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A
Project Report
On
AGE GROUP INFLUENCE FACTOR FOR PURCHASING
INURANCE POLICY
Undertaken at:
HDFC STANDARD LIFE INSURANCE CO.LTD.
VALSAD
Submitted by:
JIGNESH P.YAGNIK
(07MBA60)
Guided by:
Mr. ANIL SARAOGI
MBA (2007-09)
SHRIMAD RAJCHANDRA INSTITUTE OF MANAGEMENT
AND COMPUTER APPLICATION
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ACKNOWLEDGEMENT
At this stage of my long educational journey, I look back and find that
though mine is a fairly sail, it has been memorizing extravagance of
memorable experience. At this gratifying moment of completion of my
research problem, I feel obliged to record my gratitude to those who
have helped me.
I wish to convey my special thanks to Mr. Jigar desai (Branch Manager)
and Mr. Digant desai (Sales Manager) at HDFC standard Life Insurance
co. Ltd., who has been a constant source of inspiration and
encouragement to me.
I feel immense pleasure in expressing my deep sense of respect and
indebtedness to my institute project guide, Mr.Anil saraogi, Faculty,
Shrimad Rajchandra Institute of Management & Computer application
(SRIMCA), Tarsadi for his valuable guidance throughout preparation of
this report.
I feel immense pleasure to thank Dr. B. C Patel, Director, Shrimad
Rajchandra Institute of Management & Computer application (SRIMCA),
Tarsadi for making available all facilities in fulfilling the requirements
for the research work. And I also thank those who helped me directly or
indirectly.
Jignesh p yagnik
(07MBA60)
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Executive Summary
Purpose:
The primary purpose is to study the factor which influences the
various age groups to buy the insurance policy.
The life insurance market in India is an underdeveloped market.
The penetration of life insurance products was 19 percent of the total
400 million of the insurable population.
Today, everyone in the world wants to secure their future. All
wants to eliminate the risk of uncertain life. So my study is to know
which factor influences various age groups to buy the insurance policy.
Design/Methodology/Approach:
The data has been collected through both primary and secondary
data collection methods. This project report includes secondary data
about the life insurance industry, company details, etc. Primary data
are collected through questionnaire filled by the respondents during
face to face interaction.
At the initial stage the research design is exploratory because
the research started with review of literature available on the Internet
and in books. Based on that, the final research statement was framed.
The subsequent research design is descriptive as it aims to describe
the factor which influences the various age groups to buy the
insurance policy.
Sampling design is non-probability and sampling method is
convenience sampling because of time and money constraints. The
sample size consisted of 160 respondents across the population.
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For the data analysis, various statistical tests like one sample t-
test, independent sample t test and measures of central tendencies
were applied through SPSS software.
Findings
1. 71.25% of respondents have given 1st rank to LIC; other
insurance companies have got very less frequency in
getting 1st rank. So we can say that LIC is at first position
in peoples mind.
2. 45.625% respondents preferred to invest in fix deposit.
3. 27% of respondent have unit linked endowment plan.
4. 95% of people prefer HDFC standard life to purchase new
policy.
5. 45% of people says that brand name influence them to
buy the policy.
6. For the age group of (18-25) return on investment is the
most important factor.
7. For the age group of (26-35) tax benefits is the most
important factor.
8. For the age group of (36-45) risk cover, transparency is
the most important factor.
9. For the age group of 46&above death benefit, withdrawal
option in the policy is the most important factor.
10. Total customer satisfaction index is 85%.
11. The policy holder are satisfied with risk cover ,tax benefits
safety ,transparency ,lock in period, withdrawal option in the
policy
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Table of content
Sr. No. Topic Page No.
1 INTRODUCTION
A. Industry profile
B. Company profile
2 RESEARCH METHODOLOGY
3 DATA ANALYSIS & INTERPRETATION
4 FINDINGS
5 CONCLUSIONS
6 RECOMMENDATIONS
7 BIBLIOGRAPHY
8 APPENDIX
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Ch-1 INTRODUCTION
[A] INDUSTRY PROFILE
1.1 Insurance
Insurance is basically a sharing device. The losses to assist
resulting from natural calamities like fire, flood, earthquake, accidents,
etc are met out of the common pool contributed by large number of
persons who are exposed to similar risks. This contribution of many is
used to pay the losses suffered by unfortunate few. However the basic
principle is that loss should occur as a result of natural calamities or
unexpected events which are beyond the human control. Secondly
insured person should not make my gains out of insurance.
1.2 Classification of Insurance
Insurance business can be divided into two broad categories, life
and non-life.
1. Life insurance is concerned with making provision for a
specific event happening to the individual, such as death.
2. Non-life is more commonly concerned with the provision for
specific event, which affects a property, such as fire, flood, theft etc.
1.3 Life Insurance Market
The Life Insurance market in India is an underdeveloped market that
was only tapped by the state owned LIC till the entry of private
insurers. The penetration of life insurance products was 19 percent of
the total 400 million of the insurable population. With the entry of the
private insurers the rules of the game have changed.
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The 12 private insurers in the life insurance market have already
grabbed nearly 9 percent of the market in terms of premium income.
The new business premiums of the 12 private players has tripled to Rs
1000 core in 2002- 03 over last year. Meanwhile, state owned LIC's
new premium business has fallen.
The private insurers also seem to be scoring big in other ways- they
are persuading people to take out bigger policies. For instance, the
average size of a life insurance policy before privatization was around
Rs 50,000. That has risen to about Rs 80,000. But the private insurers
are ahead in this game and the average size of their policies is around
Rs 1.1 lakhs to Rs 1.2 lakhs- way bigger than the industry average.
The state owned companies still dominate segments like endowments
and money back policies. But in the annuity or pension products
business, the private insurers have already wrested over 33 percent of
the market. And in the popular unit-linked insurance schemes they
have a virtual monopoly, with over 90 percent of the customers.4
1.4 Important milestones in the life insurance business inIndia:
1912: The Indian Life Assurance Companies Act enacted as the first
statute to regulate the life insurance business.
1928: The Indian Insurance Companies Act enacted to enable the
government to collect statistical information about both life and non-
life insurance businesses.
1938: Earlier legislation consolidated and amended to by the
Insurance Act with the objective of protecting the interests of the
insuring public.
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1956: 245 Indian and foreign insurers and provident societies taken
over by the central government and nationalized. LIC formed by an Act
of Parliament- LIC Act 1956- with a capital contribution of Rs. 5 core
from the Government of India. (2)
1.5 Present Scenario
The Government of India liberalized the insurance sector in March
2000 with the passage of the Insurance Regulatory and Development
Authority (IRDA) Bill, lifting all entry restrictions for private players and
allowing foreign players to enter the market with some limits on direct
foreign ownership. Under the current guidelines, there is a 26 percent
equity cap for foreign partners in an insurance company. There is a
proposal to increase this limit to 49 percent.
