Homework Answers
a. The hypothesis for variable exper will be
Ho: β<or=0
Hi: β> 0
The test statisic will be
t= β'/se(β')
t=0.010510/ 0.001569
t= 6.69
At 5% level of significant, critical t value is 2
6.69>2 implies the result is statistically significant.
b. If the number of dependents goes up by one unit, then ceteris paribus, the value of wages goes up by 1.32%.
c. Confidence interval for the variable educ will be
β'-t(0.05)*se(β') , β'+t(0.05)se(β')
0.0994- 2(0.00786) , 0.0994+2(0.00786)
(0.08368 , 0.11512)
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9. A regression of log(wage) is run on a set of following variables: educ (years of education), e...
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...
QUESTION 1 Consider the following OLS regression line (or sample regression function): wage =-2.10+ 0.50 educ...
QUESTION 1 Consider the following OLS regression line (or sample regression function): wage =-2.10+ 0.50 educ (1), where wage is hourly wage, measured in dollars, and educ years of formal education. According to (1), a person with no education has a predicted hourly wage of [wagehat] dollars. (NOTE: Write your answer in number format, with 2 decimal places of precision level; do not write your answer as a fraction. Add a leading minus sign symbol, a leading zero and trailing...
hi can someone answer part e) f) g) h) with workings olease thanks 2) A cross-sectional study suggests the following wage equation: In(wage,)-α + βι EDUC' + β:FEMALE + β3EXPER, + β4FEMALE EXP...
hi can someone answer part e) f) g) h) with workings olease
thanks
2) A cross-sectional study suggests the following wage equation: In(wage,)-α + βι EDUC' + β:FEMALE + β3EXPER, + β4FEMALE EXPERi + ei Where: In(wage): Natural logarithm of f hourly wage; EDUC: Years of education; EXPER: Years of work experience; FEMALE: Dummy which equals 1 if the respondent is female and 0 otherwise; FEMALE EXPER:Interaction between FEMALE, and EXPER a) What is meant by the population level regression...
3. [40 pts Log of wage rate was regressed on the following explanatory variables: log(wage)0.389 ...
3. [40 pts Log of wage rate was regressed on the following explanatory variables: log(wage)0.389 0.227 female0.082educ0.0056 female educ (0.119) (0.168) (0.008) (0.0131) 0,00n() (0.005) (0.00024) where the female dummy takes on one if the worker is a woman. a) Explain carefully why the interaction term female educ is included in the above regression. (b) Consider two workers: a female worker and a male one with the same amount of experience and tenure. Both of them have no formal education....
Interpreting regression results 2. This is the result of a regression where goals is the dependent...
Interpreting regression results 2. This is the result of a regression where goals is the dependent variable and minutes played is the explanatory variable. a. Write out the simple linear regression equation that predicts goals based on time played using the output displayed here. If the average soccer player played one additional game (90 minutes), how many additional goals would you predict them to have scored? b. Call: 1m(formula goalstimeplayed, data -data) Residuals: Min 1Q Median 3Q Max 5.0572-1.6294 -0.3651...
please be detailed in your response :) thank you! 0 pts) You are given the following estimated equation: In(wage) 0.1279+0.0904educ + 0.041 exper-0 (0.1059) (0.0075) (0.0052) (0.00012) R 0.3003 5...
please be detailed in your response :) thank you!
0 pts) You are given the following estimated equation: In(wage) 0.1279+0.0904educ + 0.041 exper-0 (0.1059) (0.0075) (0.0052) (0.00012) R 0.3003 526 in which: log(wage) log of average hourly wage - educ is the number of years of schooling: - exper is the number of years of experience -exper'=experience"experience The plot of the residuals against the fitted values from the regression above, is provided below: .5 2.5 1.5 Fitted values a. With...
Using CrunchIt, print out and attach the regression output using the Chapter 10 Exercise 01 data set. The independent variable is years of education attained (EDUC); the dependent variable is income (...
