Which of the following is a consequence of severe multicollinearity in a regression model? A. High standard errors for the estimated coefficients B. Lower standard errors for the estimated coefficients C. The OLS estimator becomes biased D. The dependent variable becomes constant
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Which of the following is a consequence of severe multicollinearity in a regression model?
A. High standard errors for the estimated coefficients
B. Lower standard errors for the estimated coefficients
C. The OLS estimator becomes biased
D. The dependent variable becomes constant
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- The best way to interpret polynomial regressions is to: A. look at the t-statistics for the relevant coefficients. B. analyze the standard error of estimated effect. C. plot the estimated regression function and to calculate the estimated effect on Y associated with a change in X for one or more values of X. D. take a derivative of Y with respect to the relevant X.What is a linear regression model? What is measured by the coefficients ofa linear regression model? What is the ordinary least squares estimator?True or False For a linear regression model including only an intercept, the OLS estimator of that intercept is equal to the sample mean of the independent variable.
- In multiple regression model: what is it means for a variable to be significant? Explain the meaning of the significant variable.(2)What would the consequence be for a regression model if theerrors were not homoscedastic?In a multiple linear regression, which of the following can cause the OLS estimators to be biased? A sample correlation coefficient of .85 independent variables. The presence of heteroskedasticity. Omitting an important variable i. between two ii. iii. Explain briefly.
- The OLS estimators of the coefficients in multiple regression will have omitted variable bias: a. i only if an omitted determinant of b. if an omitted variable is correlated with at least one of the regressors, even though it is not a determinant of the dependent variable. C. only if the omitted variable is not normally distributed. d. if an omitted determinant of is a continuous variable. Y; i is correlated with at least one of the regressors. e. if the degree of freedom is less than 50.1. For a regression model y = XB + u where u is N(0, o?1), y is nx1 matrix, X is nxp matrix, B is px1 matrix and u is nx1 matrix, a. derive the estimators B using the method of least squaresAs the number of relevant independent variables in a regression increases, the R-squared of a regression Select one: a. exhibits greater heteroskedasticity b. increases c. decreases d. stays constant
- Define coefficients of the Linear Regression Model?2. Consider a two variable regression model, which satisfies all the Gauss Markov assumptions except that the error variance is proportional to X² i.e.E(u?) = o²X? Y₁ = B₁ + B₂X₁ + Ui How would you obtain the best linear unbiased estimates from the above regression.QUESTION 1 In the equation, y = 8o + Bjx1 + 8zx2 + u, 8z is a(n) O a. intercept parameter O b. slope parameter O. dependent variable O d. independent variable QUESTION 2 If an independent variable in a multiple linear regression model is an exact linear combination of other independent variables, the model suffers from the problem of O a. perfect collinearity O b.heteroskedasticty O . homoskedasticity O d. omitted variable bias QUESTION 3 Which of the following is true of R 2? O a. R- usually decreases with an increase in the number of independent variables in a regression. O b.R2 shows what percentage of the total variation in the dependent variable, Y, is explained by the explanatory variables. OC.A low R2 indicates that the Ordinary Least Squares line fits the data well. O d. R² is also called the standard error of regression. QUESTION 4 We estimate the model Wage, = -2.91+0.568educ; + 0.033 exper; +0.115 tenure; by OLS, where wage is the hourly wage of a worker measured in dollars,…