ANALYSIS OF MOBILE PHONES

1
A study of Mobile phones
A
PROJECT REPORT ON
“A STUDY OF MOBILE PHONES”
SUBMITTED TO,
DEPARTMENT OF STATISTICS,
RAJARAM COLLEGE, KOLHAPUR.
For the SY B.Sc. statistics
By
SHINDE PRASAD VILAS
Under the guidance of,
Miss.PATIL.A.S

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RAJARAM COLLEGE, KOLHAPUR
DEPARTMENT OF STATISTICS
This is to certify that the project entitled “THE
STUDY OF MOBILE PHONES” Is being
submitted by
SHINDE PRASAD VILAS
As a partial SY B.Sc., is a record of bonafide
work carried out by them under my
Supervision and guidance. To the best of our
knowledge the matter Presented in the project
has not been submitted earlier.
Place: Kolhapur
Date:
Project Guide Examiner Head,
Department of statistics,
Rajaram College Kolhapur.

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ACKNOWLEDGEMENT
We have a great pleasure while submitting this project
report “A STUDY OF MOBILE PHONES”.
We are thankful to Dr.P.B.Lakhani, head of department, for
giving permission for doing this project. We express deep
gratitude towards our project guide Miss. A.S.PATIL, who
gave us guidance and offered us several valuable suggestions
for developing this project in systematic and presentable
manner. Also we are thankful to Miss.P.V.Ransubhe,
A.M.Makandar and Mr.S.L.Patil, all teaching and non-
teaching staff of dept. Of Statistics and Mathematics.
We are also thankful to Mr.A.V.POWAR sir. He is a lecturer in
Shahu College Kolhapur.
We take opportunity to thank those directly or indirectly
related to our project work without their active co-operation
and guidance it would have become extremely difficult to
complete this task in time.
Last but not least, we would like to express our higher regards
to our friends and our parents who inspires us in this work,
without their inspiration the work has almost impossible.

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CONTENTS
1. Introduction ……….. 5
2. Objectives ……….. 6
3. Data Collection Method ……….. 7
4. Statistical Tools Used For Data Collection ……….. 8
5. Analysis ………… 9
6. Major Findings ………… 44
7. Reference ………… 45

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INTRODUCTION
Now a days, there are many mobile companies have
launched the cell phone with advance technologies including
internet browser. According to the prices, so many People have
their own cell phone with some facilities. People use Mobile
phones for various purposes like calls, Internet etc. People also
use some feature like camera, gaming, text messaging etc. This
project is just an attempt to study the Mobile phones by using
methodology of statistics. After the China, India get second
number in mobile phone users in the world.

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OBJECTIVES
To study use of Mobile phones in India.
According to following factors:
 To verify Most of the People more than 3 hours’ time
spend on mobile phones.
 To verify the Mobile phone type & Quality are dependent.
 To verify that internet is most popular.
 To check most popular screen size of mobile phones.
 To check whether gender & internet usage are
independent or dependent.
 To check the time spend on different feature.
 To check 18+ how many people use calling even having
unlimited calls facility.

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DATA COLLECTION METHOD
In this Project, we want to study the Mobile Phones. For that
purpose the most of the people spend our valuable time mobile
phones, we have collected the data by using the statistical
sampling method.
For implementation of our goal, we have selected a sample of
200 People or students by using simple random sampling
method.
We have prepared a Questionnaire which overlaps all the
maximum characteristics of good Questionnaire.
All the question are mutually exclusive in nature.

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STATISTICAL TOOLS USED FOR DATA
ANALYSIS
1. Simple Bar Diagram
2. Pie chart
3. Sub-divided Bar Diagram
4. Multiple Bar Diagram
5. Chi-Square test for independence of attributes.
6. Correlation
7. Rank Correlation
8. Regression analysis
9. Histogram
10. Less & more than ogive curve
SOFTWARE USED
1. MS-Excel
2. R-Software

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ANALYSIS
Program:
>lb=seq(9.5,59.5,10)
>ub=seq(19.5,69.5,10)
>m=(lb+ub)/2
>m
[1] 14.5 24.5 34.5 44.5 54.5 64.5
>f=c(72,47,42,18,18,3)
>n=length(m)
>y=rep(m,f)
>m1=c(9.5,m,69.5)
>m1
[1] 9.5 14.5 24.5 34.5 44.5 54.5 64.5 69.5
>f1=c(0,f,0)
>f1
[1] 0 72 47 42 18 18 3 0
>hist(y,xlab="Age
Limits",ylab="Frequency",nclass=n,main="HISTOGRAM",col="pink")
>lines(m1,f1,"l",lwd=2,col="red")

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Result:
Mostly people are 10-20 age group.

