Quadratic and Cubic specification for STR

Reference no: EM131761976 , Length: word count:800

Project: Replication of Table 9.2

Different software will change default setting, which lead to tiny differences on estimation result.

Data description

The Massachusetts data are district-wide averages for public elementary school districts in 1998. The test score is taken from the Massachusetts Comprehensive Assessment System (MCAS) test, administered to all fourth graders in Massachusetts public schools in the spring of 1998. The test is sponsored by the Massachusetts Department of Education and is mandatory for all public schools. The data analyzed here are the overall total score, which is the sum of the scores on the English, Math, and Science portions of the test.

Data on the student-teacher ratio, the percent of students receiving a subsidized lunch and on the percent of students still learning english are averages for each elementary school district for the 1997-1998 school year and were obtained from the massachusetts department of education. Data on average district income were obtained from the 1990 U.S. Census.

Replicates Table 9.2, answer the following questions

a. Please interpret the result of column (2). Do you think it is a better model than column (1), and why?

b. The columns report the results of including quadratic and cubic specification for STR and District income. Comparing the (R²) ¯ of regressions, we can see that regression (3) and (4) have higher value. Could we get a conclusion that there is non-linear relationship between test score and both of STR and District income, if yes, please interpret each coefficient. If not, please explain why.

c. Change the dependent variable of column 3 log of TS_i, you could get higher (R²) ¯. Do you think it is a better fit regression, why?

d. Based on the result of column 5, we find no evidence about the interaction effect of HiEL of STR to explain test score. However, if you estimate the following regression functions

TS_i = α₀ + α₁ HiEL_i + α₂ HiEL*STR + ε_i

the interaction term become significant. Which regression function is your favor and why?

Attachment:- Assignment Files.rar

Reference no: EM131761976

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