Analyzing A1. Data
Objective: To analyze each of the nine variables associated with keeping it connected with
family members by using graphical and numerical descriptive techniques.
A2. Our team constructed a questionnaire containing nine questions. These questions all
pertained to how much time people spend trying to remain connected with their families, and
what their cumulative GPAs were. Our groups target of interest is Syracuse University college
undergraduates. The sample that we used represents all Syracuse University undergraduates
because the participants were randomly selected and were all from various states around the
country. With the results from our questionnaire we will be able to formulate an idea about how
trying to stay connected may, or may not, affect our academics.
A3. According to Figure 1A most students spend approximately .5 hours per week talking on the
phone with their family members. Based on our observation of the graph, the distribution is right
skewed. In addition, the numerical descriptive data featured in Table 1 shows that on average
students spend .66 hours (40 minutes) talking on the phone speaking with family members and
the standard deviation is .3417. This low standard deviation tells us that the data is close the
mean and therefore the data is reliable.
Figure 2A displays the number of visits home for each one of our participants. According to this
graph, most students visit home 3 or more times. Based on our observation of the graph, the
distribution is left-skewed. In addition, Table 2 shows us that the average students travels home
about 1.96 times per semester. The table also tells us what the standard deviation is, which we
can use to see how reliable the data is.
When interpreting Figure 3A, we can see that most of our participants talked to their family via
email and instant messaging for less than two hours per week. The graphs distribution is rightskewed. Furthermore, Table 2 shows us that the average student emails and instant messages
their family for about 1.63 hours per week (97.8 minutes).
Figure 4A displays the cumulative GPAs of our participants. According to the dotplot, we can
see that most of our participants have a cumulative GPA of 3.5. The distribution of this graph is
left-skewed. Table 4 provides us with even more information. The table tells us that the average
participant had a cumulative GPA of 3.2662. The low standard deviation shows us that this data
is reliable.
Figure 5A graphically illustrates the distribution of how much time (in hours) is spent on
homework while on family visits. Most SU students spend zero hours on homework while at
home. Numerical descriptive data tells us that the mean is .89 hours and the standard dev is
1.233. This means that on average, most students spend .89 hours or about 53 minutes on
homework while on family visits at home. A standard deviation of 1.233 can be interpreted as a
relatively close distribution of the data to the mean.
The dotplot of how many family members a student keep in constant communication (Fig 6A) is
left skewed and it shows that most people keep in contact with three or more family members.
Descriptive numerical analysis (Table 6), illustrates that the mean is 2.64 and standard deviation
is .6579.
Figure 7A is right-skewed, and tells us how many hours away the participants live from Syracuse
University. We can observe that most participants live less than 6 hours away. Table 7 indicates
that the average student lives 5.244 hour away (313.44 minutes).
Figure 8A tells us how many classes students skipped when they visited home last semester.
Most of the students said that they missed zero classes when visiting home. Also, the graphs
distribution is right-skewed telling us that the mean is greater than the median. Table 9 shows us
that the average student misses about .36 classes per semester.
Analysis of Figure 9A indicates most students do not spend any time on special events such as
birthdays and holidays. The numerical descriptive analysis in Table 9 tells us that the mean of
this variable is 2.64 the standard deviation is 3.770.
A4. There were no qualitative variables used in our project
A5. Summary
This section of the project was all about taking the results that we got from our questionnaire and
displaying them in both graphical and numerical formats. For each variable we created graphs
(nine graphs in all). We used both dotplots and histograms, since they were the most relevant for
displaying our data. For each variable we also found the mean and the standard deviation. With
the mean we could come up with an on average number for each of our variables which would
give us an idea of our target audience and the ways that are
B1. Effects on Academic Success
Objective: To determine whether certain kinds of communication with family members can have
noticeable effects on academic success through observation of the relationships between
independent (X) and dependent variables (Y).
B2. Our dependent variables include cumulative GPA and study time. These variables will help
us determine overall academic success by observing how each dependent variable responds to
the independent or explanatory variables.
B3. The independent/explanatory variables used in this statistical analysis include amount of
time spent speaking on the phone with family members, the amount of time spent through
internet communications such as emails, instant messaging, Facebook, and/or any other social
networking sites, and the number of times a student visits home per semester. The amount of
time a student spends on the phone relates to how well a students performance is because the
time they spend on the phone takes away from studying or class time. Another factor that would
take away from a students availability to study or attend class is the amount of time he or she
spends on the computer contacting family members through instant messaging, emails, and
Facebook or any of the other social networking sites. Finally, the most important factor that
would be expected to affect student performance is the number of times a student goes home
throughout the semester. From our initial survey, we found that students barely spend any time
on school work while visiting with their families and often times some students will even skip
classes to be able to catch the grey hound bus or a ride to the airport. So the more frequently a
student decides to go home, most likely the less time they will spend on studying or reading for
their next weeks classes.
