Post on 03-Apr-2018
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3. DATA ANALYSIS
3.1 EXPLORATORY DATA
Gender
Fig 3.1
We will find various factors which will affects the customer decisions of going to a branch
among male and female .Our data is dominated by the male respondents. We have taken 100
respondents out of which 56 are males and 44 are females i.e. more than 50% males.
Gender Values Percentage
Male 56 56
Female 44 44
Total 100 100
Table 3.1
Male
56%
Female
44%
Gender
Male
Female
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Age
Fig 3.2
The various age categories which we have taken for this data is defined in the chart above.
Most of our respondents are from age 20-3 year category and the least number of
respondents are less than 20. Overall we have almost all category of respondents in our
research.
Age Values Percentage
Less than 20 0 0
20-30 77 77
30-50 21 21
50 and above 2 2
Total 100 100
Table 3.2
less
than
20
0%
20-30
77%
30-50
21%
50 and above
2%Age(In Years)
less than 20
20-30
30-50
50 and above
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Occupation
Fig 3.3
The Occupation of a person effects a lot in case of decision making of a person when taking a
decision of whether to adopt personal banking or online banking decision. Most of the peoplewere salaried employees .There are also students who account for almost 45% and the
homemaker, self employed and retired persons contributing very less.
Occupation Values Percentage
Student 40 40
SalariedEmployee 48 48
Homemaker 1 1
SelfEmployed 0 0
Retired 1 1
Total 100 100
Table 3.3.
Student
45%Salaried Employee
53%
Homemaker
1%
Self Employe
0%Retired
1% Occupation
Student
Salaried Employee
Homemaker
Self Employe
Retired
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3.2 CHI - SQUARE DISTRIBUTION
In probability theory and statistics, the chi-square Distribution , with kdegrees of
freedom is the distribution of a sum of the squares ofkindependent standard normal random
variables. It is one of the most widely used probability distributions in inferential statistics,
e.g., in hypothesis or in construction ofconfidence intervals. When there is a need to contrast
it with the non central chi-squared distribution, this distribution is sometimes called
the central chi-squared distribution.
The chi-squared distribution is used in the common chi-squared tests forgoodness of fit of an
observed distribution to a theoretical one, the independence of two criteria of classification
ofqualitative data, and in confidence interval estimation for a population standard
deviation of a normal distribution from a sample standard deviation. Many other statistical
tests also use this distribution, like Friedman's analysis of variance by ranks.
3.2.1 CHI-SQUARE TEST
A chi-squared test, also referred to as chi-square test or test, is
any statistical hypothesis test in which the sampling distribution of the test statistic is a chi-
squared distribution when the null hypothesis is true, or any in which this
is asymptotically true, meaning that the sampling distribution (if the null hypothesis is true)
can be made to approximate a chi-squared distribution as closely as desired by making thesample size large enough.H1a.Gender of the person affects the frequency of opting Online Banking for comfort .
Recode_comfort_Ob * Gender Cross tabulation
Count
Gender Total
1 2
Recode_comfort_Ob
1.00 2 0 2
2.00 35 16 51
3.00 19 28 47
Total 56 44 100
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 9.499a
2 .009
Likelihood Ratio 10.315 2 .006
N of Valid Cases 100
