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1.0 Introduction

1.1 Objectives of the Study

The objective of the study is to find out whether there is statistical relationship

between the sales of credit cards and other factors such as debit card sales, new deposit

accounts, new loan accounts, direct sales team or permanent staff number etc. This study will

attempt to establish a linear model between the variables and hence will try to quantify the

relationship in a linear equation. Various other tests will be done in an attempt to identify a

proper statistical relationship.

1.2 Significance of the Study

The study is primarily an internship report but still it will be aimed at helping the

management to identify the factors that influence the credit card sales. The statistical

relationship will help to determine which factors are most important so that the management

can focus on those factors. Understanding these factors will be key to success of EBL’s credit

cards in the face of huge competition. Moreover the study will help my faculty to understand

the factors that influence credit card purchase behavior in relation to other factors of the bank.

1.3 Methodology

The study is primarily based on secondary data collected from the Consumer

Banking division of EBL. Every month an internal MIS report is created so that performance

of each branch can be measured. These data have been used in this report. The data is of May

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2008. Additionally for technical reference, some websites have been quoted in the report

which will help to further clarify the statistical relationship.

1.4 Limitations of the Study

The major limitation of the study is that it does not have any primary data. All data

have been collected from Consumer Banking division, which have been originally collected

for a different purpose.

Secondly, there might be other factors than the variables mentioned in the report that

can affect credit card sales. So those variables may be important but might skip this report. So

this is another major limitation.

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Organizational Part

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2.0 Overview of the Bank

2.1 History and Background of Eastern Bank Limited

The emergence of Eastern Bank Limited in the private sector is an important event in

the banking industry of Bangladesh. Eastern Bank Limited started its business as a public

limited company on August 8, 1992 with the primary objectives to carry on all kinds of

banking business in and outside of Bangladesh and also with a view to safeguard the interest

of the depositors of erstwhile BCCI (Bank of Credit and Commerce International (Overseas))

under the Reconstruction Scheme, 1992, framed by Bangladesh Bank.

In 1991, when BCCI had collapsed internationally, the operation of this bank had

been closed Bangladesh. After a long discussion with the BCCI employees and taking into

consideration the depositors’ interest, Bangladesh Bank then gave permission to form a bank

named Eastern Bank Limited which would take over all the assets, cash and liabilities of

erstwhile BCCI in Bangladesh, with effect from 16th August 1992. So, it can be said that EBL

is a successor of BCCI.

Eastern Bank Limited (EBL) is a second generation commercial bank with 29 online

branches across major cities in Bangladesh and 612 full time employees on year end 2007. It

offers full range of commercial banking products and services to the corporate, mid-market

and retail segment. Under the corporate banking segments the Bank has comprehensive range

of financial products including corporate deposit accounts, syndicated financing, export-

import financing, working capital and other finance, bonds and guarantees, investment and

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business counseling, infrastructure finance, cash management services etc. With urban

banking focus, the Bank is offering various alternative delivery channels like ATMs, Bills

Pay Machines, Kiosks, and Internet Banking etc. The bank has set up a brand image

attributable in part to its policy of continuous customer service excellence, innovative

products and services and maximum technology utilization. Unlike conventional branch

banking, credit proposals as well as business operations are processed centrally at EBL.

Besides Main Operation, EBL has an Offshore Banking Unit (OBU) set up in 2004 which

gives loans and takes deposits only in freely convertible foreign currency to and from non

resident person/institutions, fully foreign owned EPZ companies etc.

2.2 EBL’s Vision:

EBL’s vision is to become the Bank of Choice by transforming the way it does

business and developing a truly unique financial institution that delivers superior growth &

financial performance and be the most Recognizable Brand in the financial services in

Bangladesh.

EBL dreams to become the bank of choice of the general public which includes both

the consumer and the corporate clients. They want to build such an image that whenever

people will think of a bank, they will think of Eastern Bank. In order to build up such an

image, EBL has taken up some attractive promotional campaign with the professional help of

Unitrend Ltd. They are targeting the young generation in particular because research has

shown that people usually have a soft corner for the first bank of their lives and tend to stick

to it if they are satisfied with its service. With this plan in mind, EBL is trying to project itself

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as a vibrant and dynamic organization by introducing state-of-the-art banking technology

which will make banking easier and hassle-free to the young generation. It has adopted the

tag line “Simple math, the philosophy of easy banking” and also adopted a new logo which

looks very dynamic in its attractive colors. The blue, gold and green colors of the logo reflect

all the changes that are taking place in EBL. In the words of EBL’s promotional literatures,

“Eastern Bank Limited, the inspiration of change is happening now…the vibrant green of

mother earth, a blue sky full of possibilities and a yellow rising sun of hope comes together

for a new face of EBL.”

