R-Code

MAS 637 Homework 3 (Due December 1)

Data (BigMac.csv or BigMac.mtw)

The data you will use for this assignment is from the international bank UBS 2009 report on prices and earnings in major cities. We are only interested to see how the number of minuets of work it takes to afford a big mac relates to other cost of living variables. The variables in this data set are:

* BigMac: Minuets of labor to purchase a BigMac

* Rice: Minuets of labor to purchase 1 kg of rice

* Bread: Minuets of labor to purchase 1 kg of bread

* FoodIndex: Food price index

* Bus: Cost in US dollars for a one-way 10km ticket

* Apt: Rent in US dollars for a 3 room apartment

* TeachGI: Primary Teacher’s gross income, 1000’s of US dollars

* TeachNI: Primary Teacher’s net income, 1000’s of US dollars

* TaxRate: Tax rate paid by a primary teacher

* TeachHours: Primary Teacher’s hours of work per week

Assignment

* Fit a multiple linear regression model that uses rice and bread to explain big mac. Make any appropriate transformations that need to be made to make this model meet the assumptions of multiple linear regression. Test that each slope coefficient is equal to zero, draw conclusions about the relationship between the predictors and big mac. Interpret each of these coefficients in context of the model. Calculate the 95% prediction interval for a city that has rice=11, bread=10 (this corresponds to Miami in the data).

* Fit a multiple linear regression model that uses all of the teaching variables (TeachGI, TeachNI, TaxRate, TeachHours). Make any appropriate transformations to make your resulting model meet the assumptions of multiple linear regression. Discuss the resulting ANOVA F-Test, and R-squared for your final model.

* Fit a multiple linear regression model that uses all of the predictor variables that are available. Make the appropriate transformations and corrections to make this model meet the assumptions of...