1. You are interested in estimating the following model: Y, = B, + RX; +, . B, and B, are the OLS estimators and û is the residual. Y; represents consumption and X;: income. Both Y; and X; are measured in pounds (£). Σ- Σχ-Γ) = 8.72 ΣΧ. ΣΧ-453Σ-48.1 = 48.1 É(Y, - f)² = 24.94 E(X, - X)² =11.1 ΣΧ-5413 Σ- - 0.0309 n-10 Standard error of B; = 0.28 a) Calculate and interpret B, and B. b) Test the null hypothesis H, : B, =0 against the two-tailed alternative at 5% significance level. c) List and explain the assumptions of the Classical Linear Regression Model. d) Compare the formulas for R-squared and adjusted R-squared. Why is the adjusted R-squared often used in multiple regression analysis?
1. You are interested in estimating the following model: Y, = B, + RX; +, . B, and B, are the OLS estimators and û is the residual. Y; represents consumption and X;: income. Both Y; and X; are measured in pounds (£). Σ- Σχ-Γ) = 8.72 ΣΧ. ΣΧ-453Σ-48.1 = 48.1 É(Y, - f)² = 24.94 E(X, - X)² =11.1 ΣΧ-5413 Σ- - 0.0309 n-10 Standard error of B; = 0.28 a) Calculate and interpret B, and B. b) Test the null hypothesis H, : B, =0 against the two-tailed alternative at 5% significance level. c) List and explain the assumptions of the Classical Linear Regression Model. d) Compare the formulas for R-squared and adjusted R-squared. Why is the adjusted R-squared often used in multiple regression analysis?
MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
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Chapter1: Starting With Matlab
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could i get some help answering part b, c, d
Transcribed Image Text:1. You are interested in estimating the following model:
Y, = B, + RX; +, .
B, and B, are the OLS estimators and û is the residual. Y; represents consumption and X;:
income. Both Y; and X; are measured in pounds (£).
Σ- Σχ-Γ) = 8.72 ΣΧ.
ΣΧ-453Σ-48.1
= 48.1 É(Y, - f)² = 24.94
E(X, - X)² =11.1
ΣΧ-5413 Σ- - 0.0309 n-10
Standard error of B; = 0.28
a) Calculate and interpret B, and B.
b) Test the null hypothesis H, : B, =0 against the two-tailed alternative at 5% significance level.
c) List and explain the assumptions of the Classical Linear Regression Model.
d) Compare the formulas for R-squared and adjusted R-squared. Why is the adjusted R-squared
often used in multiple regression analysis?
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