Exam 16: Time-Series Forecasting

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TABLE 16-14 A contractor developed a multiplicative time-series model to forecast the number of contracts in future quarters, using quarterly data on number of contracts during the 3-year period from 2008 to 2010. The following is the resulting regression equation: ln Ŷ = 3.37 + 0.117 X - 0.083 Q₁ + 1.28 Q₂ + 0.617 Q₃ where Ŷ is the estimated number of contracts in a quarter X is the coded quarterly value with X = 0 in the first quarter of 2008. Q₁ is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise. Q₂ is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise. Q₃ is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise. -Referring to Table 16-14, the best interpretation of the coefficient of X (0.117) in the regression equation is

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TABLE 16-10 Business closures in Laramie, Wyoming from 2005 to 2010 were: TABLE 16-10 Business closures in Laramie, Wyoming from 2005 to 2010 were:   -Referring to Table 16-10, the values of the MAD for the two models indicate that the first-order model should be used for forecasting. -Referring to Table 16-10, the values of the MAD for the two models indicate that the first-order model should be used for forecasting.

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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, does there appear to be a relationship between year and the number of cases of wine sold? -Referring to Table 16-1, does there appear to be a relationship between year and the number of cases of wine sold?

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TABLE 16-13 Given below is the monthly time-series data for U.S. retail sales of building materials over a specific year. TABLE 16-13 Given below is the monthly time-series data for U.S. retail sales of building materials over a specific year.     The results of the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the first month is 0:                -Referring to Table 16-13, what is the p-value for the t test statistic for testing the significance of the quadratic term in the quadratic-trend model? The results of the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the first month is 0: TABLE 16-13 Given below is the monthly time-series data for U.S. retail sales of building materials over a specific year.     The results of the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the first month is 0:                -Referring to Table 16-13, what is the p-value for the t test statistic for testing the significance of the quadratic term in the quadratic-trend model? TABLE 16-13 Given below is the monthly time-series data for U.S. retail sales of building materials over a specific year.     The results of the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the first month is 0:                -Referring to Table 16-13, what is the p-value for the t test statistic for testing the significance of the quadratic term in the quadratic-trend model? TABLE 16-13 Given below is the monthly time-series data for U.S. retail sales of building materials over a specific year.     The results of the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the first month is 0:                -Referring to Table 16-13, what is the p-value for the t test statistic for testing the significance of the quadratic term in the quadratic-trend model? TABLE 16-13 Given below is the monthly time-series data for U.S. retail sales of building materials over a specific year.     The results of the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the first month is 0:                -Referring to Table 16-13, what is the p-value for the t test statistic for testing the significance of the quadratic term in the quadratic-trend model? -Referring to Table 16-13, what is the p-value for the t test statistic for testing the significance of the quadratic term in the quadratic-trend model?

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TABLE 16-13 Given below is the monthly time-series data for U.S. retail sales of building materials over a specific year. TABLE 16-13 Given below is the monthly time-series data for U.S. retail sales of building materials over a specific year.     The results of the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the first month is 0:                -Referring to Table 16-13, what is your estimated annual compound growth rate using the exponential-trend model? The results of the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the first month is 0: TABLE 16-13 Given below is the monthly time-series data for U.S. retail sales of building materials over a specific year.     The results of the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the first month is 0:                -Referring to Table 16-13, what is your estimated annual compound growth rate using the exponential-trend model? TABLE 16-13 Given below is the monthly time-series data for U.S. retail sales of building materials over a specific year.     The results of the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the first month is 0:                -Referring to Table 16-13, what is your estimated annual compound growth rate using the exponential-trend model? TABLE 16-13 Given below is the monthly time-series data for U.S. retail sales of building materials over a specific year.     The results of the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the first month is 0:                -Referring to Table 16-13, what is your estimated annual compound growth rate using the exponential-trend model? TABLE 16-13 Given below is the monthly time-series data for U.S. retail sales of building materials over a specific year.     The results of the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the first month is 0:                -Referring to Table 16-13, what is your estimated annual compound growth rate using the exponential-trend model? -Referring to Table 16-13, what is your estimated annual compound growth rate using the exponential-trend model?

