Data: We will use the dataset "MASchools" included in the package "AER". The dataset contains data on test performance,

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Data We Will Use The Dataset Maschools Included In The Package Aer The Dataset Contains Data On Test Performance 1 (460.75 KiB) Viewed 125 times

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 us 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 example, we loaded the package first and then loaded 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, however, you do not have to specify to which dataset the variables belong when you use them. For example, suppose you want to get summary statistics of the variable salary. Then, before attaching 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() is useful when you work with many variables. When you do not need the dataset anymore, you can detach it via: > detach(MASchools)

Estimate the population regression model: In(TestScore;) = Bo+ByIn(STR;) +BzIncome;+B3Lunch;+u; where In(x) denotes the natural log of x. Note that the R-function log() computes the natural log, i.e., log(x) computes the natural log of x. Choose the correct statement. a. We should not have included Income and Lunch because the hypothesis that B2 = 0 and B3 = 0 and cannot be rejected at the 5% significance level. b. The model here is worse than the models in Question 1 and Question 4 because the model here has the smallest R2 among the three models. OC. The estimated elasticity of TestScore to STR is approximately -0.0167 and it is significant at the 5% level. d. The estimation results suggest that a 1% increase in STR would reduce TestScore by 1.67 points on the test. e. The estimation results suggest that decreasing STR by one student would induce a 1.67 percent increase in TestScore.