Refer to the the table of estimated regressions below, computed using data for 1998 from the CPS, to answer the following question. The data set consists of informationon 4000 full-time full-year workers. The highest educational achievement of each worker was either a high school diploma or a bachelor's degree. The worker's ageranged from 25 to 34 years. The data set also contained information on the region of the country where the person lived, marital status, and number of children West isthe omitted region. A detailed description of the variables used in the data set is available hereResults of Regressions of Average Hourly Earnings on Gender and Education BinaryVariables and Other Characteristics Using 1998 Data from the Curren Population Survey.Dependent variable: average hourly earnings (AHE).(3)5.33Regressor(1)(2)5.475.31College (X1)(0.25)- 2.23(0.24)(0.25)(0.25)-2.53-2.23Female (X2)(0.24)(0.24)0.180.18Age (X3)(0.04)(0.04)0.54Northeast (XA)(0.22)0.64Midwest (X5)(0.26)-0.26South (X6)(0.28)12.814.983.19Intercept(0.15)(1.03)(1.09)Click to select your answer.P.DOLL) (U.04)(U.04)Northeast (X4)0.54(0.22)0.64Midwest (X5)(0.26)South (X6)-0.26(0.28)Intercept12.814.983.19(0 15)(1.03)(1.09)Summary Statistics and Joint TestsF-statistic for regional effects 06.04SER6.276.226.21R20.1340.1180.174469946994699Note: The numbers in parentheses below each estimated coefficient are the estimatedstandard errors.Evaluate the following statement: "In all of the regressions, the coefficient on Female is negative, large, and statistically significant. This provides strong statisticevidence of gender discrimination in the U.S. labor market."%3DHint Consider two identical workers that differ only in gender, and think about the causal relationship between earnings and gender.O A. True.O B. False.Click to select your answer.WD&LL

According to the data given in the question, Average variable earning for female:- in regression 1=…

Reler to thể thé table of estimated regressions below, computed using data for 1998 from the CPS, to answer the following question The data set consists of information on 4000 full-time full-year workers. The highest educational achievement of each worker was either a high school diploma or a bachelor's degree The worker's age ranged from 25 to 34 years The data set also contained information on the region of the country where the person lived, marital status, and number of children West is the omitted region A detailed description of the variables used in the data set is available here Results of Regressions of Average Hourly Earnings on Gender and Education Binary Variables and Other Characteristics Using 1998 Data from the Curren Population Survey Dependent variable: average hourly earnings (AHE). (3) (2) 5.31 (0.25) - 2.23 (0.24) 0.18 Regressor (1) 5.47 5.33 College (X) (0.25) - 2.53 (0.24) (0.25) -2.23 (0.24) 0.18 (0.04) Female (X2) Age (X3) (0.04) 0.54 (0.22) 064 (0.26) -0 26 (0.28) 3 19 (1 09) Northeast (X) Midwest (X5) South (Xg) 12.81 4.98 Intercept (0.15) (1.03) Click to select your answer.

Reler to thể thé table of estimated regressions below, computed using data for 1998 from the CPS, to answer the following question The data set consists of information on 4000 full-time full-year workers. The highest educational achievement of each worker was either a high school diploma or a bachelor's degree The worker's age ranged from 25 to 34 years The data set also contained information on the region of the country where the person lived, marital status, and number of children West is the omitted region A detailed description of the variables used in the data set is available here Results of Regressions of Average Hourly Earnings on Gender and Education Binary Variables and Other Characteristics Using 1998 Data from the Curren Population Survey Dependent variable: average hourly earnings (AHE). (3) (2) 5.31 (0.25) - 2.23 (0.24) 0.18 Regressor (1) 5.47 5.33 College (X) (0.25) - 2.53 (0.24) (0.25) -2.23 (0.24) 0.18 (0.04) Female (X2) Age (X3) (0.04) 0.54 (0.22) 064 (0.26) -0 26 (0.28) 3 19 (1 09) Northeast (X) Midwest (X5) South (Xg) 12.81 4.98 Intercept (0.15) (1.03) Click to select your answer.

