California State University San Marcos Smoking & GPA...
Transcript of California State University San Marcos Smoking & GPA...
CSUSM 1
California State University San Marcos
Smoking & GPA Analysis
Business Statistics 304
Spring 2008 Semester
Professor Fang Fang
Written by:
Walter Evans
Farzana Mohsini
Sheryl Lejano
Ryan Thomas
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Introduction
Our team set out with the intention of researching, organizing and statistically analyzing
the smoking population here at California State University San Marcos. We sought out to prove
or disprove a negative, positive or neutral correlation between a student’s smoking habits on
campus and the effect on the student’s level of success in regards to their Grade Point Average.
By proving or disproving a cause and effect relationship between student smoking and student
success, the result would be a course of action in either accommodating the smoking population
at CSUSM or making efforts to inform, council and promote a lesser degree of smoking on
campus. By doing this survey, it will help us plan and implement smoking intervention programs
for California State University San Marcos. These programs may be helpful in preventing and/or
correcting the habit of tobacco use in school. Our team understands smoking is a habit and a
social norm that has been practiced through out history. We sought it necessary to examine such
an issue given the seriousness of the diseases that will result from the habit as well as the existing
high prevelant nature of smokers on campus. The campus acts as a sample size in itself for the
community of San Marcos and really San Diego County.
Our team is aware of the numerous variables in existence that may negatively or
positively contribute to our research outcome; however the standard in which we will collect
data, develop an outline for calculations and analyze the results will be as accurate as possible
for this level of research we are performing. To properly evaluate the extensiveness of a student
smoker’s habit and the level of success a student has accomplished, our research will identify the
following points as outlined in our survey:
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Cal State San Marcos Smoking Survey
Gender: Male Female
Class: Freshman Sophomore Junior Senior
Age: 17-20 21-24 25-28 29-32 33-36 37-40 >40
What is your major? _____________
What is your G.P.A.? <= 2.0 2.01~2.4 2.41~2.80 2.81~3.20 3.21~3.60 >3.6
Do you have a family history of smoking? YES NO
When I see a person smoking, it…
makes him/her more attractive.
makes him/her less attractive
has no impact on their attractiveness.
Would you date a person who is smoker? YES NO
Do you smoke tobacco?
YES NO
If YES, how many times do you smoke a day?
1-3 times 4-6 times 7-9 times >10 times
How long have you been smoking? < 1 year 1-3 years 3-10 years >10 years
Thank you for your help on this research project! It is greatly appreciated!
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Methodology
We proceeded in our research by surveying 105 students on campus to operate as a
population size and represent California State University San Marcos. We proceeded to
randomly distribute our surveys on campus within classroom settings. We intentionally
distributed the surveys amongst classroom students in both major distinct and general elective
classes. Our data has offered us information on the following components:
Smoking Habit
• Time of day (cigarettes/day)
• Duration of smoking
• Family History of smoking
• Student smoking percentage
• Number of student smokers
Success as a Student
• Major.
• GPA Range.
• Graduation timeframe.
• Number of Credit Units on average per semester.
• Number of work hours outside of class devoted to study.
As a team we understand the social-cultural dimension of acceptance related to smoking
in the general environment. Our team is evident of the fact, television campaigns, medical
studies, and legal testimony from tobacco corporations are concluding evidence of the negative
effects of smoking for long periods of time. Our team understands the external factors and
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background information that is also relevant in the influencing of the smoking habit on and off
campus, those variables include:
• Health risks (first and second hand.)
• Cancer susceptibility.
• Based on time spent on campus in direct connection with smoking.
• Comparing and contrasting smoker comfort versus non-smoker discomfort.
• Non-smokers hidden annoyances toward smoking.
• Smoker’s awareness of nonsmokers.
• Determining possible reasons for smoking (stress, habit, social conformity?)
The following data was compiled over a multiple week time length during the day from
0900 to 1600, the optimal school hours. The data was organized utilizing an Excel Spreadsheet
for the purpose of sorting, formulating and graph/table generation. Our team performed regular
meetings inside class, outside class and on the weekends. Our teamwork was essential in
properly analyzing our research. We met on a number of occasions with our supervisor to
correctly understand and present the information prior to collecting the data and after.
