of the time series for September?</strong> A) 81 B) 86 C) 91 D) 96 <div style=padding-top: 35px>
Referring to Table 16-3, suppose the last two smoothed values are 81 and 96 (Note: they are not). What would you forecast as the value of the time series for September?

A) 81
B) 86
C) 91
D) 96
Question
TABLE 16-3
The following table contains the number of complaints received in a department store for the first 6 months of last year.
<strong>TABLE 16-3 The following table contains the number of complaints received in a department store for the first 6 months of last year.   Referring to Table 16-3, if this series is smoothed using exponential smoothing with a smoothing constant of 1/3, what would be the third value?</strong> A) 53 B) 65.33 C) 68 D) 81 <div style=padding-top: 35px>
Referring to Table 16-3, if this series is smoothed using exponential smoothing with a smoothing constant of 1/3, what would be the third value?

A) 53
B) 65.33
C) 68
D) 81
Question
TABLE 16-3
The following table contains the number of complaints received in a department store for the first 6 months of last year.
<strong>TABLE 16-3 The following table contains the number of complaints received in a department store for the first 6 months of last year.   Referring to Table 16-3, if a three-month moving average is used to smooth this series, how many values would it have?</strong> A) 2 B) 3 C) 4 D) 5 <div style=padding-top: 35px>
Referring to Table 16-3, if a three-month moving average is used to smooth this series, how many values would it have?

A) 2
B) 3
C) 4
D) 5
Question
TABLE 16-3
The following table contains the number of complaints received in a department store for the first 6 months of last year.
<strong>TABLE 16-3 The following table contains the number of complaints received in a department store for the first 6 months of last year.   Referring to Table 16-3, if a three-month moving average is used to smooth this series, what would be the second calculated value?</strong> A) 36 B) 40.5 C) 54 D) 72 <div style=padding-top: 35px>
Referring to Table 16-3, if a three-month moving average is used to smooth this series, what would be the second calculated value?

A) 36
B) 40.5
C) 54
D) 72
Question
TABLE 16-5
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 2006 to 2008. The following is the resulting regression equation:
<strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, the best interpretation of the constant 3.37 in the regression equation is:</strong> A) the fitted value for the first quarter of 2006, prior to seasonal adjustment, is log<sub>10</sub> 3.37. B) the fitted value for the first quarter of 2006, after to seasonal adjustment, is log<sub>10</sub> 3.37. C) the fitted value for the first quarter of 2006, prior to seasonal adjustment, is 10<sup>3.37</sup>. D) the fitted value for the first quarter of 2006, after to seasonal adjustment, is 10<sup>3.37</sup>. <div style=padding-top: 35px> <strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, the best interpretation of the constant 3.37 in the regression equation is:</strong> A) the fitted value for the first quarter of 2006, prior to seasonal adjustment, is log<sub>10</sub> 3.37. B) the fitted value for the first quarter of 2006, after to seasonal adjustment, is log<sub>10</sub> 3.37. C) the fitted value for the first quarter of 2006, prior to seasonal adjustment, is 10<sup>3.37</sup>. D) the fitted value for the first quarter of 2006, after to seasonal adjustment, is 10<sup>3.37</sup>. <div style=padding-top: 35px>
Referring to Table 16-5, the best interpretation of the constant 3.37 in the regression equation is:

A) the fitted value for the first quarter of 2006, prior to seasonal adjustment, is log10 3.37.
B) the fitted value for the first quarter of 2006, after to seasonal adjustment, is log10 3.37.
C) the fitted value for the first quarter of 2006, prior to seasonal adjustment, is 103.37.
D) the fitted value for the first quarter of 2006, after to seasonal adjustment, is 103.37.
Question
Which of the following terms describes the overall long-term tendency of a time series?

A) trend
B) cyclical component
C) irregular component
D) seasonal component
Question
TABLE 16-3
The following table contains the number of complaints received in a department store for the first 6 months of last year.
<strong>TABLE 16-3 The following table contains the number of complaints received in a department store for the first 6 months of last year.   Referring to Table 16-3, if a three-month moving average is used to smooth this series, what would be the last calculated value?</strong> A) 72 B) 93 C) 114 D) 126 <div style=padding-top: 35px>
Referring to Table 16-3, if a three-month moving average is used to smooth this series, what would be the last calculated value?

A) 72
B) 93
C) 114
D) 126
Question
TABLE 16-2
The monthly advertising expenditures of a department store chain (in $1,000,000s) were collected over the last decade. The last 14 months of this time series follows:
TABLE 16-2 The monthly advertising expenditures of a department store chain (in $1,000,000s) were collected over the last decade. The last 14 months of this time series follows:   Referring to Table 16-2, set up a scatter diagram (i.e., time-series plot) with months on the horizontal X-axis.<div style=padding-top: 35px>
Referring to Table 16-2, set up a scatter diagram (i.e., time-series plot) with months on the horizontal X-axis.
Question
TABLE 16-3
The following table contains the number of complaints received in a department store for the first 6 months of last year.
<strong>TABLE 16-3 The following table contains the number of complaints received in a department store for the first 6 months of last year.   Referring to Table 16-3, if this series is smoothed using exponential smoothing with a smoothing constant of 1/3, what would be the first value?</strong> A) 36 B) 39 C) 42 D) 45 <div style=padding-top: 35px>
Referring to Table 16-3, if this series is smoothed using exponential smoothing with a smoothing constant of 1/3, what would be the first value?

A) 36
B) 39
C) 42
D) 45
Question
TABLE 16-4
Given below are EXCEL outputs for various estimated autoregressive models for a company's real operating revenues (in billions of dollars) from 1985 to 2008. From the data, we also know that the real operating revenues for 2006, 2007, and 2008 are 11.7909, 11.7757 and 11.5537, respectively.
AR(1) Model:
<strong>TABLE 16-4 Given below are EXCEL outputs for various estimated autoregressive models for a company's real operating revenues (in billions of dollars) from 1985 to 2008. From the data, we also know that the real operating revenues for 2006, 2007, and 2008 are 11.7909, 11.7757 and 11.5537, respectively. AR(1) Model:   AR(2) Model:   AR(3) Model:   Referring to Table 16-4 and using a 5% level of significance, what is the appropriate AR model for the company's real operating revenue?</strong> A) AR(1) B) AR(2) C) AR(3) D) Any of the above. <div style=padding-top: 35px> AR(2) Model:
<strong>TABLE 16-4 Given below are EXCEL outputs for various estimated autoregressive models for a company's real operating revenues (in billions of dollars) from 1985 to 2008. From the data, we also know that the real operating revenues for 2006, 2007, and 2008 are 11.7909, 11.7757 and 11.5537, respectively. AR(1) Model:   AR(2) Model:   AR(3) Model:   Referring to Table 16-4 and using a 5% level of significance, what is the appropriate AR model for the company's real operating revenue?</strong> A) AR(1) B) AR(2) C) AR(3) D) Any of the above. <div style=padding-top: 35px> AR(3) Model:
<strong>TABLE 16-4 Given below are EXCEL outputs for various estimated autoregressive models for a company's real operating revenues (in billions of dollars) from 1985 to 2008. From the data, we also know that the real operating revenues for 2006, 2007, and 2008 are 11.7909, 11.7757 and 11.5537, respectively. AR(1) Model:   AR(2) Model:   AR(3) Model:   Referring to Table 16-4 and using a 5% level of significance, what is the appropriate AR model for the company's real operating revenue?</strong> A) AR(1) B) AR(2) C) AR(3) D) Any of the above. <div style=padding-top: 35px>
Referring to Table 16-4 and using a 5% level of significance, what is the appropriate AR model for the company's real operating revenue?

A) AR(1)
B) AR(2)
C) AR(3)
D) Any of the above.
Question
After estimating a trend model for annual time-series data, you obtain the following residual plot against time: <strong>After estimating a trend model for annual time-series data, you obtain the following residual plot against time:   The problem with your model is that</strong> A) the cyclical component has not been accounted for. B) the seasonal component has not been accounted for. C) the trend component has not been accounted for. D) the irregular component has not been accounted for. <div style=padding-top: 35px> The problem with your model is that

A) the cyclical component has not been accounted for.
B) the seasonal component has not been accounted for.
C) the trend component has not been accounted for.
D) the irregular component has not been accounted for.
Question
If you want to recover the trend using exponential smoothing, you will choose a weight (W) that falls in the range

A) [0, 0.2].
B) [0.2, 0.4].
C) [0.6, 0.8].
D) [0.9, 1.0].
Question
Which of the following terms describes the up and down movements of a time series that vary both in length and intensity?

