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population multiple regression model with three independent varia ption of the CLM model. y = Bo + B₁x₁ + B₂X₂ + B3X3 + U you want to test the null hypothesis Ho: P₁ - 3B2 = 1 d ß₂ be the OLS estimators, respectively. Write Var(B₁ - the variances of B₁and B₂and the covariance between them. -statistic to test Ho: B₁-3ẞ2 = 1 = Bo - 3B₂, 0₁ B₁ - 382, Write a regression equation to get ₁a ectly.

population multiple regression model with three independent varia ption of the CLM model. y = Bo + B₁x₁ + B₂X₂ + B3X3 + U you want to test the null hypothesis Ho: P₁ - 3B2 = 1 d ß₂ be the OLS estimators, respectively. Write Var(B₁ - the variances of B₁and B₂and the covariance between them. -statistic to test Ho: B₁-3ẞ2 = 1 = Bo - 3B₂, 0₁ B₁ - 382, Write a regression equation to get ₁a ectly.

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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5. The following population multiple regression model with three independent variables is said to satisfy the assumption of the CLM model. y = Bo + B₁x₁ + B₂X₂ + B3X3 + U After estimation, you want to test the null hypothesis Ho: P₁ - 3ß₂ = 1 1. Let B₁and B₂ be the OLS estimators, respectively. Write Var( B₁ – B₂)using the variances of ß₁and ß₂and the covariance between them. 2. Write a t-statistic to test Ho: B₁ - 3B₂ = 1 3. Let, 0o = Bo - 3ß₂, Ô₁ = B₁ – 3B₂, Write a regression equation to get ₁ and its standard error directly.
Transcribed Image Text:5. The following population multiple regression model with three independent variables is said to satisfy the assumption of the CLM model. y = Bo + B₁x₁ + B₂X₂ + B3X3 + U After estimation, you want to test the null hypothesis Ho: P₁ - 3ß₂ = 1 1. Let B₁and B₂ be the OLS estimators, respectively. Write Var( B₁ – B₂)using the variances of ß₁and ß₂and the covariance between them. 2. Write a t-statistic to test Ho: B₁ - 3B₂ = 1 3. Let, 0o = Bo - 3ß₂, Ô₁ = B₁ – 3B₂, Write a regression equation to get ₁ and its standard error directly.
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