TRUE AACSB Reflective Thinking Blooms Knowledge Difficulty Medium Learning

True aacsb reflective thinking blooms knowledge

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TRUEAACSB: Reflective ThinkingBloom's: KnowledgeDifficulty: MediumLearning Objective: 5Topic: Exponential Smoothing16-60
Chapter 16 - Time Series Forecasting5. Simple exponential smoothing is an appropriate method for prediction purposes when there is a significant trend present in a time series data. FALSEAACSB: Reflective ThinkingBloom's: KnowledgeDifficulty: MediumLearning Objective: 5Topic: Exponential Smoothing6. The forecaster who uses MSD (mean squared deviations) to measure the effectiveness of forecasting methods would prefer method 1 that results in several smaller forecast errors to method 2 that results in one large forecast error equal to the sum of the absolute values of several small forecast errors given by method 1. TRUEAACSB: Reflective ThinkingBloom's: KnowledgeDifficulty: MediumLearning Objective: 6Topic: Forecasting7. When a forecaster uses multiplicative decomposition model or time series regression model she or he assumes that the time series components are changing over time. FALSEAACSB: Reflective ThinkingBloom's: KnowledgeDifficulty: MediumLearning Objective: 4Topic: Multiplicative decomposition16-61
Chapter 16 - Time Series Forecasting8. Removing the seasonal affect by dividing the actual time series observation by the estimated seasonal factor associated with the time series observation is called deseasonalization. TRUEAACSB: Reflective ThinkingBloom's: KnowledgeDifficulty: MediumLearning Objective: 4Topic: Depersonalization9. When using moving averages to estimate the seasonal factors, we need to compute the centered moving average if there are odd number of seasons. FALSEAACSB: Reflective ThinkingBloom's: KnowledgeDifficulty: MediumLearning Objective: 4Topic: Moving averages10. When deseasonalizing a time series observation the actual time series observation is divided by its seasonal factor. TRUEAACSB: Reflective ThinkingBloom's: KnowledgeDifficulty: MediumLearning Objective: 4Topic: Deseasonalizing11. Dummy variables are used to model increasing seasonal variation. FALSEAACSB: Reflective ThinkingBloom's: KnowledgeDifficulty: MediumLearning Objective: 2Topic: Time series regression16-62
Chapter 16 - Time Series Forecasting12. While a simple index is calculated by using the values of one time series, an aggregate index is computed based on the accumulated values of more than one time series. TRUEAACSB: Reflective ThinkingBloom's: KnowledgeDifficulty: MediumLearning Objective: 9Topic: Index numbers13. Paasche index more accurately provides a year-to-year comparison of the annual cost of selected products in the market-basket than Laspeyres index. TRUEAACSB: Reflective ThinkingBloom's: KnowledgeDifficulty: HardLearning Objective: 9Topic: Index numbers14. Simple exponential forecasting method would not be used to forecast seasonal data.