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# Regression analysis in Minitab

See attached data file.

G = Total U.S. gasoline consumption, computed as total expenditure divided by price index.
Pop = U.S. total population in millions
GasP = Price index for gasoline,
Income = Per capita disposable income,
Pnc = Price index for new cars,
Puc = Price index for used cars,
Ppt = Price index for public transportation,
Pd = Aggregate price index for consumer durables,
Pn = Aggregate price index for consumer nondurables,
Ps = Aggregate price index for consumer services,

Use Minitab

I. Create regression models (four of them) to predict the consumption of gasoline using the following independent variables. Create a table with the coefficients and their related statistics:
a. Price index for gasoline and the per capita disposable income
b. Price index for gasoline, per capita disposable income, and the price index for new cars
c. Price index for gasoline, per capita disposable income, the price index for new cars, and the price index for used cars.
d. Price index for gasoline, per capita disposable income, the price index for new cars, the price index for used cars, and the price index for public transportation

II. Calculate the following elasticities, using the means of the variables, obtaining separate estimates from each of the above 4 regressions.
a. Own price elasticity of gasoline
b. Income elasticity
c. Cross price elasticity with new cars
d. Cross price elasticity with used cars
e. Cross price elasticity with public transportation

III. a. Compare the regression models from question I. What are the strengths and weaknesses of each model?
b. Consider the model in part c - what may this model, reasonably, be used for, and what does it leave out?
c. If you were going to do a retail level demand study for one particular company's gasoline products (for example, GP), what would you do differently from the above?

#### Solution Summary

Step by step method for computing regression model in Minitab for Gas data is given in the answer.

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