Exam 16: Time Series Forecasting

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When using simple exponential smoothing,the more recent the time series observation,the _________ its corresponding weight.

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A restaurant has been experiencing higher sales during the weekends of compared to the weekdays.Daily restaurant sales patterns for this restaurant over a week are an example of _________ component of time series.

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A positive autocorrelation implies that negative error terms will be followed by negative error terms.

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  Based on the information given in the table above,what is the MSD? Based on the information given in the table above,what is the MSD?

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The linear regression trend model was applied to a time series sales data based on the last 24 months' sales.The following partial computer output was obtained.  The linear regression trend model was applied to a time series sales data based on the last 24 months' sales.The following partial computer output was obtained.    Test the significance of the time term at  \alpha  = .05.State the critical t value and make your decision using a two-sided alternative. Test the significance of the time term at α\alpha = .05.State the critical t value and make your decision using a two-sided alternative.

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The price and quantity of several food items are listed below for the years 1990 and 2000. The price and quantity of several food items are listed below for the years 1990 and 2000.   Compute the Laspeyres index using 1990 as the base year. Compute the Laspeyres index using 1990 as the base year.

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Consider the quarterly production data (in thousands of units)for the XYZ manufacturing company below.The normalized (adjusted)seasonal factors are .9982,.9263,1.139,.9365 for winter,spring,summer and fall respectively. Consider the quarterly production data (in thousands of units)for the XYZ manufacturing company below.The normalized (adjusted)seasonal factors are .9982,.9263,1.139,.9365 for winter,spring,summer and fall respectively.   Based on the following deseasonalized observations (d<sub>t</sub>)given below,a trend line was estimated.   The following MINITAB output gives the straight-line trend equation fitted to the deseasonalized observations.Based on the trend equation given below,calculate the trend value for each period in the time series. The regression equation is Deseasonalized = 10.1 + 1.91 Time  Based on the following deseasonalized observations (dt)given below,a trend line was estimated. Consider the quarterly production data (in thousands of units)for the XYZ manufacturing company below.The normalized (adjusted)seasonal factors are .9982,.9263,1.139,.9365 for winter,spring,summer and fall respectively.   Based on the following deseasonalized observations (d<sub>t</sub>)given below,a trend line was estimated.   The following MINITAB output gives the straight-line trend equation fitted to the deseasonalized observations.Based on the trend equation given below,calculate the trend value for each period in the time series. The regression equation is Deseasonalized = 10.1 + 1.91 Time  The following MINITAB output gives the straight-line trend equation fitted to the deseasonalized observations.Based on the trend equation given below,calculate the trend value for each period in the time series. The regression equation is Deseasonalized = 10.1 + 1.91 Time Consider the quarterly production data (in thousands of units)for the XYZ manufacturing company below.The normalized (adjusted)seasonal factors are .9982,.9263,1.139,.9365 for winter,spring,summer and fall respectively.   Based on the following deseasonalized observations (d<sub>t</sub>)given below,a trend line was estimated.   The following MINITAB output gives the straight-line trend equation fitted to the deseasonalized observations.Based on the trend equation given below,calculate the trend value for each period in the time series. The regression equation is Deseasonalized = 10.1 + 1.91 Time

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  Based on the information given in the table above,what is the MAD? Based on the information given in the table above,what is the MAD?

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The upward or downward movement that characterizes a time series over a period of time is referred to as ____.

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Consider the quarterly production data (in thousands of units)for the XYZ manufacturing company below.The normalized (adjusted)seasonal factors are .9982,.9263,1.139,.9365 for winter,spring,summer and fall respectively. Consider the quarterly production data (in thousands of units)for the XYZ manufacturing company below.The normalized (adjusted)seasonal factors are .9982,.9263,1.139,.9365 for winter,spring,summer and fall respectively.   Based on the following deseasonalized observations (d<sub>t</sub>)given below,a trend line was estimated.The linear regression trend equation is: tr<sub>t</sub> = 10.1 + 1.91 (t).Use the forecasting equation   and calculate the forecasted demand for the fall quarter of 1998 and summer quarter of 2000. Based on the following deseasonalized observations (dt)given below,a trend line was estimated.The linear regression trend equation is: trt = 10.1 + 1.91 (t).Use the forecasting equation Consider the quarterly production data (in thousands of units)for the XYZ manufacturing company below.The normalized (adjusted)seasonal factors are .9982,.9263,1.139,.9365 for winter,spring,summer and fall respectively.   Based on the following deseasonalized observations (d<sub>t</sub>)given below,a trend line was estimated.The linear regression trend equation is: tr<sub>t</sub> = 10.1 + 1.91 (t).Use the forecasting equation   and calculate the forecasted demand for the fall quarter of 1998 and summer quarter of 2000. and calculate the forecasted demand for the fall quarter of 1998 and summer quarter of 2000.

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If a time series exhibits increasing seasonal variation,one approach is to first use a ______________ transformation that produces a transformed time series that exhibits constant seasonal variation.Then,_________ variables can be used to model the time series with constant seasonal variation.

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In the multiplicative decomposition method,the centered moving averages provide an estimate of trend x _____.

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The linear regression trend model was applied to a time series sales data based on the last 24 months' sales.The following partial computer output was obtained. The linear regression trend model was applied to a time series sales data based on the last 24 months' sales.The following partial computer output was obtained.   Write the prediction equation. Write the prediction equation.

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Two forecasting models were used to predict the future values of a time series.The forecasts are shown below with the actual values: Two forecasting models were used to predict the future values of a time series.The forecasts are shown below with the actual values:   Calculate the mean squared deviation (MSD)for Model 1. Calculate the mean squared deviation (MSD)for Model 1.

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Which of the following time series forecasting methods would not be used to forecast seasonal data?

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The linear regression trend model was applied to a time series of sales data based on the last 16 months of sales.The following partial computer output was obtained: The linear regression trend model was applied to a time series of sales data based on the last 16 months of sales.The following partial computer output was obtained:   Write the prediction equation. Write the prediction equation.

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When there is first-order autocorrelation,the error term in period t is related to the error term in period _____.

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When a forecaster uses the _________________ method she or he assumes that the time series components are changing quickly over time.

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The demand for a product for the last six years has been 15,15,17,18,20,and 19.The manager wants to predict the demand for this time series using the following simple linear trend equation: trt = 12 + 2t.What are the forecast errors for the 5th and 6th years?

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Consider the quarterly production data (in thousands of units)for the XYZ manufacturing company below.The normalized (adjusted)seasonal factors are .9982,.9263,1.139,.9365 for winter,spring,summer,and fall respectively.Calculate the deseasonalized production value for each observation in the time series. Consider the quarterly production data (in thousands of units)for the XYZ manufacturing company below.The normalized (adjusted)seasonal factors are .9982,.9263,1.139,.9365 for winter,spring,summer,and fall respectively.Calculate the deseasonalized production value for each observation in the time series.

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