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Results
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Data Collection and Organization
# Gender Class Age Major G.P.A. History Attractiveness Date? Smoke Per Day Years
1 m Senior 21-24 PSCI 2.41-2.80 no less yes no 0 0
2 m Senior 21-24 PSCI 2.81-3.20 no less no no 0 0
3 m Senior >40 PSCI 3.21-3.60 no less n/a no 0 0
4 f Junior 21-24 Social sci 3.21-3.60 yes less yes no 0 0
5 m sophomore 29-32 Criminology 2.01-2.40 no less no no 0 0
6 m sophomore 17-20 PSCI 2.81-3.20 yes none no no 0 0
7 f sophomore 17-20 Social sci 3.21-3.60 yes less no no 0 0
8 m sophomore 17-20 PSCI >3.6 no less no no 0 0
9 f Senior 25-28 Comm 2.41-2.80 no less no no 0 0
10 f Senior 25-28 PSCI 2.81-3.20 yes none yes no 0 0
11 f Senior 21-24 Comm 2.81-3.20 no less no no 0 0
12 m sophomore 17-20 PSCI 2.41-2.80 yes less no no 0 0
13 m Junior 21-24 Finance 2.81-3.20 yes less no no 0 0
14 f sophomore 17-20 PSCI 3.21-3.60 no less yes no 0 0
15 m Junior 17-20 PSCI 2.81-3.20 no less no no 0 0
16 f junior 21-24 PSCI 2.81-3.20 yes less yes no 0 0
17 m senior 21-24 PSCI 2.41-2.80 yes less no no 0 0
18 m junior 21-24 Business 2.81-3.20 no less no no 0 0
19 m junior 29-32 PSCI 3.21-3.60 no less no no 0 0
20 f senior 21-24 n/a 3.21-3.60 yes none yes no 0 0
21 m sophomore 17-20 PSCI 2.81-3.20 yes none yes yes 7 to 9 1 to 3
22 m senior 29-32 PSCI 2.41-2.80 yes none yes yes >10 3 to 10
23 m junior 25-28 History 3.21-3.60 no none yes yes 1 to 3 1 to 3
24 m junior 21-24 Finance 3.21-3.60 no less no no 0 0
25 m junior 21-24 Management 2.81-3.20 no less no no 0 0
26 f junior 17-20 Comm 2.81-3.20 no less no no 0 0
27 f junior 17-20 Human Devlp 3.21-3.60 yes less yes yes 1 to 3 < 1
28 m junior n/a Bussiness 2.41-2.80 no less no no 0 0
29 f junior 17-20 Bussiness 2.41-2.80 no less no no 0 0
30 f junior 17-20 Finance 3.21-3.60 no less no no 0 0
31 m junior 25-28 Mass Media 2.81-3.20 yes less no no 0 0
32 f junior 21-24 Literature >3.6 yes less no no 0 0
33 f junior 17-20 Management >3.6 no less no no 0 0
34 m junior 25-28 Mass Media 2.81-3.20 yes less no no 0 0
35 f junior 21-24 Nursing >3.6 yes less no no 0 0
36 m junior 25-28 Accounting 3.21-3.60 yes less no no 0 0
37 f junior 21-24 Human Devlp 2.81-3.20 yes less no no 0 0
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38 f senior 21-24 Psychology 3.21-3.60 yes less no no 0 0
39 m junior 21-24 Marketing 2.81-3.20 yes none no no 0 0
40 m junior 21-24 Management 2.41-2.80 yes less no no 0 0
41 m junior 21-24 Management 2.41-2.80 yes none yes yes 1 to 3 <1
42 m junior 21-24 Management 2.41-2.80 yes less no no 0 0
43 m junior 21-24 Accounting 2.81-3.20 no less no no 0 0
44 m junior 25-28 n/a >3.6 yes less no no 0 0
45 f senior 21-24 Management 2.01-2.40 no less no no 0 0
46 m senior 21-24 Management 2.41-2.80 n/a n/a n/a yes n/a n/a
47 m senior 21-24 Marketing 2.41-2.80 no less no no 0 0
48 m junior 21-24 Management 2.41-2.80 yes less no no 0 0
49 m junior 17-20 Management 2.81-3.20 yes n/a no yes 1 to 3 <1
50 m junior 21-24 Management 3.21-3.60 yes less no no 0 0
51 f senior 21-24 ssm 2.41-2.80 no less no no 0 0
52 f junior 21-24 Finance 3.21-3.60 no less no no 0 0
53 m freshman 29-32 Bussiness 3.21-3.60 no less no no 0 0
54 f junior 21-24 Finance 3.21-3.60 no less no no 0 0
55 m junior 21-24 Bussiness 3.21-3.60 no less and none no no 0 0
56 m senior 21-24 Bussiness 3.21-3.60 no less no no 0 0
57 m junior 21-24 Finance 2.81-3.20 yes depends n/a no 0 0
58 m junior 21-24 ssm 3.21-3.60 yes none yes yes 1 to 3 3 to 10
59 m senior 21-24 Marketing 3.21-3.60 yes less no no 0 0
60 m senior >40 Management 2.81-3.20 no less no no 0 0
