Consider 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=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?

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

See similar textbooks

Similar questions

Consider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary=11,722.40+3182.56(Education)+1202.44(Experience)Estimated Salary=11,722.40+3182.56(Education)+1202.44(Experience) Suppose an employee with 66 years of education has been with the company for 33 years (note that education years are the number of years after 8th8th grade). According to this model, what is his estimated annual salary?
In this question we look at the relation between the logarithm of weakly earnings and years of education. Using data from the national longitudinal study of youth, we find the following results for a regression of log weekly earnings and years of education, experience, experience squared and an intercept: Log (earnings) = 4.016 + 0.092 . educi + 0.079 .expei + 0.002 . experi 2 ( 0.222) ( 0.008) ( 0.025) (0.001) c). Labour economist studying the relation between education and earnings are often concerned about what they call “ability bias”. Suppose that individuals differ in ability, and that the correct specification of the regression function is one that includes ability: log ( earnings )I = β1 + β 2 ⋅ educI + β 3 ⋅ experI − β 4 ⋅ experI2 + β5 ⋅ abilityI + εI . In this regression, what do you expect the sign of β5 (the coefficient on ability) to be?
The table below shows the profit, P(x), in dollars, from selling x items. 1 2 3 6 9 14 P(x) 66.1 82.4 88.9 49.6 -77.9 -486.4 Using your calculator to do a quadratic regression, express the profit as a function of the number of items sold. P(x) = Round all numbers to 1 decimal place. Using your quadratic regression, estimate the profit from selling 5 items? Select an answer v Round to 2 decimal places. Using your quadratic regression, estimate the number of items that need to be sold to make a profit of $-25.6 ? Select an answer
If we run a regression where y (bankruptcy) = f (factors potentially predicting bankruptcy), what is the dependent variable?
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?
QUESTION 17 I am trying to figure out how to measure an athlete's productivity. So, I have run a linear regression of a NBA player's salary (dependent variable) on a player's statistics including average points, assists, rebounds per game, and turnovers per game (the independent variables). The final model is: Salary = 1,000,000 * Points per game + 50,000 * Assists per game + 20,000 * Rebounds per game - 30,000 * Turnovers per game %3! Last year, Lebron James averaged 25 points per game, 8 assists per game, 8 rebounds per game and 4 turnovers per game. What is Lebron's predicted salary?
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
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...
7 Suppose the following model describes the relationship between annual salary (salary) and the number of previous years of labor market experience (exper): log(salary) = 10.6 + .027 exper. (i) What is salary when exper = 0? When exper = 5? (Hint: You will need to exponentiate.) (ii) Use equation (A.28) to approximate the percentage increase in salary when exper increases by five years. (iii) Use the results of part (i) to compute the exact percentage difference in salary when exper = 5 and еxper = 0. Comment on how this compares with the approximation in part (ii).
Suppose you run the following regression: outcome=alpha0 + alpha1*female + alpha2*married + epsilon. You know that female equals 1 for females and 0 otherwise. You know that married equals 1 if the person is married and 0 otherwise. What is the estimated outcome for non-married females?
Suppose you run the following regression: outcome=alpha0 + alpha1*female + alpha2*married + epsilon. You know that female equals 1 for females and 0 otherwise. You know that married equals 1 if the person is married and 0 otherwise. What is the estimated outcome for married females?
How do you interpret the R-squared obtained from running this regression?