The chairman of the marketing department at a large state university decided to
ID: 3266408 • Letter: T
Question
The chairman of the marketing department at a large state university decided to undertake a study to relate the starting salary for marketing majors after graduation to the grade point average (GPA) for marketing majors in courses within the major. To do this, records of seven recent marketing graduates were randomly selected, and the data shown below were obtained. Conduct a simple linear regression with 95% confidence (or alpha = .05) to answer the questions below. Round all numerical answers to 2 decimal places. If your final answer is within .05 of the correct answer, and your answer is marked incorrect, please email me to let me know the specific question and question part this happened on so I can correct it. Marketing Major Salary (Y) (in thousands of dollars) GPA (X) 1 33.8 3.26 2 29.8 2.60 3 33.5 3.35 4 30.4 2.86 5 36.4 3.82
Explanation / Answer
Answer:
The chairman of the marketing department at a large state university decided to undertake a study to relate the starting salary for marketing majors after graduation to the grade point average (GPA) for marketing majors in courses within the major. To do this, records of seven recent marketing graduates were randomly selected, and the data shown below were obtained. Conduct a simple linear regression with 95% confidence (or alpha = .05) to answer the questions below. Round all numerical answers to 2 decimal places. If your final answer is within .05 of the correct answer, and your answer is marked incorrect, please email me to let me know the specific question and question part this happened on so I can correct it. Marketing Major Salary (Y) (in thousands of dollars) GPA (X)
1 33.8 3.26
2 29.8 2.60
3 33.5 3.35
4 30.4 2.86
5 36.4 3.82
The regression line is
Y=14.76+5.67 x
Regression Analysis
r²
0.973
n
5
r
0.986
k
1
Std. Error
0.515
Dep. Var.
y
ANOVA table
Source
SS
df
MS
F
p-value
Regression
28.4131
1
28.4131
107.24
.0019
Residual
0.7949
3
0.2650
Total
29.2080
4
Regression output
confidence interval
variables
coefficients
std. error
t (df=3)
p-value
95% lower
95% upper
Intercept
14.7595
1.7553
8.408
.0035
9.1733
20.3458
x
5.6704
0.5476
10.356
.0019
3.9278
7.4130
Regression Analysis
r²
0.973
n
5
r
0.986
k
1
Std. Error
0.515
Dep. Var.
y
ANOVA table
Source
SS
df
MS
F
p-value
Regression
28.4131
1
28.4131
107.24
.0019
Residual
0.7949
3
0.2650
Total
29.2080
4
Regression output
confidence interval
variables
coefficients
std. error
t (df=3)
p-value
95% lower
95% upper
Intercept
14.7595
1.7553
8.408
.0035
9.1733
20.3458
x
5.6704
0.5476
10.356
.0019
3.9278
7.4130
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