#1 Suppose we can model health expenditures using the function: Expenditures= $4,000+0.05*Income a. Predict the change in expenditures resulting from a $2,000 increase in annual income b. What are predicted household expenditures when income= $20,000; $40,000; and $80,000. Provide a graph of these results. c. Suppose that we specify an alternate model where expenditures are now equal to $4,000+0.15*income. Describe how this would affect our graphs from part b.

#2 A researcher finds that individuals who drive yellow cars are more likely to earn higher incomes. Do you think this correlation represents an association or a causal relationship? Why? If you think it reflects an association, can you think of a third (outside) variable that may help to explain this relationship?

#3 Using last week's excel filePreview the document, run a linear regression to estimate life expectancy (dependent variable). Use GDP per capita, smoking prevalence, and doctors per 1,000 residents as the independent variables (regressors). If you're using excel, you may have to move the columns around until all three of the independent variables are next to each other. Your final equation should look like: Life expectancy= _____ + _(GDP per capita) + _(Smoking prevalence) + _(doctors per 1,000 residents). What are the significant variables in the regression? Provide a brief interpretation for the relationship between each independent variable and life expectancy (e.g, a $1,000 increase in per capita income would be associated with a _____ year increase/decrease in life expectancy).