Case Study: Soft Drink Demand Estimation

CASE 1 - DEMAND ESTIMATION: Soft Drinks

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 a multiple linear regression program by MS Excel.

2. Interpret each coefficient of independent variable in the soft drink demand estimated function in question 1.

3. Given your answer in question 1, please comment on whether the regression estimated function is a good fit or not. What is the interpretation of coefficient of determination (R-square)? May we use the estimated function to predict for the future demand? Explain why.

4. How many cans/capita/year on soft drink should be for a state in which 6-pack price=$1.95, Income/Capita=$23,500, and Mean Temp= 68�°F?

5. Now omit the price and temperature from the regression equation. Should a marketing plan for soft drinks be designed that relocates most canned drink machines into low-income neighborhoods? Why or why not?

Answer Summary

The results of the multiple regression analysis is featured in this solution.

CASE STUDY: SOFT DRINK DEMAND ESTIMATION

Demand can be estimated with experimental data, time-series data, or cross-section data. Sara Lee Corporation generates experimental data in test stores where the effect of an NFL-licensed Carolina Panthers logo on Champion sweatshirt sales can be carefully examined. Demand forecasts usually rely on time-series data. In contrast, cross-section data appear in Table 1. 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 United States.

1. Estimate the demand for soft drinks using a multiple regression program available on your computer

SPSS...