Use the data discrim.RData to answer the questions. The data set is provided by Graddy (1997)

and Wooldridge (2018). These are 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.

You will investigate the relationship between the price of medium soda (psoda) and the proportion

of the population that is black (prpblck). First you set up a SLR model but also consider a MLR

model additionally controlling median family income (income)

Interpret the R2 from the OLS summary outputs for both SLR and MLR estimations. How

does the R-squared change when we control additional explanatory variable(s)?

(5) Note that prpblck is a proportion and so it makes little sense to increase it by one. Such an

increase can happen only if prpblck goes from zero to one. Instead, consider a .10 increase in

prpblck, which means a 10 percentage point increase. How much is the price of soda estimated to

increase as prpblck increases by .10 (ten percentage points) based your SLR OLS estimation?

(6) Interpret the estimated coefficient on the prpblck variable when you control for income (i.e.

based on your MLR OLS estimation) considering a .10 increase in prpblck. Is the discrimination

effect larger or smaller compared to Q(5)?

Using r studio what wouod need to be inputed to solve these questions with a data set