A) trend
B) cyclical component
C) irregular component
D) seasonal component
Question
TABLE 16-3
The following table contains the number of complaints received in a department store for the first 6 months of last year.
<strong>TABLE 16-3 The following table contains the number of complaints received in a department store for the first 6 months of last year.   Referring to Table 16-3, if this series is smoothed using exponential smoothing with a smoothing constant of 1/3, what would be the second value?</strong> A) 39 B) 42 C) 45 D) 53 <div style=padding-top: 35px>
Referring to Table 16-3, if this series is smoothed using exponential smoothing with a smoothing constant of 1/3, what would be the second value?

A) 39
B) 42
C) 45
D) 53
Question
TABLE 16-3
The following table contains the number of complaints received in a department store for the first 6 months of last year.
<strong>TABLE 16-3 The following table contains the number of complaints received in a department store for the first 6 months of last year.   Referring to Table 16-3, suppose the last two smoothed values are 81 and 96 (Note: they are not). What would you forecast as the value of the time series for July?</strong> A) 81 B) 86 C) 91 D) 96 <div style=padding-top: 35px>
Referring to Table 16-3, suppose the last two smoothed values are 81 and 96 (Note: they are not). What would you forecast as the value of the time series for July?

A) 81
B) 86
C) 91
D) 96
Question
The cyclical component of a time series

A) represents periodic fluctuations which reoccur within 1 year.
B) represents periodic fluctuations which usually occur in 2 or more years.
C) is obtained by adding up the seasonal indexes.
D) is obtained by adjusting for calendar variation.
Question
TABLE 16-2
The monthly advertising expenditures of a department store chain (in $1,000,000s) were collected over the last decade. The last 14 months of this time series follows:
TABLE 16-2 The monthly advertising expenditures of a department store chain (in $1,000,000s) were collected over the last decade. The last 14 months of this time series follows:   Referring to Table 16-2, advertising expenditures appear to be increasing in a linear rather than curvilinear manner over time.<div style=padding-top: 35px>
Referring to Table 16-2, advertising expenditures appear to be increasing in a linear rather than curvilinear manner over time.
Question
TABLE 16-4
Given below are EXCEL outputs for various estimated autoregressive models for a company's real operating revenues (in billions of dollars) from 1985 to 2008. From the data, we also know that the real operating revenues for 2006, 2007, and 2008 are 11.7909, 11.7757 and 11.5537, respectively.
AR(1) Model:
<strong>TABLE 16-4 Given below are EXCEL outputs for various estimated autoregressive models for a company's real operating revenues (in billions of dollars) from 1985 to 2008. From the data, we also know that the real operating revenues for 2006, 2007, and 2008 are 11.7909, 11.7757 and 11.5537, respectively. AR(1) Model:   AR(2) Model:   AR(3) Model:   Referring to Table 16-4, if one decides to use AR(3), what will the predicted real operating revenue for the company be in 2011?</strong> A) $11.59 billion B) $11.68 billion C) $11.84 billion D) $12.47 billion <div style=padding-top: 35px> AR(2) Model:
<strong>TABLE 16-4 Given below are EXCEL outputs for various estimated autoregressive models for a company's real operating revenues (in billions of dollars) from 1985 to 2008. From the data, we also know that the real operating revenues for 2006, 2007, and 2008 are 11.7909, 11.7757 and 11.5537, respectively. AR(1) Model:   AR(2) Model:   AR(3) Model:   Referring to Table 16-4, if one decides to use AR(3), what will the predicted real operating revenue for the company be in 2011?</strong> A) $11.59 billion B) $11.68 billion C) $11.84 billion D) $12.47 billion <div style=padding-top: 35px> AR(3) Model:
<strong>TABLE 16-4 Given below are EXCEL outputs for various estimated autoregressive models for a company's real operating revenues (in billions of dollars) from 1985 to 2008. From the data, we also know that the real operating revenues for 2006, 2007, and 2008 are 11.7909, 11.7757 and 11.5537, respectively. AR(1) Model:   AR(2) Model:   AR(3) Model:   Referring to Table 16-4, if one decides to use AR(3), what will the predicted real operating revenue for the company be in 2011?</strong> A) $11.59 billion B) $11.68 billion C) $11.84 billion D) $12.47 billion <div style=padding-top: 35px>
Referring to Table 16-4, if one decides to use AR(3), what will the predicted real operating revenue for the company be in 2011?

A) $11.59 billion
B) $11.68 billion
C) $11.84 billion
D) $12.47 billion
Question
TABLE 16-3
The following table contains the number of complaints received in a department store for the first 6 months of last year.
<strong>TABLE 16-3 The following table contains the number of complaints received in a department store for the first 6 months of last year.   Referring to Table 16-3, if this series is smoothed using exponential smoothing with a smoothing constant of 1/3, how many values would it have?</strong> A) 3 B) 4 C) 5 D) 6 <div style=padding-top: 35px>
Referring to Table 16-3, if this series is smoothed using exponential smoothing with a smoothing constant of 1/3, how many values would it have?

A) 3
B) 4
C) 5
D) 6
Question
The MAD is a measure of the average of the absolute discrepancies between the actual and the fitted values in a given time series.
Question
TABLE 16-5
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 2006 to 2008. The following is the resulting regression equation:
<strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, to obtain a forecast for the fourth quarter of 2009 using the model, which of the following sets of values should be used in the regression equation?</strong> A) X = 15, Q<sub>1</sub> = 0, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 B) X = 15, Q<sub>1</sub> = 1, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 C) X = 16, Q<sub>1</sub> = 0, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 D) X = 16, Q<sub>1</sub> = 1, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 <div style=padding-top: 35px> <strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, to obtain a forecast for the fourth quarter of 2009 using the model, which of the following sets of values should be used in the regression equation?</strong> A) X = 15, Q<sub>1</sub> = 0, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 B) X = 15, Q<sub>1</sub> = 1, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 C) X = 16, Q<sub>1</sub> = 0, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 D) X = 16, Q<sub>1</sub> = 1, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 <div style=padding-top: 35px>
Referring to Table 16-5, to obtain a forecast for the fourth quarter of 2009 using the model, which of the following sets of values should be used in the regression equation?

A) X = 15, Q1 = 0, Q2 = 0, Q3 = 0
B) X = 15, Q1 = 1, Q2 = 0, Q3 = 0
C) X = 16, Q1 = 0, Q2 = 0, Q3 = 0
D) X = 16, Q1 = 1, Q2 = 0, Q3 = 0
Question
If a time series does not exhibit a long-term trend, the method of exponential smoothing may be used to obtain short-term predictions about the future.
Question
TABLE 16-5
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 2006 to 2008. The following is the resulting regression equation:
<strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, to obtain a forecast for the first quarter of 2009 using the model, which of the following sets of values should be used in the regression equation?</strong> A) X = 12, Q<sub>1</sub> = 0, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 B) X = 12, Q<sub>1</sub> = 1, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 C) X = 13, Q<sub>1</sub> = 0, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 D) X = 13, Q<sub>1</sub> = 1, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 <div style=padding-top: 35px> <strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, to obtain a forecast for the first quarter of 2009 using the model, which of the following sets of values should be used in the regression equation?</strong> A) X = 12, Q<sub>1</sub> = 0, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 B) X = 12, Q<sub>1</sub> = 1, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 C) X = 13, Q<sub>1</sub> = 0, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 D) X = 13, Q<sub>1</sub> = 1, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 <div style=padding-top: 35px>
Referring to Table 16-5, to obtain a forecast for the first quarter of 2009 using the model, which of the following sets of values should be used in the regression equation?