The opening up of the sector is likely to lead to greater spread and
deepening of insurance in India and this may also include restructuring
and revitalizing of the public sector companies. In the private sector 12
life insurance and 8 general insurance companies have been
registered. A host of private Insurance companies operating in both lifeand non-life segments have started selling their insurance policies
since 2001.
1.6 GDP contribution
With largest number of life insurance policies in force in the world,
Insurance happens to be a mega opportunity in India. Its a business
growing at the rate of 15-20 per cent annually and presently is of the
order of Rs 450 billion. Together with banking services, it adds about 7
per cent to the countrys GDP. Gross premium collection is nearly 2 per
cent of GDP and funds available with LIC for investments are 8 per cent
of GDP.
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Yet, nearly 80 per cent of Indian population is without life insurance
cover while health insurance and non-life insurance continues to be
below international standards. And this part of the population is also
subject to weak social security and pension systems with hardly any
old age income security. This itself is an indicator that growth potential
for the insurance sector is immense.
[B] COMPANY PROFILE
1.7History of the HDFC Standard Life
HDFC Standard Life first came together for a possible joint
venture, to enter the Life Insurance market, in January 1995. It was
clear from the outset that both companies shared similar values and
beliefs and a strong relationship quickly formed. In October 1995 the
companies signed a 3 year joint venture agreement.
Around this time Standard Life purchased a 5% stake in HDFC,
further strengthening the relationship.
The next three years were filled with uncertainty, due to changes
in government and ongoing delays in getting the IRDA (Insurance
Regulatory and Development authority) Act passed in parliament.
Despite this both companies remained firmly committed to the
venture.
In October 1998, the joint venture agreement was renewed and
additional resource made available. Around this time Standard Life
purchased 2% of Infrastructure Development Finance Company Ltd.
(IDFC). Standard Life also started to use the services of the HDFC
Treasury department to advise them upon their investments in India.
Towards the end of 1999, the opening of the market looked very
promising and both companies agreed the time was right to move the
operation to the next level. Therefore, in January 2000 an expert team
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from the UK joined a handpicked team from HDFC to form the core
project team, based in Mumbai. Around this time Standard Life
purchased a further 5% stake in HDFC and a 5% stake in HDFC Bank.
1.8 Incorporation of HDFC Standard Life Insurance Company
Limited
The company was incorporated on 14th August 2000 under the
name of HDFC Standard Life Insurance Company Limited.
Their ambition from the beginning was to be the first private
company to re-enter the life insurance market in India. On the 23rd of
October 2000, this ambition was realized when HDFC Standard Life
was the first life company to be granted a certificate of registration.
HDFC are the main shareholders in HDFC Standard Life, with
81.4%, while Standard Life owns 18.6%. Given Standard Life's existing
investment in the HDFC Group, this is the maximum investment
allowed under current regulations.
HDFC and Standard Life have a long and close relationship built
upon shared values and trust. The ambition of HDFC Standard Life is tomirror the success of the parent companies and be the yardstick by
which all other insurance companies in India are measured.
All information and material on this site are provided on an "as
is" basis, and are without guarantees or warranties of any kind,
express or implied. Furthermore, any ideas and/or information
provided or gained from this site would not necessarily reflect the
views of HDFC Standard Life or its directors or employees. You are notpermitted to modify copy, reproduce, upload, post or distribute in any
way any material from this site unless expressly permitted by HDFC
Standard Life.
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The materials and/or information available or obtained
at/through this site is/are not guaranteed or warranted in terms of
completeness, correctness, accuracy, reliability or otherwise
howsoever by HDFC Standard Life or its directors or employees.
The information obtained at/or through this site is not and should
not be construed as an offer for a policy or any other assistance. The
terms and conditions on which the policies are sold by HDFC Standard
Life are subject to changes from
Time to time depending on various factors. While the site may be
updated with changes
Periodically, HDFC does not guarantee that this site reflects the
latest amendments/ information at all times or at any time. The terms
and conditions are also largely dependent on the prevalent IRDA
Regulations. HDFC Standard Life does not guarantee that this site is
complete or accurate in its information content as regards the above.
1.9 Customer service
Claims
We understand that bereavement can be difficult to deal with,
especially when you have to arrange for all the formalities in case of
insurance claims.
At HDFC Standard Life we lend a helping hand by enabling faster
settlement of claims and help the family financially at the time of
distress
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To help you arrange the documents we have drawn up a list of
documents that you may be required to send along with the claims
form. This list is for your reference only and the complete list may vary
for each claim.
Policy Servicing
This section is designed to give you information that you may
require incase you wish to make changes in Personal details or Policy
details in your existing policy. The changes that you can avail of are:
Change in Personal Details
Changes you can avail of are:
Changes in your mailing address
Change of Nominee or Appointee
Change in Policy Benefits
Reduction in term of the policy
Removal of additional benefits (Riders)
Reduction in the level cover/premium of your policy
Change in frequency of premium payment
In case of unit linked policies in addition to the above you
can also avail of the following:
Paying additional premium (Top-up)
Changing your current investment composition (FundSwitch)
Changing your future premium direction (Premium
Redirection)
Increase your regular premium
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1.10ACHEVMENT AND AWARDS
May, 2008
Received PC Quest Best IT Implementation Award 2008
HDFC Standard Life received the PC Quest Best IT
Implementation Award 2008 for Consultant Corner, the applications for
its financial consultants, providing centralized control over a vast
geographical spread for key business units such as inventory, training,
licensing, etc. Read more about the Consultant Corner tool in the
HDFCSLinNews Section.
HDFC Standard Life has won the PC Quest Best ITImplementation Award for two years consequently. Last year, the
company received the award for Wonders, its path-breaking
implementation of an enterprise-wide workflow system
March, 2008
Silver Abby at Goa fest 2008
HDFC Standard Life's radio spot for Pension Plans won a Silver Abby in
the radio writing craft category at the Goa fest 2008 organized by the
Advertising Agencies Association of India (AAAI). The radio commercial
Pata Nahin Chala touched several changes in life in the blink of an
eye through an old mans perspective. The objective was drive
awareness and ask people to invest in a pension plan to live life to the
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fullest even after retirement, without compromising on ones self-
respect.
HDFC Standard Life received Laadli Media Award 2007 for its 'Big
car' TV commercial. It showed how a daughter wants to be more
responsible towards her family and asks her dad to upgrade to a bigger
car by offering him the extra money required to buy the car.
HDFC Standard Life received this award for two years
consecutively. In 2006, it won for the 'Papa' TV commercial, which
challenged the stereotype parents saving only for their son's education
or daughter's wedding. The company took a bold step by showing
parents saving for their daughter's education abroad, demonstratingprogressive thinking.
