Suppose you are interested in studying the effect of time spent in an SATpreparation course on actual SAT score. You propose the following regression ofSAT score on hours spent in preparation for SAT and family income:Y = Bo + BiX + B2X2 + uWhereY = score on SATX₁ = time spent in studying in an SAT preparation courseX₂ = income of the student's familyu = error termAn important assumption in the regression model with two independent variables isthat E(u|x₁,x₂)=0. Assume for simplicity that the only other relevant factor(contained in the error term) that affects Y is natural ability. That is, natural abilityand u are essentially equivalent in your regression.In the context of your proposed regression, this assumption assumes which of thefollowing? Natural ability plays no role in influencing SAT score.Natural ability is, on average, unrelated to hours spent in preparation forSAT but may be related to family income.Natural ability is, on average, unrelated to family income but may berelated to hours spent in preparation for SAT.Natural ability is, on average, unrelated to hours spent in preparation forSAT and family income.

The objective of the question is to understand the assumption made in the regression model about the…

Answered: Suppose you are interested in studying…

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

See similar textbooks

Similar questions

Using data from a random sample of 1000 working adults, we obtain the following estimated regression to study the effect of experience (exper) on log of wage (log(wage)). log(wage) = 5.423 -0.034ezper +0.009ezper² + 0.082educ + 0.157male What other regression do you need to run to test the null hypothesis that, holding other factors fixed, experience has no effect on log(wage)? Explain what test you would perform.
Imagine you are an economist working for the Government of Econville. You are tasked with developing a model to predict the GDP of the country based on various factors such as interest rates, inflation, unemployment rate, and population growth. You collect quarterly data for the past 20 years and start building your model. After running your initial regression, you notice some peculiar patterns in the residuals: (1) residuals do not have identical variances across different levels of the independent variables; (2) two or more independent variables in a regression model are highly correlated with each other; (3) the correlation of a variable with its own past values. You suspect that your model might be suffering from 3 potential issues in the regression analysis that can affect reliability and validity. List 2 factors in your model that might be causing the Multicollinearity and give a reason
The dependent variable in the regression in our cost driver analysis is which of the following? Company sales Total overhead cost for the entire period of time Total overhead cost per month
Mita, the manufacturer of copiers, has been spending increasing amounts of money on radio and television advertising in recent years. An analyst employed by Mita wanted to estimate a simple linear regression of the company's annual copier sales versus advertising dollars. Th regression results included SSE = 12593 and SSR = 87663. What is the coefficient of determination for this regression? 0.874 0.935 0.144 0.126
Define coefficients of the Linear Regression Model?
E3
Imagine you are an economist working for the Government of Econville. You are tasked with developing a model to predict the GDP of the country based on various factors such as interest rates, inflation, unemployment rate, and population growth. You collect quarterly data for the past 20 years and start building your model. After running your initial regression, you notice some peculiar patterns in the residuals: (1) residuals do not have identical variances across different levels of the independent variables; (2) two or more independent variables in a regression model are highly correlated with each other; (3) the correlation of a variable with its own past values. You suspect that your model might be suffering from 3 potential issues in the regression analysis that can affect reliability and validity. what are the implications of Heteroscedasticity if this potential issue in your model?
A home appraisal company would like to develop a regression model that would predict the selling price of a house based on the age of the house in years (X1), the living area of the house in square feet (X2), and the number of bedrooms (X3). The following regression model was chosen using a data set of house statistics: y=88,399554791.3333x231,471.1372x3 The first house from the data set had the following values: Selling price $324,000 Age - 22 years Square Feet 2.000 Bedrooms 3 The residual for this house is 23,558 -41,480 10,216 -16,095 27
Imagine you are an economist working for the Government of Econville. You are tasked with developing a model to predict the GDP of the country based on various factors such as interest rates, inflation, unemployment rate, and population growth. You collect quarterly data for the past 20 years and start building your model. After running your initial regression, you notice some peculiar patterns in the residuals: (1) residuals do not have identical variances across different levels of the independent variables; (2) two or more independent variables in a regression model are highly correlated with each other; (3) the correlation of a variable with its own past values. You suspect that your model might be suffering from 3 potential issues in the regression analysis that can affect reliability and validity. List 2 factors in your model that might be causing the Heteroscedasticity and give a reason
RQ7. A teacher is trying to predict student test grades (Q). She believes test grades are a function of incoming GPA, hours studying, and hours spent on social media (a distraction). She runs a regression and it produces these coefficients: Variable Coefficient Intercept GPA Hours Studying Social Media 70.0 3.5 2.4 -4.0 For a given student Julian, his GPA is 2.0, he studies 4 hours for the exam, and he spends 6 hours on Facebook. Predict his exam score (round to the nearest whole number).
Imagine you are an economist working for the Government of Econville. You are tasked with developing a model to predict the GDP of the country based on various factors such as interest rates, inflation, unemployment rate, and population growth. You collect quarterly data for the past 20 years and start building your model. After running your initial regression, you notice some peculiar patterns in the residuals: (1) residuals do not have identical variances across different levels of the independent variables; (2) two or more independent variables in a regression model are highly correlated with each other; (3) the correlation of a variable with its own past values. You suspect that your model might be suffering from 3 potential issues in the regression analysis that can affect reliability and validity. Based on Addressing Heteroscedasticity list one test you would employ to test this potential issue?
You are given a sample of 600 working individuals, with data on their location and earnings. You construct the following model: wage=Bo+Binorth+u, where the dependent variable is the annual earnings (wage) in $1,000. The explanatory variable is north (which equals one if a worker lives in the north, and equals zero otherwise). An OLS regression yields B 24.8. B₁ = 2.1. = As we have learned in class, education matters a lot in explaining wages-more years of education lead to higher wages. In the above econometric model, education belongs to the error term, u. We also know that in our population of interest, workers in the north have lower drop-out rates than workers in the south. These facts imply that B₁ is biased and inconsistent because the estimation is subject to omitted variable bias. The correct model should be: wage Bo+Binorth + B₂educ + e, where educ denotes the number of years of education. The OLS estimate of B1 from our incorrect model presented at the beginning of this…

