Data: We will use the dataset "MASchools" included in the package "AER". The dataset contains data on test performance, school characteristics and student demographic backgrounds for school districts in Massachusetts. (The lectures use a similar dataset for schools in CA.) For the questions in this exercise, therefore, you should load the dataset "MASchools" via: data("MASchools", package = "AER") There are alternative ways to load the data in R. In tutorial sessions for examplo, wo loaded the packago first and then loadod the data. R-tips: You might want to attach the dataset via: attach(MASchools) You do not need to attach the dataset for this assessment. By attaching the dataset, nowever, you do not have to specify to which datasot the variables belong when you use them. For example, suppose you want to get summary statistics of the variable salary. Then, before altaching the dataset, you have to do this via summary(MASchools\$salary) But, once you attach the dataset "MASchools", you can do the same thing simply by summary(salary) The R command attach 0 is usoful when you work with many variables. When you do not nood the dataset anymore, you can detach it va: To learn the effect of student-teacher ratio on test scores, we use two variables: (1) student-teacher ratio (STR) and (2) 4 th grade score (TestScore), which is the sum of math, English, and science in the dataset MASchool. What is the sample median of the student-teacher ratio (two decimal places)? QUESTION 2 Estimate the population regression model: TestScore =0+1STR+u where E[u ISTR ]=0. What is the OLS estimate of the slope coefficient (two decimal places)? QUESTION 3 Consider the ostimated regression equation in Question 2. What is the robust standard error for the OLS ostimato of the slope coefficient (two decimal places)? For this question, the standard error type in R has to be either "stata" or "HC1" QUESTION 4 The minimum and maximum of STR are 11.40 and 27.00 , respectively, in the data. Using the estimated regression equation in Questions 2 and 3, predict the difference in TestScores between the school district with STR =11,40 and the school district with STR = 27.00 . Report the difference in absolute value (two decimal places). Note that this exercise replicates Section 5.3 in Stock and Watson, but with the MA data instead of CA data. Hence, it would be helpful to read the section once again. Generate a new variable D in R that equals either 0 or 1 , depending on whether the student-teacher ratio is less than 17 , i.e, Di={1ifthestudent-teacherratioinithdistrict