Using CrunchIt, print out and attach the regression output using
the Chapter 10 Exercise 01 data set. The independent variable is
years of education attained (EDUC); the dependent variable is
income (INC), which is measured in dollars.
(This is the regression output I got from the data)
a. Based on your regression output, is the coefficient on EDUC
statistically different from 0 at the 1% level? Why?
b. Calculate the predicted value for INC when EDUC=10.
Results Multiple Linear Regression...
How do I interpret the p-values in terms of rejecting or failing to reject H0 at...
How do I interpret the p-values in terms of rejecting or failing
to reject H0 at a 95% confidence level? What does the intercept
column mean in terms of p-value? How does the p-value of the F test
compare and what does it mean? In the simple linear regression I'd
conclude age isn't related to pulmonary disease (what does
intercept p-value mean) but for the multiple regression I'd say age
and height aren't related to pulmonary disease but smoking is...
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...
QUESTION 1 Consider the following OLS regression line (or sample regression function): wage =-2.10+ 0.50 educ (1), where wage is hourly wage, measured in dollars, and educ years of formal education. According to (1), a person with no education has a predicted hourly wage of [wagehat] dollars. (NOTE: Write your answer in number format, with 2 decimal places of precision level; do not write your answer as a fraction. Add a leading minus sign symbol, a leading zero and trailing...
hi can someone answer part e) f) g) h) with workings olease
thanks
2) A cross-sectional study suggests the following wage equation: In(wage,)-α + βι EDUC' + β:FEMALE + β3EXPER, + β4FEMALE EXPERi + ei Where: In(wage): Natural logarithm of f hourly wage; EDUC: Years of education; EXPER: Years of work experience; FEMALE: Dummy which equals 1 if the respondent is female and 0 otherwise; FEMALE EXPER:Interaction between FEMALE, and EXPER a) What is meant by the population level regression...
3. [40 pts Log of wage rate was regressed on the following explanatory variables: log(wage)0.389 0.227 female0.082educ0.0056 female educ (0.119) (0.168) (0.008) (0.0131) 0,00n() (0.005) (0.00024) where the female dummy takes on one if the worker is a woman. a) Explain carefully why the interaction term female educ is included in the above regression. (b) Consider two workers: a female worker and a male one with the same amount of experience and tenure. Both of them have no formal education....
Interpreting regression results 2. This is the result of a regression where goals is the dependent variable and minutes played is the explanatory variable. a. Write out the simple linear regression equation that predicts goals based on time played using the output displayed here. If the average soccer player played one additional game (90 minutes), how many additional goals would you predict them to have scored? b. Call: 1m(formula goalstimeplayed, data -data) Residuals: Min 1Q Median 3Q Max 5.0572-1.6294 -0.3651...
please be detailed in your response :) thank you!
0 pts) You are given the following estimated equation: In(wage) 0.1279+0.0904educ + 0.041 exper-0 (0.1059) (0.0075) (0.0052) (0.00012) R 0.3003 526 in which: log(wage) log of average hourly wage - educ is the number of years of schooling: - exper is the number of years of experience -exper'=experience"experience The plot of the residuals against the fitted values from the regression above, is provided below: .5 2.5 1.5 Fitted values a. With...
Using CrunchIt, print out and attach the regression output using
the Chapter 10 Exercise 01 data set. The independent variable is
years of education attained (EDUC); the dependent variable is
income (INC), which is measured in dollars.
(This is the regression output I got from the data)
a. Based on your regression output, is the coefficient on EDUC
statistically different from 0 at the 1% level? Why?
b. Calculate the predicted value for INC when EDUC=10.
Results Multiple Linear Regression...
How do I interpret the p-values in terms of rejecting or failing
to reject H0 at a 95% confidence level? What does the intercept
column mean in terms of p-value? How does the p-value of the F test
compare and what does it mean? In the simple linear regression I'd
conclude age isn't related to pulmonary disease (what does
intercept p-value mean) but for the multiple regression I'd say age
and height aren't related to pulmonary disease but smoking is...
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