DataAnalysis Footfalls

7/28/2019 DataAnalysis Footfalls

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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_theory


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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


sided)

Pearson Chi-Square .823a

2 .663

Likelihood Ratio .821 2 .663N of Valid Cases 100


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


sided)

Pearson Chi-Square .823a

2 .663

Likelihood Ratio .821 2 .663




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


sided)


2 .000






.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


sided)


2 .006






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& 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


sided)


2 .000







& 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


sided)


4 .319





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


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


sided)


4 .063






.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


sided)


4 .063






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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


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)


4 .546







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


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


sided)


4 .045






.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


sided)


4 .088






.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


sided)


6 .001






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.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


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


sided)


6 .000







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


sided)


6 .000







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


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


sided)


6 .938







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


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


sided)


6 .701








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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


sided)


6 .806







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.

DataAnalysis Footfalls

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