Question

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= β_{0} +β_{1} IQ + u

- According to the STATA output, what are the minimum and the
maximum for education years (denoted as
*educ*) in the sample? (4 points) - 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) - What does the value of R-squared mean? (3 points)
- Interpret the coefficient in front of
*IQ*. (5 points) - What is the predicted change in wage if IQ increases by 10 points holding other factors fixed? (3 points)

**STATA output**

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

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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 + 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 + 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 + 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 + 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 + 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 + 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 +
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...

7)
Consider the following regression model
Yi = β0 + β1X1i + β2X2i + β3X3i + β4X4i + β5X5i + ui
This model has been estimated by OLS. The Gretl output is
below.
Model 1: OLS, using observations 1-52
coefficient
std. error
t-ratio
p-value
const
-0.5186
0.8624
-0.6013
0.5506
X1
0.1497
0.4125
0.3630
0.7182
X2
-0.2710
0.1714
-1.5808
0.1208
X3
0.1809
0.6028
0.3001
0.7654
X4
0.4574
0.2729
1.6757
0.1006
X5
2.4438
0.1781
13.7200
0.0000
Mean dependent var
1.3617
S.D. dependent...

Hello, I have computed a regression model where the dependent
variable is "earn" as in how much money the student will earn after
college. My independent variables include "public" as in was this
college public(1) or private(0), "academic ability" (a score
calculated as the average score from SAT/ACT data of admitted
students), "Average Cost" of tuition and "population"
(of the city the college is in). What is the impact on
earnings of higher population of the college
area/city?
SUMMARY OUTPUT
Regression...

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