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.

Answered: Image /qna-images/answer/30f6dd42-e89c-4d2f-b035-bc4b16fc2b04.jpg

Answered: Estimate the population regression…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

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Suppose that we want to build a model that predicts the group membership of a hurricane, either tropical (0) or non-tropical (1) based on the latitude of formation of the huricane. The response variable is the binary variable Type.new (type of hurricane) and the predictor variable is FirstLat (First Latitude). Using R, we build a model by applying the glm () function. For the logistic regression model, we specify family = "binomial". The data is available at https://userpage.fu-berlin.de/soga/200/2010 data sets hurricanes.xlsx. The R code is #set up filename my.filename 1z|) <2e-16 *** 0.96148 -9.446 0.37283 0.03947 9.447 <2e-16 *** --- *# Signif. codes: 0 '*** 0.001 **' 0.01 '*' 0.05'.' 0.1 '' 1 # (Dispersion parameter for binomial family taken to be 1) Null deviance: 463.11 on 336 degrees of freedom *# Residual deviance: 232.03 on 335 degrees of freedom *# AIC: 236.03 *# Number of Fisher Scoring iterations: 6 What is the interpretation of the p-value of the predictor variable…
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As part of a study at a large university, data were collected on n = 224 freshmen computer science (CS) majors in a particular year. The researchers were interested in modeling y, a student's grade point average (GPA) after three semesters, as a function of the following independent variables (recorded at the time the students enrolled in the university): X 1= average high school grade in mathematics (HSM) X 2 = average high school grade in science (HSS) X 3 = average high school grade in English (HSE) X 4 = SAT mathematics score (SATM) x 5 = SAT verbal score (SATV) A first-order model was fit to the data with the following results: SOURCE DF MS F VALUE PROB>F MODEL 28.64 5.73 11.69 0001 ERROR 218 106 82 0.49 TOTAL 223 135.46 ROOT MSE DEP MEAN 0.700 R-SQUARE 0.211 4.635 ADJ R-SQ 0.193 PARAMETER STANDARD T FOR O VARIABLE ESTIMATE ERROR PARAMETER -0 PROB> ITI INTERCEPT 2.327 0.039 5.817 0.0001 0.0003 XI HSM) X2 (HSS) X3 (HSE) X4 (SATM) X5 (SATV) 0.146 0.037 3.718 a.036 0.038 0.950 0.3432…
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Consider the logit regression log(odds(QualExam) = ßo + B, • ParEduc + B, • Awards. where QualExam is a binary variable that indicates passing the exam if equal to 1, and failing the exam if 0, ParEduc indicates the parents' education level, and Awards is a binary variable that indicates having experience of obtaining award(s) if equal to 1, and not having experience if 0. Given the parents' average education level unchanged, the odds ratio is expected to be _ for an individual with awards to pass the exam comparing to those without awards. For an individual without awards and the parents' education level of 4, the estimated probability of passing the exam is approximately_. ParEduc Awards Intercept -10.53 2.98 0.48 O A. 0.48; 80%. O B. 1.616; 80%. O C. 1.616; 4%. O D. 0.48; 4%.
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When we fit a logistic regression model with a single explanatory term, x, we often assume that logit (pi) Bo + ₁x₂ where, for the ith observation, p; is the probability of a trial being successful and *; is the value of the explanatory variable. Which of the following is a good reason for using the logit link function? We can't use more than one explanatory term unless we use this link function. It allows us to model nonconstant variance in the response variable. It ensures that the probability of success is between 0 and 1. It ensures that the variance of the response is the same for all observations.

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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.