61 m junior 21-24 Finance >3.6 no none no no 0 0
62 m junior 17-20 Management 3.21-3.60 no less no no 0 0
63 f freshman 17-20 Literature 3.21-3.60 no less yes no 0 0
64 m junior 21-24 Accounting 3.21-3.60 yes less no no 0 0
65 m sophomore 17-20 Bussiness 2.41-2.80 no none yes yes 1 to 3 1 to 3
66 m junior 17-20 Psychology 2.41-2.80 no less no no 0 0
67 m sophomore 17-20 PSCI 3.21-3.60 yes none yes no 0 0
68 m junior 25-28 Management 2.81-3.20 no none yes yes 1 to 3 3 to 10
69 f freshman 17-20 n/a 3.21-3.60 yes less no no 0 0
70 f senior 29-32 Accounting n/a no less no no 0 0
71 f senior 25-28 Nursing 3.21-3.60 no less no no 0 0
72 m junior 21-24 Bio 3.21-3.60 no less yes no 0 0
73 m senior 21-24 Information sys 2.81-3.20 yes less no no 0 0
74 m junior 21-24 Accounting 3.21-3.60 no none yes no 0 0
75 F Junior 17-20 Accounting 2.41-2.8 No less No yes n/a 1
76 F junior 21-24 Human Development 3.21-3.60 yes less NO no 0 0
77 F senior >40 Chimistry 3.21-3.60 no less No no 0 0
78 M junior 29-32 Management 3.21-3.60 yes less NO no 0 0
79 F junior 21-24 n/a 2.41-2.80 no less NO NO 0 0
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80 F junior 21-24 Finance >3.6 yes less No No 0 0
81 M junior 21-24 Business 3.21-3.60 yes less No No 0 0
82 F Senior 25-28 Liberal studies 2.01-2.4 yes no Impact yes yes >10 >10
83 F junior 21-24 History >3.6 yes More attractive No no 0 0
84 F senior 25-28 Management 3.21-3.60 no LESS NO NO 0 0
85 F junior 21-24 Marketing 3.81-3.20 yes less yes no 0 0
86 F junior 21-24 Management >3.6 no less NO NO 0 0
87 M senior 21-24 Management 2.41-2.8 no no Impact no No 0 0
88 F senior 21-24 Management 3.21-3.60 yes less NO NO 0 0
89 F senior 21-24 Management 3.81-3.20 yes no Impact yes yes 4 to 6 1 to 3
90 M senior >40 Business 3.21-3.20 yes less NO NO 0 0
91 F junior 21-24 Management >3.6 yes less NO NO 0 0
92 F junior 21-24 Business 3.21-3.60 NO LESS NO NO 0 0
93 M senior 25-28 HTM >3.6 no less NO NO 0 0
94 M senior 37-40 Management 3.21-3.60 yes no Impact YES YES >10 3 to 10
95 F senior 25-28 Management 3.21-3.60 YES less YES NO 0 0
96 M junior 21-24 Business 2.81-3.20 yes no Impact NO NO 0 0
97 M junior 25-28 Finance 2.81-3.20 yes less NO NO 0 0
98 M senior 21-24 Finance 3.81-3.20 no less yes NO 0 0
99 M junior 21-24 Finance 3.21-3.60 yes less NO NO 0 0
100 M junior 21-24 Management 2.41-2.80 yes no Impact yes no 0 0
101 F junior >40 N/A 3.21-3.60 YES less NO NO 0 0
102 F senior 39-32 Finance 3.21-3.60 yes less NO NO 0 0
103 F junior 21-24 Management 2.41-2.80 no less NO NO 0 0
104 M junior 21-24 HTM 3.21-3.60 yes less NO yes 4 to 6 <1
105 M senior 21-24 Business 2.81-3.20 no less NO NO 0 0
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Frequency Distribution of Age
Mean Standard Deviation and Frequency Mean Standard Deviation and Frequency
In the histogram above pertaining to the smoking sample, most of the smokers are in the
younger range between ages 17 and 24. The mean is about 24 years old because of the outlier of
1 person being in the higher age range. The data shown above for the sample of non-smokers
illustrates most of the non-smoking students surveyed are between 21-24 years old. By
examining the graphical illustrations and comparing them to the actuall numberred data collected
Mean = 23.56666667
Standard Deviation = 5.548058433
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it would indicate the majority of the students surveyed altogether were between the ages of 21
and 24. This would coincide with the facts that the majority of college students based on team
observations are between these same ages.
