chap16-a

# chap16-a - Statistics Chapter 16 Analyzing and Forecasting...

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Chap 16-1 Statistics Chapter 16 Analyzing and Forecasting Time-Series Data

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Chap 16-2 Chapter Goals After completing this chapter, you should be able to: Develop and explain basic forecasting models Identify the components present in a time series Compute and interpret basic index numbers Apply trend-based forecasting models, including linear trend, nonlinear trend, and seasonally adjusted trend Use smoothing-based forecasting models, including single and double exponential smoothing
Chap 16-3 The Importance of Forecasting Governments forecast unemployment, interest rates, and expected tax revenues 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 Supply Chain

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Chap 16-4 Developing a Forecasting Model Steps in forecast modeling: model specification model fitting model diagnosis Goal: use the simplest available model that meets forecasting needs
Chap 16-5 Forecasting Horizon Intermediate term – less than one month Short term – one to three months Medium term – three months to two years Long term – two years or more Forecasting period: the unit of time for which forecasts are to be made Forecasting interval: the frequency with which new forecasts are prepared

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Chap 16-6 Time-Series Data Numerical data obtained at regular time intervals The time intervals can be annually, quarterly, daily, hourly, etc. Example: Year: 2003 2004 2005 2006 2007 Sales: 75.3 74.2 78.5 79.7 80.2
Chap 16-7 Time Series Plot the vertical axis measures the variable of interest the horizontal axis corresponds to the time periods U.S. Inflation Rate 0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 Year Inflation Rate (%) A time-series plot is a two-dimensional plot of time series data

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Chap 16-8 Time-Series Components Time-Series Cyclical Component Random Component Trend Component Seasonal Component
Chap 16-9 Upward trend Trend Component Long-run increase or decrease over time (overall upward or downward movement) Data taken over a long period of time Sales Time

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Chap 16-10 Downward linear trend Trend Component Trend can be upward or downward Trend can be linear or non-linear Sales Time Upward nonlinear trend Sales Time (continued)
Chap 16-11 Seasonal Component Short-term regular wave-like patterns Observed within 1 year Often monthly or quarterly Sales Time (Quarterly) Winter Spring Summer Fall

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Chap 16-12 Cyclical Component Long-term wave-like patterns Regularly occur but may vary in length Often measured peak to peak or trough to trough Sales 1 Cycle Year
Chap 16-13 Random Component Unpredictable, random, “residual” fluctuations

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## This note was uploaded on 02/23/2011 for the course OM 300 taught by Professor Bobsanders during the Spring '11 term at Essex County College.

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chap16-a - Statistics Chapter 16 Analyzing and Forecasting...

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