Question

The following multiple regression model uses wage, which is hourly earnings in dollars, as dependent variable,...

The following multiple regression model uses wage, which is hourly earnings in dollars, as dependent variable, IQ as in IQ test scores as independent variables to run a regression as follows. STATA commands and outputs are given on the STATA output page. Answer the following questions. (23 points)

wage= β0 +β1 IQ + u

  1. According to the STATA output, what are the minimum and the maximum for education years (denoted as educ) in the sample? (4 points)
  2. Write down the estimation result in an equation form based on the STATA output. Make sure to include all estimated coefficients, number of observations and R-squared. (8 points)
  3. What does the value of R-squared mean? (3 points)
  4. Interpret the coefficient in front of IQ. (5 points)
  5. What is the predicted change in wage if IQ increases by 10 points holding other factors fixed? (3 points)

STATA output

Math   Advanced-Math   
0 0
Add a commentTranscribed image text
Next >

Homework Answers

Answer #1

Answer:-

Ques A )

Min for education years = 9

Max for education years = 18

Ques b ) Equation is as follows :

Wage = 116.9916 + 8.30306 * IQ

Where R squared = 0.0955

Number of observation = 935

Ques C ) R squared = 0.0955

This means that the model explains 9.55% variations in the wages.

Part D ) Coefficient of IQ = 8.30306

1 point increase in IQ will increase wages by 8.30306 units.

Part E ) If IQ increases by 10 points, wages will increase by 10 * 8.3036 = 83.036 units

Please like it if you have any issue mention in comment

0 0
Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Log In

Sign Up

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
Using data from 50 workers, a researcher estimates Wage = β0 + β1 Education + β2Experience...
Using data from 50 workers, a researcher estimates Wage = β0 + β1 Education + β2Experience +β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. A portion of the regression results is shown in the following table. Coefficients Standard Error t Stat p-Value   Intercept 7.42          4.14           1.79           0.0797           Education 1.53          0.39          ...
Using data from 50 workers, a researcher estimates Wage = β0 + β1 Education + β2...
Using data from 50 workers, a researcher estimates Wage = β0 + β1 Education + β2 Experience +β3 Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. A portion of the regression results is shown in the following table. Coefficients Standard Error t Stat p-value   Intercept 7.58 4.42 1.71    0.0931      Education 1.68 0.37    4.54...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience +...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience + β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 6.03 3.70 1.63 0.1100 Education 1.25 0.34 3.68 0.0006 Experience 0.51 0.15 3.40 0.0014 Age −0.03...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience +...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience + β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 8.36 4.05 2.06 0.0447 Education 1.96 0.35 5.60 0.0000 Experience 0.43 0.17 2.53 0.0149 Age −0.02...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience +...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience + β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 7.56 4.35 1.74 0.0889 Education 1.53 0.38 4.03 0.0002 Experience 0.42 0.12 3.50 0.0010 Age −0.04...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience +...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience + β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 8.23 4.40 1.87 0.0678 Education 1.23 0.38 3.24 0.0022 Experience 0.53 0.18 2.94 0.0051 Age −0.08...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience +...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience + β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 6.69 4.23 1.58 0.1206 Education 1.13 0.34 3.32 0.0018 Experience 0.37 0.10 3.70 0.0006 Age −0.09...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience +...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience + β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 7.17 4.26 1.68 0.0991 Education 1.81 0.35 5.17 0.0000 Experience 0.45 0.10 4.50 0.0000 Age −0.01...
15. Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience...
15. Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience + β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 6.89 3.61 1.91 0.0626 Education 1.29 0.34 3.79 0.0004 Experience 0.44 0.16 2.75 0.0085 Age...
Exercise 14-23 Algo Using data from 50 workers, a researcher estimates Wage = β0 + β1Education...
Exercise 14-23 Algo Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience + β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 6.03 3.70 1.63 0.1100 Education 1.25 0.34 3.68 0.0006 Experience 0.51 0.15 3.40...
ADVERTISEMENT
Need Online Homework Help?

Get Answers For Free
Most questions answered within 1 hours.

Ask a Question
ADVERTISEMENT