Frequency Distribution of GPA
Mean = 3.133317308
Standard Deviation = 0.37522654
Mean = 3.163932584
Standard Deviation = 0.365036947
Mean = 2.951666667
Standard Deviation = 0.396172161
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Out of our 105 sample, there are only 11 students who have above a 3.60 grade point
average. All of those students are non-smokers. The mean GPA is 3.16. The mean of the grade
point average for smoking students is slightly lower than the non-smoking students. Above a
3.60 GPA demonstrates great success as a student, the fact our data shows them all as non-
smokers is evidence against smoking as a student. The relationship between smoking and GPA
was our team’s largest concern. The data is illustrated in bar graphs to show the variations.
The mean for cigarettes per day was determined by using midpoints. The mean is about 5
cigarettes per day. The mean for years smoking is 3.4. There aren’t too many students who have
been smoking over 10 years possibly because they are still young and have not lived long enough
to smoke for over 10 years. In addition given their young adult status their smoking habit remain
socially based and lacks the dependent stage such as in a chain smoker at a later age, this would
give reason for the greater number of smokers with the fewest number of cigarettes a day.
Mean = 4.769230769
Standard Deviation = 3.491748515
Mean = 3.4
Standard Deviation = 3.10617959
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About half of the non-smoking students have a family history of smoking. This
information could mean that students who do have a family history of smoking are trying to
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avoid it because they see smoking in the home first hand. It could also mean that students who
have no family history of smoking are raised that way. Out of the smoking students, 10 have a
family history of smoking while 4 do not. This could possibly be a factor for the smoker. The smoking
lifestyle of acceptance exists in the smokers, our data indicates this. As small of a sample as ours is the
compare and contrast of the numbers stills exists.
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None of the smokers find smoking to be more attractive; but there are 3 smokers who
find it less attractive. 10 students say that there is no impact on attractiveness. There was only 1
student who finds smoking more attractive. Most of the students find smoking less attractive.
The only person who finds smoking more attractive is a non-smoker. This is very interesting
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because it is not expected. But as a team we analyze this data to mean that smoking has very
little to do with attractiveness from a dating perspective and visa versa.
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Out of the 15 smoking students, 11 said that they would date a smoker and a surprising 3
students said they would not date a smoker. This is really interesting considering that the 3
students are smokers themselves. Out of 25 students who would date a smoker, 14 of them are
non-smokers. The majority of smokers would not date smoker.
The sample consists of 105 students; out of the 105, only 15 students are smokers. To
find the mean and standard deviation, the midpoints were used because our survey had ordinal
data. The mean and standard deviation is not shown on the pie charts where it is not necessary.
Some of the histograms do not add up to the correct total of sample data because there was no
answer from the survey. However, we did not throw out the entire survey; instead used all of the
information that was available.
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Conditional Probabilities Relating to Data Collected
Probability Rule 4: Addition Rule for Any Two Events, E1 and E2 P(E1 or
E2)=P(E1)+P(E2)-P(E1 and E2).