A) X = 12, Q1 = 0, Q2 = 0, Q3 = 0
B) X = 12, Q1 = 1, Q2 = 0, Q3 = 0
C) X = 13, Q1 = 0, Q2 = 0, Q3 = 0
D) X = 13, Q1 = 1, Q2 = 0, Q3 = 0
Question
A trend is a persistent pattern in annual time-series data that has to be followed for several years.
Question
A least squares linear trend line is just a simple regression line with the years recoded.
Question
TABLE 16-5
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 2006 to 2008. The following is the resulting regression equation:
<strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, using the regression equation, which of the following values is the best forecast for the number of contracts in the third quarter of 2009?</strong> A) 49091 B) 133352 C) 421697 D) 1482518 <div style=padding-top: 35px> <strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, using the regression equation, which of the following values is the best forecast for the number of contracts in the third quarter of 2009?</strong> A) 49091 B) 133352 C) 421697 D) 1482518 <div style=padding-top: 35px>
Referring to Table 16-5, using the regression equation, which of the following values is the best forecast for the number of contracts in the third quarter of 2009?

A) 49091
B) 133352
C) 421697
D) 1482518
Question
Given a data set with 15 yearly observations, there are only seven 9-year moving averages.
Question
TABLE 16-5
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 2006 to 2008. The following is the resulting regression equation:
<strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, in testing the coefficient for Q<sub>1</sub> in the regression equation (-0.083), the results were a t-statistic of -0.66 and an associated p-value of 0.530. Which of the following is the best interpretation of this result?</strong> A) The number of contracts in the first quarter of the year is significantly different from the number of contracts in an average quarter (α = 0.05). B) The number of contracts in the first quarter of the year is not significantly different from the number of contracts in an average quarter (α = 0.05). C) The number of contracts in the first quarter of the year is significantly different from the number of contracts in the fourth quarter for a given coded quarterly value of X (α = 0.05). D) The number of contracts in the first quarter of the year is not significantly different from the number of contracts in the fourth quarter for a given coded quarterly value of X (α = 0.05). <div style=padding-top: 35px> <strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, in testing the coefficient for Q<sub>1</sub> in the regression equation (-0.083), the results were a t-statistic of -0.66 and an associated p-value of 0.530. Which of the following is the best interpretation of this result?</strong> A) The number of contracts in the first quarter of the year is significantly different from the number of contracts in an average quarter (α = 0.05). B) The number of contracts in the first quarter of the year is not significantly different from the number of contracts in an average quarter (α = 0.05). C) The number of contracts in the first quarter of the year is significantly different from the number of contracts in the fourth quarter for a given coded quarterly value of X (α = 0.05). D) The number of contracts in the first quarter of the year is not significantly different from the number of contracts in the fourth quarter for a given coded quarterly value of X (α = 0.05). <div style=padding-top: 35px>
Referring to Table 16-5, in testing the coefficient for Q1 in the regression equation (-0.083), the results were a t-statistic of -0.66 and an associated p-value of 0.530. Which of the following is the best interpretation of this result?

A) The number of contracts in the first quarter of the year is significantly different from the number of contracts in an average quarter (α = 0.05).
B) The number of contracts in the first quarter of the year is not significantly different from the number of contracts in an average quarter (α = 0.05).
C) The number of contracts in the first quarter of the year is significantly different from the number of contracts in the fourth quarter for a given coded quarterly value of X (α = 0.05).
D) The number of contracts in the first quarter of the year is not significantly different from the number of contracts in the fourth quarter for a given coded quarterly value of X (α = 0.05).
Question
MAD is the summation of the residuals divided by the sample size.
Question
In selecting a forecasting model, we should perform a residual analysis.
Question
TABLE 16-5
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 2006 to 2008. The following is the resulting regression equation:
<strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, using the regression equation, which of the following values is the best forecast for the number of contracts in the second quarter of 2010?</strong> A) 144212 B) 391742 C) 1238797 D) 4355119 <div style=padding-top: 35px> <strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, using the regression equation, which of the following values is the best forecast for the number of contracts in the second quarter of 2010?</strong> A) 144212 B) 391742 C) 1238797 D) 4355119 <div style=padding-top: 35px>
Referring to Table 16-5, using the regression equation, which of the following values is the best forecast for the number of contracts in the second quarter of 2010?

A) 144212
B) 391742
C) 1238797
D) 4355119
Question
The principle of parsimony indicates that the simplest model that gets the job done adequately should be used.
Question
TABLE 16-5
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 2006 to 2008. The following is the resulting regression equation:
<strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, in testing the coefficient of X in the regression equation (0.117) the results were a t-statistic of 9.08 and an associated p-value of 0.0000. Which of the following is the best interpretation of this result?</strong> A) The quarterly growth rate in the number of contracts is significantly different from 0% (α = 0.05). B) The quarterly growth rate in the number of contracts is not significantly different from 0% (α = 0.05). C) The quarterly growth rate in the number of contracts is significantly different from 100% (α = 0.05). D) The quarterly growth rate in the number of contracts is not significantly different from 100% (α = 0.05). <div style=padding-top: 35px> <strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, in testing the coefficient of X in the regression equation (0.117) the results were a t-statistic of 9.08 and an associated p-value of 0.0000. Which of the following is the best interpretation of this result?</strong> A) The quarterly growth rate in the number of contracts is significantly different from 0% (α = 0.05). B) The quarterly growth rate in the number of contracts is not significantly different from 0% (α = 0.05). C) The quarterly growth rate in the number of contracts is significantly different from 100% (α = 0.05). D) The quarterly growth rate in the number of contracts is not significantly different from 100% (α = 0.05). <div style=padding-top: 35px>
Referring to Table 16-5, in testing the coefficient of X in the regression equation (0.117) the results were a t-statistic of 9.08 and an associated p-value of 0.0000. Which of the following is the best interpretation of this result?

A) The quarterly growth rate in the number of contracts is significantly different from 0% (α = 0.05).
B) The quarterly growth rate in the number of contracts is not significantly different from 0% (α = 0.05).
C) The quarterly growth rate in the number of contracts is significantly different from 100% (α = 0.05).
D) The quarterly growth rate in the number of contracts is not significantly different from 100% (α = 0.05).
Question
Each forecast using the method of exponential smoothing depends on all the previous observations in the time series.
Question
TABLE 16-5
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 2006 to 2008. The following is the resulting regression equation:
<strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, the best interpretation of the coefficient of Q<sub>3</sub> (0.617) in the regression equation is:</strong> A) the number of contracts in the third quarter of a year is approximately 62% higher than the average over all 4 quarters. B) the number of contracts in the third quarter of a year is approximately 62% higher than it would be during the fourth quarter. C) the number of contracts in the third quarter of a year is approximately 314% higher than the average over all 4 quarters. D) the number of contracts in the third quarter of a year is approximately 314% higher than it would be during the fourth quarter. <div style=padding-top: 35px> <strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, the best interpretation of the coefficient of Q<sub>3</sub> (0.617) in the regression equation is:</strong> A) the number of contracts in the third quarter of a year is approximately 62% higher than the average over all 4 quarters. B) the number of contracts in the third quarter of a year is approximately 62% higher than it would be during the fourth quarter. C) the number of contracts in the third quarter of a year is approximately 314% higher than the average over all 4 quarters. D) the number of contracts in the third quarter of a year is approximately 314% higher than it would be during the fourth quarter. <div style=padding-top: 35px>
Referring to Table 16-5, the best interpretation of the coefficient of Q3 (0.617) in the regression equation is:

A) the number of contracts in the third quarter of a year is approximately 62% higher than the average over all 4 quarters.
B) the number of contracts in the third quarter of a year is approximately 62% higher than it would be during the fourth quarter.
C) the number of contracts in the third quarter of a year is approximately 314% higher than the average over all 4 quarters.
D) the number of contracts in the third quarter of a year is approximately 314% higher than it would be during the fourth quarter.
Question
The method of least squares may be used to estimate both linear and curvilinear trends.
Question
TABLE 16-5
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 2006 to 2008. The following is the resulting regression equation:
<strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, the best interpretation of the coefficient of X (0.117) in the regression equation is:</strong> A) the quarterly compound growth rate in contracts is around 30.92%. B) the annual compound growth rate in contracts is around 30.92%. C) the quarterly compound growth rate in contracts is around 11.7%. D) the annual compound growth rate in contracts is around 11.7%. <div style=padding-top: 35px> <strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, the best interpretation of the coefficient of X (0.117) in the regression equation is:</strong> A) the quarterly compound growth rate in contracts is around 30.92%. B) the annual compound growth rate in contracts is around 30.92%. C) the quarterly compound growth rate in contracts is around 11.7%. D) the annual compound growth rate in contracts is around 11.7%. <div style=padding-top: 35px>
Referring to Table 16-5, the best interpretation of the coefficient of X (0.117) in the regression equation is:

A) the quarterly compound growth rate in contracts is around 30.92%.
B) the annual compound growth rate in contracts is around 30.92%.
C) the quarterly compound growth rate in contracts is around 11.7%.
D) the annual compound growth rate in contracts is around 11.7%.
Question
Given a data set with 15 yearly observations, a 3-year moving average will have fewer observations than a 5-year moving average.
Question
Given a data set with 15 yearly observations, there are only thirteen 3-year moving averages.
Question
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, exponential smoothing with a weight or smoothing constant of 0.4 will be used to smooth the wine sales. The value of E<sub>5</sub>, the smoothed value for 2005 is ________.<div style=padding-top: 35px>
Referring to Table 16-6, exponential smoothing with a weight or smoothing constant of 0.4 will be used to smooth the wine sales. The value of E5, the smoothed value for 2005 is ________.
Question
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, exponential smoothing with a weight or smoothing constant of 0.4 will be used to forecast wine sales. The forecast for 2009 is ________.<div style=padding-top: 35px>
Referring to Table 16-6, exponential smoothing with a weight or smoothing constant of 0.4 will be used to forecast wine sales. The forecast for 2009 is ________.
Question
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, exponential smoothing with a weight or smoothing constant of 0.2 will be used to smooth the wine sales. The value of E<sub>4</sub>, the smoothed value for 2004 is ________.<div style=padding-top: 35px>
Referring to Table 16-6, exponential smoothing with a weight or smoothing constant of 0.2 will be used to smooth the wine sales. The value of E4, the smoothed value for 2004 is ________.
Question
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, 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.<div style=padding-top: 35px>
Referring to Table 16-6, 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.
Question
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, construct a centered 3-year moving average for the wine sales.<div style=padding-top: 35px>
Referring to Table 16-6, construct a centered 3-year moving average for the wine sales.
Question
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, exponential smoothing with a weight or smoothing constant of 0.4 will be used to smooth the wine sales. The value of E<sub>2</sub>, the smoothed value for 2002 is ________.<div style=padding-top: 35px>
Referring to Table 16-6, exponential smoothing with a weight or smoothing constant of 0.4 will be used to smooth the wine sales. The value of E2, the smoothed value for 2002 is ________.
Question
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, a centered 3-year moving average is to be constructed for the wine sales. The moving average for 2002 is ________.<div style=padding-top: 35px>
Referring to Table 16-6, a centered 3-year moving average is to be constructed for the wine sales. The moving average for 2002 is ________.
Question
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, a centered 5-year moving average is to be constructed for the wine sales. The moving average for 2006 is ________.<div style=padding-top: 35px>
Referring to Table 16-6, a centered 5-year moving average is to be constructed for the wine sales. The moving average for 2006 is ________.
Question
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, 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 2002 is ________.<div style=padding-top: 35px>
Referring to Table 16-6, 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 2002 is ________.
Question
TABLE 16-7
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 16-7, the number of arrivals will be smoothed with a 3-term moving average. The first smoothed value will be ________.
Question
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, a centered 3-year moving average is to be constructed for the wine sales. The moving average for 2005 is ________.<div style=padding-top: 35px>
Referring to Table 16-6, a centered 3-year moving average is to be constructed for the wine sales. The moving average for 2005 is ________.
Question
TABLE 16-7
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 16-7, the number of arrivals will be smoothed with a 3-term moving average. The last smoothed value will be ________.
Question
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, construct a centered 5-year moving average for the wine sales.<div style=padding-top: 35px>
Referring to Table 16-6, construct a centered 5-year moving average for the wine sales.
Question
TABLE 16-7
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 16-7, the number of arrivals will be smoothed with a 5-term moving average. The first smoothed value will be ________.
Question
TABLE 16-7
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 16-7, the number of arrivals will be smoothed with a 3-term moving average. There will be a total of ________ smoothed values.
Question
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, a centered 5-year moving average is to be constructed for the wine sales. The number of moving averages that will be calculated is ________.<div style=padding-top: 35px>
Referring to Table 16-6, a centered 5-year moving average is to be constructed for the wine sales. The number of moving averages that will be calculated is ________.
Question
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, exponential smoothing with a weight or smoothing constant of 0.2 will be used to forecast wine sales. The forecast for 2009 is ________.<div style=padding-top: 35px>
Referring to Table 16-6, exponential smoothing with a weight or smoothing constant of 0.2 will be used to forecast wine sales. The forecast for 2009 is ________.
Question
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, exponentially smooth the wine sales with a weight or smoothing constant of 0.4.<div style=padding-top: 35px>
Referring to Table 16-6, exponentially smooth the wine sales with a weight or smoothing constant of 0.4.
Question
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, a centered 5-year moving average is to be constructed for the wine sales. The moving average for 2003 is ________.<div style=padding-top: 35px>
Referring to Table 16-6, a centered 5-year moving average is to be constructed for the wine sales. The moving average for 2003 is ________.
Question
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, exponentially smooth the wine sales with a weight or smoothing constant of 0.2.<div style=padding-top: 35px>
Referring to Table 16-6, exponentially smooth the wine sales with a weight or smoothing constant of 0.2.
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Deck 16: Time-Series Analysis and Index Numbers
1
Which of the following statements about moving averages is not true?

A) It can be used to smooth a series.
B) It gives equal weight to all values in the computation.
C) It is simpler than the method of exponential smoothing.
D) It gives greater weight to more recent data.
It gives greater weight to more recent data.
2
Which of the following is not an advantage of exponential smoothing?

A) It enables us to perform one-period ahead forecasting.
B) It enables us to perform more than one-period ahead forecasting.
C) It enables us to smooth out seasonal components.
D) It enables us to smooth out cyclical components.
It enables us to perform more than one-period ahead forecasting.
3
Which of the following statements about the method of exponential smoothing is not true?

A) It gives greater weight to more recent data.
B) It can be used for forecasting.
C) It uses all earlier observations in each smoothing calculation.
D) It gives greater weight to the earlier observations in the series.
It gives greater weight to the earlier observations in the series.
4
To assess the adequacy of a forecasting model, one measure that is often used is

A) quadratic trend analysis.
B) the MAD.
C) exponential smoothing.
D) moving averages.
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5
The annual multiplicative time-series model does not possess ________ component.

A) a trend
B) a cyclical
C) an irregular
D) a seasonal
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6
A model that can be used to make predictions about long-term future values of a time series is

A) linear trend.
B) quadratic trend.
C) exponential trend.
D) All of the above.
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7
The overall upward or downward pattern of the data in an annual time series will be contained in the ________ component.

A) trend
B) cyclical
C) irregular
D) seasonal
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8
When a time series appears to be increasing at an increasing rate, such that percentage difference from observation to observation is constant, the appropriate model to fit is the

A) linear trend.
B) quadratic trend.
C) exponential trend.
D) None of the above.
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9
The fairly regular fluctuations that occur within each year would be contained in the ________ component.

A) trend
B) cyclical
C) irregular
D) seasonal
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10
The method of moving averages is used

A) to plot a series.
B) to exponentiate a series.
C) to smooth a series.
D) in regression analysis.
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11
TABLE 16-1
The number of cases of chardonnay wine sold by a Paso Robles winery in an 8-year period follows.
<strong>TABLE 16-1 The number of cases of chardonnay wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-1, does there appear to be a relationship between year and the number of cases of wine sold?</strong> A) No, there appears to be no relationship between the year and the number of cases of wine sold by the vintner. B) Yes, there appears to be a slight negative linear relationship between the year and the number of cases of wine sold by the vintner. C) Yes, there appears to be a slight positive relationship between the year and the number of cases of wine sold by the vintner. D) Yes, there appears to be a negative nonlinear relationship between the year and the number of cases of wine sold by the vintner.
Referring to Table 16-1, does there appear to be a relationship between year and the number of cases of wine sold?