In order to achieve superior growth & financial performance for its shareholders, EBL

is radically transforming the way it does business. Already the bank has restructured from the

traditional geographical matrix (branch-based banking) to business unit matrix. The

restructuring program is discussed in detail in the later sections. The bank is also centralizing

most of the business functions in the head office to ensure greater control and efficiency.

2.3 EBL’s Mission:

In line with its vision, EBL has developed a mission statement which reads as follows and is

self-explanatory:

• To deliver service excellence to all its customers, both internal & external.

• To constantly challenge its systems, procedures & training to maintain a cohesive and

professional team in order to achieve service excellence.

• To create an enabling environment and embrace a team based culture where people

will excel.

• To ensure to maximize shareholders value.

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2.4 Present Business Philosophy:

The philosophy of the present management of EBL is to develop the Bank into an

ideal and unique banking institution. The perception is that EBL should be quite different

from other privately owned and managed commercial bank operating in Bangladesh. EBL is

to grow as a leader in the industry rather than a follower. The leadership will be in the area of

service; constant effort being made to add new dimension so that clients can get "something

additional" in the matter of services to commensurate with the needs and requirements of the

country's growing society and developing economy.

Till 2000, EBL was operating in a Geographical Matrix where the business of the bank

was concentrated in the 22 branches divided into zones. But in 2001, the new dynamic

management of EBL led by the Managing Director Mr. Iftekhar has changed the Geographic

Matrix into Business Matrix. The bank has been restructured into three main businesses

which are responsible for earning the incomes of the bank. These are:

Corporate Banking

Consumer Banking

Treasury

All other departments of the bank act as support units for these 3 units and help them in every

possible way. Under this arrangement, the responsibilities and functions of the branches have

been reduced dramatically. Many of the activities like credit evaluation & approval,

monitoring of loans, trade services activities etc. are now centralized in the Head Office. The

branches of the bank are now termed as the “Sales & Service Centers” which are solely

concentrated on delivering services to the corporate and consumer clients and maintain

relationship with them.

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2.5 Management Aspects:

Like any other business organization, all the major decisions in EBL are made by the

top management. The board of directors being at the highest level of organizational structure

plays an important role in policy formulation. The board of directors is not directly concerned

with the day-to-day operation of bank. They have delegated this duty to the management

committee. The board mainly establishes the objectives and policies of the bank. There are

three (3) committee of the board for different purposes:

1. Executive committee comprising of 7 members of the board;

2. Committee of the board for Administrative matter,

3. Committee to examine Bad Loan Cases.

The Chief Executive Officer (CEO), who is assisted by 3 Executive Vice Presidents

(EVPs), looks after the day-to-day affairs of the Bank. Human Resource Department, MDs

Secretariat and Audit and Compliance Department are under direct control of the CEO. The

three EVPs are in charge of Operations, Credit and Corporate Banking respectively. They

control the affairs of these departments through the managers who are in charge of various

departments under these divisions.

Mid and lower level employees get the direction and instruction from the top executives

about the duties and tasks they have to perform. Management of Eastern Bank Ltd. assumes

that employees are members of the team, who actively participate in accomplishing the

organization goal. The chief executive provides the guideline and broad direction to the

managers and employees but delegates’ responsibility for determining how tasks and goals

are to be accomplished. The present top management is as follows:

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• Mr Ali Reza Iftekhar - Managing Director & CEO

• Mr Mahbubul Alam Tayiab - Executive Vice President & Head of Op.

• Mr Mamoon Mahmood Shah- Senior Executive Vice President & DMD

• Mr. Mukhlesur Rahman- Senior Executive Vice President & DMD

• Mr Zaglul Abbas - EVP & Head of Credit Risk Management

• Mr Md. Fakrul Alam- Executive Vice President

• Md. Sirajul Islam - Head of Human Resources -

The management hierarchy of Eastern Bank Limited is given below:

BOARD OF DIRECTORS

SENIOR ASSISTANT VICE PRESIDENT

MANAGING DIRECTOR

EXECUTIVE VICE PRESIDENT

SENIOR VICE PRESIDENT

VICE PRESIDENT

CHAIRMAN

FIRST ASSISTANT VICE PRESIDENT

ASSISTANT VICE PRESIDENT

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Figure 1: Management Hierarchy of EBL

