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Which of these statements is/are FALSE? Select all that apply. Hint: There are two statements that are false. Imperfect multicollinearity can make it difficult to distinguish the individual effects on the dependent variable of one regressor from that of another regressor. OLS estimators cannot be computed in the presence of perfect multicollinearity. In the presence of imperfect multicollinearity, the OLS estimators will be biased. Under imperfect multicollinearity, two or more regressors are highly correlated. When two variables in a regression are highly correlated, then the coefficients on at least one individual regressor will be imprecisely estimated. We can still compute the OLS estimators in the presence of imperfect multicollinearity. The effect of imperfect multicollinearity is to increase the variance of the OLS estimators. Imperfect multicollinearity increases the standard errors of the regression coefficients, and hence t- and F- tests are invalid.

Which of these statements is/are FALSE? Select all that apply. Hint: There are two statements that are false. Imperfect multicollinearity can make it difficult to distinguish the individual effects on the dependent variable of one regressor from that of another regressor. OLS estimators cannot be computed in the presence of perfect multicollinearity. In the presence of imperfect multicollinearity, the OLS estimators will be biased. Under imperfect multicollinearity, two or more regressors are highly correlated. When two variables in a regression are highly correlated, then the coefficients on at least one individual regressor will be imprecisely estimated. We can still compute the OLS estimators in the presence of imperfect multicollinearity. The effect of imperfect multicollinearity is to increase the variance of the OLS estimators. Imperfect multicollinearity increases the standard errors of the regression coefficients, and hence t- and F- tests are invalid.

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter10: Statistics
Section10.3: Measures Of Spread
Problem 1GP
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Which of these statements is/are FALSE? Select all that apply. Hint: There are two statements that are false. Imperfect multicollinearity can make it difficult to distinguish the individual effects on the dependent variable of one regressor from that of another regressor. OLS estimators cannot be computed in the presence of perfect multicollinearity. In the presence of imperfect multicollinearity, the OLS estimators will be biased. Under imperfect multicollinearity, two or more regressors are highly correlated. When two variables in a regression are highly correlated, then the coefficients on at least one individual regressor will be imprecisely estimated. We can still compute the OLS estimators in the presence of imperfect multicollinearity. The effect of imperfect multicollinearity is to increase the variance of the OLS estimators. Imperfect multicollinearity increases the standard errors of the regression coefficients, and hence t- and F- tests are invalid.
Transcribed Image Text:Which of these statements is/are FALSE? Select all that apply. Hint: There are two statements that are false. Imperfect multicollinearity can make it difficult to distinguish the individual effects on the dependent variable of one regressor from that of another regressor. OLS estimators cannot be computed in the presence of perfect multicollinearity. In the presence of imperfect multicollinearity, the OLS estimators will be biased. Under imperfect multicollinearity, two or more regressors are highly correlated. When two variables in a regression are highly correlated, then the coefficients on at least one individual regressor will be imprecisely estimated. We can still compute the OLS estimators in the presence of imperfect multicollinearity. The effect of imperfect multicollinearity is to increase the variance of the OLS estimators. Imperfect multicollinearity increases the standard errors of the regression coefficients, and hence t- and F- tests are invalid.
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