A) No, there appears to be no relationship between the year and the number of cases of wine sold by the vintner.
B) Yes, there appears to be a slight negative linear relationship between the year and the number of cases of wine sold by the vintner.
C) Yes, there appears to be a slight positive relationship between the year and the number of cases of wine sold by the vintner.
D) Yes, there appears to be a negative nonlinear relationship between the year and the number of cases of wine sold by the vintner.
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12
When using the exponentially weighted moving average for purposes of forecasting rather than smoothing,

A) the previous smoothed value becomes the forecast.
B) the current smoothed value becomes the forecast.
C) the next smoothed value becomes the forecast.
D) None of the above.
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13
You need to decide whether you should invest in a particular stock. You would like to invest if the price is likely to rise in the long run. You have data on the daily average price of this stock over the past 12 months. Your best action is to

A) compute moving averages.
B) perform exponential smoothing.
C) estimate a least square trend model.
D) compute the MAD statistic.
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14
Which of the following methods should not be used for short-term forecasts into the future?

A) exponential smoothing
B) moving averages
C) linear trend model
D) autoregressive modeling
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15
The effect of an unpredictable, rare event will be contained in the ________ component.

A) trend
B) cyclical
C) irregular
D) seasonal
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16
In selecting an appropriate forecasting model, the following approaches are suggested:

A) Perform a residual analysis.
B) Measure the size of the forecasting error.
C) Use the principle of parsimony.
D) All of the above.
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17
After estimating a trend model for annual time-series data, you obtain the following residual plot against time: <strong>After estimating a trend model for annual time-series data, you obtain the following residual plot against time:   The problem with your model is that:</strong> A) The cyclical component has not been accounted for. B) The seasonal component has not been accounted for. C) The trend component has not been accounted for. D) The irregular component has not been accounted for. The problem with your model is that:

A) The cyclical component has not been accounted for.
B) The seasonal component has not been accounted for.
C) The trend component has not been accounted for.
D) The irregular component has not been accounted for.
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18
The method of least squares is used on time-series data for

A) eliminating irregular movements.
B) deseasonalizing the data.
C) obtaining the trend equation.
D) exponentially smoothing a series.
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19
Based on the following scatter plot, which of the time-series components is not present in this quarterly time series? <strong>Based on the following scatter plot, which of the time-series components is not present in this quarterly time series?  </strong> A) trend B) seasonal C) cyclical D) irregular

A) trend
B) seasonal
C) cyclical
D) irregular
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20
TABLE 16-1
The number of cases of chardonnay wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-1 The number of cases of chardonnay wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-1, set up a scatter diagram (i.e., a time-series plot) with year on the horizontal X-axis.
Referring to Table 16-1, set up a scatter diagram (i.e., a time-series plot) with year on the horizontal X-axis.
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21
The following is the list of MAD statistics for each of the models you have estimated from time-series data: <strong>The following is the list of MAD statistics for each of the models you have estimated from time-series data:   Based on the MAD criterion, the most appropriate model is</strong> A) linear trend. B) quadratic trend. C) exponential trend. D) AR(2). Based on the MAD criterion, the most appropriate model is

A) linear trend.
B) quadratic trend.
C) exponential trend.
D) AR(2).
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22
TABLE 16-3
The following table contains the number of complaints received in a department store for the first 6 months of last year.
<strong>TABLE 16-3 The following table contains the number of complaints received in a department store for the first 6 months of last year.   Referring to Table 16-3, suppose the last two smoothed values are 81 and 96 (Note: they are not). What would you forecast as the value of the time series for September?</strong> A) 81 B) 86 C) 91 D) 96
Referring to Table 16-3, suppose the last two smoothed values are 81 and 96 (Note: they are not). What would you forecast as the value of the time series for September?

A) 81
B) 86
C) 91
D) 96
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23
TABLE 16-3
The following table contains the number of complaints received in a department store for the first 6 months of last year.
<strong>TABLE 16-3 The following table contains the number of complaints received in a department store for the first 6 months of last year.   Referring to Table 16-3, if this series is smoothed using exponential smoothing with a smoothing constant of 1/3, what would be the third value?</strong> A) 53 B) 65.33 C) 68 D) 81
Referring to Table 16-3, if this series is smoothed using exponential smoothing with a smoothing constant of 1/3, what would be the third value?

A) 53
B) 65.33
C) 68
D) 81
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24
TABLE 16-3
The following table contains the number of complaints received in a department store for the first 6 months of last year.
<strong>TABLE 16-3 The following table contains the number of complaints received in a department store for the first 6 months of last year.   Referring to Table 16-3, if a three-month moving average is used to smooth this series, how many values would it have?</strong> A) 2 B) 3 C) 4 D) 5
Referring to Table 16-3, if a three-month moving average is used to smooth this series, how many values would it have?

A) 2
B) 3
C) 4
D) 5
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25
TABLE 16-3
The following table contains the number of complaints received in a department store for the first 6 months of last year.
<strong>TABLE 16-3 The following table contains the number of complaints received in a department store for the first 6 months of last year.   Referring to Table 16-3, if a three-month moving average is used to smooth this series, what would be the second calculated value?</strong> A) 36 B) 40.5 C) 54 D) 72
Referring to Table 16-3, if a three-month moving average is used to smooth this series, what would be the second calculated value?

A) 36
B) 40.5
C) 54
D) 72
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26
TABLE 16-5
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 2006 to 2008. The following is the resulting regression equation:
<strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, the best interpretation of the constant 3.37 in the regression equation is:</strong> A) the fitted value for the first quarter of 2006, prior to seasonal adjustment, is log<sub>10</sub> 3.37. B) the fitted value for the first quarter of 2006, after to seasonal adjustment, is log<sub>10</sub> 3.37. C) the fitted value for the first quarter of 2006, prior to seasonal adjustment, is 10<sup>3.37</sup>. D) the fitted value for the first quarter of 2006, after to seasonal adjustment, is 10<sup>3.37</sup>. <strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, the best interpretation of the constant 3.37 in the regression equation is:</strong> A) the fitted value for the first quarter of 2006, prior to seasonal adjustment, is log<sub>10</sub> 3.37. B) the fitted value for the first quarter of 2006, after to seasonal adjustment, is log<sub>10</sub> 3.37. C) the fitted value for the first quarter of 2006, prior to seasonal adjustment, is 10<sup>3.37</sup>. D) the fitted value for the first quarter of 2006, after to seasonal adjustment, is 10<sup>3.37</sup>.
Referring to Table 16-5, the best interpretation of the constant 3.37 in the regression equation is:

A) the fitted value for the first quarter of 2006, prior to seasonal adjustment, is log10 3.37.
B) the fitted value for the first quarter of 2006, after to seasonal adjustment, is log10 3.37.
C) the fitted value for the first quarter of 2006, prior to seasonal adjustment, is 103.37.
D) the fitted value for the first quarter of 2006, after to seasonal adjustment, is 103.37.
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27
Which of the following terms describes the overall long-term tendency of a time series?

A) trend
B) cyclical component
C) irregular component
D) seasonal component
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28
TABLE 16-3
The following table contains the number of complaints received in a department store for the first 6 months of last year.
<strong>TABLE 16-3 The following table contains the number of complaints received in a department store for the first 6 months of last year.   Referring to Table 16-3, if a three-month moving average is used to smooth this series, what would be the last calculated value?</strong> A) 72 B) 93 C) 114 D) 126
Referring to Table 16-3, if a three-month moving average is used to smooth this series, what would be the last calculated value?

