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Beyond the Trend Line

Beyond the Trend Line. Extending that Line into the Future St. Louis CMG February 12, 2008. Wayne Bell – UniGroup, Inc. Methods of Forecasting. Excel Trendlines – Manual Extensions Percentage Rate of Growth Regression Moving Averages. Excel Trendlines. Linear vs. Exponential.

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Beyond the Trend Line

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  1. Beyond the Trend Line Extending that Line into the Future St. Louis CMG February 12, 2008 Wayne Bell – UniGroup, Inc.

  2. Methods of Forecasting • Excel Trendlines – Manual Extensions • Percentage Rate of Growth • Regression • Moving Averages

  3. Excel Trendlines Linear vs. Exponential

  4. Percentage Rate of Growth Used if you have a known value and are given a rate of growth Normally, you are given an annual rate-of-growth. Three methods: Linear Growth – Straight Line Monthly Compound Growth Annual Compound Growth

  5. Linear Growth – Straight Line • Calculate the amount of growth. • Current value is 100 • Growth Rate is 50% • Amount is 50 per year • Divide by 12 for monthly increase • Add this increase to the prior month • Xn+1=Xn*(1+(%/(12+%*n)))

  6. Linear Growth – Straight Line

  7. Monthly Compound Growth • Annual percentage divided by 12 • Increase the current monthly value by this amount • Xn+1=Xn*(1+(%/12)) • The amount at the end of a year will be more than expected • In this case, the base is 100 • Increase is 50% per year • Actual increase is 163.2 or 63.2%

  8. Monthly Compound Growth

  9. Annual Compound Growth • Produces an exponential growth • The beginning months of the year have a lower growth than the end months of the year • At the end of the year, the value is exactly the percentage growth expected • Xn+1=Xn*((1+%)^(1/12))

  10. Annual Compound Growth

  11. Regression • Linear Regression – produces a straight line that best fits a single set of data • Linear Trend Line • Exponential Regression – produces an exponential curve that best fits a single set of data • Exponential Trend Line

  12. Regression • Excel’s Data Analysis Toolkit has a Regression Tool • Can be used to judge the correlation of one or more dependent variables to a dependent variable. • Can provide the intercept and slope coefficients to “draw the line” for current and future data points. • The Regression Tool is for Linear Regression only • Exponential Regression can be performed with a minor change to the data • Simply take the log (Excel Function ‘LN’) of the dependent variable.

  13. Regression – Sample Output

  14. Regression – Sample Output • Items in Red are quick checks on the validity of the Regression • Items in Green are ‘Rule of Thumb’ values • In the case of this Sample, you can calculate the expected value for any Date: • New_Value = Intercept_Coefficient + Date_Coefficient * Date

  15. TREND / GROWTH Example • Data is composed of two types of data • Independent – What is known, such as Date • Dependent – What is unknown – In this case, the Value is dependent on the Date • =TREND(Dependent Variable Range, Independent Variable Range, New Independent Variable) • =TREND($B$2:$B$34,$A$2:$A$34,A2) • B Column is the known Dependent variables • A Column is the known Independent variables

  16. TREND / GROWTH Example

  17. Moving Averages • The Moving Average projects values in the forecast period, based on the average value of the variable over a specific number of preceding periods. • A Moving Average provides trend information that a simple average of all historical data would mask. • The number of periods in the Moving Average affects the outcome: • Small number of periods • Large number of periods

  18. Moving Average • Simple average of previous values (Sample of 5) • =AVERAGE(A2:A6) • =AVERAGE(A3:A7) - OR - • Moving Average in Analysis Toolpak

  19. Moving Average

  20. Weighted Moving Average • Assumes that the most current value is a better predictor than an older value. • Build a table of Weights • Incorporate these Weights into the Moving Average • Table: • Weights % • Month-1 50.0 38% • Month-2 35.0 27% • Month-3 25.0 19% • Month-4 15.0 12% • Month-5 5.0 4%

  21. Weighted Moving Average

  22. Summary • Known Starting Point – Known Rate of Increase – Long Term Forecast • Linear Trending • Compound Trending • Data History – Grow at same Rate – Long Term Forecast • Linear Regression • Exponential Regression

  23. Summary • Old Data not as important as Current Values – Short Term Forecast • Moving Average • Weighted Moving Average • Can combine methods. • Use Moving Average to determine ‘Next’ value • Use Regression or Trending for Long Term Forecast

  24. Summary • Know Your Data • Chart your current data • Forecast your current data and overlay the charts • Forecast 2005 and 2006 into 2007. • Compare the forecast to the actual 2007. • Choose the forecast method that best fits your data! • The link for this presentation is: • http://regions.cmg.org/regions/stlcmg/files/Download/Beyond%20the%20Trend%20Line.ppt • The link for the datasheet is: • http://regions.cmg.org/regions/stlcmg/files/Download/Beyond%20the%20Trend%20Line.xls • For more information please contact me at: Wayne_Bell@UniGroupInc.com

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