![Q4.. [40 points] Consider the multiple linear regression model given by y - XB -+ s, where y and e are vectors of size 8 × 1, X ls a matrix of size 8 x 3 and Disa vector of sze 3 × 1. Also, the following information are available e = 22 y -2 and XTy 3 1. [10 points) Estimate the regression coefficients in the model given above? 2. [4 points] Estimate the variance of the error term e? 3. 4 points] Write the covariance matrix of the model given above? 4. [10 points) Write the ANOVA table corresponding to the above model and test the significance of t at α = 0.05? 5. [4 pointsjCalculate the Mallows Cp coefficient? 6. [4 points] Calculate the standard and adjusted coefficients of determination? 7 4 points Compute a %95 confidence interval of the mean response when X1-001 and x2-0.02?](http://img.homeworklib.com/questions/11c400b0-1448-11ec-ae18-2346ad9130a7.png?x-oss-process=image/resize,w_560)
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Q4.. [40 points] Consider the multiple linear regression model given by y - XB -+ s,...
II. Derivations (You must show all your work for full credit.) i. Given the model y=XB+ɛ,...
II. Derivations (You must show all your work for full credit.) i. Given the model y=XB+ɛ, derive the least squares estimate for ß? (10 points) ii. Show that B=(x+x)"x"y is an unbiased estimate for B.(10 points) ii. Given vlə) = E[(@–B\–B)], derive the variance- covariance matrix for the least squares estimator (10 points). iv. Given the model y=XB+ɛ, the transformation matrix T, and TTT=22-1, derive the GLS estimator (10 points).
a. Consider the multiple regression model y = XB + €, with E(e) = 0 and var(e linear function c'3 of B. Show that t...
a. Consider the multiple regression model y = XB + €, with E(e) = 0 and var(e linear function c'3 of B. Show that the change in the estimate d'3 when the ith observation is deleted is d'B-d'B 021. Consider a = d'Ce re C = (X'X)-1x{. ii
a. Consider the multiple regression model y = XB + €, with E(e) = 0 and var(e linear function c'3 of B. Show that the change in the estimate d'3 when the...
(13 points) Suppose you have a simple linear regression model such that Y; = Bo +...
(13 points) Suppose you have a simple linear regression model such that Y; = Bo + B18: +€4 with and N(0,0%) Call: 1m (formula - y - x) Formula: F=MSR/MSE, R2 = SSR/SSTO ANOVA decomposition: SSTOSSE + SSR Residuals: Min 1Q Modian -2.16313 -0.64507 -0.06586 Max 30 0.62479 3.00517 Coefficients: Estimate Std. Error t value Pr(> It) (Intercept) 8.00967 0.36529 21.93 -0.62009 0.04245 -14.61 <2e-16 ... <2e-16 .. Signif. codes: ****' 0.001 '** 0.01 '* 0.05 0.1'' 1 Residual standard...
4. Consider the linear model Y = XB+e, where e MV N(0,021). (1) Derive the formula...
4. Consider the linear model Y = XB+e, where e MV N(0,021). (1) Derive the formula for , the least square estimate of B, using the matrix notation (2) Show that ß is an unbiased estimate for B. (3) Derive the formula for var(), using matrix notation.
Consider the following linear regression model 1. For any X = x, let Y = xB+U,...
Consider the following linear regression model 1. For any X = x, let Y = xB+U, where B erk. 2. X is exogenous. 3. The probability model is {f(u; ) is a distribution on R: Ef [U] = 0, VAR; [U] = 62,0 >0}. 4. Sampling model: {Y}}}=1 is an independent sample, sequentially generated using Y; = xiß +Ui, where the U; are IID(0,62). (i) Let K > 0 be a given number. We wish to estimate B using least-squares...
Econometrics 13) Consider the classical linear regression model y = XB + E, EN(0,021) The data...
Econometrics
13) Consider the classical linear regression model y = XB + E, EN(0,021) The data are collected in such a way that the X matrix is orthogonal, that is X'X = 1. We want to test the null hypothesis that Ho: B1 + B2 + ... + Bx = 0. For this particular hypothesis, the standard t-test for a single linear restriction r' B = q reduces to ki bi a) t= i=1 b) t = svk Ek=1b c)t...
Using multiple linear regression, estimate the value of a in the given regression model. Use 4 decimal places. MODEL: y=ax^b e^cx
Using multiple linear regression, estimate the value of a in the given regression model. Use 4 decimal places. MODEL: y=ax^b e^cx
Q. 1 Consider the multiple linear regression model Y = x3 + €, where e indep...
Q. 1 Consider the multiple linear regression model Y = x3 + €, where e indep MV N(0,0²V) and V +In is a diagonal matrix. a) Derive the weighted least squares estimator for B, i.e., Owls. b) Show Bwis is an unbiased estimator for B. c) Derive the variances of w ls and the OLS estimator of 8. Is the OLS estimator of still the BLUE? In one sentence, explain why or why not.