A) 72
B) 93
C) 114
D) 126
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29
TABLE 16-2
The monthly advertising expenditures of a department store chain (in $1,000,000s) were collected over the last decade. The last 14 months of this time series follows:
TABLE 16-2 The monthly advertising expenditures of a department store chain (in $1,000,000s) were collected over the last decade. The last 14 months of this time series follows:   Referring to Table 16-2, set up a scatter diagram (i.e., time-series plot) with months on the horizontal X-axis.
Referring to Table 16-2, set up a scatter diagram (i.e., time-series plot) with months on the horizontal X-axis.
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30
TABLE 16-3
The following table contains the number of complaints received in a department store for the first 6 months of last year.
<strong>TABLE 16-3 The following table contains the number of complaints received in a department store for the first 6 months of last year.   Referring to Table 16-3, if this series is smoothed using exponential smoothing with a smoothing constant of 1/3, what would be the first value?</strong> A) 36 B) 39 C) 42 D) 45
Referring to Table 16-3, if this series is smoothed using exponential smoothing with a smoothing constant of 1/3, what would be the first value?

A) 36
B) 39
C) 42
D) 45
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31
TABLE 16-4
Given below are EXCEL outputs for various estimated autoregressive models for a company's real operating revenues (in billions of dollars) from 1985 to 2008. From the data, we also know that the real operating revenues for 2006, 2007, and 2008 are 11.7909, 11.7757 and 11.5537, respectively.
AR(1) Model:
<strong>TABLE 16-4 Given below are EXCEL outputs for various estimated autoregressive models for a company's real operating revenues (in billions of dollars) from 1985 to 2008. From the data, we also know that the real operating revenues for 2006, 2007, and 2008 are 11.7909, 11.7757 and 11.5537, respectively. AR(1) Model:   AR(2) Model:   AR(3) Model:   Referring to Table 16-4 and using a 5% level of significance, what is the appropriate AR model for the company's real operating revenue?</strong> A) AR(1) B) AR(2) C) AR(3) D) Any of the above. AR(2) Model:
<strong>TABLE 16-4 Given below are EXCEL outputs for various estimated autoregressive models for a company's real operating revenues (in billions of dollars) from 1985 to 2008. From the data, we also know that the real operating revenues for 2006, 2007, and 2008 are 11.7909, 11.7757 and 11.5537, respectively. AR(1) Model:   AR(2) Model:   AR(3) Model:   Referring to Table 16-4 and using a 5% level of significance, what is the appropriate AR model for the company's real operating revenue?</strong> A) AR(1) B) AR(2) C) AR(3) D) Any of the above. AR(3) Model:
<strong>TABLE 16-4 Given below are EXCEL outputs for various estimated autoregressive models for a company's real operating revenues (in billions of dollars) from 1985 to 2008. From the data, we also know that the real operating revenues for 2006, 2007, and 2008 are 11.7909, 11.7757 and 11.5537, respectively. AR(1) Model:   AR(2) Model:   AR(3) Model:   Referring to Table 16-4 and using a 5% level of significance, what is the appropriate AR model for the company's real operating revenue?</strong> A) AR(1) B) AR(2) C) AR(3) D) Any of the above.
Referring to Table 16-4 and using a 5% level of significance, what is the appropriate AR model for the company's real operating revenue?

A) AR(1)
B) AR(2)
C) AR(3)
D) Any of the above.
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32
After estimating a trend model for annual time-series data, you obtain the following residual plot against time: <strong>After estimating a trend model for annual time-series data, you obtain the following residual plot against time:   The problem with your model is that</strong> A) the cyclical component has not been accounted for. B) the seasonal component has not been accounted for. C) the trend component has not been accounted for. D) the irregular component has not been accounted for. The problem with your model is that

A) the cyclical component has not been accounted for.
B) the seasonal component has not been accounted for.
C) the trend component has not been accounted for.
D) the irregular component has not been accounted for.
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33
If you want to recover the trend using exponential smoothing, you will choose a weight (W) that falls in the range

A) [0, 0.2].
B) [0.2, 0.4].
C) [0.6, 0.8].
D) [0.9, 1.0].
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34
Which of the following terms describes the up and down movements of a time series that vary both in length and intensity?

A) trend
B) cyclical component
C) irregular component
D) seasonal component
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35
TABLE 16-3
The following table contains the number of complaints received in a department store for the first 6 months of last year.
<strong>TABLE 16-3 The following table contains the number of complaints received in a department store for the first 6 months of last year.   Referring to Table 16-3, if this series is smoothed using exponential smoothing with a smoothing constant of 1/3, what would be the second value?</strong> A) 39 B) 42 C) 45 D) 53
Referring to Table 16-3, if this series is smoothed using exponential smoothing with a smoothing constant of 1/3, what would be the second value?

A) 39
B) 42
C) 45
D) 53
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36
TABLE 16-3
The following table contains the number of complaints received in a department store for the first 6 months of last year.
<strong>TABLE 16-3 The following table contains the number of complaints received in a department store for the first 6 months of last year.   Referring to Table 16-3, suppose the last two smoothed values are 81 and 96 (Note: they are not). What would you forecast as the value of the time series for July?</strong> A) 81 B) 86 C) 91 D) 96
Referring to Table 16-3, suppose the last two smoothed values are 81 and 96 (Note: they are not). What would you forecast as the value of the time series for July?

A) 81
B) 86
C) 91
D) 96
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37
The cyclical component of a time series

A) represents periodic fluctuations which reoccur within 1 year.
B) represents periodic fluctuations which usually occur in 2 or more years.
C) is obtained by adding up the seasonal indexes.
D) is obtained by adjusting for calendar variation.
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38
TABLE 16-2
The monthly advertising expenditures of a department store chain (in $1,000,000s) were collected over the last decade. The last 14 months of this time series follows:
TABLE 16-2 The monthly advertising expenditures of a department store chain (in $1,000,000s) were collected over the last decade. The last 14 months of this time series follows:   Referring to Table 16-2, advertising expenditures appear to be increasing in a linear rather than curvilinear manner over time.
Referring to Table 16-2, advertising expenditures appear to be increasing in a linear rather than curvilinear manner over time.
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39
TABLE 16-4
Given below are EXCEL outputs for various estimated autoregressive models for a company's real operating revenues (in billions of dollars) from 1985 to 2008. From the data, we also know that the real operating revenues for 2006, 2007, and 2008 are 11.7909, 11.7757 and 11.5537, respectively.
AR(1) Model:
<strong>TABLE 16-4 Given below are EXCEL outputs for various estimated autoregressive models for a company's real operating revenues (in billions of dollars) from 1985 to 2008. From the data, we also know that the real operating revenues for 2006, 2007, and 2008 are 11.7909, 11.7757 and 11.5537, respectively. AR(1) Model:   AR(2) Model:   AR(3) Model:   Referring to Table 16-4, if one decides to use AR(3), what will the predicted real operating revenue for the company be in 2011?</strong> A) $11.59 billion B) $11.68 billion C) $11.84 billion D) $12.47 billion AR(2) Model:
<strong>TABLE 16-4 Given below are EXCEL outputs for various estimated autoregressive models for a company's real operating revenues (in billions of dollars) from 1985 to 2008. From the data, we also know that the real operating revenues for 2006, 2007, and 2008 are 11.7909, 11.7757 and 11.5537, respectively. AR(1) Model:   AR(2) Model:   AR(3) Model:   Referring to Table 16-4, if one decides to use AR(3), what will the predicted real operating revenue for the company be in 2011?</strong> A) $11.59 billion B) $11.68 billion C) $11.84 billion D) $12.47 billion AR(3) Model:
<strong>TABLE 16-4 Given below are EXCEL outputs for various estimated autoregressive models for a company's real operating revenues (in billions of dollars) from 1985 to 2008. From the data, we also know that the real operating revenues for 2006, 2007, and 2008 are 11.7909, 11.7757 and 11.5537, respectively. AR(1) Model:   AR(2) Model:   AR(3) Model:   Referring to Table 16-4, if one decides to use AR(3), what will the predicted real operating revenue for the company be in 2011?</strong> A) $11.59 billion B) $11.68 billion C) $11.84 billion D) $12.47 billion
Referring to Table 16-4, if one decides to use AR(3), what will the predicted real operating revenue for the company be in 2011?