Laadli Media Awards, instituted in 2007, by Population First, an
NGO working on women's rights and social development, is given to
professionals in print and electronic media and ad makers for gender
sensitive news reports, articles, print, TV ads, and films.
March, 2008
Unit Linked Savings Plan Tops Mint Best TV Ads Survey
The Unit Linked Savings Plan advertisement of HDFC Standard
Life, one of the leading private insurance companies in India, has
topped Mints Top Television Advertisement survey conducted, for
February 2008. HDFC Standard Lifes Unit Linked Savings Plan
advertisement was ranked 4th in terms of a combined score of ad
awareness and brand recall and 3rd in terms of ad diagnostic scores
(likeability, enjoyment, believability, and claim). The respondents were
between 18 and 40 years. Mints exclusive report, New voices in a
makeover outlines the survey in detail.
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Mr Deepak M Satwalekar, Managing Director and CEO, HDFC
Standard Life, received the QIMPRO Gold Standard Award 2007 in the
business category at the 18th annual Qimpro Awards function. The
award celebrates excellence in individual performance and highlights
the quality achievements of extraordinary individuals in an era of
global competition and expectations.
January, 2008
Sar Utha Ke Jiyo Among Indias 60 Glorious Advertising
Moments
HDFC Standard Lifes advertising slogan honored as one of 60
Glorious Advertising & Marketing Moments' over the last 60
years in India, by 4Ps Business and Marketing magazine. The
magazine said that HDFC Standard Life is one of the first private
insurers to break the ice using the idea of self respect (Sar UthaKe
Jiyo) instead of 'death' to convey its brand proposition. This was then,
followed by others including ICCI Prudential, thus giving HDFC
Standard Life the credit of bringing up one such glorious advertising
and marketing moment in the last 60 years.
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1.11 Products
Each of us leads a unique life and so has unique needs, HDFCStandard Life offers a range of products and invites you to choose the
one that suits you best
Plan Benefits
1. Savings Plans
Endowment Assurance Plan Life Insurance with Savings
Unit Linked Endowment Plan Life Insurance & Savings with choice
of investment funds
Childrens Plan Financial Security for your child
Unit Linked Young Star Plan Financial security for your child
with choice of investment funds
Money Back Plan Life Insurance with Savings
2. Investment Plans
Single Premium Whole Of Life Plan Investment with Life Insurance
3. Protection Plans
Term Assurance Plan Life Insurance at an affordable price
Loan Cover Term Assurance Plan Life Insurance customized for home
loans
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4. Retirement Plans
Personal Pension Plan Savings for retirement
Unit Linked Pension Plan Retirement Savings with a choice
of investment funds
1.12 MISSION, VISION, VALUE OF COMPANY
HDFC MISSION
We aim to be the top new life insurance company in the market. This
does not just mean being the largest or the most productive company
in the market, rather it is a combination of several things like-
Customer service of the highest order
Value for money for customers
Professionalism in carrying out business
Innovative products to cater to different needs of different
customers
Use of technology to improve service standards
Increasing market share
HDFCs VISION
The most successful and admired life insurance company, which
means that we are the most trusted company, the easiest to deal with,
offer the best value for money, and set the standards in the industry.
HDFCs VALUES
Values that we observe while we work:
Integrity
Innovation
Customer centric
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People Care One for all and all for one
Team work
Joy and Simplicity
1.13 Board Members
Mr. Deepak S Parekh Chair man
Mr. Keki M Mistry-Managing director
Mr. Alexander M Crombie
Ms. Marcia D Campbell
Mr. Keith N Skeoch
Mr. Gautam R Divan
Mr. Ranjan Pant
Mr. Ravi Narain
Mr. Deepak M Satwalekar
Ms. Renu S. Karnad-Executive director
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Ch-2 RESEARCH METHODOLOGY
2.1 Problem statement:
To study the factor which influence the various age group to
buy the insurance policy?2.2 Research Objectives
1. Primary objective:
To identify the factor which influence the various age group to
buy the insurance policy?
2. Secondary objectives:
To measure the satisfaction level of the customer of the HDFC
standard life insurance company in the valsad area.
To study the awareness of the life insurance company in the
valsad area.
2.3 Research Design
During the primary research, I have done pilot testing of people in
valsad area. From that I have prepared the questionnaire and then
done my final survey in valsad.
At the initial stage the research is exploratory because it started with
review of literature which was available on internet as well as in books.
After framing the research statement, the research is now Descriptive
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Design because it describes the present scenario at a particular point
time and consists of a sample of the population of interests. Thus it
provides the overall picture at a given time.
Pre-testing
Before the final survey, 10 people were surveyed first to check the
validity of the questionnaire.
2.4 Defining target population
Sampling Design
In this project Descriptive design is used. This is one shot research
study at a given point of time and consists of a sample of the
population of interest. This gives the overall picture at a given time.
Sampling plan
Sampling plan helps to collect the data more accurately from the
market. Sampling plan is made to make the research more effective
when the time available fir research is limited.
Sample description
The people who are living in valsad and surrounding region.
Sample size
For my project, the sample size taken for the survey purpose is of 160
people from valsad region.
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Time duration: 12th May to 07th July. Two months period was
available for the research and data collection.
Sampling frame:
General people of valsad including shop keepers, businessmen,
farmers, service holders, house wives, etc.
Sampling technique
Instead of probability sampling, here Non-probability Convenience
sampling technique is used because of time constraints.
Execution of sampling process
I have collected the data from the people of valsad region through
personal interview with them.
2.5 Research instrument
Questionnaire was used for the purpose of data collection as the
research instrument.
Questionnaire consists of _
Close ended questions ( Many questions includes use of scale)
Open ended questions
2.6 Data collection
For the preparation of the project both types of data are used. i.e.
Primary Data
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Secondary Data
Primary Data:
Primary data are those data which are collected by the researcher for
the first time for his use. These data are pure and therefore more
reliable. Primary data gives the original picture of the study or situation
for which they are collected.
In my project, the primary data are collected through the use of survey
method. In the survey the respondents were personally interviewed for
data collection.
Secondary Data
Secondary data are those data which are once collected by any other
person in past for his purpose and now being used by the researcher
for his purpose. These data are less reliable compared to primary data
because these data may be obsolete with the passing of time and may
have bias information.
In my project, most of the secondary data are collected from internet.
Some data regarding the company were obtained with the help of he
company guide. Some data related to the topic were collected from the
books related to that topic.
2.7 Limitations of the research
As many of the respondents were not that much familiar with
English, I needed to explain each and every question in Guajarati
or Hindi to them.
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The use of SPSS software is new. It took much time to be
understood and to be applied in the project.
