Math 1040
Term Project
Emily Chapman
Douglas Richards
April 2012
Math 1040-003
Purpose
 The
purpose of our project was to see if
there is a relation between a student’s
cumulative Grade Point Average (GPA)
and the number of hours worked per
week.
Study Design
 Our
group randomly asked 46 random
students on campus what their GPA and
how many hours they work per week. We
informed them that the study was
completely anonymous to eliminate any
biases. We conducted the study at Salt
Lake Community College.
Data Table
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Grade Point
Average (GPA)
3.75
3.00
3.875
2.84
4.0
3.75
3.0
3.8
2.6
2.8
3.6
3.6
3.5
3.6
3.9
3.8
3.18
3.0
3.6
3.0
3.7
3.2
3.1
Hours Worked
Per Week
24.5
26
25
24
20
0
25
20
30
40
40
8
20
0
53
50
10
22
36
30
20
40
35
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
3.9
2.8
3.0
2.8
3.0
2.8
3.5
3.6
3.1
3.8
3.7
3.8
2.9
3.8
2.4
3.7
3.4
3.3
2.5
3.9
3.6
2.9
2.7
15
0
20
40
40
20
40
16
40
0
20
30
24
36
8
42
25
25
20
0
0
20
24
Statistics
1st Quantitative Variable
Column
n
Mean
Variance
Std. Dev.
Std. Err.
Grade Point
Average (GPA)
46
3.3281522
0.20233707
0.44981894
0.066322185
Median Range Min Max
3.45
1.6
2.4
4
Q1
Q3
3
3.75
1st Quantitative Variable
 Histogram
 Boxplot
Statistics
2nd Quantitative Variable
Column
n
Mean
Variance
Std. Dev.
Std. Err. Median Range Min Max Q1 Q3
Hours Worked Per Week 46 23.98913 191.44987 13.836541 2.0400867
24
53
0
53 20 36
2nd Quantitative Variable
 Histogram
 Boxplot
Correlation & Line of
Regression
 Statistics
for Testing Correlation
 Correlation between GPA and Hours
Worked per Week is: -0.042509433
Equation for Line of Regression

Simple linear regression results:
Dependent Variable: GPA
Independent Variable: Hours Worked Per Week
GPA = 3.3613043 - 0.0013819601 Hours Worked Per Week
Sample size: 46
R (correlation coefficient) = -0.0425
R-sq = 0.001807052
Estimate of error standard deviation: 0.45449057
Parameter estimates:
Parameter
Intercept
Estimate
3.3613043
Slope
Std. Err.
Alternative DF
0.1352343
T-Stat
≠ 0 44 24.855413 <0.0001
-0.0013819601 0.00489656
≠ 0 44 -0.2822308
Analysis of variance table for regression model:
Source DF
Model
SS
MS
F-stat
P-value
1 0.016453512 0.016453512 0.079654224 0.7791
Error
44
9.088715
Total
45
9.105168
0.20656168
P-Value
0.7791
Scatter Plot With Line of
Regression
Difficulties/Surprises
Encountered

One difficulty we encounter was when we
were collecting our data. We had to word our
question just right so people would be honest
but also not be afraid to give us the
information. For example we stated the
questions “would be anonymous” and it is a
“random” sample. With using the word
“anonymous” many felt more open to giving
us the information. I was surprised that we
found no correlation between the amount of
hours students worked and the number of
hours they worked per week.
Analysis
 The
critical value for 46 is .243 and the
absolute value of our critical value of our
“r” value is .04. Since our value is less than
the critical value there is no linear
relationship between x and y (Hours per
Week and GPA).
Interpretation and Conclusions
 Our
conclusion is there is no linear
relationship between the amount of hours
a student works per week and their grade
point average. This is determined by
comparing our absolute value “r” value to
the critical value.