2.5.1 Corporate Banking Division:

Corporate Banking Division came into existence because of the restructuring of

EBL’s business processes. Previously all the loan disbursement and monitoring activities

were carried out by the officers of individual branches, which resulted in poor management

and control of the process. To check this trend, EBL decided to centralize its loan

disbursement and monitoring activities in line with the model followed by the foreign banks

in the country. As such, EBL was the first private commercial bank in the country to have a

separate Corporate Banking Division which started operation on 10th January of 2002. This

division is responsible for bringing in profitable new corporate clients and retaining present

clients by meeting their various needs. Corporate Banking delivers banking services like

products, credit facilities, tailored financial solution to the specific needs of clients, resolves

credit issues, and develops the relationship between the clients and the bank. At present,

PRINCIPAL OFFICER

SENIOR OFFICER

OFFICER

SENIOR PRINCIPAL OFFICER

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corporate Banking Division is the main revenue earner of EBL, generating about 70% of the

total revenue.

The whole corporate division is divided into two broad areas, Area 1 that comprises

of Dhaka and Outstation Branches and Area 2 comprising of Chittagong branches. Area 1

consists of six relationship units. Five units in Area 1 are looking after the bank’s assets

(loans and advances) and one unit is responsible for liabilities, while Area 2 has three asset

units and one liabilities unit. Every asset unit has a unit head, who is in charge of that unit

and responsible for all the business activities of that unit. Generally one Relationship

Manager (RM) and one Associate Relationship Manager (ARM) work under the Unit Head.

All Unit heads work under the Head of Corporate, and they report directly to him. The

management hierarchy of the Corporate Banking Division is given below:

Figure 3: Management Hierarchy of Corporate Banking

Relationship Manager Associate Relationship Manager

Head of Corporate

Unit Head

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The broad functions of this division are as follows:

Targeting corporate clients and building business relationship with them

Designing customized service for the clients

Evaluating financial strength of the clients

Making possible recommendations for further financial expansion

2.5.2 Credit Risk Management and Administration:

The primary objective of this division is to evaluate the credit worthiness and debt

payment capability of present loan customers and loan applicants. The respective branches

send all loans and advances proposals from the prospective borrowers to the Head office

Credit Risk Management (HOCRM) for an approval. If this department finds the loan

proposal attractive, it either approves it or sends it for board approval. It is also responsible

for keeping track of the credit portfolio by obtaining regular information from the branches. It

sets prices for credits and ensures affecting it at the branches. This department also monitors

the various loan accounts of the branches and prepares various statements for Bangladesh

Bank.

The Credit Risk Management Department is assisted by the Credit Administration

Department, which is mainly concerned with the post-approval functions of the division. The

aspects that are critically tracked and monitored by Credit Admin are

Credit expiry

Past dues

Excess over limit

Document deficiency

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Reporting

Credit Administration is involved in basically 2 broad functions:

Loan Monitoring

Documentation

Loan Monitoring:

The important aspects of this part are:

Follow approval terms

Proper loan disbursement

Monitor interest payments

Monitor principal repayment

Balance with general ledger

Documentation:

The important functions of this part are:

Look at sanction terms

Fill up loan documentation checklist

Ensure Proper loan documentation

Obtain client sign off

Filing with the Registered Joint Stock Corporation ( RJSC)

Registered mortgage deed execution

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2.5.3 Consumer Banking Division:

The mission of Consumer Banking is to serve individual customer throughout every

stage of their life stage. The consumer banking activities are being carried out through the 29

branches of Eastern Bank Limited operating countrywide. Among these branches 15 branches

are located in Dhaka, 6 in Chittagong, 4 in Sylhet and 1 each in Khulna, Jessore, Bogra and

Rajshahi. Previously these branches used to conduct all kind of business activities, including

processing credit issue, conducting trade service, consumer service etc. But after the

restructuring process, all these branches are now mainly focusing only on delivering service

to individual and corporate customers and are therefore termed as “Sales & Services

Centers”. At present, EBL does not have too many attractive consumer products, and

therefore this division is producing only 15% of the total revenue. However, EBL has decided

to give more focus on consumer banking and is developing modern delivery channels like

ATMs, tele-banking, internet banking, credit cards, etc. and many other new consumer

products and services to meet specific financial demands of the customers as well as to make

their life easy & convenient.

The broad functions of this division are:

Settlement of accounts

Building strong relationship with individual customers

Identifying individual needs of the customers and thus helping design products that will

meet their needs.

Providing locker services

Providing ancillary services

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2.5.4 Human Resources Division:

The employees are Eastern Bank’s most valuable resource. Having competent and

professional employees is becoming increasingly important in today’s competitive world, and

EBL has a significant competitive advantage in this respect. Many of its employees have

worked here since the BCCI area and therefore have vast experience in their respective fields.

