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# Lecture 7 forecasting

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• This and subsequent slide frame a discussion on time series - and introduce the various components.\n
• This and subsequent slide frame a discussion on time series - and introduce the various components.\n
• This and subsequent slide frame a discussion on time series - and introduce the various components.\n
• This and subsequent slide frame a discussion on time series - and introduce the various components.\n
• This and subsequent slide frame a discussion on time series - and introduce the various components.\n
• This and subsequent slide frame a discussion on time series - and introduce the various components.\n
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• This teleology is based on the number of explanatory variables &amp; nature of relationship between X &amp; Y.\n
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• This slide probably merits discussion - additional to that for the linear trend model. \nYou might make the point here that the dependent and independent variable are not necessarily of the same nature - they need not both be dollars, for example.\n\nYou might also wish to note that setting x = 0 may not have a useful physical interpretation.\n
• This slide probably merits discussion - additional to that for the linear trend model. \nYou might make the point here that the dependent and independent variable are not necessarily of the same nature - they need not both be dollars, for example.\n\nYou might also wish to note that setting x = 0 may not have a useful physical interpretation.\n
• This slide probably merits discussion - additional to that for the linear trend model. \nYou might make the point here that the dependent and independent variable are not necessarily of the same nature - they need not both be dollars, for example.\n\nYou might also wish to note that setting x = 0 may not have a useful physical interpretation.\n
• This slide probably merits discussion - additional to that for the linear trend model. \nYou might make the point here that the dependent and independent variable are not necessarily of the same nature - they need not both be dollars, for example.\n\nYou might also wish to note that setting x = 0 may not have a useful physical interpretation.\n
• This slide probably merits discussion - additional to that for the linear trend model. \nYou might make the point here that the dependent and independent variable are not necessarily of the same nature - they need not both be dollars, for example.\n\nYou might also wish to note that setting x = 0 may not have a useful physical interpretation.\n
• This slide probably merits discussion - additional to that for the linear trend model. \nYou might make the point here that the dependent and independent variable are not necessarily of the same nature - they need not both be dollars, for example.\n\nYou might also wish to note that setting x = 0 may not have a useful physical interpretation.\n
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• Note potential problem with multicollinearity. This is solved somewhat by centering on the mean.\n
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• ### Lecture 7 forecasting

1. 1. Mktg 559
2. 2. Mktg 559 45%
3. 3. Mktg 559 45% 25%
4. 4. Mktg 559 45% 25% 20%
5. 5. Mktg 559 45% 25% 20% 10%
6. 6. Mktg 559 Rotten 45% 86% 25% 75% 20% 18% 10% 42%
7. 7. Mktg 559 Rotten Lee’s 45% 86% \$25 m 25% 75% \$12 m 20% 18% \$10 m 10% 42% \$17 m
8. 8. Mktg 559 Rotten Lee’s BO 45% 86% \$25 m \$30 m 25% 75% \$12 m \$10 m 20% 18% \$10 m \$8 m 10% 42% \$17 m \$7 m
9. 9. Mktg 559 Rotten Lee’s BO Cost 45% 86% \$25 m \$30 m \$100 m 25% 75% \$12 m \$10 m \$22 m 20% 18% \$10 m \$8 m \$18 m 10% 42% \$17 m \$7 m \$16 m
10. 10. do you thinkthe Bass Diffusion Model applies to ....
11. 11. Revolutionary productsorEvolutionary products?
12. 12. Usability featuresorDesirability features?
13. 13. ForecastingTime Series and Regressionfor New Products
14. 14. 1967 predictionsArtificial human organsCredit cards would eliminate moneyLasers would be in common use
15. 15. 1967 predictionsNo cars in city centersHunger reductionCars will be driven by robots
16. 16. Box Office ReturnsCan one predict the successful diffusion of anew MOVIE?