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Program:
> lb=seq(9.5,59.5,10)
> ub=seq(19.5,69.5,10)
> f=c(72,47,42,18,18,3)
> lcf=cumsum(f);lcf
[1] 72 119 161 179 197 200
> k=length(f);k
[1] 6
> mcf=1:k
> for(i in 1:k)
+ {
+ mcf[i]=sum(f[k:i])
+ }
> mcf
[1] 200 128 81 39 21 3
> plot(lb,mcf,"l",xlim=c(9.5,69.5),xlab="Age Limits",ylab="lcf & mcf",main="More
& Less than frequency curve",lwd=2,col=12)
> lines(ub,lcf,"l",lwd=2,col=14)

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Result:
The median of age is 28.

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Program:
> x=c("0-50","50-100","100-150","150-200")
> a=c(6,8,4,13)
> b=c(21,15,23,25)
> c=c(23,27,23,12)
> d1=data.frame(a,b,c)
> d1
a b c
1 6 21 23
2 8 15 27
3 4 23 23
4 13 25 12
> barplot(t(d1),beside=F,xlab="size",ylab="Frequency",main="Size of
Handset",col=1:3,names.arg=x)
> legend(locator(1),legend=c("small","Medium","Large"),fill=1:3)

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Range:
1) Small (Less than 3 inch)
2) Medium (3 to 4.5 inch)
3) Large (more than 4.5 inch)
Conclusion:
 Most of the People large (more than 4.5 inch) size of handset refer.
 Most of the People large (more than 4.5 inch) size is most popular.

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Program:
> Screen=c("Small","Medium","Large","Extra Large")
> choose=c(55,66,53,46)
> choose
[1] 55 66 53 46
> d=data.frame(Screen,choose)
> d
Screen choose
1 Small 55
2 Medium 66
3 Large 53
4 Extra Large 46
> barplot(choose,xlab="Screen",ylab="choice",main="Size of refer
Screen",col=c(2,5,11,12))
> legend(locator(1),legend=Screen,fill=c(2,5,11,12))

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Range:
 Small (Less than 2.5 inch)
 Medium (2.5 to 3.5 inch)
 Large (3.5 to 4.5 inch)
 3) Extra Large (more than 4.5 inch)
Result:
Most of the people medium (2.5 to 3.5 inch) size popular.
Most of the people medium (2.5 to 3.5 inch) size refer

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Hypothesis:
H0: Gender & Internet usages are independent
H1: Gender & Internet usages are not independent
Observation Table:
Male Female Total
Yes 81 58 139 0.695
No 16 13 29 0.145
May be 22 10 32 0.16
Total 119 81 200
82.705 56.295
Expected 17.255 11.745 Chisq value 0.4847153
Frequency 19.04 12.96
Observation:
Chi-square tabulated value =5.991
chisq(cal) < chisq(tabulated)
Accept Ho
Conclusion:
Gender & Internet usages are independent

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Program:
> x=c("100-249","250-399","400-549","550+")
> y=c(122,47,23,8)
> pie(y, main="Money willing spend on mobile per month", col=c(3,2,1,7), label=x)
> legend(locator(1),legend=x,fill=c(3,2,1,7))

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Limitations:
 550+ rupees means Money spend more than 550 rupees.
Result:
 Mostly people are 100-249 rupees willing spend on a mobile.
 4% people are 550+ rupees willing spend on a mobile.

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Program:
> x=c("Less than 30 min.","30min to 1 hour","1 to 2 hour","more than 2 hour")
> y=c(22,38,46,94)
> pie(y, main="Time Spend on Mobile in a day", col=2:5, label=x)
> legend(locator(1),legend=x,fill=2:5)

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Result:
 Most People spend more than 2 hour’s time on their
mobile phones. .