Also the new employees are recruited with sound academic background and given proper

training after recruitment to groom up for their responsibilities.

The Mission of HR Division is to make EBL the Employer of Choice. They plan to

inculcate a high performance culture where the employees will work with fun and pride. The

broad functions of this division are:

Building Employer Image: The HR department has taken steps in building relationship with

recognized educational institutes in order to create positive awareness about EBL as an

employer. This is being done by extending internships to students, sponsoring student events

and by participating in job fairs run by different universities.

Staffing: Another important task of the HR division is to prepare all formalities regarding

appointment and joining of the successful candidates.

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Training: HR department emphasizes on training and development in order to minimize the

knowledge and skill gaps and enhance business awareness among the bank employees. To

this effect, this department arranges various training programs for the bank employees both at

the Bank’s own Training Institute at Shantinagar and at various outside locations, like

Bangladesh Institute of bank Management (BIBM).

Performance Management: Last year, EBL has introduced a performance-oriented culture

and a participative performance appraisal process. Each employee is given annual

performance target based on a discussion between the employee and his/her supervisor. At

year end, a performance evaluation called Annual Classified Report (ACR) is carried out

based on the targets and Key Responsibility Areas (KRAs). From now on, all increments,

bonuses and promotions will be given based on the results of this performance evaluation.

2.5.5 Audit and Compliance Division:

The main function of this division is to provide legal assistance to the branches and to

ensure strict adherence of rules and policies by all concerned officials of the bank through

routine and surprise inspection and audit. The functions of this division are as follows:

Implement rescheduling process of stuck-up loan to the branches for obtaining repayment

schedule through strong persuasion and serve final notice etc. as the condition required.

Monitor the individual cases with respect to their securities, value of securities, and

finally review of possibility of recovery of bank’s stuck-up classified loan.

Investigate suspicious or irregular matters being directed by higher management. Also

conduct such inquiry being requested by affected branch-in-charges.16

Time to time follow-up of stuck-up advances of branches and keep the branches under

constant pressure.

Inspect all branches’ operations at least once in a year.

Carry out surprise audit as felt necessary.

2.5.6 Finance and Accounts Division:

Finance and Accounts division is a very important division for any Bank, because its task

is to

Maintain daily liquidity positions, treasury bills, call money, debentures, placement of

funds etc.

Monthly-accrued interest calculation of all interests bearing accounts, inter-branch

calculation for Head Office, amortization of all fixed and other assets.

Preparation of statement of accounts and profit and loss account for the bank.

Weekly deposit and advance analysis of the bank.

Cost of fund analysis.

Maintenance of accounts, preparation of annual report of the bank, maintenance of

provident fund accounts, maintenance of income and expenditure posting, maintenance of

salaries and wages of the employees etc.

Fulfilling reporting requirements of Bangladesh Bank.

2.5.7 Information Technology Division:

Previously, Eastern Bank had a very low level of automation. There were hardly any

PC in the whole Bank before 2001. But when the new management took over in 2001, they

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gave huge emphasis on computerizing the bank’s operations. After 2 years, almost all the

operations in the bank are now automated. The Bank is also shifting to a new IT platform,

which aims at maintaining, operating and strengthening the technology base of the bank to

enable error free production of information that ensures ongoing efficiency and profitability

of operation. A world class banking software called FlexCube has been installed which will

centralize operations and provide Online Banking, Internet Banking, Automated Teller

Machine, Telephone Banking, Point of sale dispenser, Credit Card facility etc.

With this implementation of state-of-the-art Information Technology, the IT division has

become a very important contributor to the bank’s overall efficiency and profitability. At

present, the IT department serves the following functions:

Development of softwares for bank’s operation according to need, their maintenance

and purchase of new softwares.

Maintenance of computer hardwares and upgrading the PCs whenever required.

Training of the staff so that they can perform properly in the automated environment.

Preparing training materials.

Troubleshooting with the new software.

2.5.8 International Division:

International Division is responsible for assisting the authorized branches to deal in

foreign trades, that is, import and export businesses on account of the customers of the bank

by giving approval for transactions and controlling them at various stages.

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It deals with all correspondents of foreign banks having arrangement with the bank.

Every year new agents are added. The larger the number of correspondents and the wider the

coverage area, the richer will be the international connections of the bank.

The functions performed by this division are as follows:

Correspondent banking relationship

Supervision of foreign exchange transaction of other units

Monitoring on compliance of Bangladesh Bank regulations

Supervise sale / purchase of foreign currencies

Reconciliation of Nostro Accounts

2.5.9 Administration:

This department looks after the administrative matters and procurement and supply of all

tangible goods to the branches. Some of the main functions of this division are described as

follows:

Make arrangement for branch opening such as making lease agreement, internal

decorations etc.

