Question

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?

Answer #1

Here we consider a estimated regression model relating annual salary to years of education and work experience.

Estimated regression equation:-

Salary = 11756.80 + 2723.3(education) + 1092.64(experience)

Suppose an employee with 11 years of education has been with the company for 2 years.

Now we want to find what is his estimated annual salary.

According to this estimated regression model his annual salary is given by,

Salary = 11756.80 + 2723.3 × 11 + 1092.64 × 2

= 11756.80 + 29956.3 + 2185.25 = 43898.35

Hence according to this model his annual salary is 43898.35

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?

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...

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
1)According to the regression equation above, what is the
estimated change in salary resulting from...

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,
FEMALEiFEMALEi, where FEMALEi=1FEMALEi=1 if employee i is female
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
According to the regression equation above, what is the
estimated starting salary for married women?...

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?

Based on a sample of 30 observations the population regression
model to predict salary of employees (dependent variable, numerical
continuous) based on independent variables:
education, measured as number of years of education
(numerical, discrete but so many values),
work experience, measured as number of years of work
experience (numerical, continuous, for example 3.5 means 3 years 6
month work experience)
department, department of the employee, There are three main
departments in the organization (categorical nominal) Assume it
affects only intercept...

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...

Consider the following estimated multiple regression model
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
if freshman, =0 otherwise). GPA = -0.3507+0.1096(Attendance) +
0.1697(Exam Score) -0.1380(Freshman) Suppose two students, one a
junior and one a freshman, attended the same number of classes and
both got a score of 88 on the final exam. What would be the
expected difference in the GPa for...

Suppose the following table was generated from the sample data
of 20 employees relating annual salary to years of education and
gender. According to the results, is there a salary difference
between men and women at the 0.05 level of significance? If yes,
write the difference in salary in the space provided, rounded to
two decimal places. Else, select "There is not enough
evidence."
Coefficients
Standard Error
t Stat
P-Value
Intercept
−15910.435186
1986.259992
−8.010248
0.000000
Education
4129.465091
114.058730
36.204726
0.000000...

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