http://en.wikipedia.org/wiki/Probability_theoryhttp://en.wikipedia.org/wiki/Degrees_of_freedom_(statistics)http://en.wikipedia.org/wiki/Degrees_of_freedom_(statistics)http://en.wikipedia.org/wiki/Independence_(probability_theory)http://en.wikipedia.org/wiki/Standard_normalhttp://en.wikipedia.org/wiki/Probability_distributionhttp://en.wikipedia.org/wiki/Inferential_statisticshttp://en.wikipedia.org/wiki/Confidence_intervalhttp://en.wikipedia.org/wiki/Noncentral_chi-squared_distributionhttp://en.wikipedia.org/wiki/Chi-squared_testhttp://en.wikipedia.org/wiki/Goodness_of_fithttp://en.wikipedia.org/wiki/Statistical_independencehttp://en.wikipedia.org/wiki/Data_analysishttp://en.wikipedia.org/wiki/Confidence_intervalhttp://en.wikipedia.org/wiki/Standard_deviationhttp://en.wikipedia.org/wiki/Standard_deviationhttp://en.wikipedia.org/wiki/Friedman_testhttp://en.wikipedia.org/wiki/Statisticalhttp://en.wikipedia.org/wiki/Hypothesis_testhttp://en.wikipedia.org/wiki/Sampling_distributionhttp://en.wikipedia.org/wiki/Chi-squared_distributionhttp://en.wikipedia.org/wiki/Chi-squared_distributionhttp://en.wikipedia.org/wiki/Null_hypothesishttp://en.wikipedia.org/wiki/Null_hypothesishttp://en.wikipedia.org/wiki/Chi-squared_distributionhttp://en.wikipedia.org/wiki/Chi-squared_distributionhttp://en.wikipedia.org/wiki/Sampling_distributionhttp://en.wikipedia.org/wiki/Hypothesis_testhttp://en.wikipedia.org/wiki/Statisticalhttp://en.wikipedia.org/wiki/Friedman_testhttp://en.wikipedia.org/wiki/Standard_deviationhttp://en.wikipedia.org/wiki/Standard_deviationhttp://en.wikipedia.org/wiki/Confidence_intervalhttp://en.wikipedia.org/wiki/Data_analysishttp://en.wikipedia.org/wiki/Statistical_independencehttp://en.wikipedia.org/wiki/Goodness_of_fithttp://en.wikipedia.org/wiki/Chi-squared_testhttp://en.wikipedia.org/wiki/Noncentral_chi-squared_distributionhttp://en.wikipedia.org/wiki/Confidence_intervalhttp://en.wikipedia.org/wiki/Inferential_statisticshttp://en.wikipedia.org/wiki/Probability_distributionhttp://en.wikipedia.org/wiki/Standard_normalhttp://en.wikipedia.org/wiki/Independence_(probability_theory)http://en.wikipedia.org/wiki/Degrees_of_freedom_(statistics)http://en.wikipedia.org/wiki/Degrees_of_freedom_(statistics)http://en.wikipedia.org/wiki/Probability_theory7/28/2019 DataAnalysis Footfalls
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a. 2 cells (33.3%) have expected count less than 5. The minimum
expected count is .88.
The significant value is .009 at 2 degree of freedom with confidence of 33.33%, is less than
.05, hence the hypothesis is accepted. Thus this signifies there is a relationship between
Gender and Comfort of Online Banking. This implies Gender of the person affects the
frequency of opting for comfort online banking.
H1b.Gender of the person affects the Frequency of opting Online Banking for
efficiency.
Recode_efficiency_OB * Gender Crosstabulation
Count
Gender Total
1 2
Recode_efficiency_OB
1.00 3 2 5
2.00 35 24 59
3.00 18 18 36
Total 56 44 100
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square .823a
2 .663
Likelihood Ratio .821 2 .663N of Valid Cases 100
a. 2 cells (33.3%) have expected count less than 5. The minimum
expected count is 2.20.
The significant value is .663 at 2 degree of freedom with confidence of 33.3%, is greater than
.05, hence the hypothesis is rejected. Thus this signifies there is no relationship between
Gender & efficiency of online banking. This implies that Gender of the person does not affect
the Frequency of opting online banking for efficiency.
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H1c.Gender of the person affects the Frequency of opting Online banking for security.
Recode_Security_OB * Gender Crosstabulation
Count
Gender Total
1 2
Recode_Security_OB
1.00 3 2 5
2.00 35 24 59
3.00 18 18 36
Total 56 44 100
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square .823a
2 .663
Likelihood Ratio .821 2 .663
N of Valid Cases 100
a. 2 cells (33.3%) have expected count less than 5. The minimum
expected count is 2.20.
The significant value is .663 at 2 degree of freedom with confidence of 33.3%, is greater
than .05, hence the hypothesis is rejected. Thus this signifies there is no relationship between
Gender & security of online banking. This implies that Gender of the person does not affect
the Frequency of opting online banking for security.
H1d.Gender of the person affects the Frequency of opting Personal Banking for
accessibility.
