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利用Excel进行统计分析-Chapter16-Time Series Forecasting and Index Numbers
Statistics for Managers Using Microsoft? Excel 5th Edition Chapter 16 Time Series Forecasting and Index Numbers Learning Objectives In this chapter, you learn: About seven different time-series forecasting models: moving averages, exponential smoothing, the linear trend, the quadratic trend, the exponential trend, the autoregressive, and the least-squares models for seasonal data. To choose the most appropriate time-series forecasting model About price indexes and the difference between aggregated and simple indexes The Importance of Forecasting Governments forecast unemployment, interest rates, and expected revenues from income taxes for policy purposes Marketing executives forecast demand, sales, and consumer preferences for strategic planning College administrators forecast enrollments to plan for facilities and for faculty recruitment Retail stores forecast demand to control inventory levels, hire employees and provide training Time Series Plot Time-Series Components Trend Component Long-run increase or decrease over time (overall upward or downward movement) Data taken over a long period of time Seasonal Component Short-term regular wave-like patterns Observed within 1 year Often monthly or quarterly Cyclical Component Long-term wave-like patterns Regularly occur but may vary in length Often measured peak to peak or trough to trough Irregular Component Unpredictable, random, “residual” fluctuations Due to random variations of Nature Accidents or unusual events “Noise” in the time series Multiplicative Time-Series Model for Annual Data Multiplicative Time-Series Model with a Seasonal Component Used primarily for forecasting Allows consideration of seasonal variation Smoothing the Annual Time Series Calculate moving averages to get an overall impression of the pattern of movement over time Moving Average: averages of consecutive time series values for a chosen period of length L Moving Averages Used for smoothing A series of arithmetic means over time Result
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