QUESTION 7 In order to estimate the effect on wage of years of schooling, researcher B analysed the dataset that researcher A collected in the previous question. Researcher B considers the following regression equation, log(income), = Bo+B1educ,+ .… + Up where educ, is years of school of individual and ... includes additional terms associated with other characteristics in the data set. When researcher B was carefully examining omitted factors in the error term U, she realised that unobserved factors such as motivation, and work ethic, would determine the income level and be correlated with the variable finterest educ: If researcher B's conjecture regarding the unobserved factors is correct, then: O a. The OLS estimator a would not be computed. O b The OLS estimator B1 would overestimate the effect on income, of educ; O C. The OLS estimator B, would would suffer imperfect multicollinearity. Od. The OLS estimator B1 would have a large standard error. Oe. The OLS estimator B, would follow the Student t-distribution with n-k degrees of freedom, where k is the number of coefficients to estimate in the model.

QUESTION 7 In order to estimate the effect on wage of years of schooling, researcher B analysed the dataset that researcher A collected in the previous question. Researcher B considers the following regression equation, log(income), = Bo+B1educ,+ .… + Up where educ, is years of school of individual and ... includes additional terms associated with other characteristics in the data set. When researcher B was carefully examining omitted factors in the error term U, she realised that unobserved factors such as motivation, and work ethic, would determine the income level and be correlated with the variable finterest educ: If researcher B's conjecture regarding the unobserved factors is correct, then: O a. The OLS estimator a would not be computed. O b The OLS estimator B1 would overestimate the effect on income, of educ; O C. The OLS estimator B, would would suffer imperfect multicollinearity. Od. The OLS estimator B1 would have a large standard error. Oe. The OLS estimator B, would follow the Student t-distribution with n-k degrees of freedom, where k is the number of coefficients to estimate in the model.

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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Concept explainers

Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.

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