We use the dataset "MASchools" in the package "AER" like we did in R-Exercise 2. The dataset contains information on tes

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We use the dataset "MASchools" in the
package "AER" like we did in R-Exercise
2. The dataset contains information on test performance, school
characteristics and student demographic backgrounds for school
districts in Massachusetts (MA). In this exercise, we shall use
R-functions in the
packages "estimatr" and "AER" (or "car").
We wish to learn the effect of student-teacher ratio (STR) on the
4th grade score (TestScore), which is defined as the sum of math,
English, and science. In order to control for omitted variable
bias, we shall include a few other additional variables and
consider various functional forms. For all estimation exercises
here the type of standard error should
be "HC1" or
equivalently "stata".
You can access the data using the following steps

We Use The Dataset Maschools In The Package Aer Like We Did In R Exercise 2 The Dataset Contains Information On Tes 1 (33.46 KiB) Viewed 94 times

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We Use The Dataset Maschools In The Package Aer Like We Did In R Exercise 2 The Dataset Contains Information On Tes 2 (72.76 KiB) Viewed 94 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 "MA Schools", 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)

QUESTION 1 Estimate the population regression model: TestScore;= Bo+B1 STR; + B2Income, +B3Lunch; + u; where E[u,STR,] = 0. Here, Income, and Lunch, are the per capita income and percentage of qualified students for reduced-price lunch in school district ;. What is the t value for the OLS estimate of the slope coefficient on STR (two decimal places)? QUESTION 2 After estimationg the regression model in Question 1, you want to test Ho: B2= B2 = 0 at the 5% significance level. Choose the correct statement. O a. We reject He because the standard error of is approximately 0.128. Ob "We reject Ho because the maximum of the p-values associated with B2 and is larger than 0.05. O c. We do not have sufficient evidence to reject Ho because R2 = 0.67. d. We have to test two restrictions jointly and the critical value for this test is 3. Oe. For this test, the F statistic is 154.9 and we use the F distribution with degrees of freedom 3 and 216. QUESTION 3 Using the estimated coefficients in Question 1 above, predict TestScore for "Dover", which is one of the school districts in the sample (two decimal places).