Sarah wants to compare her estimates to that of another researcher who used incomes measured in thousands of rands and educational achievement measured in months. What will be values of the parameter estimates (estimated in Part 2), and their standard errors (given in Part 2), be if earn is measured in thousands of rands, and educ is measured in months? Question 1β^∗1= Question 2β^∗2= . Question 5 Sarah runs a test on the residuals of her model and discovers that σ2=245.34 for individuals with 1 year of education, 356.12 for individuals with 8 years of education, and 386.32 for individuals with 15 years of education. What is the implication so this finding? A. The error term may be heteroscedastic, violating one of the assumptions required to make OLS estimators BLUE. B. The error terms are heteroscedastic, thus the estimators are no longer consistent. C. There may be autocorrelation present since residuals for individuals with the same education level have equal variance. D. There are no implications, the regression estimates remain linear, unbiased and efficient. E. The t distribution may no longer be appropriate for inference.Question 6 Taking another look at the data, Sarah realises that the positive relationship between education and income holds only up to a particular value for education (16 years) then the relationship becomes negative. How should she model this relationship? A. ln(earn)=β1+β2ln(educ)+ui B. earn=β1+β2ln(educ)+ui C. earn=β1+β2educ+β3(educ)2+ui D. earn=β1+β2(educ)2+ui E. ln(earn)=β1+β2educ+β31educ+uiAdditional informationPart 2 InstructionsSarah wants to estimate the relationship between individual earnings in the labour market (earn) and years of education achieved (educ). She collects data from a sample of 122 individuals on their monthly salaries (Y), measured in rands, and education levels (X), measured in years of education.Based on these data, she derives the following estimates:∑XY=41421943.8∑X2=15472∑X=1242∑Y=3401031

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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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Sarah wants to compare her estimates to that of another researcher who used incomes measured in thousands of rands and educational achievement measured in months. What will be values of the parameter estimates (estimated in Part 2), and their standard errors (given in Part 2), be if earn is measured in thousands of rands, and educ is measured in months?

Question 1

β^∗1=

Question 2

β^∗2= .

Question 5

Sarah runs a test on the residuals of her model and discovers that σ2=245.34 for individuals with 1 year of education, 356.12 for individuals with 8 years of education, and 386.32 for individuals with 15 years of education. What is the implication so this finding?

A. The error term may be heteroscedastic, violating one of the assumptions required to make OLS estimators BLUE.
B. The error terms are heteroscedastic, thus the estimators are no longer consistent.
C. There may be autocorrelation present since residuals for individuals with the same education level have equal variance.
D. There are no implications, the regression estimates remain linear, unbiased and efficient.
E. The t distribution may no longer be appropriate for inference.

Question 6

Taking another look at the data, Sarah realises that the positive relationship between education and income holds only up to a particular value for education (16 years) then the relationship becomes negative. How should she model this relationship?

A. ln(earn)=β1+β2ln(educ)+ui
B. earn=β1+β2ln(educ)+ui
C. earn=β1+β2educ+β3(educ)2+ui
D. earn=β1+β2(educ)2+ui
E. ln(earn)=β1+β2educ+β31educ+ui

Additional information
Part 2 Instructions
Sarah wants to estimate the relationship between individual earnings in the labour market (earn) and years of education achieved (educ). She collects data from a sample of 122 individuals on their monthly salaries (Y), measured in rands, and education levels (X), measured in years of education.

Based on these data, she derives the following estimates:

∑XY=41421943.8
∑X2=15472
∑X=1242
∑Y=3401031

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