June 20, 2007 MBA 555 – Professor Gordon H. Dash, Jr.

Determinants of Sam Adams Beer

Leah SemonelliIryna Sieczkiewicz Meghan SmithAdamson E. Streit

2 of 23June 20, 2007MBA 555 – Professor Gordon H.

Dash, Jr.

Agenda• Objective

• Hypotheses

• Variable Identification

• Methodology

• Statistics

• Results

• Conclusion

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 of 23June 20, 2007MBA 555 – Professor Gordon H.

Dash, Jr.

Hypotheses• H1: Weather conditions influence Sam

Adams sales

• H2: Advertising affects Sam Adams sales

• H3: Major sporting events lead to quarterly increases in Sam Adams sales

• H4: Major holidays lead to quarterly increases in Sam Adams sales

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 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 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 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 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 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 of 23June 20, 2007MBA 555 – Professor Gordon H.

Dash, Jr.

Statistics, Cont’d• Variables are statistically

significant:– Precipitation levels

– Avg. Temperature

– Advertising Expense

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 (oF)• P = Avg. Precipitation (In.)

13 of 23June 20, 2007MBA 555 – Professor Gordon H.

Dash, Jr.

Result’s Cont’d• Regression Predictive Model Plot

Predictive Ability (OLS)Dependent Variable: Barrels, thousands

Actual Predicted

Observation454239363330272421181512963

Act

ual &

Pre

dict

ed

440

420

400

380

360

340

320

300

280

260

14 of 23June 20, 2007MBA 555 – Professor Gordon H.

Dash, Jr.

Result’s Cont’d• Model Data

Root MSE 29.714

SSQ (Res) 31785.83

Dep. Mean 322.975

Coef of Var. (CV) 9.20%

R2 75.86%

Adj. R2 72.18%

P value = 0.00002; CI =99.998%

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 of 23June 20, 2007MBA 555 – Professor Gordon H.

Dash, Jr.

Result’s Cont’d• VIF Parameters

Variable VIF

Advertising, $ thousands 1.089

Temperature 2.718

Precipitation 2.576

Avg. VIF 2.128

NOTE: WinORS calculates our VIF at 2.128, which is the same as above

17 of 23June 20, 2007MBA 555 – Professor Gordon H.

Dash, Jr.

Result’s Cont’d• Residual Error (Constant Var.)

White's Test for Homoscedasticity 13.294

P-Value for White's 0.14974

Note: Ho for White’s Test states that residuals are Homoscedastic

18 of 23June 20, 2007MBA 555 – Professor Gordon H.

Dash, Jr.

Result’s Cont’d• Constant Variance Plot

Constant Variance Test (OLS)Dependent Variable: Barrels, thousands

Predicted396384372360348336324312300288276264252

Res

idua

l

60

48

36

24

12

0

-12

-24

-36

-48

19 of 23June 20, 2007MBA 555 – Professor Gordon H.

Dash, Jr.

Result’s Cont’d• Outliers & Normality Plot

Normal Probability Chart (OLS)Dependent Variable: Barrels, thousands

Expected Residual7260483624120-12-24-36-48-60-72

Sor

ted

Res

idua

l

60

48

36

24

12

0

-12

-24

-36

-48

20 of 23June 20, 2007MBA 555 – Professor Gordon H.

Dash, Jr.

Result’s Cont’d• Elasticities

Variable Avg. Elasticity

Advertising 0.33869

Temperature 0.32025

Precipitation 0.38340

All variables are inelastic!!

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

R2 ~ 75% at CI = 99%

22 of 23June 20, 2007MBA 555 – Professor Gordon H.

Dash, Jr.

Result’s Cont’dReferencesSecurities & Exchange Commission

http://www.sec.gov

National Oceanic and Atmospheric Administrationhttp://www.ncdc.noaa.gov/oa/climate/research/cag3/cag3.html

Class Notes, Professor Gordon H. Dash, Jr.