A) $11.59 billion
B) $11.68 billion
C) $11.84 billion
D) $12.47 billion
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40
TABLE 16-3
The following table contains the number of complaints received in a department store for the first 6 months of last year.
<strong>TABLE 16-3 The following table contains the number of complaints received in a department store for the first 6 months of last year.   Referring to Table 16-3, if this series is smoothed using exponential smoothing with a smoothing constant of 1/3, how many values would it have?</strong> A) 3 B) 4 C) 5 D) 6
Referring to Table 16-3, if this series is smoothed using exponential smoothing with a smoothing constant of 1/3, how many values would it have?

A) 3
B) 4
C) 5
D) 6
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41
The MAD is a measure of the average of the absolute discrepancies between the actual and the fitted values in a given time series.
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42
TABLE 16-5
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 2006 to 2008. The following is the resulting regression equation:
<strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, to obtain a forecast for the fourth quarter of 2009 using the model, which of the following sets of values should be used in the regression equation?</strong> A) X = 15, Q<sub>1</sub> = 0, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 B) X = 15, Q<sub>1</sub> = 1, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 C) X = 16, Q<sub>1</sub> = 0, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 D) X = 16, Q<sub>1</sub> = 1, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 <strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, to obtain a forecast for the fourth quarter of 2009 using the model, which of the following sets of values should be used in the regression equation?</strong> A) X = 15, Q<sub>1</sub> = 0, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 B) X = 15, Q<sub>1</sub> = 1, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 C) X = 16, Q<sub>1</sub> = 0, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 D) X = 16, Q<sub>1</sub> = 1, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0
Referring to Table 16-5, to obtain a forecast for the fourth quarter of 2009 using the model, which of the following sets of values should be used in the regression equation?

A) X = 15, Q1 = 0, Q2 = 0, Q3 = 0
B) X = 15, Q1 = 1, Q2 = 0, Q3 = 0
C) X = 16, Q1 = 0, Q2 = 0, Q3 = 0
D) X = 16, Q1 = 1, Q2 = 0, Q3 = 0
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43
If a time series does not exhibit a long-term trend, the method of exponential smoothing may be used to obtain short-term predictions about the future.
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44
TABLE 16-5
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 2006 to 2008. The following is the resulting regression equation:
<strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, to obtain a forecast for the first quarter of 2009 using the model, which of the following sets of values should be used in the regression equation?</strong> A) X = 12, Q<sub>1</sub> = 0, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 B) X = 12, Q<sub>1</sub> = 1, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 C) X = 13, Q<sub>1</sub> = 0, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 D) X = 13, Q<sub>1</sub> = 1, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 <strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, to obtain a forecast for the first quarter of 2009 using the model, which of the following sets of values should be used in the regression equation?</strong> A) X = 12, Q<sub>1</sub> = 0, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 B) X = 12, Q<sub>1</sub> = 1, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 C) X = 13, Q<sub>1</sub> = 0, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0 D) X = 13, Q<sub>1</sub> = 1, Q<sub>2</sub> = 0, Q<sub>3</sub> = 0
Referring to Table 16-5, to obtain a forecast for the first quarter of 2009 using the model, which of the following sets of values should be used in the regression equation?

A) X = 12, Q1 = 0, Q2 = 0, Q3 = 0
B) X = 12, Q1 = 1, Q2 = 0, Q3 = 0
C) X = 13, Q1 = 0, Q2 = 0, Q3 = 0
D) X = 13, Q1 = 1, Q2 = 0, Q3 = 0
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45
A trend is a persistent pattern in annual time-series data that has to be followed for several years.
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46
A least squares linear trend line is just a simple regression line with the years recoded.
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47
TABLE 16-5
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 2006 to 2008. The following is the resulting regression equation:
<strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, using the regression equation, which of the following values is the best forecast for the number of contracts in the third quarter of 2009?</strong> A) 49091 B) 133352 C) 421697 D) 1482518 <strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, using the regression equation, which of the following values is the best forecast for the number of contracts in the third quarter of 2009?</strong> A) 49091 B) 133352 C) 421697 D) 1482518
Referring to Table 16-5, using the regression equation, which of the following values is the best forecast for the number of contracts in the third quarter of 2009?

A) 49091
B) 133352
C) 421697
D) 1482518
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48
Given a data set with 15 yearly observations, there are only seven 9-year moving averages.
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49
TABLE 16-5
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 2006 to 2008. The following is the resulting regression equation:
<strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, in testing the coefficient for Q<sub>1</sub> in the regression equation (-0.083), the results were a t-statistic of -0.66 and an associated p-value of 0.530. Which of the following is the best interpretation of this result?</strong> A) The number of contracts in the first quarter of the year is significantly different from the number of contracts in an average quarter (α = 0.05). B) The number of contracts in the first quarter of the year is not significantly different from the number of contracts in an average quarter (α = 0.05). C) The number of contracts in the first quarter of the year is significantly different from the number of contracts in the fourth quarter for a given coded quarterly value of X (α = 0.05). D) The number of contracts in the first quarter of the year is not significantly different from the number of contracts in the fourth quarter for a given coded quarterly value of X (α = 0.05). <strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, in testing the coefficient for Q<sub>1</sub> in the regression equation (-0.083), the results were a t-statistic of -0.66 and an associated p-value of 0.530. Which of the following is the best interpretation of this result?</strong> A) The number of contracts in the first quarter of the year is significantly different from the number of contracts in an average quarter (α = 0.05). B) The number of contracts in the first quarter of the year is not significantly different from the number of contracts in an average quarter (α = 0.05). C) The number of contracts in the first quarter of the year is significantly different from the number of contracts in the fourth quarter for a given coded quarterly value of X (α = 0.05). D) The number of contracts in the first quarter of the year is not significantly different from the number of contracts in the fourth quarter for a given coded quarterly value of X (α = 0.05).
Referring to Table 16-5, in testing the coefficient for Q1 in the regression equation (-0.083), the results were a t-statistic of -0.66 and an associated p-value of 0.530. Which of the following is the best interpretation of this result?

A) The number of contracts in the first quarter of the year is significantly different from the number of contracts in an average quarter (α = 0.05).
B) The number of contracts in the first quarter of the year is not significantly different from the number of contracts in an average quarter (α = 0.05).
C) The number of contracts in the first quarter of the year is significantly different from the number of contracts in the fourth quarter for a given coded quarterly value of X (α = 0.05).
D) The number of contracts in the first quarter of the year is not significantly different from the number of contracts in the fourth quarter for a given coded quarterly value of X (α = 0.05).
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50
MAD is the summation of the residuals divided by the sample size.
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51
In selecting a forecasting model, we should perform a residual analysis.
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52
TABLE 16-5
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 2006 to 2008. The following is the resulting regression equation:
<strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, using the regression equation, which of the following values is the best forecast for the number of contracts in the second quarter of 2010?</strong> A) 144212 B) 391742 C) 1238797 D) 4355119 <strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, using the regression equation, which of the following values is the best forecast for the number of contracts in the second quarter of 2010?</strong> A) 144212 B) 391742 C) 1238797 D) 4355119
Referring to Table 16-5, using the regression equation, which of the following values is the best forecast for the number of contracts in the second quarter of 2010?

A) 144212
B) 391742
C) 1238797
D) 4355119
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53
The principle of parsimony indicates that the simplest model that gets the job done adequately should be used.
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54
TABLE 16-5
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 2006 to 2008. The following is the resulting regression equation:
<strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, in testing the coefficient of X in the regression equation (0.117) the results were a t-statistic of 9.08 and an associated p-value of 0.0000. Which of the following is the best interpretation of this result?</strong> A) The quarterly growth rate in the number of contracts is significantly different from 0% (α = 0.05). B) The quarterly growth rate in the number of contracts is not significantly different from 0% (α = 0.05). C) The quarterly growth rate in the number of contracts is significantly different from 100% (α = 0.05). D) The quarterly growth rate in the number of contracts is not significantly different from 100% (α = 0.05). <strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, in testing the coefficient of X in the regression equation (0.117) the results were a t-statistic of 9.08 and an associated p-value of 0.0000. Which of the following is the best interpretation of this result?</strong> A) The quarterly growth rate in the number of contracts is significantly different from 0% (α = 0.05). B) The quarterly growth rate in the number of contracts is not significantly different from 0% (α = 0.05). C) The quarterly growth rate in the number of contracts is significantly different from 100% (α = 0.05). D) The quarterly growth rate in the number of contracts is not significantly different from 100% (α = 0.05).
Referring to Table 16-5, in testing the coefficient of X in the regression equation (0.117) the results were a t-statistic of 9.08 and an associated p-value of 0.0000. Which of the following is the best interpretation of this result?