Recode_Accessibility_PB * Gender Crosstabulation
Count
Gender Total
1 2
Recode_Accessibility_PB
1.00 1 6 7
2.00 41 15 56
3.00 14 23 37
Total 56 44 100
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Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 16.632a
2 .000
Likelihood Ratio 17.278 2 .000
N of Valid Cases 100
a. 2 cells (33.3%) have expected count less than 5. The minimum
expected count is 3.08.
The significant value is .000 at 2 degree of freedom with confidence of 33.3%, is less than
.05, hence the hypothesis is accepted. Thus this signifies there is relationship between Gender
& accessibility of personal banking. This implies that Gender of the person affects the
Frequency of opting Personal Banking for accessibility.
H1e.Gender of the person affects the Frequency of opting Personal Banking for
efficiency.
Recode_Efficiency_PB * Gender Crosstabulation
Count
Gender Total
1 2
Recode_Efficiency_PB
1.00 2 1 3
2.00 42 20 62
3.00 12 23 35
Total 56 44 100
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 10.305a
2 .006
Likelihood Ratio 10.392 2 .006
N of Valid Cases 100
a. 2 cells (33.3%) have expected count less than 5. The minimum
expected count is 1.32.
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The significant value is .006 at 2 degree of freedom with confidence of 33.3%, is less than
.05, hence the hypothesis is accepted. Thus this signifies there is relationship between Gender
& efficiency of personal banking. This implies that Gender of the person affects the
Frequency of opting Personal Banking for efficiency.
H1f.Gender of the person affects the Frequency of opting Personal Banking for comfort.
Recode_Comfort_PB * Gender Crosstabulation
Count
Gender Total
1 2
Recode_Comfort_PB
1.00 3 0 3
2.00 46 22 68
3.00 7 22 29
Total 56 44 100
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 18.049a
2 .000
Likelihood Ratio 19.519 2 .000
N of Valid Cases 100
a. 2 cells (33.3%) have expected count less than 5. The minimum
expected count is 1.32.
The significant value is .000 at 2 degree of freedom with confidence of 33.3%, is less than
.05, hence the hypothesis is accepted. Thus this signifies there is relationship between Gender
& comfort of personal banking. This implies that Gender of the person affects the Frequency
of opting Personal Banking for comfort.
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H2a.Age of the person affects the frequency of opting Online banking for comfort.
Recode_comfort_Ob * Age Crosstabulation
Count
Age Total
2 3 4
Recode_comfort_Ob
1.00 1 1 0 2
2.00 38 13 0 51
3.00 38 7 2 47
Total 77 21 2 100
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 4.701a
4 .319
Likelihood Ratio 5.330 4 .255
N of Valid Cases 100
a. 5 cells (55.6%) have expected count less than 5. The minimum
expected count is .04.
The significant value is .319 at 4 degree of freedom with confidence of 55.6%, is greater than.05, hence the hypothesis is rejected. Thus this signifies there is no relationship between Age
and Comfort of Online Banking. This implies Age of the person does not affect the frequency
of opting for comfort online banking.
H2b.Age of the person affects the Frequency of opting Online Banking for efficiency.
Recode_efficiency_OB * Age Crosstabulation
Count
Age Total
2 3 4
Recode_efficiency_OB
1.00 3 2 0 5
2.00 43 16 0 59
3.00 31 3 2 36
Total 77 21 2 100
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Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 8.923a
4 .063
Likelihood Ratio 10.010 4 .040
N of Valid Cases 100
a. 5 cells (55.6%) have expected count less than 5. The minimum
expected count is .10.
The significant value is .063 at 4 degree of freedom with confidence of 55.6%, is greater than
.05, hence the hypothesis is rejected. Thus this signifies there is no relationship between Age
& efficiency of online banking. This implies that age of the person does not affect the
Frequency of opting online banking for efficiency.
H2c. Age of the person affects the Frequency of opting Online Banking for security.
Recode_Security_OB * Age Crosstabulation
Count
Age Total
2 3 4
Recode_Security_OB
1.00 3 2 0 5
2.00 43 16 0 59
3.00 31 3 2 36
Total 77 21 2 100
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 8.923a
4 .063
Likelihood Ratio 10.010 4 .040
N of Valid Cases 100
a. 5 cells (55.6%) have expected count less than 5. The minimum
expected count is .10.
