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- 1. Ch 3: Forecasting: Techniques and Routes IntroductionForecasting is the establishment of futureexpectations by the analysis of past data,or the formation of opinions.Forecasting is an essential element ofcapital budgeting.Capital budgeting requires the commitmentof significant funds today in the hope of longterm benefits. The role of forecasting is theestimation of these benefits. 1
- 2. Forecasting Techniques and Routes Techniqu Routes es Top-down Qualitative route Quantitativ Bottom-up e route Delphi methodSimple Nominal groupregressions techniqueMultiple Jury of executiveregressions opinionTime trends Scenario projectionMoving averages 2
- 3. Quantitative ForecastingQuantitative: Regression with related variableData set of ‘Sales’ as related to both timeand the number of households. HISTORICAL DATA YEAR HOUSEHOLDS SALES 1991 815 2109 1992 927 2530 1993 1020 2287 1994 987 3194 1995 1213 3785 1996 1149 3372 1997 1027 3698 1998 1324 3908 1999 1400 3725 2000 1295 4129 2001 1348 4532 2002 1422 4487 3
- 4. Quantitative ForecastingQuantitative: Sales plotted related to households. SalesUnits Related to Number of Households 5000 Sales Units 4000 3000 Sales 2000 1000 0 0 500 1000 1500 Numbe r of Households 4
- 5. Quantitative Forecasting Quantitative: Sales regressed on households.SUMMARY OUTPUT SALES REGRESSED AS A FUNCTION Edited output OF HOUSEHOLDS from the Excel regression. Regression StatisticsMultiple R 0.824389811R Square 0.67961856Adjusted R Square 0.644020623 <== "Strength" of the regressionStandard Error 429.2094572Observations 11 Coefficients Standard Error t Stat P-valueY Axis Intercept -348.218 913.798 -0.381 0.712Number of Households 3.316 0.759 4.369 0.002 5
- 6. Quantitative Forecasting Quantitative: Sales regressed on households.Predicting with the regression output.Regression equation is:Sales(for year) = -348.218 + ( 3.316 x households). Assuming that a separate data set forecasts the number of households at 1795 for the year 2006, then:Sales(year 2006) = -348.218 + ( 3.316 x 1795) = 5,604 units. 6
- 7. Quantitative ForecastingQuantitative: Multiple RegressionSales as a function of both time andthe number of households. HISTORICAL DATA YEAR HOUSEHOLDS SALES 1991 815 2109 1992 927 2530 1993 1020 2287 1994 987 3194 1995 1213 3785 1996 1149 3372 1997 1027 3698 1998 1324 3908 1999 1400 3725 2000 1295 4129 2001 1348 4532 2002 1422 4487 7
- 8. Quantitative Forecasting: Multiple Regression Line Information From the Excel spreadsheet.SUMMARY OUTPUT MULTIPLE REGRESSION: SALES ON YEARS and HOUSEHOLDS Regression Statistics Multiple R 0.9216 R Square 0.8494 Adjusted R Square 0.8118 <== "Strength" of regression. Standard Error 312.1217 Observations 11 Coefficients Standard Error t Stat P-value Lower 95%Y Axis Intercept -382643.9164 127299.584 -3.006 0.017 -676197.474Calendar Year 193.3326 64.376 3.003 0.017 44.880Households 0.1368 1.194 0.115 0.912 -2.616 8
- 9. Quantitative Forecasting:Using Multiple RegressionMultiple regression equation is:Sales in year = -382643.91 +(193.33 x Year) + (0.1368 xHouseholds) sales for the year 2005 is:Forecast ofSales in year 2005 = -382643.91 + (193.33 x 2005) + (0.1368 x 1586) = 5200 Units(Note: the sales forecast relies upon a separateforecast of the number of households, given as 1 586,for 2005.) 9
- 10. Quantitative ForecastingQuantitative: Time Series RegressionSales plotted as a function of time. Plot of Past Sales Units By Year 5000 4000 Sales Units 3000 Sales 2000 1000 0 1990 1995 2000 2005 Year 10
- 11. Quantitative Forecasting:Fitted Regression Line Sales Regression: Line Fit Plot 5000 4000 Actual 3000 Sales 2000 Predicted 1000 0 1990 1995 2000 2005 Year 11
- 12. Quantitative Forecasting:Regression Line InformationFrom the Excel spreadsheet.EDITED SUMMARY OUTPUT REGRESSION OF SALES ON YEARS Regression StatisticsMultiple R 0.9215R Square 0.8492Adjusted R Square 0.8324 <== "Strength" of regression.Standard Error 294.5125Observations 11 Coefficients Standard Error t Stat P-valueY axis intercept -395541.56 56077.1544 -7.0535 0.0001Slope of line 199.87 28.0807 7.1178 0.0001 12
- 13. Quantitative Forecasting:Regression Line UseEquation for the regression line is:Sales in year = -395541.56 + (199.87 x Year)Forecast of sales for the year 2005 is:Sales in 2005 = -395541.56 + (199.87 x 2005) = 5198 Units(Note: the large negative Y axis intercept resultsfrom using the actual calendar years as the X axisscale.) 13
- 14. Quantitative Forecasting:Regression: Auto Forecast by Excel. Sales by Year, With Automatic Three Year Prediction 6000 5000 SALES 4000 Sales 3000 Simple Linear 2000 Regression, Forecast Out to 1000 Year 2005 0 1990 1995 2000 2005 2010 Year 14
- 15. Quantitative Forecasting:Moving Average- Auto Plot Sales Units Per Year With Fitted Two Year Moving Average 5000 4000 Sales Units SALES 3000 2000 2 per. Mov. Avg. 1000 (SALES) 0 1990 1995 2000 2005 Years 15
- 16. Quantitative Forecasting:Notes on Excel Auto Plot.Excel will plot, and automatically forecast, adata series which has a functional relationship.For example, a regression trend line.The auto plot is driven through the ‘Chart’menu as ‘Add Trendline’. A particular forecastis specified via the dialog box.Future point data values cannot be readfrom the automated trendline.Non-functional relationships, such as amoving average, can be plotted, butcannot be automatically forecast. 16
- 17. Forecasting Routes Top-Down where international and national events affect the future behaviour of local variables. 17
- 18. Forecasting Routes Where local events affect the future behaviour of local variables. Bottom-Up 18
- 19. Forecasting: Summary Sophisticated forecasting is essential for capital budgeting decisions Quantitative forecasting uses historical data to establish relationships and trends which can be projected into the future Qualitative forecasting uses experience and judgment to establish future behaviours Forecasts can be made by either the‘top down’ or ‘bottom up’ routes. Back to the Future! 19

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