SEE MORE QUESTIONS

Recommended textbooks for you

ENGR.ECONOMIC ANALYSIS

Economics

ISBN:

9780190931919

Author:

NEWNAN

Publisher:

Oxford University Press

Principles of Economics (12th Edition)

Economics

ISBN:

9780134078779

Author:

Karl E. Case, Ray C. Fair, Sharon E. Oster

Publisher:

PEARSON

Engineering Economy (17th Edition)

Economics

ISBN:

9780134870069

Author:

William G. Sullivan, Elin M. Wicks, C. Patrick Koelling

Publisher:

PEARSON

ENGR.ECONOMIC ANALYSIS

Economics

ISBN:

9780190931919

Author:

NEWNAN

Publisher:

Oxford University Press

Principles of Economics (12th Edition)

Economics

ISBN:

9780134078779

Author:

Karl E. Case, Ray C. Fair, Sharon E. Oster

Publisher:

PEARSON

Engineering Economy (17th Edition)

Economics

ISBN:

9780134870069

Author:

William G. Sullivan, Elin M. Wicks, C. Patrick Koelling

Publisher:

PEARSON

Principles of Economics (MindTap Course List)

Economics

ISBN:

9781305585126

Author:

N. Gregory Mankiw

Publisher:

Cengage Learning

Managerial Economics: A Problem Solving Approach

Economics

ISBN:

9781337106665

Author:

Luke M. Froeb, Brian T. McCann, Michael R. Ward, Mike Shor

Publisher:

Cengage Learning

Managerial Economics & Business Strategy (Mcgraw-…

Economics

ISBN:

9781259290619

Author:

Michael Baye, Jeff Prince

Publisher:

McGraw-Hill Education

SEE MORE TEXTBOOKS