The survey was conducted in valsad only so, it can not cover the
preference of other areas client.
2.7Statistical tests used
In this project report, I have used One Sample T-test, paired sample t-
test and measures of central tendency.
One Sample t-test
The one sample t-test is the statistical test which is used to test the
difference between sample statistic and a hypothesized populationparameter. It is used when the types of data are interval in nature.
Paired sample t-test
The paired sample procedure compares the mean of two variables for
a single group. The procedure computes the difference between the
values of the two variables for each case and test weather the average
differs from zero.
For example in a study of high blood pressure, all patients are measure
again. Thus, each subject has two measures, often call before and after
measure. An alternative design for which this test is use is a match
pairs or case control study, in which each record in the data file
contains the response for the patients and also for his or her matched
control subject. In a blood pressure study, patient and also for his or
her matched control subject.
In a blood pressure study, patients and controls might be matched by
75 year old patient with a 75 year old control group member.
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One sample t-test measure the different between the hypotheses
mean and the calculated mean while paired t-test measure the mean
difference between two parameters.
Measures of central tendency
There are three parameters for the measure of central tendency.
Mean is used when the data are of scale type in nature.
Median is used when the data are of ordinal i.e. interval type innature.
Mode is used when the data are of nominal type in nature.
2.8 How to conduct statistical test
I have used SPSS software for applying statistical tests in my research.
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Ch-3 DATA ANALYSIS & INTERPRETATION
Q.1 Kindly rank from 1 to 7 with respect to which ever
company comes first to last in your mind when you think about
insurance?
Purpose:This question helps in knowing the respondents top of the
mind
Awareness regarding various life insurance companies.
RANK=1 PERCENTLIC 114 71.25%HDFC 24 15%ICICI 6 3.75%AVIVA 1 0.625%MAX 4 2.5%BAJAJ 1 0.625%RELIANCE 10 6.25%TOTAL 160 100
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From the above table, we can say that from 160 respondents 114 have
given 1st rank to LIC. Other insurance companies have got very less
frequency in getting 1st rank. So we can say that LIC is at first position
in peoples mind.
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Q-2.Rank the following investment options from 1 to 6
which you prefer most for investment?
Purpose: the purpose of asking this question is to know that which
investment option is mostly used by the respondents
RANK
PERCEN
T
EQUITY 18 11.25FD 73 45.625MUTUAL
FUND 18 11.25INSURANCE 44 27.5POSTAL 6 3.75BONDS 1 0.625TOTAL 160 100
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Here we can see that two investment options bank fix deposit and
Insurance has got higher frequency i.e. 46%and 27% respectively. So
we can say on the bases of the above data that from all the investment
options, these two options viz. fix deposit and Insurance are mostly
used and preferred by the people.
Q.3 what type of insurance policy you have taken?
Purpose: This question is asked to know that which type of life
insurance plan the people have.
PLAN NUMBER PERSENTAGE
life 5 2
investment plan 17 7
pension plan 37 15
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children 12 5
money back 9 4
endowment 55 23
unit licked 30 12
unit licked
endowment 65 27
others 12 5TOTAL
230
As we can see in the above table as well as chart, about 27% of people
has the unit licked endowment plan. This shows that people are more
interested in the unit linked endowment plan.
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Q.4 put the make in below attributes which you prefer
most while purchasing insurance policy?
Purpose. This question is asked to know which factor according to the
policy holder is most important while they have purchase a life
insurance policy.
Desired level (18-25)
ATTRIBUTES 1 2 3 4 5 MEANRisk cover 10 20 3 1 2.8Tax benefits 0 0 3 22 10 4.2Return 0 0 0 3 32 4.91Flexibility 0 0 7 15 7 3.3Safety 0 0 11 14 10 3.9Death
benefits
0 0 0 14 21 4.6
Value added
service
0 10 20 2 3 2.94
Transparenc
y
0 0 3 11 21 4.5
Fund option 8 12 10 5 0 2.3Policy term 10 15 7 4 0 2.2
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Lock in
period
0 0 12 10 13 4
Withdrawal
option in the
policy
0 0 7 12 15 4.1
Risk cover
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (3).in other words, we
hypothesize that policy holder are neutral about the risk cover factor.
i.e. H0: x==3
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words the
policy holders are not neutral about risk cover.
i.e. x, i.e.H1x3
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
RISKCOVER 35 2.8000 .63246 .10690
One-Sample Test
Test Value = 3
t df Sig. (2- Mean 95% Confidence
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tailed)
Differen
ce
Interval of the
Difference
RISKCOVER -1.871 34 .070 -.20000 -.4173 .0173
Inference
Here the test is performed at 95%significant level and the t value
comes out as .070 which is greater then0.05, means here null
hypothesis is accepted. It can be said that there is no significance
difference between calculated mean and hypothesis mean.
So we can say that according to policy holder of this age group they
are neutral about risk cover factor.
Tax benefits
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (4).in other words, we
hypothesize that according to policy holders tax benefits are importantfor them.
i.e. H0: x==4
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders tax benefits are not important for them.
i.e. x, i.e.H1x4
Significant level: 0.05
One-Sample Statistics
N Mean Std. Std.
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Deviation
Error
Mean
TAX
BENEFITS35 4.2000 .58410 .09873
One-Sample Test
Test Value = 4
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
TAX
BENEFITS2.026 34 .051 .20000 -.0006 .4006
Inference
Here the test is performed at 95%significant level and the t value
comes out as .051 which is greater then 0.05, means here null
hypothesis is accepted. It can be said that there is no significance
difference between calculated mean and hypothesis mean.
So we can say that according to policy holder of this age group tax
benefit factor is important for them.
Returns
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (5).in other words, we
hypothesize that according to policy holders return is completely
important for them.
i.e. H0: x==5
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Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other according to
policy holders return is not completely important for them.
i.e. x, i.e.H1x5
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
RETURNS 34 4.9118 .28790 .04937
One-Sample Test
Test Value = 5
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
RETURNS -1.787 33 .083 -.08824 -.1887 .0122
InferenceHere the test is performed at 95%significant level and the t value
comes out as .083 which is greater then0.05, means here null
hypothesis is accepted. It can be said that there is no significance
difference between calculated mean and hypothesis mean.
So we can say that according to policy holder of this age group return
on investment is completely important for them
Flexibility
One sample t- test
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Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (3).in other words, we
hypothesize that according to policy holders flexibility is neutral for
them.
i.e. H0: x==3
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words the
policy holders are not neutral about flexibility.
i.e. x, i.e.H1x3
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
FLEXIBILITY 35 3.3143 1.65869 .28037
One-Sample Test
Test Value = 3
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
FLEXIBILITY 1.121 34 .270 .31429 -.2555 .8841
Inference
Here the test is performed at 95%significant level and the t value
comes out as .270 which is greater then0.05, means here null
hypothesis is accepted. It can be said that there is no significance
difference between calculated mean and hypothesis mean.