A) The quarterly growth rate in the number of contracts is significantly different from 0% (α = 0.05).
B) The quarterly growth rate in the number of contracts is not significantly different from 0% (α = 0.05).
C) The quarterly growth rate in the number of contracts is significantly different from 100% (α = 0.05).
D) The quarterly growth rate in the number of contracts is not significantly different from 100% (α = 0.05).
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55
Each forecast using the method of exponential smoothing depends on all the previous observations in the time series.
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56
TABLE 16-5
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 2006 to 2008. The following is the resulting regression equation:
<strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, the best interpretation of the coefficient of Q<sub>3</sub> (0.617) in the regression equation is:</strong> A) the number of contracts in the third quarter of a year is approximately 62% higher than the average over all 4 quarters. B) the number of contracts in the third quarter of a year is approximately 62% higher than it would be during the fourth quarter. C) the number of contracts in the third quarter of a year is approximately 314% higher than the average over all 4 quarters. D) the number of contracts in the third quarter of a year is approximately 314% higher than it would be during the fourth quarter. <strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, the best interpretation of the coefficient of Q<sub>3</sub> (0.617) in the regression equation is:</strong> A) the number of contracts in the third quarter of a year is approximately 62% higher than the average over all 4 quarters. B) the number of contracts in the third quarter of a year is approximately 62% higher than it would be during the fourth quarter. C) the number of contracts in the third quarter of a year is approximately 314% higher than the average over all 4 quarters. D) the number of contracts in the third quarter of a year is approximately 314% higher than it would be during the fourth quarter.
Referring to Table 16-5, the best interpretation of the coefficient of Q3 (0.617) in the regression equation is:

A) the number of contracts in the third quarter of a year is approximately 62% higher than the average over all 4 quarters.
B) the number of contracts in the third quarter of a year is approximately 62% higher than it would be during the fourth quarter.
C) the number of contracts in the third quarter of a year is approximately 314% higher than the average over all 4 quarters.
D) the number of contracts in the third quarter of a year is approximately 314% higher than it would be during the fourth quarter.
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57
The method of least squares may be used to estimate both linear and curvilinear trends.
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58
TABLE 16-5
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 2006 to 2008. The following is the resulting regression equation:
<strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, the best interpretation of the coefficient of X (0.117) in the regression equation is:</strong> A) the quarterly compound growth rate in contracts is around 30.92%. B) the annual compound growth rate in contracts is around 30.92%. C) the quarterly compound growth rate in contracts is around 11.7%. D) the annual compound growth rate in contracts is around 11.7%. <strong>TABLE 16-5 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 2006 to 2008. The following is the resulting regression equation:     Referring to Table 16-5, the best interpretation of the coefficient of X (0.117) in the regression equation is:</strong> A) the quarterly compound growth rate in contracts is around 30.92%. B) the annual compound growth rate in contracts is around 30.92%. C) the quarterly compound growth rate in contracts is around 11.7%. D) the annual compound growth rate in contracts is around 11.7%.
Referring to Table 16-5, the best interpretation of the coefficient of X (0.117) in the regression equation is:

A) the quarterly compound growth rate in contracts is around 30.92%.
B) the annual compound growth rate in contracts is around 30.92%.
C) the quarterly compound growth rate in contracts is around 11.7%.
D) the annual compound growth rate in contracts is around 11.7%.
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59
Given a data set with 15 yearly observations, a 3-year moving average will have fewer observations than a 5-year moving average.
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60
Given a data set with 15 yearly observations, there are only thirteen 3-year moving averages.
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61
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, exponential smoothing with a weight or smoothing constant of 0.4 will be used to smooth the wine sales. The value of E<sub>5</sub>, the smoothed value for 2005 is ________.
Referring to Table 16-6, exponential smoothing with a weight or smoothing constant of 0.4 will be used to smooth the wine sales. The value of E5, the smoothed value for 2005 is ________.
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62
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, exponential smoothing with a weight or smoothing constant of 0.4 will be used to forecast wine sales. The forecast for 2009 is ________.
Referring to Table 16-6, exponential smoothing with a weight or smoothing constant of 0.4 will be used to forecast wine sales. The forecast for 2009 is ________.
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63
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, exponential smoothing with a weight or smoothing constant of 0.2 will be used to smooth the wine sales. The value of E<sub>4</sub>, the smoothed value for 2004 is ________.
Referring to Table 16-6, exponential smoothing with a weight or smoothing constant of 0.2 will be used to smooth the wine sales. The value of E4, the smoothed value for 2004 is ________.
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64
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, 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 16-6, 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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65
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, construct a centered 3-year moving average for the wine sales.
Referring to Table 16-6, construct a centered 3-year moving average for the wine sales.
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66
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, exponential smoothing with a weight or smoothing constant of 0.4 will be used to smooth the wine sales. The value of E<sub>2</sub>, the smoothed value for 2002 is ________.
Referring to Table 16-6, exponential smoothing with a weight or smoothing constant of 0.4 will be used to smooth the wine sales. The value of E2, the smoothed value for 2002 is ________.
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67
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, a centered 3-year moving average is to be constructed for the wine sales. The moving average for 2002 is ________.
Referring to Table 16-6, a centered 3-year moving average is to be constructed for the wine sales. The moving average for 2002 is ________.
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68
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, a centered 5-year moving average is to be constructed for the wine sales. The moving average for 2006 is ________.
Referring to Table 16-6, a centered 5-year moving average is to be constructed for the wine sales. The moving average for 2006 is ________.
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69
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, 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 2002 is ________.
Referring to Table 16-6, 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 2002 is ________.
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70
TABLE 16-7
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 16-7, the number of arrivals will be smoothed with a 3-term moving average. The first smoothed value will be ________.
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71
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, a centered 3-year moving average is to be constructed for the wine sales. The moving average for 2005 is ________.
Referring to Table 16-6, a centered 3-year moving average is to be constructed for the wine sales. The moving average for 2005 is ________.
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72
TABLE 16-7
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 16-7, the number of arrivals will be smoothed with a 3-term moving average. The last smoothed value will be ________.
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73
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, construct a centered 5-year moving average for the wine sales.
Referring to Table 16-6, construct a centered 5-year moving average for the wine sales.
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74
TABLE 16-7
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 16-7, the number of arrivals will be smoothed with a 5-term moving average. The first smoothed value will be ________.
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75
TABLE 16-7
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 16-7, the number of arrivals will be smoothed with a 3-term moving average. There will be a total of ________ smoothed values.
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76
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, a centered 5-year moving average is to be constructed for the wine sales. The number of moving averages that will be calculated is ________.
Referring to Table 16-6, a centered 5-year moving average is to be constructed for the wine sales. The number of moving averages that will be calculated is ________.
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77
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, exponential smoothing with a weight or smoothing constant of 0.2 will be used to forecast wine sales. The forecast for 2009 is ________.
Referring to Table 16-6, exponential smoothing with a weight or smoothing constant of 0.2 will be used to forecast wine sales. The forecast for 2009 is ________.
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78
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, exponentially smooth the wine sales with a weight or smoothing constant of 0.4.
Referring to Table 16-6, exponentially smooth the wine sales with a weight or smoothing constant of 0.4.
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79
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, a centered 5-year moving average is to be constructed for the wine sales. The moving average for 2003 is ________.
Referring to Table 16-6, a centered 5-year moving average is to be constructed for the wine sales. The moving average for 2003 is ________.
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80
TABLE 16-6
The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.
TABLE 16-6 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   Referring to Table 16-6, exponentially smooth the wine sales with a weight or smoothing constant of 0.2.
Referring to Table 16-6, exponentially smooth the wine sales with a weight or smoothing constant of 0.2.
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