CASE 1 – DEMAND ESTIMATION and ELASTICITY: Soft Drinks in the U.S.
Demand can be estimated with experimental data, time-series data, or cross-section
data. In this case, cross-section data appear in the Excel file. Soft drink consumption in
cans per capita per year is related to six-pack price, income per capita, and mean
temperature across the 48 contiguous states in the United States.

QUESTIONS
1. Given the data, please construct the demand estimation for soft drink consumption in
the United States by
(1) a multiple-linear regression equation, and
(2) a log-linear (exponential) regression equation.

2. Given the MS Excel output in Question 1, please compare the two regression
equations’ coefficient of determination (R-square), F-test and t-test. Which equation
is a good (better) fit? Which equation shows the stronger overall significance to
predict future demand? Which equation will you choose as a better estimation for
quantity demanded? Which equation will you choose as a better estimation for
elasticities? Explain your answer in the language of statistics.

3. Please interpret the coefficient of each independent variable in the multiple-linear soft
drink demand estimated equation.

4. Given the multiple-linear equation, how many cans/capita/year on soft drink should
be for a state in which 6-pack price=\$2.99, Income/Capita=\$48,500, and Mean
Temp= 64°F?

5. Given the log-linear equation, please provide the price elasticity of demand and
income elasticity. Comment on whether the demand is elastic or inelastic and
whether the soft drink is a necessity, normal good or luxury good.

6. Now omit both price and temperature from the regression equation then run the
simple linear regression again. Given the Excel output of only one independent
variable, income, should a marketing plan for soft drinks be designed that relocates
most canned drink vending machines into low-income neighborhood’s? Please
explain your answer in the language of economics.