Print all security papers and bank stationery.

Purchase necessary stationery items.

Distribute this stationery to the branches.

Purchase all sorts of furniture and fixtures for the branches.

Install and maintain different facilities for the branches etc.

Issuance of power of attorney to the officers of the branches.19

Issuance of different circulars of Bangladesh Bank and other banks.

General correspondence with Bangladesh Bank and other banks.

Advertise in the different media about tender notice, general meetings and public

interests.

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3.0 EBL Cards

3.1 Types of Credit Cards

The following diagram shows the different types of credit cards that EBL currently offers.

3.2 Features of EBL Simple Credit Card

Onetime Fee, Lifetime Free

To avail EBL Credit Card services, customer needs to pay the

Issuance/Joining/Subscription/ Annual Fee only once. After that there is no annual fee for

customer as long as customer transact at least 18 times in a whole year. It is a lifetime Card.

As per market standard, an average cardholder uses his/her card almost 24 times annually.

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So, without doing anything extra - customer can have the free renewal option – for the

following year.

Balance Transfer

EBL is happy to introduce Balance Transfer for the first time in Bangladesh. If

customer has Other Bank Credit Card(s), customer has the option to transfer his current

outstanding balance at a much cheaper rate to his EBL Credit Card. This saves his money,

time and gives customer the convenience to manage all his expenses from one point. EBL

want to be his lifetime partner. That is why EBL care about his current outstanding amount.

EBL are offering Balance Transfer (BT) @22% per annum. The standard market interest

average of Bank Credit Card is 30%. EBL are offering customer to switch his cards @8%

less interest.

Card Cheque

Through EBL Credit Card customer can enjoy a full-fledged cheque book facility.

Customer can make payment (account payee only) to any person or organization of his

choice. This cheque book is useful with situation where customer cannot use his credit card

(e.g. tuition fees, rent, etc). EBL are offering the FIRST CHEQUEBOOK FREE. There is a

charge for subsequent Chequebooks.

Worldwide Acceptance

EBL Credit Card is accepted at over 5,000 VISA merchant outlets around the country

and over 24 million VISA outlets worldwide. Customer can use this Card for a wide range of

products and services such as hotels, restaurants, airline & travel agents, shopping malls and

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departmental stores, hospitals and diagnostic centers, jewelers, electronics & computer shops,

leather goods and shoe stores, mobile phone and internet service providers, fuel station and

what not! This number is progressively on the rise. No matter wherever customer is – with

EBL Credit Cards, customer will always have the purchase power.

Immediate Cash Advance

With an EBL Credit Card in his wallet, customer will not be strapped for cash ever

again. He can withdraw cash up to 50% of his total credit limit 24-hours a day from any EBL

ATM; or any VISA participating member bank ATMs across the country; or from any VISA

ATMs worldwide. You do not need to worry of carrying cash anywhere – not even on a

foreign trip when there is no one to give customer hard cash at time of need.

Supplementary Card

EBL Credit Card gives customer the opportunity to share the benefits of his card with

his dear ones by providing customer supplementary cards. Being a primary cardholder,

customer has the option to set a spending limit for each of his supplementary cards. This is

perfect for customer if customer is thinking of giving his children their pocket money in a

controlled way. You can easily set-up their spending limit and also have an eye on their

spending behavior. All transactions on his supplementary card will separately be shown on

his monthly statement.

Risk Assurance Program

Risk Assurance Program is a Double Benefit Insurance Plan for the EBL Credit

Cardholders. The entire dues on the credit card in the event of death or permanent total

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disability of EBL credit cardholder will be waived and the cardholder or his/her family will

receive equal amount to meet immediate expenses – under Risk Assurance Program. Very

nominal charge will be applicable for this coverage. Cardholders can decide to cancel this

program by sending a letter to us at any time.

Great Discounts

EBL have collaborated with hundreds of vendors for giving the EBL Credit Card

users a hefty discount on their products and services. The great discounts in turn allows

customer either to save money or shop more with the same amount customer are spending

now.

Shopper’s Guide

EBL Credit Card provides customer a list of shops and outlets where using the card

will entitle customer to receive certain discount. EBL have chosen a large number of shops

and retail outlets for the purpose. The printed guide comes with every details (i.e. shop name,

address, discount value etc.) thus making his life easier.