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So we can say that according to policy holder of this age group
flexibility factor is neutral for them.
Safety
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (4).in other words, we
hypothesize that according to policy holders safety is important for
them.
i.e. H0: x==4
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders safety is not important for them.
i.e. x, i.e.H1x4
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
SAFTY 35 3.9714 .78537 .13275
One-Sample Test
Test Value = 4
t df Sig. (2- Mean 95% Confidence
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tailed)
Differen
ce
Interval of the
Difference
SAFTY -.215 34 .831 -.02857 -.2984 .2412
Inference
Here the test is performed at 95%significant level and the t value
comes out as .831 which is greater then0.05, means here null
hypothesis is accepted. It can be said that there is no significance
difference between calculated mean and hypothesis mean.So we can say that according to policy holder of this age group safety
factor is important for them.
Death benefits
One sample t- test
Null hypothesis (Ho): There is no significant different betweencalculated mean and hypothesized mean (5).in other words, we
hypothesize that according to policy holders death benefit is
completely important for them.
i.e. H0: x==5
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders death benefits are not completely
important for them.
i.e. x, i.e.H1x5
Significant level: 0.05
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One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
DEATH
BENEFITS35 4.6000 .49705 .08402
One-Sample Test
Test Value = 5
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
DifferenceDEATH
BENEFITS-4.761 34 .000 -.40000 -.5707 -.2293
Inference
Here the test is performed at 95%significant level and the t value
comes out as .000 which is less then0.05, means here null hypothesis
is rejected. It can be said that there is significance difference between
calculated mean and hypothesis mean.
Value added service
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (3).in other words, we
hypothesize that policy holder are neutral about the value added
service factor.
i.e. H0: x==3
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words the
policy holders are not neutral about value added service.
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i.e. x, i.e.H1x3
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
VALUEADDEDSE
RVICE34 2.941 .77621 .13312
One-Sample Test
Test Value = 3
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
VALUEADDEDSE
RVICE
-.442 33 .661 -.05882 -.3297 .2120
Inference
Here the test is performed at 95%significant level and the t value
comes out as .661 which is greater then0.05, means here null
hypothesis is accepted. It can be said that there is no significance
difference between calculated mean and hypothesis mean.
So we can say that according to policy holder of this age group value
added service is neutral for them.
Transparency
One sample t- test
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Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (4).in other words, we
hypothesize that according to policy holders transparency is important
for them.
i.e. H0: x==4
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders transparency is not important for them.
i.e. x, i.e.H1x4
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
TRANSPARENCY 35 4.5143 .65849 .11131
One-Sample Test
Test Value = 5
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
TRANSPARENCY-4.364
3
4.000 -.48571 -.7119 -.2595
InferenceHere the test is performed at 95%significant level and the t value
comes out as .000 which is less then0.05, means here null hypothesis
is rejected. It can be said that there is significance difference between
calculated mean and hypothesis.
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Fund option
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (2).in other words, we
hypothesize that according to policy holders fund options are not
important for them.
i.e. H0: x==2
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders fund options are important for them.
i.e. x, i.e.H1x2
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
FUNDOPTION 35 2.3429 .99832 .16875
One-Sample Test
Test Value = 2
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
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FUND OPTION 2.032 34 .050 .34286 -.0001 .6858
Inference
Here the test is performed at 95%significant level and the t value
comes out as .050 which is equal to 0.05, means here null hypothesis
is accepted. It can be said that there is no significance difference
between calculated mean and hypothesis mean.
So we can say that according to policy holder of this age group fund
option is not important for them.
Policy term
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (2).in other words, we
hypothesize that according to policy holders policy term is not
important for them.
i.e. H0: x==2
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders policy term is important for them.
i.e. x, i.e.H1x2
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
POLICY TERM 35 2.0857 .91944 .15541
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One-Sample Test
Test Value = 2
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
POLICY TERM .552 34 .585 .08571 -.2301 .4016
Inference
Here the test is performed at 95%significant level and the t value
comes out as .585 which is greater then 0.05, means here null
hypothesis is accepted. It can be said that there is no significance
difference between calculated mean and hypothesis mean.
So we can say that according to policy holder of this age group policyterm is not important for them.
Lock in period
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (4).in other words, we
hypothesize that according to policy holders lock in period is important
for them.
i.e. H0: x==4
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders lock in period is not important for them.
i.e. x, i.e.H1x4
Significant level: 0.05
One-Sample Statistics
N Mean Std. Std.
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Deviation
Error
Mean
LOCK IN
PERIOD35 4.0286 .85700 .14486
One-Sample Test
Test Value = 4
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
LOCKINPERI
OD.197 34 .845 .02857 -.2658 .3230
Inference
Here the test is performed at 95%significant level and the t value
comes out as .845 which is greater then0.05, means here null
hypothesis is accepted. It can be said that there is no significance
difference between calculated mean and hypothesis mean.
So we can say that according to policy holder of this age group lock in
period is important for them.
Withdrawal option in the policy
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (4).in other words, we
hypothesize that according to policy holders withdrawal option in thepolicy is important for them.
i.e. H0: x==4
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
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according to policy holders withdrawal option in the policy is not
important for them.
i.e. x, i.e.H1x4
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
WITHROWALOPTION IN
THE POLICY
35 4.1143 1.05081 .17762
One-Sample Test
Test Value = 4
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
WITHROWAL
OPTION IN
THE POLICY
.643 34 .524 .11429 -.2467 .4753
Inference
Here the test is performed at 95%significant level and the t value
comes out as .524 which is greater then0.05, means here null
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hypothesis is accepted. It can be said that there is no significance
difference between calculated mean and hypothesis mean.
So we can say that according to policy holder of this age group
withdrawal option in the policy is important for them.
The respondents in this age group of 18-25 are looking for more
returns rather then risk cover and value added service.
Death benefits, withdrawal option in the policy are almost important.
While policy term and fund option are least important.
Desired level (26-35)
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ATTRIBUTES 1 2 3 4 5 MEAN
Risk cover 0 0 0 25 20 4.44
Tax benefits 0 0 0 3 42 4.93
Return 0 0 2 16 27 4.55
Flexibility 0 0 5 18 23 4.48
Safety 0 0 0 14 31 4.69
Death
benefits
0 0 0 24 21 4.46
Value added
service
0 0 4 20 21 4.37
Transparenc
y
0 0 1 19 25 4.53
Fund option 14 11 12 7 3.2
Policy term 11 7 14 10 3 2.7
Lock in
period
0 0 20 14 11 3.91
Withdrawal
option in the
policy
0 0 8 20 17 4.2
Risk cover
One sample t- test
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Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (4).in other words, we
hypothesize that according to policy holders withdrawal option in the
policy is important for them.
i.e. H0: x==4
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders withdrawal option in the policy is not
important for them.
i.e. x, i.e.H1x4
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviatio
n
Std.
