Data and Packages: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 forschool 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 gradescore (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 functionalforms. For all estimation exercises here the type of standard error should be "HC1" or equivalently "stata". QUESTION 6Estimate the population regression model:In (TestScore)=B+B ln(STR.) + B Income + Lunch + uwhere 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 correctstatement.Oa.We should not have included Income and Lunch because the hypothesis that ₂0 and ₂ = 0 and cannot be rejected at the 5% significance level.3O 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.O C.The estimated elasticity of TestScore to STR is approximately -0.0167 and it is significant at the 5% level.O d.The estimation results suggest that a 1% increase in STR would reduce TestScore by 1.67 points on the test.O e.The estimation results suggest that decreasing STR by one student would induce a 1.67 percent increase in TestScore.

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Answered: Estimate the population regression…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Suppose a logistic regression model is fitted for the probability of car ownership for residents of a certain city in Oman (Y=1 if a resident owns a car, Y=0 if a resident does not own a car). Suppose the explanatory variables used are x1=no. of years a resident spent in schooling and x2 is gender of the resident of the city (x2=1 for a male and x2=0 for a female resident) a) Interpret el and e82 b) if BO= -1.6, B1=0.4 and B2=3, estimate the probability of a resident in the city owning a car.
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Consider the multiple regression model to investigate the relationship between the number of fishes (Y) per section of the stream and the following independent variables: dissolved oxygen (3 < oxy < 10, in mg/liter), maximum depth (1 < maxdepth < 8, in feet), and water temperature (5< temp < 20, in °C). The location of the stream was also considered (lowland and upland). Y-hat = 23.09 + 0.199*oxy + 0.3361*maxdepth +8.6730*temp + 3.8290* lowland. Which of the following is(are) TRUE about the estimated regression coefficient for location of the stream? 1. The location of the stream is a categorical variable, so it is represented by a dummy variable with upland stream as the reference variable. II. Holding other factors constant, the number of fishes in lowland is 3.8290 higher than upland streams. O A. I only O B. II only O C. Both I and II O D. Neither I nor II
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Estimate the population regression model: In (TestScore) =B+B In(STR) +B Income + B₂Lunch + u 0 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.