Scenario 38-3. Consider the following statistical model of workers salaries and years of job tenure, where e; stands for the random error: SALARY = 0 + ẞ1 × YEARS; + ej. Refer to Scenario 38-3. This model is called 20000 a singular regression. a multiple regression. an instrumental regression. a biased regression.
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- 10. 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 performanceConsider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary=11,681.31+3418.97(Education)+1194.78(Experience) Suppose two employees at the company have been working there for five years. One has a bachelor's degree (8 years of education) and one has a master's degree (10 years of education). How much more money would we expect the employee with a master's degree to make?Consider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary=10,160.10+3147.75(Education)+1230.34(Experience) Suppose an employee with 8 years of education has been with the company for 20 years (note that education years are the number of years after 8th grade). According to this model, what is his estimated annual salary?
- Consider the following ANOVA table for a multiple regression model relating housing prices (in thousands of dollars) to the number of bedrooms in the house and the size of the lot on which the house was built (in square feet). There were 77 total observations. Estimated Price = 27,756.55 + 2431.81 (Bedrooms) + 0.2732 (Lot Size) ANOVA df SS MS F Significance F Regression 2 357,061.4229 178,530.7115 21.0765 5.6943E-08 Residual 74 626,824.0170 Total 76 983,885.4399 8470.5948 Compute the adjusted coefficient of determination for this regression model. Round your answer to four decimal places.Using data from a random sample of 1000 working adults, we obtain the following estimated regression to study the effect of experience (exper) on log of wage (log(wage)). log(wage) = 5.423 -0.034ezper +0.009ezper² + 0.082educ + 0.157male What other regression do you need to run to test the null hypothesis that, holding other factors fixed, experience has no effect on log(wage)? Explain what test you would perform.Consider the following ANOVA table for a multiple regression model relating housing prices (in thousands of dollars) to the number of bedrooms in the house and the size of the lot on which the house was built (in square feet). There were 90 total observations. Estimated Price = 24,838.74 + 2339.88 (Bedrooms) + 0.2685 (Lot Size) ANOVA df SS MS F Regression 2 382,993.4921 Residual 87 627,839.9910 Significance F 191,496.7461 26.5358 1.0066E-09 7216.5516 Total 89 1,010,833.4831 Compute the adjusted coefficient of determination for this regression model. Round your answer to four decimal places.
- What are the measures of fit that are commonly used for multiple regressions? How can an adjusted R2 take on negative values?Define Interpretation of coefficients in polynomial regression models?13. Collinearity in a multiple regression analysis Suppose you want to examine the effects of a training program on future earnings using the following model: earn98= 4.64 +2.376train +0.371earn96 +0.366educ- 1.86 age +2.534 married (1.14) (0.43) (0.016) (0.062) (0.013) (0.4) where earn 98- 1998 earnings, in thousands of dollars train -1 if the individual participated in the training program, and =0 otherwise earn 96- 1996 earnings, in thousands of dollars educ years of education age = age, in years married-1 if the individual is married, and -0 otherwise Suppose that there is a high degree of correlation (but not perfect) between earnings in 1996, education, age, and marital status. True or False: We should be concerned about this high degree of correlation because it affects our ability to reliably estimate the impact of the training program on 1998 earnings, T. True False
- Consider the following regression model: wage-Bi+Bamalerpumalexedu Buedutu, where wage is the hourly wage measured in dollars: male is a dummy variable for males edu is the years of education: maleedu is the interaction of male and edu variables. The parameter estimates for B parameters are P-1.27: B1.29: Br-0,16: Be-0.82. What is the predicted marginal effect of years of education for males?Discuss and explain each of the assumptions of the simple linear regression model.5 1 point Scenario 38-4. Consider the following OLS model estimating the effect of job tenure and IQ on worker salary: SALARY = =-49.11.86 × YEARS +0.57 × IQi. (29.1) (1.41) (0.35) Refer to Scenario 38-4. This model is an example of a natural experiment. a singular regression. a control experiment. a multiple regression.