Table 1. Smoker Nonsmoker Male 11 51 Total 105 Female 4 39 Table 2. GPA>3.0 GPA<3.0 Smoking Male 7 4 Total 15 Smokers Smoking Female 2 2
Graphical Representation
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Table 3. GPA>3.0 GPA<3.0 Total 90 Nonsmokers Nonsmoking Male 38 13 51 Males
Nonsmoking Female 30 939 Females
The table and graphical representation shows an almost even result on both female and male ends, however there is a small difference and improvement in the male smoking GPA compared to the female’s GPA. Table 4. Age > 20 Age ≤20 Total 105 Male 51 11 62 Males Female 33 10 43 Females Table 5.
Age > 20 Age ≤20 Total 15
Male Smokers 8 3 11 Males Smokers
Female Smokers 2 2 4 Females Smokers
Probability Calculations
Event 1= Male
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P(E1)=62/105 0.590476 Event 2= Female 0.409524 P(E2)=43/105 P(E1 or E2)=P(E1)+P(E2)‐P(E1 and E2) .590476+.409524‐0=1.0 Probability of Either a Male or Female 100% P(E1 and E2) 11/105=.10476 Probability of Male and Smoker Event 1= Male P(E1)=62/105 0.590476 Event 2=Smoker P(E2)=15/105 0.142857 P(E1 or E2)=P(E1)+P(E2)‐P(E1 and E2) 34.2% .590476+.142857‐.10476= 0.342859 Probability of Either Male or Smoker
P(E1 and E2) 4/105=.0380 Probability of Female and Smoker
Event 1= Female P(E1)=43/105 0.409524
Event 2=Smoker P(E2)=15/105 0.142857 14.2%
P(E1 or E2)=P(E1)+P(E2)‐P(E1 and E2) .409524+.142857‐.0380= 0.514381 Probability of Either Female or Smoker
P(E1 and E2) 51/105=.4857 Probability of Male and Nonsmoker
Event 1= Male P(E1)=62/105 .5905 Probability
Event 2=Non‐smoker
P(E2)=90/105 .857 Probability P(E1 or E2)=P(E1)+P(E2)‐P(E1 and E2) 96.18%
.5905+.857‐.4857= 0.9618 Probability of Either Male or Nonsmoker
P(E1 and E2) 39/105=.3714 Probability of Female and Nonsmoker
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Event 1= Female P(E1)=43/105 0.409524 Probability
Event 2=Nonsmoker P(E2)=90/105 0.857 Probability
P(E1 or E2)=P(E1)+P(E2)‐P(E1 and E2) 89.5% .409524+.857‐.3714= 0.895124 Probability of Either Female or Nonsmoker
We see a dominant probability in locating a male or nonsmoking candidate the reason being is based on our set of data the dominant characteristics were male people and or nonsmokers.
Event 1= Male P(E1)=62/105 .5905 Probability
Event 2= GPA > 3.0 P(E2)=77/105 .73333 Probability
P(E1 and E2) 42.8% 45/105=.42857 Probability of Male and GPA > 3.0
Event 1= Female P(E1)=43/105 0.409524
Event 2= GPA > 3.0 P(E2)=77/105 .73333 Probability
P(E1 and E2) 30.5% 32/105=.30476 Probability of Female and GPA > 3.0
Event 1= Male Smoker 11/105=.10476 Probability of Male and Smoker
Event 2= GPA > 3.0 P(E2)=77/105 .73333 Probability
P(E1 and E2) 7% 7/105=.066667 Probability of Male Smoker and GPA > 3.0 Event 1= Female Smoker
4/105=.0380 Probability of Female and Smoker Event 2= GPA > 3.0
P(E2)=77/105 .73333 Probability
P(E1 and E2) 2%
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2/105=.0190 Probability of Female Smoker and GPA > 3.0 Our team sought it necessary to develop the critical conditional probability sequences that we thought most impacted our question of research. Because the sample of smokers collected is in such low standard compared to the greater population the conditional probabilities are lower as well. For instance both female gender and GPA>3.0 are two of the fewest occurring events therefore when a event1 and event 2 probability is set up the lowest percentage of occurrence is the result.