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Mobile phones & power bank are independent or not using MS Excel.
Hypothesis:
Ho: Mobile phones & power bank are independent.
Ha: Mobile phones & power bank are not independent.
Observation table:
Keypad Touch phone Total
Yes 5 39 44 0.22
No 47 109 156 0.78
Total 52 148 200
11.44 32.56 Expected
40.56 115.44 Value
Chisq value 0.012205

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Calculation:
At 5% level of significance
chisquare table value = 3.841
chisquare calculate value = 0.01225
chisquare calculate value < chisquare table value.
Accept Ho.
Therefore 'Ho' is true.
Result:
 Mobile phones & power bank are independent.

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Program:
> feature=c("CAMERA","INTERNET", "GAMING","TEXT MESSANGING")
> feature
[1] "CAMERA" "INTERNET" "GAMING" "TEXT MESSANGING"
> choose=c(59,127,53,31)
> choose
[1] 59 127 53 31
> d=data.frame(feature,choose)
> d
feature choose
1 CAMERA 59
2 INTERNET 127
3 GAMING 53
4 TEXT MESSANGING 31
> barplot(choose,xlab="feature",ylab="choice",main="simple bar
diagram",col=c(2,3,5,8))
> legend(locator(1),legend=feature,fill=c(2,3,5,8))

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Result:
 Internet is most popular as well as favorite feature.

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Analysis of approximately call receive call in a day.
Program:
> x=c("0-6","6-12","12-18","18+")
> y=c(56,59,40,45)
> pie(y, main="Recieve Call in a Day", col=c("red","green","blue","pink"), label=x)
> legend(locator(1),legend=x,fill=c("red","green","blue","pink"))

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Conclusion:
 Most people receive 6-12 calls per day.
 Most 18+ people use 22.5 % calling even having unlimited calls
facility
Program:
> x=c("camera","Internet","gaming","messanging","other")
> a=c(95,39,111,119,49)
> b=c(91,61,44,59,85)

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> c=c(11,35,33,14,39)
> d=c(2,39,9,5,23)
> e=c(1,26,3,3,4)
> d1=data.frame(a,b,c,d,e)
> d1
a b c d e
1 95 91 11 2 1
2 39 61 35 39 26
3 111 44 33 9 3
4 119 59 14 5 3
5 49 85 39 23 4
>barplot(t(d1),beside=T,xlab="Feature",ylab="population",ylim=c(0,125),main="Ti
me spend on different feature",col=1:5,names.arg=x)
> legend(locator(1),legend=c("Don't use","less than 30min.","30min to
1hour","1to 2hour","2 to3hour"),fill=1:5)

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Limitations:
Some People use any feature more than three hours.
Result:
 Most of the people does not use camera , game , messanging
daily.
 Some people use less than 30 min Internet & other features.
 13 % people use internet 2 to 3 hours in day today life.
 30% people use internet less than 30 min in daily life.

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Hypothesis:
Ho: Mobile phone type & Quality are independent.
Ha: Mobile phone type & Quality are dependent.
Observation table:
Touch keypad Total
low 20 39 59 0.1475
Good 19 41 60 0.15
Very Good 36 49 85 0.2125
Better 45 22 67 0.1675
Best 80 49 129 0.3225
Total 200 200 400
29.5 29.5
30 30
42.5 42.5 Expected
33.5 33.5 Value
64.5 64.5
2.39918E-06

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Calculation:
At 5% level of significance
chi-square table value = 9.400
chi-square calculate value = 2.39918E-06
chi-square calculate value > chi-square table value.
Reject Ho.
Therefore 'Ha' is true.
Result:
Alternative Hypothesis is true.
The Mobile phone type & Quality are dependent.

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Program:
> x=c("Keypad”, “Touch screen")
> a=c(20,39)
> b=c(19,41)
> c=c(36,49)
> d=c(45,22)
> e=c(80,49)
> d1=data.frame(a,b,c,d,e)
> d1
a b c d e
1 20 19 36 45 80
2 39 41 49 22 49
> barplot(t(d1),beside=F,xlab="Mobile
phones",ylab="Rate",ylim=c(0,250),main="Quality of keypad & touch
phone",col=6:10,names.arg=x)
> legend(locator(1),legend=c("Low Good","Good","Very
Good","Better","Best"),fill=6:10)

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Result :
 Mobile phones depends upon its qualities.

