O E. studies the statistical dependence of an outcome variable on an exogenous variable (or variables) with the view of estimating the average response of the regressand for given values of predictor variables.
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- "In the regression model InY=b0+b1*InX+u, the coefficient b1 is interpreted as" O the intercept O A covariance O A regressor O An elasticity1.1 Which of the following is NOT a good reason for including a disturbance term in a regression equation?/ A. To allow for random influences on the dependent variable/ B. To allow for errors in the measurement of the dependent variable/ C. It captures omitted determinants of the dependent variable D. To allow for the non-zero mean of the dependent variable/ 1.2 Consider the equation Y = B1 + B2X2 + u. A null hypothesis of H0: B2 = 0 means that/ A. X2 has no effect on the expected value of Y / B. B2 has no effect on the expected value of Y/ C. X2 has no effect on the expected value of B2 / D. Y has no effect on the expected value of X2/ 1.3 The OLS residuals in the multiple regression model/ A. can be calculated by subtracting the fitted values from the actual values / B. are zero because the predicted values are another name for forecasted values / C. are typically the same as the population regression function errors / D. cannot be calculated because there…Which of the following statements about the R-squared statistic of a linear estimation (or linear regression) is true? An R-squared statistic of 0.6 means the variation of the independent (explanatory) variable explains about 60% of the variation of the dependent variable. O An R-squared statistic of 0.6 means 60% the variation of the dependent variable cannot be explained by the variation of the independent (explanatory) variable. O An R-squared statistic of 0.6 means the variation of the dependent variable explains about 60% of the variation of the independent (explanatory) variable.
- If you are interested in investigating themarginal effect of the percentage change of lot size on the changeof house price, then in your linear regression analysis you should apply thelogarithm transformation on: Select one: a. The house price variable. b. Both the house price variable and the lot size variable. c. The lot size variable. d. The bedroom variable. e. Both the bedroom variable and the house price variable.2. Suppose you run a regression with quantity as your dependent variable and advertising as one of your independent variables. The p-value on advertising is .08. The marketing team is arguing that their advertising efforts are impacting sales, but the finance/economics department is arguing that there isn’t evidence that the advertising is impacting sales. What side would you take and why? Note that this question squarely hits the idea that stats is part science/part art...3. Boyoung is writing a paper about the effect of Sunday liquor sales on drunk driving. She has panel data on which states allow liquor to be sold on Sunday in which years and wishes to estimate a difference-in-differences model. She writes the following regression equation: Year DUIRate;; = atate +a? + BTreatmentt +yControl;t + Eit i Which of the following changes does Boyoung need to make? a) She should include a constant in the regression equation. b) She should not include controls because they're already accounted for by fixed effects. c) She should include a time trend instead of time-fixed effects. d) None of the above are changes she needs to make.
- QUESTION 2 Continue to use the example from Question 1. Suppose each product is randomly assigned to a process by a computer program, but some products get reassigned on the factory floor (for practical reasons). Let Z¡ denote the original assignment and X¡ the actual process used to produce i. In a regression of Y¡ on X¡ and Wj, OLS is: d. Potentially biased because W; should not be included b. Potentially biased, but an IV regression using Z¡ as an instrument can be used to obtain a consistent estimator C. Unbiased because the products were randomly assigned in the beginning d. Unbiased as long as Zj is also included as a control variable10. Residual analysis Consider a regression of y on several independent variables, and the resulting predicted values of the dependent variable. The residual for the ith observation Consider a data set for a large sample of professional basketball players. Each observation contains the salary, as well as various performance statistics such as points, rebounds, and assists for each player. Suppose a regression of salary on all performance statistics is run, and the residuals are obtained. The player with the lowest (most negative) resid represents which of the following? (Assume the regression reasonably predicts salaries in most cases.) The most fairly paid player relative to her on-court performance The most overpaid player relative to her on-court performance The highest-paid player, regardless of her on-court performance The most underpaid player relative to her on-court performanceA static model is postulated when a change in the independent variable at time T is believed to have an effect on the dependent variable at period Da change in the independent variable at time t'is believed to have an effect on the dependent varlable for al successive time periods. Ca change in the independent variable at time tis telieved to have an immediate effect on the dependent variable. da change in the independent variable at time T does not have any effect on the dependent variable
- If you included both time and entity fixed effects in the regression model which includes a constant, then: O a. you have perfect multicollinearity. O b. you can exclude the constant and one entity binary variable to estimate the model. you can exclude the constant and one time binary variable to estimate the model. O d. you can exclude one time binary variable and one entity binary variable to estimate the model. O e. All of the above. Of. None of the above.Consider the following regression estimates (FN2) Linear regression belavg abvavg female married _cons b. 1 wage O C. 5 O d. 4 Robust Coef. Std. Err. 3047845 3150202 2820787 -1.063254 .0693348 -2.751963 .9686236 .2612646 6.699098 .2889831 Number of obs F(4, 1255) Prob > F R-squared Root MSE t P>|t| -3.49 0.001 0.22 0.826 -9.76 0.000 3.71 0.000 23.18 0.000 |||||||||| = = .45606 6.132155 = -1.661197 -.5486894 -3.305361 = = [95% Conf. Interval] 1,260 56.35 0.0000 0.1121 4.3987 assume that MLR 1-6 hold. In the regression above, how many coefficients (including the constant) are statistically significant at the 1% level? a. 3 -.4653108 .687359 -2.198565 1.481187 7.266042Can I include the dummy variables in regression equation like Y=a+bX+u where the X is the vector of x variables that contain dummy variables with 5 categories? how should I write my general regression equation with this?