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 2

Excel Trendlines Linear vs. Exponential 3

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 4

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 X n+1 =X n *(1+(%/(12+%*n))) 5

Linear Growth – Straight Line 6

Monthly Compound Growth Annual percentage divided by 12 Increase the current monthly value by this amount X n+1 =X n *(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 or 63.2% 7

Monthly Compound Growth 8

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 X n+1 =X n *((1+%)^(1/12)) 9

Annual Compound Growth 10

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 11

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. 12

Regression – Sample Output SUMMARY OUTPUT Regression Statistics Multiple R R Square >0.8 Adjusted R Square Standard Error Observations33 ANOVA dfSSMSFSignificance F Regression E-21<0.01 Residual Total CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0% Intercept E Date E E <0.01Both Positive or Both Negative 13

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 14

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 15

TREND / GROWTH Example 16

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 17

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

Moving Average 19

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 % Month % Month % Month % Month % 20

Weighted Moving Average 21

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 22

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 23

Summary Know Your Data Chart your current data Forecast your current data and overlay the charts Forecast 2005 and 2006 into Compare the forecast to the actual Choose the forecast method that best fits your data! The link for this presentation is: The link for the datasheet is: For more information please contact me at: 24