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter7: Production Economics

Section: Chapter Questions

Problem 1.3CE

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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.
Oil Recovery A U.S. state's Bureau of Economic Geology published a study on the economic impact of using carbon dioxide enhanced oil recovery (EOR) technology to extract additional oil from fields that have reached the end of their conventional economic life. The following table gives the approximate number of jobs for the citizens that would be created at various levels of recovery. Percent Recovery (%) 20 40 80 100 Jobs Created (Millions) 6 6 12 18 Find the regression line. j(r) = Use the regression line to estimate the number of jobs (in millions) that would be created at a recovery level of 50%. million jobs
First regression: wage = B1 + B2educ + B3 exper + B4(educ x exper) + e Second regression: wage = B1 + Bzeduc + B3exper + e where wage denotes hourly wages. We estimate both regressions in R and obtain the output: ## Coefficients: ETTI ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 1.50 1.50 0.3173 ## (Intercept) 2.0 1.0 0.0455 * ## educ 0.50 0.50 1 0.3173 ## educ 0.5 0.5 1 0.3173 ## exper 1.50 0.50 0.0027 ** ## exper 2.0 0.5 4 6.33e-05 *** ## educ:exper 0.10 0.05 0.0455 * ## ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 '' 1 ## Signif. codes: O '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 where in the left panel, the row educ:exper refers to the estimates for B4 in the first regression. Suppose we are interested in testing the null hypothesis that the marginal returns to education do not depend on experience using an F-test. Recall the F-statistic is given by ((SSER - SSE)/J)/(SSEu/(n – K)). Which of…
Describe the “overall” regression F-statistic & F-statistic when q=1?
A finance manager employed by an automobile dealership believes that the number of cars sold in his local market can be predicted by the interest rate charged for a loan. Interest Rate (%) Number of Cars Sold (100s) 3 5 10 7 8 2 The finance manager performed a regression analysis of the number of cars sold and interest rates using the sample of data above. Shown below is a portion of the regression output. Regression Statistics Multiple R0.998868 R2 0.997738 Coefficient |14.88462 Interest Rate -1.61538 Intercept 1. Are there factors other than interest rate charged for a loan that the finance manager should consider in predicting future car sales? 2. Is interest rate charged for a loan the most important factor to be considered in predicting future car sales? Explain your reasoning.The dealership's vice- president of marketing has requested a sales forecast at the prevailing interest rate of 7%. 3. As finance manager, what reasons would you convey to the vice-president in recommending…
SoCal Edison reported the following data for operating revenue and net income for 2001 through 2005. Year Operating Revenue (Millions), X Net Income (Millions), Y 2001 2270 96.9 2002 1482 89.1 2003 2138 103.9 2004 2260 81.6 2005 2600 78.1 Determine the least-squares regression line and interpret its slope. Estimate the net income if the operating revenue figure is $2500 million.
Question 9 5 pts For a multiple regression model, SSR = 555 and SSE = 661. What is the multiple coefficient of determination? (Enter the number below.)
Using the regression results in column (1):a. Is the college–high school earnings difference estimated from thisregression statistically significant at the 5% level? Construct a 95%confidence interval of the difference.b. Is the male–female earnings difference estimated from this regressionstatistically significant at the 5% level? Construct a 95% confidenceinterval for the difference.
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
Results of Regressions of Average Hourly Earnings on Gender and Education Binary Variables and Other Characteristics Using Data from the Current Population Survey Dependent variable: average hourly earnings (AHE). Regressor (1) (2) (3) 5.59 5.55 College (X,) 5.57 Female (X2) -2.69 -2.67 -2.67 0.30 0.30 Age (X3) 0.70 Northeast (X4) 0.61 Midwest (Xs) -0.28 South (X) Intercept 12.94 4.49 3.83 Summary Statistics SER 6.40 6.34 6.33 0.180 0.194 0.198 0.193 ok97 0.180 4100 4100 4100 Using the regression results in column (2): On average, a worker eans $ per hour for each year that he or she ages.
A finance manager employed by an automobile dealership believes that the number of cars sold in his local market can be predicted by the interest rate charged for a loan. Interest Rate (%) Number of Cars Sold (100s) 3 10 5 7 6 5 8 2 The finance manager performed a regression analysis of the number of cars sold and interest rates using the sample of data above. Shown below is a portion of the regression output. Regression Statistics Multiple R 0.998868 R2 0.997738 Coefficient Intercept 14.88462 Interest Rate -1.61538 2. Is interest rate charged for a loan the most important factor to be considered in predicting future car sales? Explain reasoning.The dealership's vice-president of marketing has requested a sales forecast at the prevailing interest rate of 7%. 3. As finance manager, what reasons would you convey to the vice-president in recommending this forecasting model? 4. Is the prediction of car sales…
A finance manager employed by an automobile dealership believes that the number of cars sold in his local market can be predicted by the interest rate charged for a loan. Interest Rate (%) Number of Cars Sold (100s) 3 10 5 7 6 5 8 2 The finance manager performed a regression analysis of the number of cars sold and interest rates using the sample of data above. Shown below is a portion of the regression output. Regression Statistics Multiple R 0.998868 R2 0.997738 Coefficient Intercept 14.88462 Interest Rate -1.61538 1. Are there factors other than interest rate charged for a loan that the finance manager should consider in predicting future car sales? 2. Is interest rate charged for a loan the most important factor to be considered in predicting future car sales? Explain reasoning.The dealership's vice-president of marketing has requested a sales forecast at the prevailing interest rate of 7%. 3. As…