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TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters, using quarterly data on its revenues during the 4-year period from 2005 to 2009. The following is the resulting regression equation: log₁₀ TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters, using quarterly data on its revenues during the 4-year period from 2005 to 2009. The following is the resulting regression equation: log₁₀   = 6.102 + 0.012 X - 0.129 Q₁ - 0.054 Q₂ + 0.098 Q₃ where   is the estimated number of contracts in a quarter. X is the coded quarterly value with X = 0 in the first quarter of 2005. Q₁ is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise. Q₂ is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise. Q₃ is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise. -Referring to Table 16-12, using the regression equation, what is the forecast for the revenues in the third quarter of 2010? = 6.102 + 0.012 X - 0.129 Q₁ - 0.054 Q₂ + 0.098 Q₃ where TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters, using quarterly data on its revenues during the 4-year period from 2005 to 2009. The following is the resulting regression equation: log₁₀   = 6.102 + 0.012 X - 0.129 Q₁ - 0.054 Q₂ + 0.098 Q₃ where   is the estimated number of contracts in a quarter. X is the coded quarterly value with X = 0 in the first quarter of 2005. Q₁ is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise. Q₂ is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise. Q₃ is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise. -Referring to Table 16-12, using the regression equation, what is the forecast for the revenues in the third quarter of 2010? is the estimated number of contracts in a quarter. X is the coded quarterly value with X = 0 in the first quarter of 2005. Q₁ is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise. Q₂ is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise. Q₃ is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise. -Referring to Table 16-12, using the regression equation, what is the forecast for the revenues in the third quarter of 2010?

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TABLE 16-10 Business closures in Laramie, Wyoming from 2005 to 2010 were: TABLE 16-10 Business closures in Laramie, Wyoming from 2005 to 2010 were:   -Referring to Table 16-10, the fitted values for the second-order autoregressive model are ________, ________, ________, and ________. -Referring to Table 16-10, the fitted values for the second-order autoregressive model are ________, ________, ________, and ________.

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TABLE 16-14 A contractor developed a multiplicative time-series model to forecast the number of contracts in future quarters, using quarterly data on number of contracts during the 3-year period from 2008 to 2010. The following is the resulting regression equation: ln Ŷ = 3.37 + 0.117 X - 0.083 Q₁ + 1.28 Q₂ + 0.617 Q₃ where Ŷ is the estimated number of contracts in a quarter X is the coded quarterly value with X = 0 in the first quarter of 2008. Q₁ is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise. Q₂ is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise. Q₃ is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise. -Referring to Table 16-14, to obtain a forecast for the fourth quarter of 2011 using the model, which of the following sets of values should be used in the regression equation?

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TABLE 16-11 The manager of a health club has recorded mean attendance in newly introduced step classes over the last 15 months: 32.1, 39.5, 40.3, 46.0, 65.2, 73.1, 83.7, 106.8, 118.0, 133.1, 163.3, 182.8, 205.6, 249.1, and 263.5. She then used Microsoft Excel to obtain the following partial output for both a first- and second-order autoregressive model. TABLE 16-11 The manager of a health club has recorded mean attendance in newly introduced step classes over the last 15 months: 32.1, 39.5, 40.3, 46.0, 65.2, 73.1, 83.7, 106.8, 118.0, 133.1, 163.3, 182.8, 205.6, 249.1, and 263.5. She then used Microsoft Excel to obtain the following partial output for both a first- and second-order autoregressive model.   -Referring to Table 16-11, using the first-order model, the forecast of mean attendance for month 16 is ________. -Referring to Table 16-11, using the first-order model, the forecast of mean attendance for month 16 is ________.

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The principle of parsimony indicates that the simplest model that gets the job done adequately should be used.

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The overall upward or downward pattern of the data in an annual time series will be contained in the ________ component.

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Each forecast using the method of exponential smoothing depends on all the previous observations in the time series.