17. 17. Box Office ReturnsCan one predict the successful diffusion of anew MOVIE? Bass Diffusion Model
18. 18. Box Office ReturnsUrband Legend:Total Sales = 2.5 * first week sales
19. 19. Box Office ReturnsUrband Legend:Total Sales = 2.5 * first week sales Time Series Analysis
20. 20. Box Office Returns“Seven” took 198 days to get to \$100 mil“Spiderman 3” took 2 days to get there
21. 21. Box Office Return- Regression models with critic ratings, star cast etc- Stock market games
22. 22. Box Office Return- Regression models with critic ratings, star cast etc- Stock market games Regression Analysis
23. 23. For existing companies the need is to determine how much of the current product they are likely to sell.. Markets Existing New Time Series Analysis Existing Regression AnalysisProducts New Forecasting–
24. 24. Time Series Simplest Method is EXTRAPOLATIONVolumeof Sales Time Past Present Future Forecasting–
25. 25. Typical Time Series DataYear Sales1996 371997 401998 411999 372000 452001 502002 432003 472004 562005 522006 552007 542008 Forecasting–
26. 26. Typical Time Series DataYear Sales1996 37 Set of evenly spaced numerical data1997 401998 411999 372000 452001 502002 432003 472004 562005 522006 552007 542008 Forecasting–
27. 27. Typical Time Series DataYear Sales1996 37 Set of evenly spaced numerical data1997 401998 411999 372000 452001 502002 432003 472004 562005 522006 552007 542008 Forecasting–
28. 28. Typical Time Series DataYear Sales1996 37 Set of evenly spaced numerical data1997 401998 411999 372000 452001 502002 432003 472004 562005 522006 552007 542008 Forecasting–
29. 29. Typical Time Series DataYear Sales1996 37 Set of evenly spaced numerical data1997 401998 411999 372000 452001 502002 432003 472004 562005 522006 552007 542008 Forecasting–
30. 30. Typical Time Series DataYear Sales1996 37 Set of evenly spaced numerical data1997 401998 411999 372000 452001 502002 43 Forecast based only on past values2003 472004 562005 522006 552007 542008 Forecasting–
31. 31. What would a plot of the data tell you?Year Sales1996 371997 401998 411999 372000 452001 502002 432003 472004 562005 522006 552007 542008 Forecasting–
32. 32. Plot data and connect the dotsYear Sales1996 371997 401998 411999 372000 452001 502002 432003 472004 562005 522006 552007 542008 Forecasting–
33. 33. Connect the dots and add a trend lineYear Sales1996 371997 401998 411999 372000 452001 502002 432003 472004 562005 522006 552007 542008 Forecasting–
34. 34. Lets try moving averages, lag functionsYear Sales 3 year1996 371997 401998 41 39.331999 37 39.332000 45 41.002001 50 44.002002 43 46.002003 47 46.672004 56 48.672005 52 51.672006 55 54.32007 54 53.72008 Forecasting–
35. 35. Weighted Average Moving Average weights equally WeightedPeriod Year Sales Avg 1 1996 37 What would happen if you differentially weighted the data? 2 1997 40 3 1998 41 39.9 4 1999 37 38.8 t-1 0.5 5 2000 45 41.8 t-2 0.3 6 2001 50 45.9 7 2002 43 45.5 t-3 0.2 8 2003 47 46.4 9 2004 56 50.7 10 2005 52 52.2 11 2006 55 54.3 12 2007 54 53.9 13 2008 Forecasting–
36. 36. Exponential Smoothing Exp Sophisticated weighted averageYear Sales Smooth1996 37 371997 40 37.01998 41 39.7 This Forecast =1999 37 40.92000 45 37.42001 50 44.2 last forecast2002 43 49.4 +2003 47 43.6 alpha * (last actual - last forecast)2004 56 46.72005 52 55.12006 55 52.32007 54 54.72008 54.1 Forecasting–
37. 37. Exponential Smoothing Tool Single-parameter exponential smoothing is easy with Excel’s ToolPak. Clickon Tools on the menu bar, select the Data Analysis option, and then in the Data Analysis dialog box, click on Exponential Smoothing. 22 Forecasting–