Error
Mean
RISKCOVER 45 4.4444 .50252 .07491
One-Sample Test
Test Value = 4
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
RISKCOVER 5.933 44 .000 .44444 .2935 .5954
Inference
Here the test is performed at 95%significant level and the t value
comes out as .00 which is less then0.05, means here null hypothesis is
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rejected. It can be said that there is significance difference between
calculated mean and hypothesis mean.
Tax benefits
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (5).in other words, we
hypothesize that according to policy holders tax benefits in the policy
is completely important for them.
i.e. H0: x==5
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders tax benefits in the policy is not completely
important for them.
i.e. x, i.e.H1x5
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviatio
n
Std.
Error
Mean
TAXBENEFITS 45 4.93 .25226 .0376
One-Sample Test
Test Value = 5
t df Sig. (2-
tailed)
Mean
Differen
95% Confidence
Interval of the
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ce Difference
TAXBENEFITS 1.7 44 .083 .06667 .1425 .0091
Inference
Here the test is performed at 95%significant level and the t value
comes out as .083 which is greater then0.05, means here null
hypothesis is accepted. It can be said that there is no significance
difference between calculated mean and hypothesis mean.
So we can say that according to policy holder of this age group tax
benefit is completely important for them.
Returns
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (5).in other words, we
hypothesize that according to policy holders return is completely
important for them.
i.e. H0: x==5
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other according to
policy holders return is not completely important for them.
i.e. x, i.e.H1x5
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
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RETURNS 45 4.5556 .58603 .08736
One-Sample Test
Test Value = 5
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
RETURNS -5.087 44 .000 -.44444 -.6205 -.2684
Inference
Here the test is performed at 95%significant level and the t value
comes out as .000 which is greater then0.05, means here null
hypothesis is rejected. It can be said that there is significance
difference between calculated mean and hypothesis mean.
Flexibility
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (4).in other words, we
hypothesize that according to policy holders flexibility is important for
them.
i.e. H0: x==4
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other wordsaccording to policy holders flexibility is not important for them.
i.e. x, i.e.H1x4
Significant level: 0.05
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One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
FLEXIBILITY 45 4.4889 .62603 .09332
One-Sample Test
Test Value = 4
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
DifferenceFLEXIBILITY 5.239 44 .000 .48889 .3008 .6770
Inference
Here the test is performed at 95%significant level and the t value
comes out as .000 which is less then0.05, means here null hypothesis
is rejected. It can be said that there is significance difference between
calculated mean and hypothesis mean.
Safety
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (5).in other words, we
hypothesize that according to policy holders safety is completely
important for them.
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i.e. H0: x==5
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders safety is not completely important for
them.
i.e. x, i.e.H1x5
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
SAFTY 45 4.6889 .46818 .06979
One-Sample Test
Test Value = 5
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
SAFTY -4.458 44 .000 -.31111 -.4518 -.1705
Inference
Here the test is performed at 95%significant level and the t value
comes out as .000 which is less then0.05, means here null hypothesis
is rejected. It can be said that there is significance difference between
calculated mean and hypothesis mean.
Death benefits
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One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (4).in other words, we
hypothesize that according to policy holders death benefit is important
for them.
i.e. H0: x==4
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders death benefits are not important for them.
i.e. x, i.e.H1x4
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
DEATHBENEFITS 45 4.4667 .50452 .07521
One-Sample Test
Test Value = 4
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
DEATHBENEFITS 6.205 44 .000 .46667 .3151 .6182
Inference
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Here the test is performed at 95%significant level and the t value
comes out as .000 which is less then0.05, means here null hypothesis
is rejected. It can be said that there is significance difference between
calculated mean and hypothesis mean
Value added service
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (4).in other words, we
hypothesize that according to policy holders value added service is
important for them.
i.e. H0: x==4
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders value added service is not important for
them.
i.e. x, i.e.H1x4
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
VALUE ADDED
SERVICE45 4.3778 .61381 .09150
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One-Sample Test
Test Value = 4
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
VALUE ADDED
SERVICE4.129 44 .000 .37778 .1934 .5622
Inference
Here the test is performed at 95%significant level and the t value
comes out as .000 which is less then0.05, means here null hypothesis
is rejected. It can be said that there is significance difference betweencalculated mean and hypothesis mean
Transparency
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (5).in other words, we
hypothesize that according to policy holders transparency is
completely important for them.
i.e. H0: x==5
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders transparency is not completely important
for them.
i.e. x, i.e.H1x5
Significant level: 0.05
One-Sample Statistics
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N Mean
Std.
Deviation
Std.
Error
Mean
TRANSPARENCY 45 4.5333 .54772 .08165
One-Sample Test
Test Value = 5
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
TRANSPARENCY -5.71 44 .000 -.46667 -.6312 -.3021
Inference
Here the test is performed at 95%significant level and the t value
comes out as .000 which is less then0.05, means here null hypothesis
is rejected. It can be said that there is significance difference between
calculated mean and hypothesis mean
Fund option
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (3).in other words, wehypothesize that according to policy holders fund options is neutral for
them.
i.e. H0: x==3
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Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders fund options is not neutral for them.
i.e. x, i.e.H1x3
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
FUNDOPTION 45 3.2444 .85694 .12774
One-Sample Test
Test Value = 3
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
FUNDOPTION 1.914 44 .062 .24444 -.0130 .5019
Inference
Here the test is performed at 95%significant level and the t value
comes out as .062 which is greater then0.05, means here null
hypothesis is accepted. It can be said that there is no significance
difference between calculated mean and hypothesis mean.
So we can say that according to policy holder of this age group fund
option is neutral for them.
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Policy term
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (3).in other words, we
hypothesize that according to policy holders policy term is neutral for
them.
i.e. H0: x==3
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders policy term is not neutral for them.
i.e. x, i.e.H1x3
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
POLICYTERM 45 2.7111 1.03621 .15447
One-Sample Test
Test Value = 3
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
POLICYTERM -1.870 44 .068 -.28889 -.6002 .0224
Inference
Here the test is performed at 95%significant level and the t value
comes out as .068 which is greater then0.05, means here null
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hypothesis is accepted. It can be said that there is no significance
difference between calculated mean and hypothesis mean.
So we can say that according to policy holder of this age group policy
term is neutral for them.