Easy Installment Program

Easy Installment Program of EBL Credit Card allows the cardholder to convert any

retail transaction into an easy installment plan. You can purchase any high value product or

service and make payments in equal monthly installments (EMIs). The program will be

available shortly and EBL will notify customer as soon as our network partners increase.

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Auto Debit Facility

If customer has an account with any Eastern Bank branch, customer has the option of

making the payment of his monthly credit statement (either the minimum amount due or the

total amount due) directly through his bank account.

Convenient Payment Option

When customer receives his bill, customer need not pay the entire bill amount. You have

the flexibility of selecting any of the following payment options:

1. Pay the total amount due.

2. Pay only the minimum amount due (5% of his total outstanding or BDT 500

whichever is higher for local card and for Dual card 5% of his total outstanding or

USD 10 whichever is higher) and the balance can be carried forward to subsequent

statements.

3. Pay any amount ranging from the minimum amount due to the total amount due.

You can plan his payments conveniently, without creating any extra pressure on his finances.

SMail Service

EBL cardholders can receive monthly statements via e-mail, completely free of cost.

This is a fast, reliable and efficient service, which will minimize his paperwork and maximize

convenience. His Statement Mails or SMails can be delivered to more than one e-mail

address (up to a maximum of three e-mail addresses). All customer need to do is just filling

up the form and EBL will do the rest. It’s all smile with SMail.

Global Emergency Assistance Service25

When customer travel abroad, please remember that customer have the option of

using the Global Emergency Assistance Services provided by VISA for our cardholders.

These can be availed for: 1. Reporting lost/stolen credit cards, 2. Requesting for an

emergency card replacement, 3. Emergency cash advance & 4. Miscellaneous enquiries. The

toll free telephone numbers for accessing these emergency assistance Help lines are available

in local telephone directories/yellow pages and other local listings in each country.

EBL Mobile Alert

EBL Mobile Alert is a very simple, powerful and convenient way to know his Credit

Card statement details instantly without any postal delays. As soon as customer becomes an

EBL Credit Cardholder, he automatically join in the EBL Mobile Alert service and get faster,

reliable access to his Monthly Statement. Mobile alerts from EBL provides customer with

information on his EBL Credit Card even when customer are on the move. You would now

no longer miss a payment or exhaust his credit limit without a warning. Once his statement is

generated, EBL will notify customer about the balance status with payment information. Life

has never been so easier.

EBL Rewards Program

A special bonus plan that allows customer to earn points every time customer use his

Card, Every Tk. 50 that customer spend earns customer 1 point. The redemption of reward

points can be done against the products, services in the rewards catalogue. The enrollment is

free. EBL are preparing the rewards listing and shall mail customer the catalogue with

program details as soon as it is launched.

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Self-set Limit

EBL Credit Card is the only Card that allows customer to pre-define his own credit

limits. You can request for a limit lower than what customer are eligible for. You can even

preset the monthly spending limits on the Supplementary Cards. Any transactions over the

specified 'Spend Limit' will be declined. This monthly spending limit can be reset every

billing cycle by just calling the EBL 24-hour Cards Center and place his request with the

Customer Service Executive. His spend limit will be changed on-line and come in to force

from the next billing cycle.

Limited Lost Card Liability

In case the Card is lost or stolen, call the EBL 24-hour Cards Center and report the

loss of his Card. A new Card will be sent to customer within 72 hours of reporting this loss.

You are protected from any financial liability arising out of transactions done on his missing

Card, from the time customer report the loss to us.

24-hour Cards Center

The EBL 24-hour Cards Center is equipped with a state-of-the-art system that ensures his

queries being handled efficiently and promptly. For any card-related query or information, all

customer need to do is dial 9571760 or 9571775 Ext. 120/130

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3.3 Fees & Charges excluding VAT, all in BDT

LOCAL

CLASSIC

LOCAL

GOLD

DUAL

CLASSIC

DUAL

GOLD

Card Issuance Fee 1,300 2,500 1,300 2,500

Card Issuance Fee - Supplementary Card 1,300 2,500 1,300 2,500

Renewal Fee - Any Supplementary Card* NIL NIL NIL NIL

Card Replacement Fee 300 500 400 500

PIN Replacement Fee 150 300 200 400

Finance Charge on all Transactions (per

month)2.50% 2.50% 2.50% 2.50%

Balance Transfer Interest (per month) 1.83% 1.83% 1.83% 1.83%

Interest on Excess of Limit Amount (per

month)2.92% 2.92% 2.92% 2.92%

Late Payment Fee (BDT) 400 400 400 500

Late Payment Fee ($) X X $ 10 $ 10

Cash Advance Fee (BDT – whichever

higher)3% or 100 3% or 100 3% or 100 3% or 100

Cash Advance Fee ($) X X 3% or $5 3% or $5

Balance Transfer Fee 1% 1% 1% 1%

Duplicate Statement Fee (BDT) 300 300 300 300

Returned Cheque Fee (BDT) 200 200 200 200

Sales Voucher Retrieval Fee 200 200 200 200

Certificate Fee 200 200 200 200

Expired Card Printing Charge/Plastic Fee

(Every 2 Years, if the card is in zero

renewal mode)