B4. Figure B1 relates the minutes a student speaks on the phone to his or her family with his or
her GPA. Because the correlation coefficient is so near 0 and not -1 or 1, this means that a
students phone time is a very weak predictor of GPA as well as proving that there is a positive
relationship. Only 0.9% of GPA is predicted by the number of minutes a student uses the phone
for family time. Figure B2 compares the amount of time a student spends on the internet
contacting family to his or her GPA. The correlation coefficient proves that this is actually a
negative weak relationship and the coefficient of determinate proves that a mere 0.08% of GPA is
estimated by internet usage.
Figure B3 demonstrates the number of visits home a student makes and how it predicts his or her
GPA. The correlation coefficient being so close to 0 proves that this is a negative and weak
relationship. The coefficient of determinate states that 1.41% of GPA is predicted by the number
of visits a student makes home. Figure B4 compares the number of visits home to the amount of
time a student spends studying in this scatter plot. The relationship is negative and weak as the
correlation coefficient finds it to be near 0. The number of minutes put towards study time is
found to be 1.23% based on the number of visits home. Figure B5 relates internet usage to the
amount of time a student spends studying. The correlation coefficient is near 0, meaning that this
relationship is weak but positive. The amount of time spent on the internet is estimated to
determine 3.47% of the time spent on study. Figure B6 relates the amount of time a student
spends on the phone with family with the amount of time he or she studies. The correlation
coefficient proves the relationship to be negative and weak. The coefficient of determinate
proves the amount of a time a student spends on the phone to only predict 0.42% of the time he
or she spends studying.
B5. Regression Analysis
Regression Analysis: GPA versus Phone Time (mins)
GPA = 3.00 + 0.00367 Phone Time (mins)
S = 0.778345 R-Sq = 0.9% R-Sq(adj) = 0.0%
This regression model indicates that every minute increase in phone usage the GPA will increase
by .00367. If someone were not to talk on the phone at all they would have a 3.0 GPA. The
coefficient determinant 0% indicates that this model is not a good determinant at all.
Regression Analysis: GPA versus Phone Time (mins), Internet Time (mins)
GPA = 3.01 + 0.00360 Phone Time (mins) - 0.000079 Internet Time (mins)
S = 0.786493 R-Sq = 1.0% R-Sq(adj) = 0.0%
This regression model indicates that with every minute increase spent on the internet to keep in
touch with your family the GPA will drop by 0.000079 of a point. With every minute increase
spent keeping in touch with your family on the phone the GPA will increase by 0.00360 of a
point. If someone were to not do any of these their GPA would be 3.01 The coefficient
determinant is 0%, which indicates that this model is not a good determinant.
Regression Analysis: GPA versus Phone Time (, Internet Tim, No. of Home
GPA = 3.17 + 0.00933 Phone Time (mins) - 0.000167 Internet Time (mins)
- 0.197 No. of Home Visits
S = 0.775960 R-Sq = 5.7% R-Sq(adj) = 0.0%
This regression model indicates that every time someone visits home there GPA decreases by
0.197 of a point. With every minute increase spent on the internet to keep in touch with their
family the GPA will drop by 0.000167 of a point. Every minute increase spent talking on the
phone with family will increase the GPA by 0.00933 of a point. If someone were not to do any of
these they would start off with a 3.17 GPA. The coefficient determinant is 0%, which indicates it
is not a good regression model.
Regression Analysis: Study Time (mins) versus Phone Time (mins)
Study Time (mins) = 55.0 - 0.273 Phone Time (mins)
S = 66.1259 R-Sq = 0.7% R-Sq(adj) = 0.0. This regression model indicates that every minute
increase spent on the phone keeping in touch with your family will decrease study time by 0.273.
If you were to not spend any time on the phone you would study 55 minutes. The coefficient
determinant is 0% which indicates that it is not a good indicator.
Regression Analysis: Study Time (versus Phone Time), Internet Tim
Study Time (mins) = 43.7 - 0.196 Phone Time (mins) + 0.0788 Internet Time = (mins)
S 65.7690 R-Sq = 3.8% R-Sq(adj) = 0.0%. This regression model indicates that with every
minute increase spent on the internet the time spent study will increase by 0.0788 of a minute.
With every minute increase spent on the phone time spent studying will decrease by 0.196 of a
minute. If someone were not to spend any time on the internet or phone there study time would
be 43.7 minutes. The coefficient determinant is 0% which indicates that it is not a good deciding
factor.
