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QUESTION 1 Consider the following OLS regression line (or sample regression function): wage =-2.10+ 0.50 educ...
Consider the relationship between hourly wage rate and education attainment. A random sample of 21 male...
Consider the relationship between hourly wage rate and education attainment. A random sample of 21 male workers was collected to estimate the following model Y; = Bo + B1X; + uj, for i = 1,..., 21. Here, Y; is the logarithm of hourly wage rate, log(wage), for the i-th worker. Xi is the education level, husedu, of the i-th worker, which is measured as the years of schooling, and uị is the error term for the i-th worker. The ordinary...
educ wage 17 8.19 12 18.42 12 23.38 12 10.24 12 20.47 12 12.90 12 31.03...
educ
wage
17
8.19
12
18.42
12
23.38
12
10.24
12
20.47
12
12.90
12
31.03
17
14.33
14
12.28
10
17.77
16
15.56
12
13.31
16
15.76
11
9.62
12
14.08
13
17.93
11
29.68
12
24.24
11
17.07
16
33.74
14
36.85
12
18.42
16
10.85
18
28.37
9
8.72
13
8.27
12
16.83
14
24.07
12
11.59
The data set wages educ (click the link to open the Excel file) contains data from a survey on hourly...
Could I please get an answer to this problem? Thank you. 4. Consider the following regression...
Could I please get an answer to this problem? Thank
you.
4. Consider the following regression model of log(rage) log(wage)-As + &female + βί educ + u, where wage is hourly wage, female is a dummy variable indicating gender (1 for women and 0 for men), and educ is the number of years of education. Augment the model to allow the return to education to differ by gender
Drop down: zero or nonzero / holds of does not hold. True or False: Assuming the...
Drop down: zero or nonzero / holds of does not hold.
True or False: Assuming the model is linear in parameters, and
you obtain a random sample of observations with varying values of
education, the simple OLS slope and intercept estimates will be
unbiased.
13. Assumption SLR.4 (Zero Conditional Mean) One crucial assumption in the simple linear regression model is that the error term u has a mean of zero, conditional on the value of the explanatory variable Suppose you...
Question 1 Consider the simple regression model (only one covariate): y= BoB1 u Let B1 be the OLS estimator of B1. a) W...
Question 1 Consider the simple regression model (only one covariate): y= BoB1 u Let B1 be the OLS estimator of B1. a) What are the six assumptions needed for B1 to be unbiased, have a simple expression for its variance, and have normal distribution? (3 points) b) Under Assumptions 1-6, derive the distribution of B1 conditional on x\,..., xn. (3 points) In lecture we described how to test the null hypothesis B1 bo against the alternative hypothesis B1 bo, where...
can I please get an answer to this problem? 3. Consider the following simple model for...
can I please get an answer to this problem?
3. Consider the following simple model for wages log(wage-Ao + β,educ + u, where wage is the hourly wage and educ is the number of years of education. Define a. gender dummy female equaling 1 for women and 0 for men. Write out an enhanced model to account for possible wage differences between women and men due to the basic wage level and due to amount of education.
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer)....
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer)....
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer)....
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
Consider the relationship between hourly wage rate and education attainment. A random sample of 21 male workers was collected to estimate the following model Y; = Bo + B1X; + uj, for i = 1,..., 21. Here, Y; is the logarithm of hourly wage rate, log(wage), for the i-th worker. Xi is the education level, husedu, of the i-th worker, which is measured as the years of schooling, and uị is the error term for the i-th worker. The ordinary...
educ
wage
17
8.19
12
18.42
12
23.38
12
10.24
12
20.47
12
12.90
12
31.03
17
14.33
14
12.28
10
17.77
16
15.56
12
13.31
16
15.76
11
9.62
12
14.08
13
17.93
11
29.68
12
24.24
11
17.07
16
33.74
14
36.85
12
18.42
16
10.85
18
28.37
9
8.72
13
8.27
12
16.83
14
24.07
12
11.59
The data set wages educ (click the link to open the Excel file) contains data from a survey on hourly...
Could I please get an answer to this problem? Thank
you.
4. Consider the following regression model of log(rage) log(wage)-As + &female + βί educ + u, where wage is hourly wage, female is a dummy variable indicating gender (1 for women and 0 for men), and educ is the number of years of education. Augment the model to allow the return to education to differ by gender
Drop down: zero or nonzero / holds of does not hold.
True or False: Assuming the model is linear in parameters, and
you obtain a random sample of observations with varying values of
education, the simple OLS slope and intercept estimates will be
unbiased.
13. Assumption SLR.4 (Zero Conditional Mean) One crucial assumption in the simple linear regression model is that the error term u has a mean of zero, conditional on the value of the explanatory variable Suppose you...
Question 1 Consider the simple regression model (only one covariate): y= BoB1 u Let B1 be the OLS estimator of B1. a) What are the six assumptions needed for B1 to be unbiased, have a simple expression for its variance, and have normal distribution? (3 points) b) Under Assumptions 1-6, derive the distribution of B1 conditional on x\,..., xn. (3 points) In lecture we described how to test the null hypothesis B1 bo against the alternative hypothesis B1 bo, where...
can I please get an answer to this problem?
3. Consider the following simple model for wages log(wage-Ao + β,educ + u, where wage is the hourly wage and educ is the number of years of education. Define a. gender dummy female equaling 1 for women and 0 for men. Write out an enhanced model to account for possible wage differences between women and men due to the basic wage level and due to amount of education.
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
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