100 100 100 100

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Risk Assurance Fee (BDT) 0.35% 0.35% 0.35% 0.35%

First Card Cheque Book – 12 Leaves Free Free Free Free

Card Cheque Book Fee 12 leaves (BDT) 150 150 150 150

Card Cheque Book Fee 25 leaves (BDT) 250 250 250 250

Card Cheque Processing Fee (BDT) 1% 1% 1% 1%

Statement by Email - SMail Service Free Free Free Free

*Subject to minimum 18 transactions in a year 15% VAT applicable on all fees and charges

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Project Part

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4.0 Statistical Analysis

4.1 Objectives of this part

In this part I will do my statistical analyses on the data I have collected on EBL cards. I

will primarily focus on the following courses of actions.

At first I will show what the variables in my analysis are and why they are important.

I will present the data sheet on the variables on which I will do my statistical analyses.

My first course of action will be to show some descriptive statistics of the variables

along with graphical representations of the variables.

With the data, I will conduct estimations of population means of each of the variables

using confidence level technique.

I will then analyze the correlation among the independent variables with the

dependent variable.

Finally I will show the multiple linear regression model and carry out F-test, Durbin

Watson auto correlation test and individual t-test for beta coefficients.

31

4.2 The Variables in my Analysis

In this section I am going to describe what the variables are and what do they mean.

Credit Card Sales – They reflect the number of credit card sales in the month of May

2008. This is the main dependent variable of my model.

The following are the independent variables in my study.

Debit Card Sales – They reflect the number of debit card sales in the month of May

2008.

New Deposits Opened – They reflect the number of new deposit accounts opened in

the month of May 2008.

New Loan Accounts – They indicate the number of new loan accounts opened in the

month of May 2008.

Deposit Growth Rate – It reflects the deposit growth rate in percentage from April to

May 2008.

Loan Growth Rate – It shows the loan growth rate in percentage from April to May

2008.

Direct Sales Team – It shows the number of direct sales team in each branch in May

2008

32

Permanent Staff – It shows the number of permanent staff enrolled in each branch in

May 2008.

4.3 Data Sheet

Before starting the analysis, I will present the data sheet on which I will conduct the

statistical analysis. As I have stated in the beginning of my report, I will analyze whether

some of the factors such as new deposit accounts, direct sales team number, debit card sales

etc. influence the sale of credit cards. The following is the data sheet.

Branch Credit Cards Debit Cards New Deposits Opened New Loans Opened Deposit Growth in Taka (%)O R Nizam 154 0 203 23Sonargaon Road 140 67 150 25Motijheel 131 50 167 42Banani 96 45 144 17Gulshan 223 37 81 9Dhanmondi 223 275 0 12Principal 266 107 91 6Uttara 180 52 128 11Agrabad 216 61 93 0Shantinagar 163 69 164 10Chawkbajar 67 147 4 2Narayangonj 154 48 118 6Jubilee Road 154 0 104 14Shamoly 142 31 95 7Station Road 141 17 121 1Mirpur 123 17 60 0English Road 108 33 0 12Chandgaon 96 58 56 6Panchlaish 96 50 113 16Bashundhara 144 78 112 4Khulna 62 5 122 9Moulvibazar 62 3 43 5Rajshahi 55 157 84 3Upashahar 59 3 31 4Bogra 66 34 72 4

33

Khatunganj 63 18 58 0Biswanath 61 47 12 2Chouhatta 61 29 39 0Jessore 52 2 39 4Raozan 48 54 42 8

As we can see, the data have been arranged according to branches of Eastern Bank

Ltd. As there are 30 branches, there are 30 observations for each set of variables. These data

are of May 2008. The credit card sales number will be the dependent variable in my

regression analysis and the rest seven factors are my independent variables. With this thing

into account, I will do the calculations in the regression analysis part. Besides I will also do

estimation of population mean, analysis of variance, validity test with confidence level of

90%, durbin-watson test. There will be some descriptive statistics also but they are not the

main focus of this report.