Regression Analysis: Study Time (versus Phone Time), Internet Tim, ...
Study Time (mins) = 47.6 - 0.057 Phone Time (mins) + 0.0766 Internet Time (mins)- 4.8 No. of
Home Visits
S = 66.3473 R-Sq = 4.2% R-Sq(adj) = 0.0%. This regression model indicates that with every
home visit increase study time will decrease by 4.8 minutes. With every minute increase spent
on the internet keeping in touch study time will decrease by 0.0766 of a minute. With every
minute increase spent talking on the phone, study time will decrease by 0.057 of a minute. If
someone was not engaging i there study time would start off as 47.6 minutes. The coefficient
determinant is 0% which indicates it is not a good determining factor.
B6. Summary
According to our findings there is not a strong correlation between GPA and how often you stay
connected with your family. There is not a strong correlation between the amount of time you
study and how much you stay connected with your family either. This information would be
useful to campus universities because they will know that students are not affected by how much
they stay in contact with their family. Whether they live close to home and like to visit often or
live across the country and like to talk on the phone to relatives or remain in contact through the
internet, their GPAs and study hours will not be affected.
Appendix
Appendix A
Dotplotofnumberofvisitshome
Dotplotoftimespentonphone( hours)
0
0.0
0.2
0.4
0.6
0.8
1.0
t imespent onphone( hours)
1.2
1
2
numberofvisit shome
3
1.4
Figure 1A
Figure 2A
HistogramofGPA
18
DotplotoffacebookandAI Mtime
16
14
Fr e que ncy
12
10
8
6
0.0
1.4
2.8
4.2
5.6
facebookandAIMt ime
7.0
8.4
4
9.8
2
0
Figure 3A
2.0
2.5
3.0
GPA
3.5
4.0
Figure 4A
HistogramoftimespentonHWduringvisits
25
Dotplotofhowmanyclosefamilymembers
Fr e que ncy
20
15
10
5
0
0.0
Figure 5A
1.2
2.4
3.6
t imespent onHWduringvisit s
4.8
6.0
0
Figure 6A
1
2
howmanyclosefamilymembers
3
Dotplotofclassesskippedforvisits
Dotplotofhowfarawayfromhome
0
6
12
18
24
howfarawayfromhome
30
36
42
0
Figure 7A
1
classesskippedforvisit s
2
Figure 8A
Dotplotoftimespentforfamevents
0
3
6
9
12
t imespent forfamevent s
15
18
Figure 9A
Table 1: Descriptive Statistics: time spent talking on
phone
Variable
N N*
Mean
StDev Minimum
Q1
time spent talking on ph 50
0
0.0483 0.3417
0.0000 0.5000
Variable
Maximum
time spent talking on ph
1.5000
Mean
Table 3: Descriptive Statistics: facebook and AIM time
SE
0.6600
Median
Q3
0.5000
0.6250
Table 2 : Descriptive Statistics: number of visits home
Variable
N N*
Mean
Mean StDev Minimum
Q1 Median
number of visits home~ 50
1 1.960
0.148 1.049
0.000 1.000
2.000
Variable
number of visits home~
Q3
3.000
Maximum
3.000
SE
Variable
N N*
Mean
Mean StDev Minimum
Q1 Median
facebook and AIM time~ 49
0 1.630
0.345 2.415
0.000 0.097
1.000
Variable
facebook and AIM time~
Q3
2.000
SE
Maximum
10.000
Table 4 Descriptive Statistics: GPA
Variable
N N*
Mean SE Mean
Minimum
Q1 Median
Q3
GPA~
50
0 3.2662
0.0609
2.0000 3.0750 3.3900 3.5625
Variable
GPA~
StDev
0.4307
Maximum
3.9600
Table 5: Descriptive Statistics: time spent on HW during
visits
Variable
N N*
Mean
Mean StDev Minimum
Q1 Median
time spent on HW during
50
0 0.890
0.174 1.233
0.000 0.000
0.416
Variable
time spent on HW during
Q3
2.000
SE
Residual Error 48 29.0794
Total
49 29.3571
Unusual Observations
Maximum
6.000
Table 6: Descriptive Statistics: how many close family
members
Variable
N N*
Mean
StDev Minimum
Q1
how many close family me 50
4
0.0980 0.6928
0.0000 2.0000
Variable
Maximum
how many close family me
3.0000
Mean
SE
2.6400
Median
Q3
3.0000
3.0000
Table 7: Descriptive Statistics: how far away from home
Variable
N N*
Mean
Mean StDev Minimum
Q1 Median
how far away from home 50
0 5.244
0.990 6.998
0.167 2.000
4.000
Variable
how far away from home
Q3
5.000
SE
Maximum
42.000
Table 8 Descriptive Statistics: classes skipped for visits
Variable
N N*
Mean
StDev Minimum
Q1
classes skipped for visi 50
2
0.0937 0.6627
0.0000 0.0000
Variable
Maximum
classes skipped for visi
2.0000
Mean
SE
0.3600
Median
Q3
0.0000
1.0000
Phone
Time
Obs (mins)
St Resid
31
30.0
-4.04R
35
30.0
-4.04R
46
90.0
-0.73 X
47
90.0
0.31 X
48
90.0
0.66 X
49
90.0
-0.18 X
50
90.0
-0.09 X
0.6058
GPA
Fit
SE Fit
Residual
0.000
3.106
0.122
-3.106
0.000
3.106
0.122
-3.106
2.800
3.326
0.295
-0.526
3.550
3.326
0.295
0.224
3.800
3.326
0.295
0.474
3.200
3.326
0.295
-0.126
3.260
3.326
0.295
-0.066
R denotes an observation with a large
standardized residual.
