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.

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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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.
The following data from a random sample of individuals that are 25 years old. Note, wage is the hourly earnings in US dollars and educ is years of education. i 1 2 3 4 wage 12.18 12.18 20.09 7.39 educ 13 11 16 12 Consider the following model: In(wage)-Bo+B₁educ+u. Calculate the estimate of B₁. Round your answer to two decimal places.
What do you mean by Confidence Sets for Multiple Coefficients? How to construct a confidence set for two or more regression coefficients?
This exercise refers to the drunk driving panel data regression summarized below. Regression Analysis of the Effect of Drunk Driving Laws on Traffic Deaths Dependent variable: traffic fatility rate (deaths per 10,000). Regressor Beer tax Drinking age 18 Drinking age 19 Drinking age 20 Drinking age Mandatory jail or community service? Average vehicle miles per driver Unemployment rate Real income per capita (logarithm) Years State Effects? Time effects? (1) 0.41* (0.056) 1982-88 no no (2) (3) (4) -0.62** -0.76*** -0.42 (0.39) (0.33) (0.38) 0.023 (0.078) -0.014 (0.084) -0.023 -0.075 (0.053) (0.064) 0.034 -0.109*** (0.058) (0.058) no yes yes no yes Clustered standard errors? yes yes F-Statistics and p-Values Testing Exclusion of Groups of Variables Time effects=0 (5) -0.76** (0.36) 0.041 0.083 (0.111) (0.115) 0.006 0.015 (0.005) (0.011) -0.068* (0.016) 1.66* (0.66) 1982-88 1982-88 1982-88 1982-88 yes yes yes yes yes yes (6) -0.46 (0.39) -0.004 (0.022) 0.043 (0.101) 0.007 (0.005) -0.064*…
INSUR A life insurance company wishes to examine the relationship between the amount of life insurance held by a family and family income. From a random sample of households, the company collected the accompanying data. The data are in units of thousands of dollars. INCOME 97 38 141 29 y = x = Let INSUR 280 75 INCOME 303 81 453 137 The numerator of the slope coefficient formula for the estimated regression equation is: 357 77 a 400,201.10 199 43 b 392,354.02 251 53 384,660.80 807 184 d 377,118.44 147 45 272 70 537 128 527 117 245 55 483 116 673 204 194 46 154 51 163 48 280 69 507 140 464 136 321 71 873 206 476 144 574 111 251 65 497 130 826 171 133 32 259 82 281 73 446 146 332 77 219 48 208 55 180 48 169 42 273 69 502 127 547 126 281 80 428 143 370 77 221 49 214 51
please answer in text form and in proper format answer with must explanation , calculation for each part and steps clearly
b. The following model is a simplified version of the multiple regression model used by BEST Econometrics Group to study the trade off between time spent sleeping and working to look at other factors affecting sleep: sleep Bo + B₁totwrk + B₂educ + Page + μ₁ where sleep and totwork (total work) are measure in minutes per week and educ and age are measured in years. (i) If adults trade sleep for work, what is the sign of B₁? (ii) What signs do you think ₂ and 3 will have? (iii) Using the data in SLEEP75.RAW, the estimated equation is sleep = 3,638.25-.148totwrk-11.13educ - 2.20age, n = 706, R² = .113 If someone works five hours per week, by how many minutes is sleep predicted to fall? Is this a large tradeoff? (iv) Discuss the sign and magnitude of the estimated coefficient on educ (v) Would you say totwrk, educ, and age explain much of the variation in sleep? (vi) What other factors might affect the time spent sleeping? (vii) Are these likely to be correlated with totwrk?
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.
1. Suppose output (Q) is related to labor (L) and capital (K) in the following nonlinear way: Q = albKc When taking log to this equation, it is transformed into a linear LnQ = Ina + b In(L) + c Ln (K) One hundred twenty-three observations are used to obtain the following regression results: Dependant Variable: Observations: Variable Intercept L K Q 123 5.5215 Parameter Standard Estimate error 0.650 R-square 0.350 0.7547 0.9750 0.2950 0.1450 F-ratio 184.56 t-ratio 5.66 2.20 2.41 p-value on F 0.00001 p-value 0.0001 0.0295 0.0173 a. Write the regression equation based on the output either in the transformed linear form or the original non-linear form.
SSE SST X₂ The estimated regression equation for these data is ŷ = 7.6 +0.9x. Compute SSE, SST, and SSR (to 1 decimal). SSR 2 7 6 18 9 13 20 9 26 23 What percentage of the total sum of squares can be accounted for by the estimated regression equation (to 1 decimal)? % What is the value of the sample correlation coefficient (to 3 decimals)?
How would you describe this data?
number 1 please