Question

Consider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary=10,815.11+2563.46(Education)+897.49(Experience) . Suppose two employees at the company have been working there for five years. One has a bachelor's degree (8 years of education) and one has a master's degree (10 years of education). How much more money would we expect the employee with a master's degree to make?

Answer #1

Estimated Salary=10,815.11+2563.46(Education)+897.49(Experience)

For Employee with bachelor's degree, Education = 8

For Employee with master's degree, Education = 10

The difference in education between Employee with master's degree and bachelor's degree = 10 - 8 = 2

Slope of the Education in estimated regression line = 2563.46

So, for same number of years of experience, employee with a master's degree will make 2 * 2563.46 = 5126.92 more than the employee with bachelor's degree.

Consider the following estimated regression model relating
annual salary to years of education and work experience.
Estimated
Salary=11,756.80+2723.3(Education)+1092.64(Experience)
Suppose an employee with 11 years of education has been with the
company for 2 years (note that education years are the number of
years after 8th grade). According to this model, what is
his estimated annual salary?

Consider the following computer output of a multiple regression
analysis relating annual salary to years of education and years of
work experience.
Regression Statistics
Multiple R
0.7345
R Square
0.5395
Adjusted R Square
0.5195
Standard Error
2134.9715
Observations
49
ANOVA
df
SS
MS
F
Significance F
Regression
2
245,644,973.9500
122,822,486.9750
26.9460
1.8E-08
Residual
46
209,672,760.0092
4,558,103.4785
Total
48
455,317,733.9592
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
14271.51879
2,525.5672
5.6508
0.000000963
9187.8157
19,355.2219
Education (Years)
2351.3035...

Consider the following computer output of a multiple regression
analysis relating annual salary to years of education and years of
work experience.
Regression Statistics
Multiple R
0.7338
R Square
0.5384
Adjusted R Square
0.5183
Standard Error
2139.0907
Observations
49
ANOVA
df
SS
MS
F
Significance F
Regression
2
245,472,093.5833
122,736,046.7917
26.8234
1.9E-08
Residual
46
210,482,624.6208
4,575,709.2309
Total
48
455,954,718.2041
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
14275.75637
2,530.4400
5.6416
0.000000994
9182.2448
19,369.2679
Education (Years)
2350.2675
338.3625...

Ford would like to develop a regression model that would predict
the number of cars sold per month by a dealership employee based on
the employee's number of years of sales experience (X1), the
employee's weekly base salary before commissions (X2), and the
education level of the employee. There are three levels of
education in the sales force—high school degree, associate's
degree, and bachelor's degree. The following dummy variables have
been defined.
Degree
X3
X4
High School
0
0
Associate's...

Suppose the following regression equation was generated from
sample data relating salary to years of experience, marital status,
and the interaction term of years of experience and marital status.
Marital status is a dummy variable where MARRIEDi=1MARRIEDi=1 if
employee i is married and MARRIEDi=0MARRIEDi=0 if employee i is
single.
SALARYi=58316.795901+790.675473EXPERIENCEi+1222.075701MARRIEDi−36.766201EXPERIENCEiMARRIEDi+ei
1) In this regression equation, what is the intercept value for
married employees?
2)In this regression equation, what is the value of the slope
for married employees?

Suppose the following regression equation was generated from
sample data relating annual salary to experience, gender, and
marital status. Gender is represented by a dummy variable, FEMALEi
where FEMALEi=1 if employee i is female and FEMALEi=0 if employee i
is male. Marital status is represented by a dummy variable MARRIEDi
where MARRIEDi=1 if employee i is married and MARRIEDi=0 if
employee i is single.
SALARYi=45399.358869+522.095299EXPERIENCEi−2445.498353FEMALEi−42.875876EXPERIENCEi⋅FEMALEi+10156.223595MARRIEDi−98.638952EXPERIENCEi⋅MARRIEDi−14314.347804FEMALEi⋅MARRIEDi+ei
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continuous) based on independent variables:
education, measured as number of years of education
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work experience, measured as number of years of work
experience (numerical, continuous, for example 3.5 means 3 years 6
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Suppose the following regression equation was generated from
sample data relating annual salary to experience, gender, and
marital status. Gender is represented by a dummy variable,
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and FEMALEi=0FEMALEi=0 if employee i is male. Marital status is
represented by a dummy variable MARRIEDiMARRIEDi where
MARRIEDi=1MARRIEDi=1 if employee i is married and
MARRIEDi=0MARRIEDi=0 if employee i is single.
SALARYi=46905.309796+429.685020EXPERIENCEi−2445.945436FEMALEi−35.464377EXPERIENCEi⋅FEMALEi+10084.736168MARRIEDi−60.572088EXPERIENCEi⋅MARRIEDi−14238.984644FEMALEi⋅MARRIEDi+eiSALARYi=46905.309796+429.685020EXPERIENCEi−2445.945436FEMALEi−35.464377EXPERIENCEi⋅FEMALEiSALARYi=+10084.736168MARRIEDi−60.572088EXPERIENCEi⋅MARRIEDi−14238.984644FEMALEi⋅MARRIEDi+ei
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Consider the model ln(Yi)=β0+β1Xi+β2Ei+β3XiEi+ui, where Y is an
individual's annual earnings in dollars, X is years of work
experience, and E is years of education. Consider an individual
with a high-school degree (E=12yrs) who has been working for 20
years. The expected increase in log earnings next year (when
X=21yrs) compared to this year is, dropping units,
β1
β1+12β3
β1+β3
β0+21β1+12β2+252β3

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relating GPA to the number of classes attended and the final exam
score in a particular class, and if the student is a freshman (=1
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0.1697(Exam Score) -0.1380(Freshman) Suppose two students, one a
junior and one a freshman, attended the same number of classes and
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