1. June 20, 2007 MBA 555 – Professor Gordon H. Dash, Jr. Determinants of Sam Adams Beer Leah Semonelli Iryna Sieczkiewicz Meghan Smith Adamson E. Streit

2. 2 of 23June 20, 2007MBA 555 – Professor Gordon H. Dash, Jr. Agenda Objective Hypotheses Variable Identification Methodology Statistics Results Conclusion

3. 3 of 23June 20, 2007MBA 555 – Professor Gordon H. Dash, Jr. Objective Develop an econometric model that explains the determinants of sales in Sam Adams beer Use a variety of pertinent data to help explain this relationship

4. 4 of 23June 20, 2007MBA 555 – Professor Gordon H. Dash, Jr. Hypotheses H 1 : Weather conditions influence Sam Adams sales H 2 : Advertising affects Sam Adams sales H 3 : Major sporting events lead to quarterly increases in Sam Adams sales H 4 : Major holidays lead to quarterly increases in Sam Adams sales

5. 5 of 23June 20, 2007MBA 555 – Professor Gordon H. Dash, Jr. Variables – Dependent Quarterly beer sales of the Boston Beer Company, Inc. from 1997 to 2006

6. 6 of 23June 20, 2007MBA 555 – Professor Gordon H. Dash, Jr. Variables – Independent Nat’l Quarterly Precipitation Data Nat’l Quarterly Temperature Data No. Major U.S. Holidays per Qtr. Major Sporting Events per Qtr. Sam Adams Advertising Allowance

7. 7 of 23June 20, 2007MBA 555 – Professor Gordon H. Dash, Jr. Exogenous –Major Sporting Events –U.S. Holidays –Temperature –Precipitation Endogenous –Advertising Variable ID & Definitions

8. 8 of 23June 20, 2007MBA 555 – Professor Gordon H. Dash, Jr. Methodology Data was collected from the U.S. gov’t websites on Internet –E.g., SEC, NOAA Monthly data was converted into quarterly data using Microsoft Excel

9. 9 of 23June 20, 2007MBA 555 – Professor Gordon H. Dash, Jr. Methodology, Cont’d WinORS™ software package was used to perform all calculations Stepwise Regression was used to determine the most significant variables Ordinary Least Squares (OLS) tested data for: –Normality –Homoscedasticity –Multi-Colinearity –Serial Correlation

10. 10 of 23June 20, 2007MBA 555 – Professor Gordon H. Dash, Jr. Statistics Variables not statistically significant: –No. Major U.S. Holidays (E.g., Memorial Day, Labor Day) –No. Major Sporting Events (E.g., World Series, Superbowl)

11. 11 of 23June 20, 2007MBA 555 – Professor Gordon H. Dash, Jr. Statistics, Cont’d Variables are statistically significant: –Precipitation levels –Avg. Temperature –Advertising Expense

12. 12 of 23June 20, 2007MBA 555 – Professor Gordon H. Dash, Jr. Results The Derived Equation: Q = 122.746 + 0.0005A + 1.909T – 1.490P Q = No. Barrels sold (Thousands) A = Advertising ($ Thousands) T = Avg. Temperature ( o F) P = Avg. Precipitation (In.)

13. 13 of 23June 20, 2007MBA 555 – Professor Gordon H. Dash, Jr. Result’s Cont’d Regression Predictive Model Plot

14. 14 of 23June 20, 2007MBA 555 – Professor Gordon H. Dash, Jr. Result’s Cont’d Model Data Root MSE29.714 SSQ (Res)31785.83 Dep. Mean322.975 Coef of Var. (CV)9.20% R2R2 75.86% Adj. R 2 72.18% P value = 0.00002; CI =99.998%

15. 15 of 23June 20, 2007MBA 555 – Professor Gordon H. Dash, Jr. Result’s Cont’d Multicolinearity –Variance Inflation Factors (VIFs) Measure the impact of colinearity in a regression model on the precision of estimation. It expresses the degree to which colinearity among the predictors degrades the precision of an estimate. –In practice, VIF < 10.0 –Our VIF = 2.128

16. 16 of 23June 20, 2007MBA 555 – Professor Gordon H. Dash, Jr. Result’s Cont’d VIF Parameters VariableVIF Advertising, $ thousands1.089 Temperature2.718 Precipitation2.576 Avg. VIF2.128 NOTE: WinORS calculates our VIF at 2.128, which is the same as above

17. 17 of 23June 20, 2007MBA 555 – Professor Gordon H. Dash, Jr. Result’s Cont’d Residual Error (Constant Var.) White's Test for Homoscedasticity13.294 P-Value for White's0.14974 Note: H o for White’s Test states that residuals are Homoscedastic

18. 18 of 23June 20, 2007MBA 555 – Professor Gordon H. Dash, Jr. Result’s Cont’d Constant Variance Plot

19. 19 of 23June 20, 2007MBA 555 – Professor Gordon H. Dash, Jr. Result’s Cont’d Outliers & Normality Plot

20. 20 of 23June 20, 2007MBA 555 – Professor Gordon H. Dash, Jr. Result’s Cont’d Elasticities VariableAvg. Elasticity Advertising0.33869 Temperature0.32025 Precipitation0.38340 All variables are inelastic!!

21. 21 of 23June 20, 2007MBA 555 – Professor Gordon H. Dash, Jr. Conclusions Major Holidays & Major Sporting Events do not affect sales of Sam Adams Advertising, Temperature, & Precipitation affect Sam Adams sales –More in-depth data needed to prove or disprove R 2 ~ 75% at CI = 99%

22. 22 of 23June 20, 2007MBA 555 – Professor Gordon H. Dash, Jr. Result’s Cont’d References Securities & Exchange Commission http://www.sec.gov National Oceanic and Atmospheric Administration http://www.ncdc.noaa.gov/oa/climate/research/cag3/cag3.ht ml Class Notes, Professor Gordon H. Dash, Jr.