Lock in period
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (4).in other words, we
hypothesize that according to policy holders lock in period is important
for them.
i.e. H0: x==4
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders lock in period is not important for them.
i.e. x, i.e.H1x4
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
LOCK IN
PERIOD 45 3.8000 1.28982 .19228
One-Sample Test
Test Value = 4
t df Sig. (2- Mean 95% Confidence
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tailed)
Differen
ce
Interval of the
Difference
LOCK IN
PERIOD-1.04 44 .304 -.20000 -.5875 .1875
Inference
Here the test is performed at 95%significant level and the t value
comes out as .304 which is greater then0.05, means here null
hypothesis is accepted. It can be said that there is no significance
difference between calculated mean and hypothesis mean.
So we can say that according to policy holder of this age group lock in
period is important for them.
Withdrawal option in the policy
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (4).in other words, we
hypothesize that according to policy holders withdrawal option in the
policy is important for them.
i.e. H0: x==4
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders withdrawal option in the policy is not
important for them.
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i.e. x, i.e.H1x4
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
WITHROWAL
OPTION IN
THE POLICY
45 4.2000 .72614 .10825
One-Sample Test
Test Value = 4
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
WITHROWAL
OPTION IN
THE POLICY
1.848 44 .071 .20000 -.0182 .4182
Inference
Here the test is performed at 95%significant level and the t value
comes out as .071 which is greater then0.05, means here null
hypothesis is accepted. It can be said that there is no significance
difference between calculated mean and hypothesis mean.
So we can say that according to policy holder of this age group
withdrawal option in the policy is important for them.
According to the respondent of this age group 26-35 tax benefit is the
most important for them. So we can say that the respondent of this
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age group consider insurance as a tax saving instrument rather than
looking for returns than other attributes.
They also feel that the lock in period and the withdrawal option in the
policy are also important.
Desired level (36-45)
ATTRIBUTES 1 2 3 4 5 Weighte
d
average
Risk cover 0 0 0 3 42 4.93
Tax benefits 0 0 9 15 21 4.3
Return 0 0 12 15 18 4.1
Flexibility 0 0 4 21 20 4.3
Safety 0 0 8 22 15 4.15
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Death
benefits
0 0 0 14 31 4.68
Value added
service
0 0 17 17 11 3.86
Transparenc
y
0 0 0 2 43 4.84
Fund option 0 0 3 20 22 4.42
Policy term 3 9 10 12 11 3.35
Lock in
period
0 0 12 15 18 4.13
Withdrawal
option in the
policy
0 0 11 17 17 4.13
Risk cover
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (5).in other words, we
hypothesize that according to policy holders risk cover is completely
important for them.
i.e. H0: x==5
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
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according to policy holders risk cover is not completely important for
them.
i.e. x, i.e.H1x5
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
RISKCOVER 45 4.93 .25226 .03761
One-Sample Test
Test Value = 5
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
RISKCOVER 1.773 44 .083 .06667 .1425 .0091Inference
Here the test is performed at 95%significant level and the t value
comes out as.083 which is greater then0.05, means here null
hypothesis is accepted. It can be said that there is no significance
difference between calculated mean and hypothesis mean.
So we can say that according to policy holder of this age group risk
cover is completely important for them.
Tax benefits
One sample t- test
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One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (4).in other words, we
hypothesize that according to policy holders return is important for
them.
i.e. H0: x==4
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders return is not important for them.
i.e. x, i.e.H1x4
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.Error
Mean
RETURNS 45 4.1333 .81464 .12144
One-Sample Test
Test Value = 4
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
RETURNS 1.09 44 .278 .13333 -.1114 .3781
Inference
Here the test is performed at 95%significant level and the t value
comes out as .278 which is greater then 0.05, means here null
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hypothesis is accepted. It can be said that there is no significance
difference between calculated mean and hypothesis mean.
So we can say that according to policy holder of this age group return
on investment is important for them.
Flexibility
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (4).in other words, we
hypothesize that according to policy holders flexibility is important for
them.
i.e. H0: x==4
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders flexibility is not important for them.
i.e. x, i.e.H1x4
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
FLEXIBILITY 45 4.3556 .64511 .09617
One-Sample Test
Test Value = 4
t df Sig. (2- Mean 95% Confidence
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tailed)
Differen
ce
Interval of the
Difference
FLEXIBILITY 3.6
944 .001 .35556 .1617 .5494
Inference
Here the test is performed at 95%significant level and the t value
comes out as .001 which is less then 0.05, means here null hypothesis
is rejected. It can be said that there is significance difference between
calculated mean and hypothesis mean.
Safety
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (4).in other words, we
hypothesize that according to policy holders safety is important for
them.
i.e. H0: x==4
Alternative hypothesize (H1): There is significant differencebetween calculated mean and hypotheses mean. In other words
according to policy holders safety is not important for them.
i.e. x, i.e.H1x4
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
SAFTY 45 4.155 .70568 .10520
One-Sample Test
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Test Value = 4
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
SAFTY 1.479 44 .146 .15556 .0565 .3676
Inference
Here the test is performed at 95%significant level and the t value
comes out as .146 which is greater then 0.05, means here null
hypothesis is accepted. It can be said that there is no significance
difference between calculated mean and hypothesis mean.
So we can say that according to policy holder of this age group safety
is important for them.
Death benefits
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (5).in other words, we
hypothesize that according to policy holders death benefits is
completely important for them.
i.e. H0: x==5
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other wordsaccording to policy holders death benefits is not completely important
for them.
i.e. x, i.e.H1x5
Significant level: 0.05
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One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
DEATH
BENEFITS45 4.6889 .46818 .06979
One-Sample Test
Test Value = 5
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
DifferenceDEATH
BENEFITS-4.458 44 .000 -.31111 -.4518 -.1705
Inference
Here the test is performed at 95%significant level and the t value
comes out as .000 which is less then 0.05, means here null hypothesis
is rejected. It can be said that there is significance difference between
calculated mean and hypothesis mean.
Value added service
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (4).in other words, we
hypothesize that according to policy holders value added service is
important for them.
i.e. H0: x==4
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Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders value added service is not important for
them.
i.e. x, i.e.H1x4
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
VALUE ADDED
SERVICE45 3.8667 .78625 .11721
One-Sample Test
Test Value = 4
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
VALUE ADDED
SERVICE-1.138 44 .261 -.13333 -.3695 .1029
Inference
Here the test is performed at 95%significant level and the t value
comes out as .261 which is greater then 0.05, means here null
hypothesis is accepted. It can be said that there is no significancedifference between calculated mean and hypothesis mean.
So we can say that according to policy holder of this age group value
added service is important for them.
