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- Theoretically prove that the optimal linear regression weights are given by: û = (XT X)-?xTy?...
I only need help for the last question "What Went Wrong?". Thank you! After seeing the...
I only need help for the last question
"What Went Wrong?". Thank you!
After seeing the problem, your classmate Alice immediately argues that we can apply a linear regression model, as the labels are numbers from 0-9, very similar to the example we learned from Unit 1. Though being a little doubtful, you decide to have a try and start simple by using the raw pixel values of each image as features. Alice wrote a skeleton code run_linear_regression_on_MNIST in main.py,...
6. Consider the following regression model without an intercept: Y = B,X, +U, One possible estimator...
6. Consider the following regression model without an intercept: Y = B,X, +U, One possible estimator for this model is given by: BE ANXJ Assume that you can make all of the usual ordinary least squares assumptions about the model, including the assumption that the true model does not include an intercept. Is B, an unbiased estimator? Please prove your conclusion, being sure to state the assumptions you use. [5 points]
Consider two separate linear regression models and For concreteness, assume that the vector yi co...
please help me to solve that question
Consider two separate linear regression models and For concreteness, assume that the vector yi contains observations on the wealth ofn randomly selected individuals in Australia and y2 contains observations on the wealth of n randomly selected individuals in New Zealand. The matrix Xi contains n observations on ki explanatory variables which are believed to affect individual wealth in Australia, and he matrix X2 contains n observations on k2 explanatory variables which are believed...
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...
b- A linear regression of Y vs X leads to an R equal to 1.23, what...
b- A linear regression of Y vs X leads to an R equal to 1.23, what can you conclude? c-Given paired data (X,Y), if Y is independent of X, what would you expect R2 to be? d- Given paired data (X,Y), how could Y be closely dependent upon X yet R2 is close to 0?
Consider the multiple regression with three independent variables under the classical linear model assumptions: y Bo+BBx,+B,x,...
Consider the multiple regression with three independent variables under the classical linear model assumptions: y Bo+BBx,+B,x, +u 1. You would like to test the hypothesis: H0: B-3B, 1 What is the standard error of B-3B,? (i Write the t-statistic of B-3B ( Define 0,= B-3B.. Write a regression equation that allows you to directly obtain 0, and its standard error.
In a simple linear regression, the following information is given: x−x− = −39; y− y− =...
In a simple linear regression, the following information is given: x−x− = −39; y− y− = 40; Σ(xi−x− )(yi− y−)= −840;Σ(xi−x− )(yi− y−)= −840; Σ(xi− x−)2= 718Σ(xi− x−)2= 718 a. Calculate b1. (Negative value should be indicated by a minus sign. Round your answer to 2 decimal places.) b1 b. Calculate b0. (Round intermediate calculations to 4 decimal places and final answer to 2 decimal places.) b0 c-1. What is the sample regression equation? (Negative value should be...
Question 2 (10 points) You are given the following model y-put ei. Consider two alternative estimators of β, b2xvix? and b = Zy/X 1. Which estimator would you choose and why if the model satisfies al...
Question 2 (10 points) You are given the following model y-put ei. Consider two alternative estimators of β, b2xvix? and b = Zy/X 1. Which estimator would you choose and why if the model satisfies all the assumptions of classical regression? Prove your results. (4 points) 2. Now suppose that var(y)-hxi, where h is a positive constant (a) Obtain the correct variance of the OLS estimator. (2 points) (b) Show that the BLU estimator is now 6. Derive its variance....
Consider the following linear regression model 1. For any X x, let Y xBU, where 3...
Consider the following linear regression model 1. For any X x, let Y xBU, where 3 E R*. 2. X is exogenous 3. The probability model is {f(u;0) is a distribution on R: Ef [U] = 0, VAR, [U] = 02,0 > 0}. 4. Sampling model: Y} anidependent sample, sequentially generated using Yi x Ui,where the U IID(0,0) are (i) Let K 0 be a given number. We wish to estimate B using least-squares subject to the constraint 6BK2. Write...
3) Consider the following linear regression: y =a + Bx + Show that minimizing the sum...
3) Consider the following linear regression: y =a + Bx + Show that minimizing the sum of squared residuals ( - ) to obtain OLS estimators of the slope and the intercept results in the following algebraic properties a) b) Ex = 0 = 0 4) You run the following regression: TestScore = a + (Female) + where TestScore is measured on a scale from 400 to 1000, and female is an indicator for the gender of the student. You...
I only need help for the last question
"What Went Wrong?". Thank you!
After seeing the problem, your classmate Alice immediately argues that we can apply a linear regression model, as the labels are numbers from 0-9, very similar to the example we learned from Unit 1. Though being a little doubtful, you decide to have a try and start simple by using the raw pixel values of each image as features. Alice wrote a skeleton code run_linear_regression_on_MNIST in main.py,...
6. Consider the following regression model without an intercept: Y = B,X, +U, One possible estimator for this model is given by: BE ANXJ Assume that you can make all of the usual ordinary least squares assumptions about the model, including the assumption that the true model does not include an intercept. Is B, an unbiased estimator? Please prove your conclusion, being sure to state the assumptions you use. [5 points]
please help me to solve that question
Consider two separate linear regression models and For concreteness, assume that the vector yi contains observations on the wealth ofn randomly selected individuals in Australia and y2 contains observations on the wealth of n randomly selected individuals in New Zealand. The matrix Xi contains n observations on ki explanatory variables which are believed to affect individual wealth in Australia, and he matrix X2 contains n observations on k2 explanatory variables which are believed...
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...
b- A linear regression of Y vs X leads to an R equal to 1.23, what can you conclude? c-Given paired data (X,Y), if Y is independent of X, what would you expect R2 to be? d- Given paired data (X,Y), how could Y be closely dependent upon X yet R2 is close to 0?
Consider the multiple regression with three independent variables under the classical linear model assumptions: y Bo+BBx,+B,x, +u 1. You would like to test the hypothesis: H0: B-3B, 1 What is the standard error of B-3B,? (i Write the t-statistic of B-3B ( Define 0,= B-3B.. Write a regression equation that allows you to directly obtain 0, and its standard error.
Question 2 (10 points) You are given the following model y-put ei. Consider two alternative estimators of β, b2xvix? and b = Zy/X 1. Which estimator would you choose and why if the model satisfies all the assumptions of classical regression? Prove your results. (4 points) 2. Now suppose that var(y)-hxi, where h is a positive constant (a) Obtain the correct variance of the OLS estimator. (2 points) (b) Show that the BLU estimator is now 6. Derive its variance....
Consider the following linear regression model 1. For any X x, let Y xBU, where 3 E R*. 2. X is exogenous 3. The probability model is {f(u;0) is a distribution on R: Ef [U] = 0, VAR, [U] = 02,0 > 0}. 4. Sampling model: Y} anidependent sample, sequentially generated using Yi x Ui,where the U IID(0,0) are (i) Let K 0 be a given number. We wish to estimate B using least-squares subject to the constraint 6BK2. Write...
3) Consider the following linear regression: y =a + Bx + Show that minimizing the sum of squared residuals ( - ) to obtain OLS estimators of the slope and the intercept results in the following algebraic properties a) b) Ex = 0 = 0 4) You run the following regression: TestScore = a + (Female) + where TestScore is measured on a scale from 400 to 1000, and female is an indicator for the gender of the student. You...
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