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The significant value is .063 at 4 degree of freedom with confidence of 55.6%, is greater
than .05, hence the hypothesis is rejected. Thus this signifies there is no relationship between
Age & security of online banking. This implies that age of the person does not affect the
Frequency of opting online banking for security.
H2d. Age of the person affects the Frequency of opting Personal Banking for
accessibility.
Recode_Accessibility_PB * Age Crosstabulation
Count
Age Total
2 3 4
Recode_Accessibility_PB
1.00 6 1 0 7
2.00 46 9 1 56
3.00 25 11 1 37
Total 77 21 2 100
Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 3.074a
4 .546
Likelihood Ratio 3.139 4 .535
N of Valid Cases 100
a. 4 cells (44.4%) have expected count less than 5. The minimum
expected count is .14.
The significant value is .546 at 4 degree of freedom with confidence of 44.4%, is greater
than .05, hence the hypothesis is rejected. Thus this signifies there is no relationship between
Age & accessibility of online banking. This implies that age of the person does not affect the
Frequency of opting online banking for accessibility.
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H2e. Age of the person affects the Frequency of opting Personal Banking for efficiency.
Recode_Efficiency_PB * Age Crosstabulation
Count
Age Total
2 3 4
Recode_Efficiency_PB
1.00 2 1 0 3
2.00 54 7 1 62
3.00 21 13 1 35
Total 77 21 2 100
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 9.755a
4 .045
Likelihood Ratio 9.599 4 .048
N of Valid Cases 100
a. 5 cells (55.6%) have expected count less than 5. The minimum
expected count is .06.
The significant value is .045 at 4 degree of freedom with confidence of 55.6%, is less than
.05, hence the hypothesis is accepted. Thus this signifies there is relationship between Age &
efficiency of personal banking. This implies that age of the person affects the Frequency of
opting Personal Banking for efficiency.
H2f. Age of the person affects the Frequency of opting Personal Banking for comfort.
Recode_Comfort_PB * Age Crosstabulation
Count
Age Total
2 3 4
Recode_Comfort_PB
1.00 3 0 0 3
2.00 56 10 2 68
3.00 18 11 0 29
Total 77 21 2 100
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Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 8.111a
4 .088
Likelihood Ratio 8.760 4 .067
N of Valid Cases 100
a. 5 cells (55.6%) have expected count less than 5. The minimum
expected count is .06.
The significant value is .088 at 4 degree of freedom with confidence of 55.6%, is greater than
.05, hence the hypothesis is rejected. Thus this signifies there is no relationship between Age
& comfort of personal banking. This implies that age of the person does not affects the
Frequency of opting Personal Banking for comfort.
H3a.Occupation of the person affects the frequency of opting Online banking for
comfort.
Recode_comfort_Ob * Occupation Crosstabulation
Count
Occupation Total
1 2 3 5
Recode_comfort_Ob
1.00 0 2 0 0 2
2.00 10 40 1 0 51
3.00 30 16 0 1 47
Total 40 58 1 1 100
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 23.793a
6 .001
Likelihood Ratio 25.909 6 .000
N of Valid Cases 100
a. 8 cells (66.7%) have expected count less than 5. The minimum
expected count is .02.
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The significant value is .001 at 6 degree of freedom with confidence of 66.7%, is less than
.05, hence the hypothesis is accepted. Thus this signifies there is relationship between
Occupation and Comfort of Online Banking. This implies occupation of the person affects the
frequency of opting for comfort online banking.
H3b. Occupation of the person affects the Frequency of opting Online banking for
efficiency.
Recode_efficiency_OB * Occupation Crosstabulation
Count
Occupation Total
1 2 3 5
Recode_efficiency_OB
1.00 0 4 1 0 5
2.00 14 45 0 0 59
3.00 26 9 0 1 36
Total 40 58 1 1 100
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 46.600a
6 .000
Likelihood Ratio 36.210 6 .000
N of Valid Cases 100
a. 8 cells (66.7%) have expected count less than 5. The minimum
expected count is .05.