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Observations:
SUMMARY
OUTPUT
Regression
Statistics
Multiple R 0.211712
R Square 0.044822
Adjusted R
Square
-0.27357
Standard
Error
12.46498
Observations 5
ANOVA
Df SS MS F Significance
F
Regression 1 21.87309 21.87309 0.140776 0.732468
Residual 3 466.1269 155.3756
Total 4 488
Coefficients Standard
Error
t Stat P-value Lower 95% Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 36.24496 11.45586 3.16388 0.050719 -0.21269 72.70262 -
0.21269
72.70262
X Variable 1 0.093876 0.250202 0.375201 0.732468 -0.70238 0.89013 -
0.70238
0.89013

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Result: the regression equation is
Y=36.24496+0.093876X

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Program:
> x=c("List together","List of seprste group","Both")
> x
[1] "List together" "List of seprste group" "Both"
> choose=c(61,79,60)
> choose
[1] 61 79 60
> d=data.frame(x,choose)
> d
x choose
1 List together 61
2 List of seprste group 79
3 Both 60
> barplot(choose,xlab="x",ylab="choice",main="simple bar diagram",col=1:3)
> legend(locator(1),legend=x,fill=1:3).

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Note: List together & List of separate group is called as a 'Both'.
Conclusion:
Most People prefer separately listed call log.

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Program:
> menu=c("List","Icons")
> menu
[1] "List" "Icons"
> choose=c(67,133)
> choose
[1] 67 133
> d=data.frame(menu,choose)
> d
menu choose
1 List 67
2 Icons 133
> barplot(choose,xlab="menu",ylab="choice",main="Menu
representation",ylim=c(0,140),col=c(2,3))
> legend(locator(1),legend=menu,fill=c(2,3))

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Conclusion:
 # Icons are most popular.

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Analysis of data usages in a month.
Program:
> x=c("0-10GB","10-20 GB","20-30 GB","more than 30 GB")
> y=c(94,43,46,17)
> pie(y, main="DATA USAGES", col=3:6, label=x)
> legend(locator(1),legend=x,fill=3:6)

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Limitations:
1) If the people are not internet users then their daily data usage
0-10 GB.
Result:
1) Most people use 0 - 10 GB data daily.
2) 8.5 % people use more than 30 GB data.

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Program:
> OS=c("ANDROID","MICROSOFT", "iOS","Other")
> OS
[1] "ANDROID" "MICROSOFT" "iOS" "Other"
> choose=c(127,22,35,16)
> choose
[1] 127 22 35 16
> d=data.frame(OS,choose)
> d
OS choose
1 ANDROID 127
2 MICROSOFT 22
3 iOS 35
4 Other 16
> d
[1] 2 39 9 5 23
> barplot(choose,xlab="OS",ylab="choice",main="simple bar diagram",col=2:5)
> legend(locator(1),legend=OS,fill=2:5)

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Result:
1) The favorite Operating System is Android.

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Observation Table:
Feature Operating
system
Rank Feature Rank
Operating
system
58 127 2 1
96 22 1 3
32 35 3 2
14 16 4 4
Rank
Correlation
0.4
Observation:
Rank Correlation = 0.4
Result:
 Rank Correlation between Favorite Feature & Operating
system is 0.4.
 Rank Correlation lies between 0 to 1. Hence it is positively
correlated.

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MAJOR FINDING
 It is concluded that male and female & internet users are
independent.
 It concluded that internet is mostly popular.
 Samples are taken randomly.
 Preference of mobile users only.
 It is concluded that mobile phones & its qualities are
dependent.

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REFERENCES
1. Fundamental of Mathematical statistics by Gupta and
Kapoor.
2. SY BSc Text Books (Statistics).
3. Programmed Statistics by B.L.Agrawal.
4. 100 Statistical test.
5. Fundamental of Applied statistics by Gupta and .
Kapoor.

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