a study on factors affecting the selection of mobile phone network service providers
TRANSCRIPT
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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
Cross-products1.080 10.560
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
Sig. (1-tailed) .081Sum of Squares and
Cross-products10.190 3.140
Covariance .103 .032N 100 100
Minutes per day Pearson Correlation .141 1Sig. (1-tailed)
.081
Sum of Squares and
Cross-products3.140 48.840
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
Sig. (1-tailed) .097Sum of Squares and
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
Sig. (1-tailed) .055Sum of Squares and
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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