A residual analysis of a regression model suggests that the errors episilon i may not be...
A residual analysis of a regression model suggests that the errors episilon i may not be normally distributed. You have no intention of carrying out a hypothesis test. You just needed to obtain estimates for the slope and intercept. The normality of or lack there of The errors.
A. Is not a concern
B. Could be a concern
C.Definitely is a concern
Homework Answers
The normality of the errors is not a concern as we just need to obtain estimates of slope and intercept.
Normality assumption is needed when we have to carry out the hypothesis test .
so it is not a concern
option a is correct
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A residual analysis of a regression model suggests that the
errors episilon i may not be...
The model assumptions for multiple regression analysis are : 1. Normally distributed errors 2. Constant variance...
The model assumptions for multiple regression analysis are : 1. Normally distributed errors 2. Constant variance of the errors 3. Independent errors True False
The below image shows diagnostic plots for a linear regression analysis. Decide if these plots represent...
The below image shows diagnostic plots for a linear regression analysis. Decide if these plots represent a significant departure from the assumptions of linear regression. If they do then select the most severe violation of the assumptions revealed by the diagnostics. Normality check QQ Plot Residual Plot 3 2 101 2 3 02 04 05 08 10 Theoretical Guantiles Histogram for the Residuals Model Fit R"2# 096 0.4 0.2 00 02 04 00 02 04 06 08 10 Residual Value...
4. Consider the regression model, y1B22+ BKiK+ei -.. where errors may be heteroskedastic. Choose the most incorrect sta...
4. Consider the regression model, y1B22+ BKiK+ei -.. where errors may be heteroskedastic. Choose the most incorrect statement (a) The OLS estimators are consistent and unbiased (b) We should report the OLS estimates with the robust standard errors (c) The Gauss-Markov theorem may not apply (d) The GLS cannot be used because we do not know the error variances in practice (e) We should take care of heteroskedasticity only if homoskedasticity is rejected Consider the regression model, +BKIK+et e pet-1+...
Q5. 20% An Economics department at a large university keeps track of its majors' starting salaries....
Q5. 20% An Economics department at a large university keeps track of its majors' starting salaries. Let SAL = salary in dollars, GPA = grade point average on a 4.0 scale, COURSE = 1 if student took econometrics, and 0 otherwise. The model is estimated using information on randomly sampled 50 graduates (standard errors in parentheses): SAL=24200 + 1643 GPA +5033 COURSE, R2 = 0.74. (1078) (352) (456) The errors are assumed to be normally distributed and homoskedastic. (a) What...
JUST ANSWER B) 3. Suppose that you are provided with the following estimated regression model for academic performance...
JUST ANSWER B)
3. Suppose that you are provided with the following estimated regression model for academic performance of students in your district: Test_score = 686.0-1.10 x Student teacher ratio-0.650% Percentage of English_students, (8.7) (0.43) (0.031) where the numbers in parentheses are standard errors. (a) Construct 95% confidence intervals for the true regression coefficients of Student teacher ratio and Percentage of English-students. How do you interpret these confidence intervals? (b) You expect that the slope coefficient for Student_teacher ratio less...
I need help at the bottom of the page with - A normal probability plot suggests...
I need help at the bottom of the page with - A normal probability plot suggests that the residuals are normally distributed - thank you! A pediatrician wants to determine the relation that may exist between a child's height and head circumference. She randomly selects 5 children and measures their height and head circumference. The data are summarized below. Height_(inches)_-_x Head Circumference_(inches)_-_y 27.5 17.5 27.75 17.6 25.5 17.1 25 16.9 26.5 17.3 (a) Treating height as the explanatory variable, x,...
Consider the regression model given by: Yi = βo + β1Xi + β2Zi+ ui Suppose that...
Consider the regression model given by: Yi = βo + β1Xi + β2Zi+ ui Suppose that an econometrician wishes to test the null hypothesis given by: Ho: β1 + β2 = 1 Use this null hypothesis to specify a restricted form of the regression model (in a form that may be estimated using an OLS estimation procedure). State the equation that you could estimate as the restricted version of this model.
Consider the regression model given by: Yi = βo + β1Xi + β2Zi+ ui Suppose that...
Consider the regression model given by: Yi = βo + β1Xi + β2Zi+ ui Suppose that an econometrician wishes to test the null hypothesis given by: Ho: β1 + β2 = 0 Use this null hypothesis to specify a restricted form of the regression model (in a form that may be estimated using an OLS estimation procedure). State the equation that you could estimate as the restricted version of this model.
Question 14 3 pts Suppose that you estimate a multiple regression model, but that you inadvertently...
Question 14 3 pts Suppose that you estimate a multiple regression model, but that you inadvertently omit an explanatory variable that is correlated with the dependent variable. In this case, the coefficients on the included variables will always be biased. the coefficients on the included variables will always be unbiased, but the standard errors and test statistics will be biased. there is no effect on the coefficients of the included variables since the omitted variable has been omitted. the coefficients...
The below image shows diagnostic plots for a linear regression analysis. Decide if these plots represent a significant departure from the assumptions of linear regression. If they do then select the most severe violation of the assumptions revealed by the diagnostics. Normality check QQ Plot Residual Plot 3 2 101 2 3 02 04 05 08 10 Theoretical Guantiles Histogram for the Residuals Model Fit R"2# 096 0.4 0.2 00 02 04 00 02 04 06 08 10 Residual Value...
4. Consider the regression model, y1B22+ BKiK+ei -.. where errors may be heteroskedastic. Choose the most incorrect statement (a) The OLS estimators are consistent and unbiased (b) We should report the OLS estimates with the robust standard errors (c) The Gauss-Markov theorem may not apply (d) The GLS cannot be used because we do not know the error variances in practice (e) We should take care of heteroskedasticity only if homoskedasticity is rejected Consider the regression model, +BKIK+et e pet-1+...
Q5. 20% An Economics department at a large university keeps track of its majors' starting salaries. Let SAL = salary in dollars, GPA = grade point average on a 4.0 scale, COURSE = 1 if student took econometrics, and 0 otherwise. The model is estimated using information on randomly sampled 50 graduates (standard errors in parentheses): SAL=24200 + 1643 GPA +5033 COURSE, R2 = 0.74. (1078) (352) (456) The errors are assumed to be normally distributed and homoskedastic. (a) What...
JUST ANSWER B)
3. Suppose that you are provided with the following estimated regression model for academic performance of students in your district: Test_score = 686.0-1.10 x Student teacher ratio-0.650% Percentage of English_students, (8.7) (0.43) (0.031) where the numbers in parentheses are standard errors. (a) Construct 95% confidence intervals for the true regression coefficients of Student teacher ratio and Percentage of English-students. How do you interpret these confidence intervals? (b) You expect that the slope coefficient for Student_teacher ratio less...
Question 14 3 pts Suppose that you estimate a multiple regression model, but that you inadvertently omit an explanatory variable that is correlated with the dependent variable. In this case, the coefficients on the included variables will always be biased. the coefficients on the included variables will always be unbiased, but the standard errors and test statistics will be biased. there is no effect on the coefficients of the included variables since the omitted variable has been omitted. the coefficients...
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