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Unformatted text preview: e the normalized (adjusted) seasonal factors for
each quarter.
.9982, .9263, 1.139, .9365 for winter, spring, summer and fall respectively. Quarter: snt = (1.0124)
Winter: (1.0124)(.986) = .9982
Spring: (1.0124)(.915) = .9263
Summer: (1.0124)(1.125) = 1.139
Fall: (1.0124)(.925) = .9365 AACSB: Analytic
Bloom's: Application
Difficulty: Medium
Learning Objective: 6
Topic: Seasonal factors 11646 Chapter 01  An Introduction to Business Statistics 130. 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. 9.02, 17.27, 14.05, 18.15, 21.04, 21.59, 26.34, 24.56, 25.05, 29.15, 29.85, 34.17 where: AACSB: Analytic
Bloom's: Application
Difficulty: Medium
Learning Objective: 6
Topic: Seasonal factors 11647 Chapter 01  An Introduction to Business Statistics 131. 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 (dt) given below, a trend line was
estimated. The following MINITAB output gives the straightline 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 11648 Chapter 01  An Introduction to Business Statistics 12.01, 13.91, 15.82, 17.72, 19.63, 21.54, 23.44, 25.35, 27.25, 29.16, 31.07, and 32.97. Calculations are: and so on. AACSB: Analytic
Bloom's: Application
Difficulty: Medium
Learning Objective: 2
Topic: Seasonalized factors 11649 Chapter 01  An Introduction to Business Statistics 132. 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 (dt) given below, a trend line was
estimated. The linear regression trend equation is: trt = 10.1 + 1.91 (t). Based on this trend
equation, the following trend values are calculated for each period in the time series: Isolate the cyclical and irregular components by calculating the estimate of CLt* IRt for the
first four quarters in the time series.
.75, 1.242, .888, 1.024 11650 Chapter 01  An Introduction to Business Statistics
AACSB: Analytic
Bloom's: Application
Difficulty: Medium
Learning Objective: 2
Topic: Time series regression 133. 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 (dt) given below, a trend line was
estimated. The linear regression trend equation is: trt = 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.
16.595, 35.39 AACSB: Analytic
Bloom's: Application
Difficulty: Medium
Learning Objective: 2
Topic: Time series regression 11651 Chapter 01  An Introduction to Business Statistics 134. 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 absolute deviation (MAD) for Model 1.
1.225 AACSB: Analytic
Bloom's: Application
Difficulty: Medium
Learning Objective: 8
Topic: Exponential Smoothing 135. 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.
1.847 AACSB: Analytic
Bloom's: Application
Difficulty: Medium
Learning Objective: 8
Topic: Exponential Smoothing 11652 Chapter 01  An Introduction to Business Statistics 136. 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 absolute deviation (MAD) for Model 2.
.625 AACSB: Analytic
Bloom's: Application
Difficulty: Medium
Learning Objective: 8
Topic: Exponential Smoothing 137. 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 2
.937 AACSB: Analytic
Bloom's: Application
Difficulty: Medium
Learning Objective: 8
Topic: Exponential Smoothing 11653 Chapter 01  An Introduction to Business Statistics 138. Two forecasting models were used to predict the future values of a time series. The
forecasts are shown below with the actual values: Which model is the most accurate? Why?
Model 2 i...
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 Winter '14

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