Consider the following regression estimates (ID6)SourceModelResidualTotalwageeducexperexper2_consinverse u-shapedSSC. lineard. exponential1927.876735232.537567160.41429df-.0046123-3.96489642.6255763522 10.0240183525Coef. Std. Err..5953429.0530251.268287 .0368969.000822.7521526MS13.6388844Number of obsF(3, 522)Prob > FR-squaredAdj R-squaredRoot MSEt P>|t|0.00011.237.27 0.000-5.61 0.000-5.27 0.000=.4911741.1958023-.006227-5.442508===52664.110.00000.26920.26503.1661[95% Conf. Interval].6995118.3407717where wage is hourly wage in US$, educ is years of education, exper is years of work experience and exper2 is experience squared (exper* exper).According to these estimates, what is the functional form of the relationship between experience and predicted wage when we hold education constant?a. u-shapedb.i-.0029975-2.487272

From the given regression results, the coefficient of the constant term is -3.96489, the coefficient…

Consider the following regression estimates (ID6) Source Model Residual Total wage educ exper exper2 _cons SS a.u-shaped O b.inverse u-shaped OC. linear O d. exponential 1927.87673 5232.53756 7160.41429 df .5953429 .268287 -.0046123 -3.96489 3 522 Coef. Std. Err. MS 525 13.6388844 0530251 0368969 .000822 .7521526 642.625576 10.0240183 Number of obs F(3, 522) Prob > F R-squared t P>|t| 11.23 0.000 0.000 7.27 -5.61 0.000 -5.27 0.000 = Adj R-squared = Root MSE = .4911741 .1958023 -.006227 -5.442508 = 526 64.11 0.0000 0.2692 0.2650 3.1661 [95% Conf. Interval] .6995118 .3407717 -.0029975 -2.487272 where wage is hourly wage in US$, educ is years of education, exper is years of work experience and exper2 is experience squared (exper* exper). According to these estimates, what is the functional form of the relationship between experience and predicted wage when we hold education constant?

Consider the following regression estimates (ID6) Source Model Residual Total wage educ exper exper2 _cons SS a.u-shaped O b.inverse u-shaped OC. linear O d. exponential 1927.87673 5232.53756 7160.41429 df .5953429 .268287 -.0046123 -3.96489 3 522 Coef. Std. Err. MS 525 13.6388844 0530251 0368969 .000822 .7521526 642.625576 10.0240183 Number of obs F(3, 522) Prob > F R-squared t P>|t| 11.23 0.000 0.000 7.27 -5.61 0.000 -5.27 0.000 = Adj R-squared = Root MSE = .4911741 .1958023 -.006227 -5.442508 = 526 64.11 0.0000 0.2692 0.2650 3.1661 [95% Conf. Interval] .6995118 .3407717 -.0029975 -2.487272 where wage is hourly wage in US$, educ is years of education, exper is years of work experience and exper2 is experience squared (exper* exper). According to these estimates, what is the functional form of the relationship between experience and predicted wage when we hold education constant?

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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u have 180 minutes to complete the exam. Please use your time efficiently and read the questions carefully. ere are 30 questions, the exam is marked out of 100 marks and all questions are equally marked. Question 16 1 pts A regression analysis between sales (Y in $1,000) and advertising (X in dollars) resulted in the following equation Y = 30,000 + 4 X. The literal interpretation of bo in the regression is: O Sales when advertising is at its average is bO O The average sales is b0 O The average sales is b0 when no money is spent on advertising Next « Previous 52°F ASUS VivoBook
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Suppose you have the following wage and education data for a sample with 3 observations: obs. no. 1 2 3 wage educ ($/hr) (years) 10.73 11 13.44 12 18.05 16 Calculate the coefficient for the regression of wage on education through the origin (i.e. ₁ in the model wage = B₁ educ). Express your answer out to B1 two (2) decimal points.
Question 1) Which of the following can cause the usual OLS t statistics to be invalid (that is, not to have t distribu- tions under HO)? (i) Heteroskedasticity. (ii) A sample correlation coefficient of 95 between two independent variables that are in the model. (iii) Omitting an important explanatory variable Question 2) Which of the following can cause OLS estimators to be biased? (i) Heteroskedasticity. (ii) Omitting an important variable. (iii) A sample correlation coefficient of .95 between two independent variables both included in the model.
Question 18
Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.7339 R Square 0.5386 Adjusted R Square 0.5185 Standard Error 2137.5200 Observations 49 ANOVA SS df Regression 2 245,370,679.3850 122,685,339.6925 26.8517 MS F Significance F 1.9E-08 Total Residual 46 210,173,612.6150 48 455,544,292.0000 4,568,991.5786 Coefficients Standard Error Intercept Education (Years) 14290.37278 2350.8671 2,528.5819 338.1140 Experience (Years) 829.3167 392.5627 t Stat P-value 5.6515 0.000000961 9200.6014 6.9529 0.000000011 2.1126 0.040093183 Lower 95 % Upper 95% 19,380.1442 1670.2789 3031.4553 39.129 1619.5044 Step 2 of 2: How much would you expect your salary to increase if you had one more year of education?
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Suppose the researcher considers the following model : Wage = Bo+B,Female + u, and runs OLS, using wage data on 250 randomly selected male workers and 280 female workers. The researcher has obtained the estimated equation as Wage = 15 (1.00) 3 Female, R = 0.05. (0.5) In the equation, Wage is measured in dollars per hour, Female is a binary variable that is equal to 1 if the person is a female and O if the person is a male. The numbers in the parentheses are the standard errors of the coefficients. Which Statement is NOT correct? The coefficient of Female, -3.00, is statistically significant at 5%. O The p-value for the test that Ho: B = 0, H : B 0 is less than 0.05. This regression may suffer omitted variable bias. R Since insignificant. is too low, the wage difference between men and women is The researcher can increase R- by including more regressors in the model.
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