Event 1= Male Nonsmoker 51/105=.4857 Probability of Male and Nonsmoker
Event 2= GPA > 3.0 P(E2)=77/105 .73333 Probability
P(E1 and E2) 36.2% 38/105=.3619 Probability of Male Nonsmoker and GPA > 3.0 Event 1= Female Nonsmoker
39/105=.3714 Probability of Female and Nonsmoker Event 2= GPA > 3.0
P(E2)=77/105 .73333 Probability
P(E1 and E2) 28.6% 30/105=.2857 Probability of Female Nonsmoker and GPA > 3.0
Probability Rule 5: Addition Rule for Mutually Exclusive Events
Two Mutually Exclusive Events E1 and E2
Binomial Formula
n= Random Sample Size 30 person Sample
E1 E2
P(x)n
x ! n x
p q
x n x !
( ) ! =
−
−
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x= Number of successes n‐x= Number of Failures p= probability of a success 0.14285714 q=1‐p = Probability of a failure 0.85714286 n!=n(n‐1)(n‐2)(n‐3)…1 0!=1 by definition P(x)= 30! (.14285714) power of 4(.85714286) power of 26 4!(30‐4)! 4.16E‐04 0.0181712
100% Probability of encountering a smoker in a 30 person sample from the population.
Correlation t-Tests
Hypothesis:
Non-smoking average G.P.A. > 3.00
Smoking average G.P.A < 3.00 and
Non-smoking – smoking > 0
t Test for Differences in Two Means
Data Hypothesized Difference 3Level of Significance 0.05
Population 1 Sample Sample Size 105Sample Mean 3.16Sample Standard Deviation 0.365
Population 2 Sample Sample Size 105Sample Mean 2.95Sample Standard Deviation 0.396
Intermediate Calculations Population 1 Sample Degrees of Freedom 104Population 2 Sample Degrees of Freedom 104Total Degrees of Freedom 208Pooled Variance 0.145021Difference in Sample Means 0.21t Test Statistic -53.0847
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Since the requirement level of significance α =0.05, we reject Ho only when the sample mean is
less than 5% into the range, thus we do not declare the Hypothesis Ho wrong unless the sample
data strongly indicates this is wrong, in other words we do not declare that HA true unless the
sample data strongly indicates that is true.
This two tailed test for the difference in two means uses the sample size of 105 for both sample 1
and sample two. Using a level of significance of 0.05.
Hypothesis:
Non-smoking average G.P.A. > 3.00
Smoking average G.P.A < 3.00 and
Non-smoking – smoking > 0
Based on the data we reject the null hypothesis; there is no evidence that suggests a higher
overall average G.P.A. among non-smokers vs. smokers
t Test for Differences in Two Means
Data Hypothesized Difference 3Level of Significance 0.05
Population 1 Sample Sample Size 89Sample Mean 3.16Sample Standard Deviation 0.365
Population 2 Sample Sample Size 15Sample Mean 2.95Sample Standard Deviation 0.396
Two-Tail Test
Lower Critical Value -1.97143Upper Critical Value 1.971435p-Value 6.7E-123
Reject the null hypothesis
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Intermediate Calculations Population 1 Sample Degrees of Freedom 88Population 2 Sample Degrees of Freedom 14Total Degrees of Freedom 102Pooled Variance 0.136463Difference in Sample Means 0.21t Test Statistic -27.0596
Two-Tail Test Lower Critical Value -1.9835Upper Critical Value 1.983495p-Value 2.39E-48
Reject the null hypothesis This two tailed test for the difference in Two means uses the sample size of 89 for sample 1
(non-smokers) and a sample size of 15 for sample 2 (smokers). Using a level of significance of
0.05.
Hypothesis:
Non-smoking average G.P.A. > 3.00
Smoking average G.P.A < 3.00 and
Non-smoking – smoking > 0
Based on the data we reject the null hypothesis; this indicates that there is not a difference of
3.00 among the two samples
t Test for Differences in Two Means
Data Hypothesized Difference 0Level of Significance 0.05
Population 1 Sample Sample Size 89Sample Mean 3.16Sample Standard Deviation 0.365
Population 2 Sample Sample Size 15Sample Mean 2.95Sample Standard Deviation 0.396
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Intermediate Calculations Population 1 Sample Degrees of Freedom 88Population 2 Sample Degrees of Freedom 14Total Degrees of Freedom 102Pooled Variance 0.136463Difference in Sample Means 0.21t Test Statistic 2.036741
Two-Tail Test Lower Critical Value -1.9835Upper Critical Value 1.983495p-Value 0.044267
Reject the null hypothesis
Hypothesis: H0: non-smokers – smokers ≤ 0.0
H1: non-smokers – smokers > 0.0
The test shown here proves there is a difference between non-smokers and smokers G.P.A. and
the indicated sample mean for the two samples suggests that non-smokers have a higher overall
G.P.A. than smokers. Non-smokers sample mean G.P.A. 3.16, Smoker sample mean G.P.A.