Question 3 [4 points] Suppose the model is: Y B1+B2Xu. What is the nonlinear regression algorithm...
Question 3 [4 points] Suppose the model is: Y B1+B2Xu. What is the nonlinear regression algorithm to estimate the model (i.e., list the steps to estimate the coefficients)?
4. (35 points) Use multiple linear regression to fit the following experimental data, 12 1 4...
4. (35 points) Use multiple linear regression to fit the following experimental data, 12 1 4 5.5 1.5 5 y 13 22 16 9 9 (a) Compute the coefficients, the coefficient of determination , the standard deviation Sy, and the standard error of the estimate S/. Show your calculations. (b) Write a MATLAB script that solves part (a).
II. Derivations (You must show all your work for full credit.) i. Given the model y=XB+ɛ, derive the least squares estimate for ß? (10 points) ii. Show that B=(x+x)"x"y is an unbiased estimate for B.(10 points) ii. Given vlə) = E[(@–B\–B)], derive the variance- covariance matrix for the least squares estimator (10 points). iv. Given the model y=XB+ɛ, the transformation matrix T, and TTT=22-1, derive the GLS estimator (10 points).
a. Consider the multiple regression model y = XB + €, with E(e) = 0 and var(e linear function c'3 of B. Show that the change in the estimate d'3 when the ith observation is deleted is d'B-d'B 021. Consider a = d'Ce re C = (X'X)-1x{. ii
a. Consider the multiple regression model y = XB + €, with E(e) = 0 and var(e linear function c'3 of B. Show that the change in the estimate d'3 when the...
(13 points) Suppose you have a simple linear regression model such that Y; = Bo + B18: +€4 with and N(0,0%) Call: 1m (formula - y - x) Formula: F=MSR/MSE, R2 = SSR/SSTO ANOVA decomposition: SSTOSSE + SSR Residuals: Min 1Q Modian -2.16313 -0.64507 -0.06586 Max 30 0.62479 3.00517 Coefficients: Estimate Std. Error t value Pr(> It) (Intercept) 8.00967 0.36529 21.93 -0.62009 0.04245 -14.61 <2e-16 ... <2e-16 .. Signif. codes: ****' 0.001 '** 0.01 '* 0.05 0.1'' 1 Residual standard...
4. Consider the linear model Y = XB+e, where e MV N(0,021). (1) Derive the formula for , the least square estimate of B, using the matrix notation (2) Show that ß is an unbiased estimate for B. (3) Derive the formula for var(), using matrix notation.
Consider the following linear regression model 1. For any X = x, let Y = xB+U, where B erk. 2. X is exogenous. 3. The probability model is {f(u; ) is a distribution on R: Ef [U] = 0, VAR; [U] = 62,0 >0}. 4. Sampling model: {Y}}}=1 is an independent sample, sequentially generated using Y; = xiß +Ui, where the U; are IID(0,62). (i) Let K > 0 be a given number. We wish to estimate B using least-squares...
Econometrics
13) Consider the classical linear regression model y = XB + E, EN(0,021) The data are collected in such a way that the X matrix is orthogonal, that is X'X = 1. We want to test the null hypothesis that Ho: B1 + B2 + ... + Bx = 0. For this particular hypothesis, the standard t-test for a single linear restriction r' B = q reduces to ki bi a) t= i=1 b) t = svk Ek=1b c)t...
Q. 1 Consider the multiple linear regression model Y = x3 + €, where e indep MV N(0,0²V) and V +In is a diagonal matrix. a) Derive the weighted least squares estimator for B, i.e., Owls. b) Show Bwis is an unbiased estimator for B. c) Derive the variances of w ls and the OLS estimator of 8. Is the OLS estimator of still the BLUE? In one sentence, explain why or why not.
Question 3 [4 points] Suppose the model is: Y B1+B2Xu. What is the nonlinear regression algorithm to estimate the model (i.e., list the steps to estimate the coefficients)?
4. (35 points) Use multiple linear regression to fit the following experimental data, 12 1 4 5.5 1.5 5 y 13 22 16 9 9 (a) Compute the coefficients, the coefficient of determination , the standard deviation Sy, and the standard error of the estimate S/. Show your calculations. (b) Write a MATLAB script that solves part (a).
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