X denotes an observation whose X value gives
it large leverage.
Regression Analysis: GPA versus Phone Time (mins),
Internet Time (mins)
The regression equation is
GPA = 3.01 + 0.00360 Phone Time (mins) 0.000079 Internet Time (mins)
Predictor
T
P
Constant
11.24 0.000
Phone Time (mins)
0.65 0.519
Internet Time (mins)
-0.10 0.918
Coef
SE Coef
3.0071
0.2675
0.003596
0.005531
-0.0000786
0.0007637
Table 9 Descriptive Statistics: time spent for fam events
S = 0.786493
Variable
N N*
Mean
Mean StDev Minimum
Q1 Median
time spent for fam event 50
0 2.460
0.533 3.770
0.000 0.000
1.000
Analysis of Variance
Source
DF
SS
P
Regression
2
0.2843
0.796
Residual Error 47 29.0728
Total
49 29.3571
SE
Variable
Q3 Maximum
time spent for fam event 4.000
20.000
Regression Analysis: GPA versus Phone Time (mins)
Source
Phone Time (mins)
Internet Time (mins)
Unusual Observations
The regression equation is
GPA = 3.00 + 0.00367 Phone Time (mins)
Predictor
P
Constant
0.000
Phone Time (mins)
0.502
S = 0.778345
Coef
SE Coef
T
2.9958
0.2414
12.41
0.003672
0.005424
0.68
R-Sq = 0.9%
Analysis of Variance
Source
DF
SS
P
Regression
1
0.2777
0.502
R-Sq(adj) = 0.0%
MS
F
0.2777
0.46
R-Sq = 1.0%
Phone
Time
Obs (mins)
St Resid
13
30.0
-0.27 X
31
30.0
-4.00R
35
30.0
-4.04R
DF
1
1
R-Sq(adj) = 0.0%
MS
F
0.1421
0.23
0.6186
Seq SS
0.2777
0.0066
GPA
Fit
SE Fit
Residual
2.900
3.049
0.567
-0.149
0.000
3.104
0.125
-3.104
0.000
3.115
0.151
-3.115
R denotes an observation with a large
standardized residual.
X denotes an observation whose X value gives
it large leverage.
S = 66.1259
R-Sq = 0.7%
Analysis of Variance
Regression Analysis: GPA versus Phone Time (, Internet
Tim, No. of Home
Source
DF
SS
MS
F
Regression
1
1532 1532 0.35
Residual Error 48 209886 4373
Total
49 211418
Unusual Observations
Phone
Time Study Time
Obs (mins)
(mins)
Fit SE Fit
Residual St Resid
6
30.0
180.00 46.82
10.35
133.18
2.04R
13
30.0
180.00 46.82
10.35
133.18
2.04R
14
30.0
180.00 46.82
10.35
133.18
2.04R
26
30.0
180.00 46.82
10.35
133.18
2.04R
33
30.0
240.00 46.82
10.35
193.18
2.96R
46
90.0
0.00 30.45
25.04
-30.45
-0.50 X
47
90.0
120.00 30.45
25.04
89.55
1.46 X
48
90.0
30.00 30.45
25.04
-0.45
-0.01 X
49
90.0
0.00 30.45
25.04
-30.45
-0.50 X
50
90.0
0.00 30.45
25.04
-30.45
-0.50 X
The regression equation is
GPA = 3.17 + 0.00933 Phone Time (mins) 0.000167 Internet Time (mins)
- 0.197 No. of Home Visits
Predictor
T
P
Constant
11.14 0.000
Phone Time (mins)
1.40 0.167
Internet Time (mins)
-0.22 0.826
No. of Home Visits
-1.51 0.138
S = 0.775960
Coef
SE Coef
3.1675
0.2844
0.009326
0.006645
-0.0001672
0.0007557
-0.1969
0.1303
R-Sq = 5.7%
R-Sq(adj) = 0.0%
Analysis of Variance
Source
DF
SS
P
Regression
3
1.6599
0.439
Residual Error 46 27.6972
Total
49 29.3571
Source
Phone Time (mins)
Internet Time (mins)
No. of Home Visits
Unusual Observations
Phone
Time
Obs (mins)
St Resid
13
30.0
-0.39 X
31
30.0
-3.96R
35
30.0
-3.85R
DF
1
1
1
MS
F
0.5533
0.92
0.6021
P
0.557
R denotes an observation with a large
standardized residual.
