Dr. Ron Lembke. 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.

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Presentation transcript:

Dr. Ron Lembke

Washoe Gaming Win, What did they mean when they said it was down three quarters in a row? Look at year-over-year

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

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

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

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

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

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.

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

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

Washoe Win Definitely a general upward trend, slowed 93-94

Red line shows “de-seasonalized” data

Linear Regression

Cache Creek Thunder Valley CC Expands 9/11

Selecting Data What data to use? All of it? Representative? Overall upward trend , 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

Data

LR using 2008Q3-2010Q4 R-squared = 0.78

2011 Forecast using SR Data for LR Seasonal Indexes calculated using data

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

1.Compute Seasonal Indexes Q1Q2Q3Q ,114, ,349, ,784, ,068, ,607, ,849, ,401, ,617, ,793, ,238, ,810, ,014, ,775, ,670, ,839, ,155, ,648, ,460, ,733, ,352, ,915, ,045, ,990, ,203, ,098, ,913, ,227, ,971, ,016, ,330, ,608, ,601, ,138, ,122, ,912, ,510, ,417,340 Avg 216,252, ,220, ,145, ,277, ,223,821 Indexes

2.Deseasonalize YearQuarterGaming WinSeasonalDeseas ,114, ,179, ,349, ,902, ,784, ,981, ,068, ,234, ,607, ,925, ,849, ,389, ,401, ,910, ,617, ,508,607 Deseasonalize by dividing actual number by index Use same index value for All Q1s, same number for All Q2s, etc.

3.LR on Deseasonalized data 2008 Q4-2012Q1 PeriodDeseasonalized 1 217,236, ,775, ,645, ,417, ,921, ,422, ,400, ,528, ,997, ,415,694 Intercept = 210,576,193 Slope = -2,065,456 R-squared =0.75

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

5.Multiply by Seasonal Relatives PeriodQ Linear Trend Line Seasonal Relative Seasonalized Forecast ,789, ,627, ,820, ,613, ,850, ,513, ,881, ,292,712

Final Forecast

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