38. 38. Single-Parameter Exponential Smoothing (Figure 7-4 ) 1. Enter the smoothing 2. Enter problemconstant in D2. information in range. Notice D26 does not have a value because it is to be forecast. 3. Click on Tool, Data Analysis, and the Exponential Smoothing to get the Exponential Smoothing dialog box shown next. 23 Forecasting–
39. 39. Exponential Smoothing Dialog Box 4. Click the OK button to get the results shown previously in Figure 7-4. 1. In the InputRange line enter the range of the data.The result shown is \$D\$6:\$D\$25 2. Enter theDamping factor. It is 1 - α. 3. In the Output Range enter the location of the results. 24 Forecasting–
40. 40. Forecast usingRegression Models Forecasting–
41. 41. Linear Regression  Identify dependent (y) and independent (x) variables  Develop your equation for the trend line Y = a + bX Forecasting–
42. 42. Interpretation of Coefficients Y = a + bX 27 Forecasting–
43. 43. Interpretation of Coefficients Y = a + bXSlope (b) 27 Forecasting–
44. 44. Interpretation of Coefficients Y = a + bXSlope (b) Y changes by b for each 1 unit increase in X 27 Forecasting–
45. 45. Interpretation of Coefficients Y = a + bXSlope (b) Y changes by b for each 1 unit increase in X 27 Forecasting–
46. 46. Interpretation of Coefficients Y = a + bXSlope (b) Y changes by b for each 1 unit increase in XY-intercept (a) 27 Forecasting–
47. 47. Interpretation of Coefficients Y = a + bXSlope (b) Y changes by b for each 1 unit increase in XY-intercept (a) Average value of Y when X = 0 27 Forecasting–
48. 48. Regression is to understand relationships E(Y) = a + bX iY b>0 Y b< 0 X X Forecasting–
49. 49. A maker of golf shirts has been tracking sales and advertising dollars. Predict sales for \$53,000 advertising Sales \$ (Y) Adv.\$ (X)1 130 32 Y = 92.9 + 1.15X2 151 523 150 50 Y5 = 92.9 + 1.15(53) = 153.854 158 55 €5 ? 53 € Forecasting–
50. 50. Excel Regression ToolTools --> Data Analysis -->Regression 30 Forecasting–
51. 51. 1. In the Input Y Range 3. Click on the OK line enter the range of button to get the the Y data. The result Regressionshown here is \$C\$7:\$C \$16 Regression Dialog Box Summary Output shown next. 2. In the Input X Range line enterthe range of the X data. The resulthere shown is \$B \$7:\$B\$16 31 Forecasting–
52. 52. Excel’s Regression Tool The slope and intercept are read from E15:E16 and yield the regressionequation below. The multiple R, R squared, adjusted R, standard error, and F and t statistics are shown also. 32 Forecasting–
53. 53. What if you had data like this? Forecasting–
54. 54. Second-Order Model 2E(Y) = a + bX1i + cX 1i Linear Curvilinear effect effect Forecasting–
55. 55. Second-Order Model Worksheet Create X12 column. Run regression with Y, X1, X12. Forecasting–
56. 56. Non Linear Regression 5 € 36 Forecasting–
57. 57. Eg. Toy Manufacturer How is weekly toy sales affected by  changes in levels of advertising,  the use of sales reps vs. agents for calling on retailers, and  local school enrollments?Toy Sales = Advertising(X1)+ sales rep/agent(X2)+ school enrollment(X3) + e To do this, we need to dummy code: sales rep = 1 or agent = 0.Y = 102.18 + 3.87X1 + 115.2X2 + 6.73X3 Forecasting–
58. 58. Multiple Regression Excel’s regression tool can be used to do multiple regression. Just list ALL the X variables when designating the Input X Range; C7:D16 in this example. 38 Forecasting–
59. 59. Model-based forecasting methodsRegression with other factors  Sales = a intercept + b (advertising) + c (price)  Develop model on half of past data  Test model on other half of data Forecasting–
60. 60. PROS AND CONS? Markets Existing New NewProducts Time Series Analysis Existing Regression Analysis Forecasting–