When the regression line passes through the origin then: O The intercept is zero. The regression coefficient is zero. O The correlation is zero. O The association is zero. O All of the above.
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- 1. You are interested the causal effect of X on Y, B1. Suppose that X, and X2 are uncorrelated. You estimate B1 by regressing Y onto X1 (so that X2 is not included in the regression). Does this estimator suffer from omitted variable bias due to the exclusion of X2? (a) Yes (b) No (c) Maybe 2. Omitted variable bias violates which of the following assumptions: (a) The conditional distribution of u, given X1i X2i, ...Xki has a mean of zero (b) (Xi, X2i...Y;), i = 1, ., n are independently and identically distributed (c) Heteroskedasticity (d) Perfect multicollinearityThe 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.If we run a regression where y (bankruptcy) = f (factors potentially predicting bankruptcy), what is the dependent variable?
- 14. The regression R² is a measure of: a. whether or not X causes Y. b. the goodness of fit of your regression line. c. whether or not ESS> TSS. d. the square root of the correlation coefficient. 15. The OLS residuals, û, are defined as follows: a. Ŷ-Bo-B₁X; R b. Y – Bo – PıX c. (Y-Y)² d. Y₁ - Ŷ, 16. There exist a relationship test scores and the student-teacher ratio can be modeled as a linear function with an intercept of 798.9 and a slope of -3.28. A increase in the student- teacher ratio by 2 will: a. Reduces test scores by 3.28 on average b. Results in a test score of 798.9 c. Reduces test scores by 6.56 on average d. Reduces test scores by 6.56 for every school district(2)What would the consequence be for a regression model if theerrors were not homoscedastic?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,…
- What is a linear regression model? What is measured by the coefficients ofa linear regression model? What is the ordinary least squares estimator?If a regression equation contains an irrelevant variable, the parameter estimates will be Select one: a. Consistent and unbiased but inefficient b. Consistent and asymptotically efficient but biased c. Consistent, unbiased and efficient. d. InconsistentA company wants to use regression analysis to forecast the demand for the next quarter.In such a regression model, demand would be the independent variable. True or false?a. Trueb. False
- 1. Can you estimate a regression model for Y and X? 2. What are the assumptions of the model in 1? 3. Do you think that this model is accurate? 4. What are the related hypotheses of 37 5. Discuss and interpret your results. Y 4 4 -1 -2 11 -2 -4 0. 3. 7. 12 -3 -6 7. 144. From the regression output, report the coefficients, standard errors, t-statistics, probability and R-squared (report the results in a table). 5. Re-write the specified model in (a) with values from the regression results and interpret the coefficients.2. In a multiple regression of y on x1, x2, and x3, including additional variables on the right-hand side of the model always increases R2. (True/False).