X denotes an observation whose X value gives
it large leverage.
Seq SS
0.2777
0.0066
1.3756
GPA
Fit
SE Fit
Residual
2.900
3.110
0.561
-0.210
0.000
3.030
0.132
-3.030
0.000
2.857
0.227
-2.857
R denotes an observation with a large
standardized residual.
X denotes an observation whose X value gives
it large leverage.
Regression Analysis: Study Time (mins) versus Phone
Time (mins)
The regression equation is
Study Time (mins) = 55.0 - 0.273 Phone Time
(mins)
Predictor
P
Constant
0.010
Phone Time (mins)
0.557
R-Sq(adj) = 0.0%
Coef
SE Coef
T
55.00
20.50
2.68
-0.2727
0.4608
-0.59
Regression Analysis: Study Time ( versus Phone Time (,
Internet Tim
The regression equation is
Study Time (mins) = 43.7 - 0.196 Phone Time
(mins) + 0.0788 Internet Time (mins)
Predictor
T
P
Constant
0.057
Phone Time (mins)
0.674
Internet Time (mins)
0.223
S = 65.7690
Coef
SE Coef
43.67
22.37
1.95
-0.1960
0.4625
-0.42
0.07879
0.06386
1.23
R-Sq = 3.8%
R-Sq(adj) = 0.0%
Analysis of Variance
Source
Regression
Residual Error
Total
DF
2
47
49
SS
8117
203301
211418
Source
Phone Time (mins)
Internet Time (mins)
Unusual Observations
Phone
DF
1
1
MS
4058
4326
Seq SS
1532
6585
F
0.94
P
0.399
Time Study Time
Obs (mins)
(mins)
Residual St Resid
6
30.0
180.00
142.21
2.20R
13
30.0
180.00
76.02
1.67 X
14
30.0
180.00
136.69
2.11R
26
30.0
180.00
137.48
2.12R
33
30.0
240.00
202.21
3.13R
Fit
SE Fit
37.79
12.63
103.98
47.46
43.31
10.68
42.52
10.86
Source
Phone Time (mins)
Internet Time (mins)
No. of Home Visits
37.79
12.63
Unusual Observations
R denotes an observation with a large
standardized residual.
X denotes an observation whose X value gives
it large leverage.
Regression Analysis: Study Time ( versus Phone Time (,
Internet Tim, ...
The regression equation is
Study Time (mins) = 47.6 - 0.057 Phone Time
(mins) + 0.0766 Internet Time (mins)
- 4.8 No. of Home Visits
Predictor
Coef SE Coef
T
P
Constant
47.56
24.32
1.96
0.057
Phone Time (mins)
-0.0569
0.5681 -0.10
0.921
Internet Time (mins) 0.07664 0.06462
1.19
0.242
No. of Home Visits
-4.78
11.14 -0.43
0.670
S = 66.3473
R-Sq = 4.2%
R-Sq(adj) = 0.0%
Analysis of Variance
Source
Regression
Residual Error
Total
DF
3
46
49
SS
8928
202490
211418
DF
1
1
1
Phone
Time Study Time
Obs (mins)
(mins)
Residual St Resid
6
30.0
180.00
134.14
2.15R
13
30.0
180.00
74.54
1.63 X
14
30.0
180.00
133.56
2.05R
26
30.0
180.00
139.10
2.13R
33
30.0
240.00
208.48
3.29R
MS
2976
4402
F
0.68
P
0.571
Seq SS
1532
6585
811
Fit
SE Fit
45.86
22.71
105.46
48.00
46.44
13.02
40.90
11.59
31.52
19.38
R denotes an observation with a large
standardized residual.
X denotes an observation whose X value gives
it large leverage.