A study on Factors affecting the selection of Mobile Phone Network Service Providers

8/7/2019 A study on Factors affecting the selection of Mobile Phone Network Service Providers

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Cross-Tabulation

Monthly Income and Monthly Expense for Mobile

Frequency of changing the SIM and Monthly Income

Chi Square Tests

Age and Impact of Celebrities

Monthly income and Monthly Expense

Usage of SIM and Tariff Scheme.22

Correlation Correlation between Monthly expenditure and Age

Correlation between Minutes Per Day And Age

Correlation between SMS per day and Age

Correlation between Tariff preference and Age

ANOVA

Monthly Expenditure and Monthly Income

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Monthly Income * Monthly Expenses Cross tabulationCount

Monthly Expenses10-50 rupees 50-150 rupees 150-400 rupees >400 rupe

Monthly Income 20000 0 1 1

Total 13 34 39

Inference: Here irrespective of Monthly income, most of

spent `50-`400 per month.


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SIM changed in last 3 years * Monthly Income Crosstabulation Monthly Income

2000SIM changed in last 3 years 0-2 times 20 50 9

2-5 times 5 7 2>10 times 0 1 0

Total 25 58 11

Inference: Here, irrespective of monthly income, most

people have changed their SIM very less(0-2 times).


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Correlations

Age GroupImpact of

celebritiesAge Group Pearson Correlation 1 .104

Sig. (1-tailed) .151Sum of Squares and

Cross-products10.190 1.080

Covariance .103 .011N 100 100

Impact of celebrities Pearson Correlation .104 1Sig. (1-tailed) .151Sum of Squares and


Covariance .011 .107N 100 100

We got level as

greater

0.05. S

significant

between t

H0: There is no significant relationship between Age and Impact of Celebrities

H1: There is significant relationship between Age and Impact of Celebrities.

Inference:


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Chi-Square TestsValue df

Asymp. Sig. (2-

sided)Pearson Chi-Square 18.323a 9 .032Likelihood Ratio 16.743 9 .053Linear-by-Linear Association 8.255 1 .004N of Valid Cases 100a. 10 cells (62.5%) have expected count less than 5. The minimum

expected count is .65.

We got t

level as

less than

So the

significant

between t

H0: There is no significant relationship between Monthly income and

expense.

H1: There is significant relationship between Monthly income and mon

Inference:

Hypothesis:


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Chi-Square Tests

Value dfAsymp. Sig. (2-

sided)Pearson Chi-Square 18.425a 6 .005Likelihood Ratio 17.262 6 .008Linear-by-Linear Association 3.171 1 .075N of Valid Cases 99a. 6 cells (50.0%) have expected count less than 5. The minimum expected

count is .97.

We got level as

much lesse

there is

relationshi

variables.

H0: There is no significant relationship between Usage of SIM an

Scheme.

H1: There is significant relationship between Usage of SIM an

Scheme.

Inference:

Hypothesis:


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Correlations

Age GroupMonthly

ExpensesAge Group Pearson Correlation 1 -.030

Sig. (1-tailed) .382N 100 100

Monthly Expenses Pearson Correlation -.030 1Sig. (1-tailed) .382N 100 100

The outpu

of scat

significant

relationsh

monthly e

(since r=

greater t0.382. So

null hypot

H0: There is no relationship exist between monthly expenditure and

H1: There is relationship exist between monthly expenditure and age

Inferenc

Hypothesis:


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Correlations

Age GroupMinutes per

dayAge Group Pearson Correlation 1 .141



Covariance .103 .032N 100 100

Minutes per day Pearson Correlation .141 1Sig. (1-tailed)

.081

Sum of Squares and


Covariance .032 .493N 100 100

The output

of scatte

significant

relationship

time and a

So p-value

0.05 and i

can reject t

H0: There isno relationship exist between talk time and age.

H1: There is relationship exist between talk time and age.

Inference

Hypothesis:


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Correlations

Age GroupSMS per

dayAge Group Pearson Correlation 1 -.131


Cross-products10.190 -3.760

Covariance .103 -.038N 100 100

SMS per day Pearson Correlation -.131 1Sig. (1-tailed) .097Sum of Squares and

Cross-products-3.760 81.040

Covariance -.038 .819N 100 100

The output conf

scatter plot ha

negative rela

between SMS p

(since r=-.131)

greater than 0

0.131. So we c

null hypothesis.

H0: There isno relationship exist between SMS per day and age.

H1: There is relationship exist between SMS per day and age.

Inference:

Hypothesis:


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Correlations

Age GroupCall Tariff

preferenceAge Group Pearson Correlation 1 -.161


Cross-products10.190 -3.000

Covariance .103 -.031N 100 99

Call Tariff preference Pearson Correlation -.161 1Sig. (1-tailed) .055Sum of Squares and

Cross-products-3.000 34.000

Covariance -.031 .347N 99 99

The output of scatter

significant

relationship

tariff prefe

(since r=-.1

greater tha

0.161. So w

null hypothe

H0: There isno relationship exist between tariff preference and age.

H1: There is relationship exist between tariff preference and age.

Inference:

Hypothesis:


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Hypothesis:

ANOVAMonthly Expenses

Sum of Squares df Mean Square F Sig.Between Groups 7.051 3 2.350 3.143 .029Within Groups 71.789 96 .748Total 78.840 99

Here de

3 and 9

value ob

0.05 we

hypothes

and p-vis


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Cross Tab

Monthly Income and Monthly Expense for Mobile Here irrespective of Monthly income, most of the people spent `50-`400 per mo

Frequency of changing the SIM and Monthly Income

Here, irrespective of monthly income, most of the people have changed their SIMtimes).

Chi-Square Test

Age and Impact of Celebrities No significant relationship.

Monthly income and Monthly Expense

No Significant relationship.

Usage of SIM and Tariff Scheme

No Significant relationship.


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Correlation

Correlation between Monthly expenditure and Age Relationship exists between these two.

Correlation between Minutes Per Day And Age

Relationship exists between these two.

Correlation between SMS per day and Age

No relationship exists.

Correlation between Tariff preference and Age No relationship exists.

ANOVA

Monthly Expenditure and Monthly Income

Significant relation between Monthly Expenditure and Monthly Income.


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Documents

A study on Factors affecting the selection of Mobile Phone Network Service Providers