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42 aacsb analytic blooms application difficulty

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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 1-1646 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 1-1647 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 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 1-1648 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 1-1649 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 1-1650 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 1-1651 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 1-1652 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 1-1653 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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