Install Wooldridge Package in R and use the discrim dataset from the Wooldridge package in R to answer the following questions. Please carefully read the document of variable description. This dataset contains zip code-level data on prices for various items at fast-food restaurants, along with characteristics of the zip code population, in New Jersey and Pennsylvania. The idea is to see whether fast-food restaurants charge higher prices in areas with a larger concentration of blacks. (a) Please find the sample means of prpblck and income in the data set, along with their sample standard deviations. (b) Assume that there's no heteroscedasticity issue. Please use the lm command in R to estimate the linear model below. It is okay to use lm with default settings. Report the results in equation form, the sample size, standard error of the estimates and R-squared. Interpret the coefficient of prpblck. Is it meaningful? Is it worth looking into? psoda = Po + B * prpblck + (c) Can you use the model above to make causal claims? Why or why not? Do you see any potential threats to internal validity? (d) What could be potential confounding variables affecting causal inference in the model above? Try to be comprehensive and clearly lay out the logic behind each variable you list out. (e) Which of the potential confounding variables above are measurable and which of them are not measurable? Give reasons for your classification. Which of them do you have access to in the data set? Can you think of proxies for those that are not measurable? (f) Include the ones that are measurable and available in your regression equation and rerun the regression. How does the coefficient of prpblck change? How do the Adjusted R and Multiple R change? What is your new interpretation of the estimated relationship between prpblck and psoda?