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The method of moving averages is used

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TABLE 16-13 Given below is the monthly time-series data for U.S. retail sales of building materials over a specific year. TABLE 16-13 Given below is the monthly time-series data for U.S. retail sales of building materials over a specific year.     The results of the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the first month is 0:                -Referring to Table 16-13, what is your forecast for the 13ᵗʰ month using the linear-trend model? The results of the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the first month is 0: TABLE 16-13 Given below is the monthly time-series data for U.S. retail sales of building materials over a specific year.     The results of the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the first month is 0:                -Referring to Table 16-13, what is your forecast for the 13ᵗʰ month using the linear-trend model? TABLE 16-13 Given below is the monthly time-series data for U.S. retail sales of building materials over a specific year.     The results of the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the first month is 0:                -Referring to Table 16-13, what is your forecast for the 13ᵗʰ month using the linear-trend model? TABLE 16-13 Given below is the monthly time-series data for U.S. retail sales of building materials over a specific year.     The results of the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the first month is 0:                -Referring to Table 16-13, what is your forecast for the 13ᵗʰ month using the linear-trend model? TABLE 16-13 Given below is the monthly time-series data for U.S. retail sales of building materials over a specific year.     The results of the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the first month is 0:                -Referring to Table 16-13, what is your forecast for the 13ᵗʰ month using the linear-trend model? -Referring to Table 16-13, what is your forecast for the 13ᵗʰ month using the linear-trend model?

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TABLE 16-11 The manager of a health club has recorded mean attendance in newly introduced step classes over the last 15 months: 32.1, 39.5, 40.3, 46.0, 65.2, 73.1, 83.7, 106.8, 118.0, 133.1, 163.3, 182.8, 205.6, 249.1, and 263.5. She then used Microsoft Excel to obtain the following partial output for both a first- and second-order autoregressive model. TABLE 16-11 The manager of a health club has recorded mean attendance in newly introduced step classes over the last 15 months: 32.1, 39.5, 40.3, 46.0, 65.2, 73.1, 83.7, 106.8, 118.0, 133.1, 163.3, 182.8, 205.6, 249.1, and 263.5. She then used Microsoft Excel to obtain the following partial output for both a first- and second-order autoregressive model.   -Referring to Table 16-11, using the second-order model, the forecast of mean attendance for month 16 is ________. -Referring to Table 16-11, using the second-order model, the forecast of mean attendance for month 16 is ________.

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TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters, using quarterly data on its revenues during the 4-year period from 2005 to 2009. The following is the resulting regression equation: log₁₀ TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters, using quarterly data on its revenues during the 4-year period from 2005 to 2009. The following is the resulting regression equation: log₁₀   = 6.102 + 0.012 X - 0.129 Q₁ - 0.054 Q₂ + 0.098 Q₃ where   is the estimated number of contracts in a quarter. X is the coded quarterly value with X = 0 in the first quarter of 2005. Q₁ is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise. Q₂ is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise. Q₃ is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise. -Referring to Table 16-12, the best interpretation of the coefficient of Q₂ (-0.054) in the regression equation is = 6.102 + 0.012 X - 0.129 Q₁ - 0.054 Q₂ + 0.098 Q₃ where TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters, using quarterly data on its revenues during the 4-year period from 2005 to 2009. The following is the resulting regression equation: log₁₀   = 6.102 + 0.012 X - 0.129 Q₁ - 0.054 Q₂ + 0.098 Q₃ where   is the estimated number of contracts in a quarter. X is the coded quarterly value with X = 0 in the first quarter of 2005. Q₁ is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise. Q₂ is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise. Q₃ is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise. -Referring to Table 16-12, the best interpretation of the coefficient of Q₂ (-0.054) in the regression equation is is the estimated number of contracts in a quarter. X is the coded quarterly value with X = 0 in the first quarter of 2005. Q₁ is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise. Q₂ is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise. Q₃ is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise. -Referring to Table 16-12, the best interpretation of the coefficient of Q₂ (-0.054) in the regression equation is

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TABLE 16-10 Business closures in Laramie, Wyoming from 2005 to 2010 were: TABLE 16-10 Business closures in Laramie, Wyoming from 2005 to 2010 were:   -Referring to Table 16-10, the value of the MAD for the second-order autoregressive model is ________. -Referring to Table 16-10, the value of the MAD for the second-order autoregressive model is ________.

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TABLE 16-4 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows. TABLE 16-4 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows.   -Referring to Table 16-4, construct a centered 5-year moving average for the wine sales. -Referring to Table 16-4, construct a centered 5-year moving average for the wine sales.

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MAD is the summation of the residuals divided by the sample size.

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The method of least squares is used on time-series data for

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