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Transparency
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (5).in other words, we
hypothesize that according to policy holders transparency is
completely important for them.
i.e. H0: x==5
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders transparency is not completely important
for them.
i.e. x, i.e.H1x5
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
TRANSPARENCY 45 4.9556 .20841 .03107
One-Sample Test
Test Value = 5
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
TRANSPARENCY -1.431 44 .160 -.04444 -.1071 .0182
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Inference
Here the test is performed at 95%significant level and the t value
comes out as .160 which is greater then 0.05, means here null
hypothesis is accepted. It can be said that there is no significance
difference between calculated mean and hypothesis mean.
So we can say that according to policy holder of this age group
transparency is completely important for them.
Fund option
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (4).in other words, we
hypothesize that according to policy holders fund options is important
for them.
i.e. H0: x==4
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other wordsaccording to policy holders fund options is not important for them.
i.e. x, i.e.H1x4
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
FUND
OPTION45 4.4222 .62118 .09260
One-Sample Test
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Test Value = 4
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
FUND
OPTION4.560 44 .000 .42222 .2356 .6088
Inference
Here the test is performed at 95%significant level and the t value
comes out as .000 which is less then 0.05, means here null hypothesis
is rejected. It can be said that there is significance difference between
calculated mean and hypothesis mean.
Policy term
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (3).in other words, we
hypothesize that according to policy holders policy term is neutral for
them.
i.e. H0: x==3
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders policy term is not neutral for them.
i.e. x, i.e.H1x3
Significant level: 0.05
One-Sample Statistics
N Mean Std. Std.
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Deviation
Error
Mean
POLICY
TERM45 3.3556 1.24600 .18574
One-Sample Test
Test Value = 3
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
POLICY TERM 1.91 44 .062 .35556 .0188 .7299
Inference
Here the test is performed at 95%significant level and the t value
comes out as .062 which is greater then 0.05, means here null
hypothesis is accepted. It can be said that there is no significance
difference between calculated mean and hypothesis mean.
So we can say that the policy holder of this age group is neutral about
policy term factor.
Lock in period
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (4).in other words, we
hypothesize that according to policy holders lock in period is important
for them.
i.e. H0: x==4
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Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders lock in period is not important for them.
i.e. x, i.e.H1x4
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
LOCK IN
PERIOD45 4.1333 .81464 .12144
One-Sample Test
Test Value = 4
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
LOCK IN
PERIOD 1.098 44 .278 .13333 -.1114 .3781
Inference
Here the test is performed at 95%significant level and the t value
comes out as .278 which is greater then 0.05, means here null
hypothesis is accepted. It can be said that there is no significance
difference between calculated mean and hypothesis mean.So we can say that according to policy holder of this age group lock in
period is important for them.
Withdrawal option in the policy
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One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (4).in other words, we
hypothesize that according to policy holders withdrawal option in the
policy is important for them.
i.e. H0: x==4
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders withdrawal option in the policy is not
important for them.
i.e. x, i.e.H1x4
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
WITHROWAL
OPTION IN
THE POLICY
45 4.1333 .78625 .11721
One-Sample Test
Test Value = 4
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
WITHROWAL
OPTION IN
THE POLICY
1.138 44 .261 .13333 -.1029 .3695
Inference
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Here the test is performed at 95%significant level and the t value
comes out as .261 which is greater then 0.05, means here null
hypothesis is accepted. It can be said that there is no significance
difference between calculated mean and hypothesis mean.
So we can say that according to policy holder of this age group
withrowal option in the policy is important for them.
According to responded of this age group (36-45)Risk cover and transparency is completely important for them. so we
can say the respondent of this age group primary looking for risk cover
and transparency. While return, safety, value added service, lock in
period, withdrawal option in the policy are important for them.
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Desired level (46&above)
ATTRIBUTES 1 2 3 4 5 Weighted
average
Risk cover 0 0 7 6 22 4.42
Tax benefits 0 0 0 12 23 4.65
Return 0 0 8 14 13 4.14
Flexibility 0 4 7 12 12 3.91
Safety 0 0 0 15 20 4.57
Death
benefits
0 0 0 2 33 4.94
Value added
service
0 1 4 16 14 4.22
Transparenc
y
0 0 3 12 20 4.48
Fund option 0 0 3 15 17 4.4
Policy term 0 0 8 12 15 4.2
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Lock in
period
0 0 0 13 22 4.62
Withdrawal
option in the
policy
0 0 0 2 33 4.94
Risk cover
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (4).in other words, we
hypothesize that according to policy holders withdrawal option in the
policy is important for them.
i.e. H0: x==4
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders withdrawal option in the policy is not
important for them.
i.e. x, i.e.H1x4
Significant level: 0.05
One-Sample Statistics
N Mean Std.
Deviation
Std.
Error
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i.e. x, i.e.H1x5
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
TAX
BENEFITS35 4.6571 .48159 .08140
One-Sample Test
Test Value = 5
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
TAX
BENEFITS-4.212 34 .000 -.34286 -.5083 -.1774
Inference
Here the test is performed at 95%significant level and the t value
comes out as .000which is less then 0.05, means here null hypothesis
is rejected. It can be said that there is significance difference between
calculated mean and hypothesis mean.
Returns
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (4).in other words, we
hypothesize that according to policy holders return is important for
them.
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Flexibility
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (4).in other words, we
hypothesize that according to policy holders flexibility is important for
them.
i.e. H0: x==4
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders flexibility is not important for them.
i.e. x, i.e.H1x4
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
FLEXIBILITY 35 3.9143 1.01087 .17087
One-Sample Test
Test Value = 4
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
FLEXIBILITY -.502 34 .619 -.08571 -.4330 .2615
Inference
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Here the test is performed at 95%significant level and the t value
comes out as .619 which is greater then 0.05, means here null
hypothesis is accepted. It can be said that there is no significance
difference between calculated mean and hypothesis mean.
So we can say that according to policy holder of this age group
flexibility is important for them.
Safety
One sample t- test
Null hypothesis (Ho): There is no significant different between
calculated mean and hypothesized mean (5).in other words, wehypothesize that according to policy holders safety is completely
important for them.
i.e. H0: x==5
Alternative hypothesize (H1): There is significant difference
between calculated mean and hypotheses mean. In other words
according to policy holders safety is not completely important for
them.
i.e. x, i.e.H1x5
Significant level: 0.05
One-Sample Statistics
N Mean
Std.
Deviation
Std.
Error
Mean
SAFTY 35 4.5714 .50210 .08487
One-Sample Test
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Test Value = 5
t df
Sig. (2-
tailed)
Mean
Differen
ce
95% Confidence
Interval of the
Difference
SAFTY -5.050 34 .000 -.42857 -.6010 -.2561
Inference
Here the test is performed at 95%significant level and the t value
comes out as .000 which is less then 0.05, means here null hypothesis
is rejected. It can be said that there is significance difference between
calculated mean and hypothesis mean.
Death benefi