1. Dr. Ron Lembke

2. Washoe Gaming Win, 1993-96 What did they mean when they said it was down three quarters in a row? 1993 1994 1995 1996 Look at year-over-year

3. Seasonality Seasonality is regular up or down movements in the data Can be hourly, daily, weekly, yearly Naïve method ▫N1: Assume January sales will be same as December ▫N2: Assume this Friday’s ticket sales will be same as last

4. Seasonal Factors Seasonal factor for May is 1.20, means May sales are typically 20% above the average Factor for July is 0.90, meaning July sales are typically 10% below the average

5. Seasonality & No Trend SalesFactor Spring200200/250 = 0.8 Summer350350/250 = 1.4 Fall300300/250 = 1.2 Winter150150/250 = 0.6 Total1,0004.0 Avg1,000/4=250

6. Seasonal Factors Compute average for each period Compute overall average Divide period averages by overall to get indexes. Ok to have different # of data points

7. Seasonality & No Trend If we expected total demand for the next year to be 1,100, the average per quarter would be 1,100/4=275 Forecast Spring275 * 0.8 = 220 Summer275 * 1.4 = 385 Fall275 * 1.2 = 330 Winter275 * 0.6 = 165 Total1,100

8. Trend & Seasonality Deseasonalize to find the trend 1.Calculate seasonal factors 2.Deseasonalize the demand 3.Find trend of deseasonalized line Project trend into the future 4.Project trend line into future 5.Multiply trend line by seasonal component.

9. Seasonally Adjusting BLS report, 2012 Makes it easier to see trends BLS data, 2012

10. Washoe Gaming Win, 1993-96 Looks like a downhill slide -Silver Legacy opened 95Q3 -Otherwise, upward trend 1993 1994 1995 1996 Source: Comstock Bank, Survey of Nevada Business & Economics

11. Washoe Win 1989-1996 Definitely a general upward trend, slowed 93-94

12. 1989-2007 Red line shows “de-seasonalized” data

13. 1989-2007 Linear Regression

14. 1998-2007 Cache Creek Thunder Valley CC Expands 9/11

15. Selecting Data What data to use? All of it? Representative? Overall upward trend 2000-2003, downwards From 2003, fairly stable? From 2003 upward trend? The data you select to use has significant impact on the results you get and the conclusions you draw. ▫Spend time making sure data are representative

16. 2003-2012 Data

17. 2003-2012 LR using 2008Q3-2010Q4 R-squared = 0.78

18. 2011 Forecast using 2003-10 SR Data for LR Seasonal Indexes calculated using 2003-10 data

19. How Good Was It? Pattern fits data pretty well, but win better than expected.

20. 1.Compute Seasonal Indexes Q1Q2Q3Q4 2003 240,114,703 259,349,602 279,784,440 246,068,018 2004 231,607,546 259,849,383 297,401,507 259,617,607 2005 245,793,646 269,238,341 294,810,396 257,014,585 2006 245,775,176 269,670,481 294,839,349 257,155,338 2007 244,648,019 273,460,685 284,733,890 246,352,794 2008 227,915,101 237,045,466 258,990,669 206,203,166 2009 190,098,500 211,913,667 217,227,445 185,971,111 2010 187,016,132 198,330,968 209,608,491 175,601,589 2011 174,138,905 192,122,889 203,912,214 175,510,911 2012 175,417,340 Avg 216,252,507 241,220,165 260,145,378 223,277,235 235,223,821 Indexes 0.919 1.025 1.106 0.949

21. 2.Deseasonalize YearQuarterGaming WinSeasonalDeseas 20031 240,114,703 0.919 261,179,391 2 259,349,602 1.025 252,902,590 3 279,784,440 1.106 252,981,489 4 246,068,018 0.949 259,234,039 20041 231,607,546 0.919 251,925,921 2 259,849,383 1.025 253,389,947 3 297,401,507 1.106 268,910,866 4 259,617,607 0.949 273,508,607 Deseasonalize by dividing actual number by index Use same index value for All Q1s, same number for All Q2s, etc.

22. 3.LR on Deseasonalized data 2008 Q4-2012Q1 PeriodDeseasonalized 1 217,236,193 2 206,775,386 3 206,645,836 4 196,417,365 5 195,921,610 6 203,422,609 7 193,400,781 8 189,528,296 9 184,997,260 10 189,415,694 Intercept = 210,576,193 Slope = -2,065,456 R-squared =0.75

23. 4.Project trend line into future Intercept = 210,576,193 Slope = -2,065,456

24. 5.Multiply by Seasonal Relatives PeriodQ Linear Trend Line Seasonal Relative Seasonalized Forecast 11 1 189,789,679 1.025 194,627,812 12 2 186,820,177 1.106 206,613,451 13 3 183,850,675 0.949 174,513,237 14 4 180,881,173 0.919 166,292,712

25. Final Forecast

26. Summary 1.Calculate indexes 2.Deseasonalize 1.Divide actual demands by seasonal indexes 3.Do a LR 4.Project the LR into the future 5.Seasonalize 1.Multiply straight-line forecast by indexes