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1) OLS is an estimating procedure that minimizes the sum of the squared errors. 2) The assumptions made by the classical linear regression model (CLRM) are not necessary to compute OLS estimators. 3) The efficiency of the estimate depends on the size of the coefficient estimate, not the variance of the estimate. 4) When the sample size is small, the OLS estimators, ß, and B₂, each follow the normal distribution only if u follows the normal distribution.

1) OLS is an estimating procedure that minimizes the sum of the squared errors. 2) The assumptions made by the classical linear regression model (CLRM) are not necessary to compute OLS estimators. 3) The efficiency of the estimate depends on the size of the coefficient estimate, not the variance of the estimate. 4) When the sample size is small, the OLS estimators, ß, and B₂, each follow the normal distribution only if u follows the normal distribution.

MATLAB: An Introduction with Applications
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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9:46 ◄App Store 1) OLS is an estimating procedure that minimizes the sum of the squared errors. 2) The assumptions made by the classical linear regression model (CLRM) are not necessary to compute OLS estimators. X 3) The efficiency of the estimate depends on the size of the coefficient estimate, not the variance of the estimate. 4) When the sample size is small, the OLS estimators, B₁ and B₂, each follow the normal distribution only if u follows the normal distribution.
Transcribed Image Text:9:46 ◄App Store 1) OLS is an estimating procedure that minimizes the sum of the squared errors. 2) The assumptions made by the classical linear regression model (CLRM) are not necessary to compute OLS estimators. X 3) The efficiency of the estimate depends on the size of the coefficient estimate, not the variance of the estimate. 4) When the sample size is small, the OLS estimators, B₁ and B₂, each follow the normal distribution only if u follows the normal distribution.
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