2.95.
This also can be due to the fact that out of 105 surveys conducted that only 15 where smokers
and 89 where non-smokers hence the chances to correctly correlate the G.P.A. between the two
have been skewed.
CORRELATION
Column 1 Column 2
Column 1 1
Column 2 ‐0.57224 1
This is the output of the correlation between non-smokers (89) and smokers (15)
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There is a negative correlation between non-smokers and smokers meaning that the G.P.A. is
lower among the smokers than the non-smokers.
AGE TOTAL AGE (NON‐SMOKING) AGE (SMOKING) GPA (Total)
GPA (NON‐SMOKING) GPA (SMOKING)
AGE TOTAL 1
AGE (NON‐SMOKING) 1 1
AGE (SMOKING) ‐0.668417667 ‐0.668417667 1
GPA (Total) 0.600557338 0.607914202 ‐0.691636195 1
GPA (NON‐SMOKING) 0.607914202 0.607914202 ‐0.691636195 1 1
GPA (SMOKING) ‐0.788263423 ‐0.788263423 0.599683101 ‐0.5722359 ‐0.572235919 1
This is the correlation between all relevant sample data. Surveys that had marks (n/a) where
replaced with a zero there by possibly skewing the data slightly. There were only four surveys
that had to be adjusted with a zero.
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The lowest data point is at 2.2 and with the smokers the trend line is steep meaning it is possible
for smokers to have a high G.P.A. if there are a lot of smokers on campus, but the data we have
collected with only 15 out of 105 (14.3%) students shows that the collective G.P.A. is still lower
than that of the none smokers. This data is contradictory with a correlation of .758 in regards to
smokers obtaining high G.P.A. compared to the non-smokers. It is diffcult to concretely deduce
that the smoking GPA is lower than the non-smoking GPA based on our one sample. The shows
the possibility of earning a higher GPA as a smoker, particularly the probability of doing so in a
large crowd of smokers.
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Non-smokers are the majority of our data and therefore the correlation seems to flatten out with
only a .165 trend of not smoking in relation to their G.P.A. Below the 1 marker the data remains
very low in correlation, this is a result that we expected simply from observing the data as a
team.
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This is a direct comparison of smokers and non-smokers and as you can see the lack of sample
data to correctly measure the G.P.A. is consistent with the smoker’s trend line telling us that
over time with more data collected smokers will have a higher average G.P.A. We do not know
this for sure.
Conclusions and Implications
Based on our survey, there is no strong correlation coefficient among students who
smoke or who are non-smokers, therefore we can not prove any sort of assumption about the
smoking habit. The only reason students smoke is related to their family history and their ages,
the younger their age the more they get addicted to the social activity. The small survey and
study performed by our team could provide evidence to support a negative argument between
GPA and smoking, however the comparison shows minimul significance and therefore neutral in
the best standards. There could be claim that the smoking habit has an affect on GPA; our data
indicates that the GPA of students who smoke are lower (2.9) compared to non-smokers (3.16).
In order to restrict the smoking habit of students at California State University San
Marcos we need to tighten the smoking policy so that the student only light up in the campus
parking lots such as lots J, K, L, N, O, X, Y, and Z. The reason we are asking for these lots is
because the smoking designated areas are not in favorable places, they are located at the
computer labs, outside of classrooms, and near walkways as an example the business building
designates a smoking area directly outside. Based on observation and intuitiveness students are
perturbed when they are forced to pass through second hand smoke to reach a classroom. Our
team believes this is a major problem for the students and we also encourage CSUSM to restrict
smoking in school and by doing this it will not only increase the value of education but also help
students to get rid of the smoking habit. The external factors of smoking such as health risks and
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annoyance has been proven to have a correlation, therefore our suggestion although are not
concretely backed up by our study are backed up by facts.