The significant value is .000 at 6 degree of freedom with confidence of 66.7%, is less than
.05, hence the hypothesis is accepted. Thus this signifies there is relationship between
occupation & efficiency of online banking. This implies that occupation of the person does
not affect the Frequency of opting online banking for efficiency.
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H3c. Occupation of the person affects the Frequency of opting Online banking for
security.
Recode_Security_OB * Occupation Crosstabulation
Count
Occupation Total
1 2 3 5
Recode_Security_OB
1.00 0 4 1 0 5
2.00 14 45 0 0 59
3.00 26 9 0 1 36
Total 40 58 1 1 100
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 46.600a
6 .000
Likelihood Ratio 36.210 6 .000
N of Valid Cases 100
a. 8 cells (66.7%) have expected count less than 5. The minimum
expected count is .05.
The significant value is .000 at 6 degree of freedom with confidence of 66.7%, is less than
.05, hence the hypothesis is accepted. Thus this signifies there is relationship between
occupation & security of online banking. This implies that occupation of the person affects
the Frequency of opting online banking for security.
H3d. Occupation of the person affects the Frequency of opting Personal Banking for
accessibility.
Recode_Accessibility_PB * Occupation Crosstabulation
Count
Occupation Total
1 2 3 5
Recode_Accessibility_PB
1.00 3 4 0 0 7
2.00 21 33 1 1 56
3.00 16 21 0 0 37
Total 40 58 1 1 100
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Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 1.790a
6 .938
Likelihood Ratio 2.536 6 .864
N of Valid Cases 100
a. 8 cells (66.7%) have expected count less than 5. The minimum
expected count is .07.
The significant value is .938 at 6 degree of freedom with confidence of 66.7%, is greater
than .05, hence the hypothesis is rejected. Thus this signifies there is no relationship between
occupation & accessibility of online banking. This implies that occupation of the person does
not affect the Frequency of opting online banking for accessibility.
H3e. Occupation of the person affects the Frequency of opting Personal Banking for
efficiency.
Recode_Efficiency_PB * Occupation Crosstabulation
Count
Occupation Total
1 2 3 5
Recode_Efficiency_PB
1.00 1 2 0 0 3
2.00 21 39 1 1 62
3.00 18 17 0 0 35
Total 40 58 1 1 100
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 3.817a
6 .701
Likelihood Ratio 4.465 6 .614
N of Valid Cases 100
a. 8 cells (66.7%) have expected count less than 5. The minimum
expected count is .03.
The significant value is .701 at 6 degree of freedom with confidence of 66.7%, is greater
than .05, hence the hypothesis is rejected. Thus this signifies there is no relationship between
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occupation & efficiency of personal banking. This implies that occupation of the person does
not affect the Frequency of opting Personal Banking for efficiency.
H3f. Occupation of the person affects the Frequency of opting Personal Banking forcomfort.
Recode_Comfort_PB * Occupation Crosstabulation
Count
Occupation Total
1 2 3 5
Recode_Comfort_PB
1.00 1 2 0 0 3
2.00 24 42 1 1 68
3.00 15 14 0 0 29
Total 40 58 1 1 100
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 3.025a
6 .806
Likelihood Ratio 3.583 6 .733
N of Valid Cases 100
a. 8 cells (66.7%) have expected count less than 5. The minimum
expected count is .03.
The significant value is .806 at 6 degree of freedom with confidence of 66.7%, is greater
than .05, hence the hypothesis is rejected. Thus this signifies there is no relationship between
occupation & comfort of personal banking. This implies that occupation of the person does
not affect the Frequency of opting Personal Banking for comfort.
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4. Conclusion
From the above discussion of the data shows the relationship between the demographic
variables and the various factors of Online Banking and Personal Banking.
The overall mean of the data shows that the respondents are more interested in Online
banking (Mean =2.325) as compared to personal banking (Mean= 2.235) on the various
factors such as Comfort, Efficiency, Security and Accessibility of both the facilities provided
by the bank.
As our assumption declares that if online banking is preferred then the number of footfalls
will decrease in a bank so this comes out to be true as online banking is preferred over
personal banking as shown by the figures evaluated.