4.4 Basic Descriptive Statistics

It is important to know some of the basic descriptive statistics of the variables that I

am including in my model. Although they are not the key focus of this report as inferential

statistics are, nevertheless they are very important in knowing the central tendency of their

distribution and also the degree of dispersion. Understanding such statistics help us to

determine the skewness of the distribution of the variables and hence will help us infer more

about the distribution of the population means.

Descriptive Statistics in a shapshotCredit Debit New New Direct Deposit Loan Permanent

34

Card

Sales

Card

Sales

Deposit

Accounts

Loan

Accounts

Sales

Team

Growth

Rate

Growth

Rate

Staff

N Valid 30 30 30 30 30 30 30 30

Missing 0 0 0 0 0 0 0 0

Mean 120.20 53.13 84.87 8.73 7.50 2.1193 .4903 11.33

Median 115.50 46.00 87.50 6.00 7.00 1.2700 .4750 10.00

Std.

Deviation

60.168 57.108 53.095 9.040 2.013 3.64780 .59593 3.951

Percentiles 25 62.00 17.00 41.25 2.75 6.00 .3225 .1350 8.00

50 115.50 46.00 87.50 6.00 7.00 1.2700 .4750 10.00

75 154.00 62.50 121.25 12.00 9.00 2.5525 .7725 13.25

As we can see I have shown the mean, median, standard deviation and quartiles of

each of the eight variables. The mean and median shows us the central tendency and the

standard deviation shows us the dispersion. For example, credit card sales variable has high

degree of dispersion due to high standard deviation compared to variables such as permanent

staff, which change very slowly from the mean value.

4.5 Graphical Representation of Each Variable

A. Credit Card Sales

The following is the frequency table of credit card sales figure. Since all the values are

numerical value, the frequency table is quite large.

Frequency Table of Credit Card SalesValue Frequency Percent Valid Percent Cumulative

Percent48 1 3.3 3.3 3.352 1 3.3 3.3 6.755 1 3.3 3.3 10.059 1 3.3 3.3 13.3

35

61 2 6.7 6.7 20.062 2 6.7 6.7 26.763 1 3.3 3.3 30.066 1 3.3 3.3 33.367 1 3.3 3.3 36.796 3 10.0 10.0 46.7

108 1 3.3 3.3 50.0123 1 3.3 3.3 53.3131 1 3.3 3.3 56.7140 1 3.3 3.3 60.0141 1 3.3 3.3 63.3142 1 3.3 3.3 66.7144 1 3.3 3.3 70.0154 3 10.0 10.0 80.0163 1 3.3 3.3 83.3180 1 3.3 3.3 86.7216 1 3.3 3.3 90.0223 2 6.7 6.7 96.7266 1 3.3 3.3 100.0

Total 30 100.0 100.0

Interpretation of Chart: The following bar chart in the next page illustrates the

credit card sales figure according to percentage by branch. We can clearly see that credit

cards are mostly sold in Principle branch in Dilkusha, Dhanmondi and Gulshan with sales

percentage of around 7%, 6% and 6% respectively. This illustrates the main targeted market

of the credit cards.

36

B. Debit Card Sales

The following is the frequency table of credit card sales figure. Since all the values are

numerical value, the frequency table is quite large.

Frequency Table of Debit Card SalesValues Frequency Percent Valid Percent Cumulative

Percent0 2 6.7 6.7 6.72 1 3.3 3.3 10.03 2 6.7 6.7 16.75 1 3.3 3.3 20.0

17 2 6.7 6.7 26.718 1 3.3 3.3 30.029 1 3.3 3.3 33.331 1 3.3 3.3 36.733 1 3.3 3.3 40.034 1 3.3 3.3 43.337 1 3.3 3.3 46.745 1 3.3 3.3 50.047 1 3.3 3.3 53.3

37

48 1 3.3 3.3 56.750 2 6.7 6.7 63.352 1 3.3 3.3 66.754 1 3.3 3.3 70.058 1 3.3 3.3 73.361 1 3.3 3.3 76.767 1 3.3 3.3 80.069 1 3.3 3.3 83.378 1 3.3 3.3 86.7

107 1 3.3 3.3 90.0147 1 3.3 3.3 93.3157 1 3.3 3.3 96.7275 1 3.3 3.3 100.0

Total 30 100.0 100.0

The following chart illustrated the debit card sales figure according to percentage by branch.

Interpretation of Chart: If we look at the percentage of debit card sales, the highest

concencentration is in Dhanmondi which is around 17% and it is not surprising considering

the market for debit cards there.

38

C. New Deposit Accounts

The following is the frequency table of new deposit accounts.

Frequency Table of New Deposit AccountsValues Frequency Percent Valid Percent Cumulative

Percent0 2 6.7 6.7 6.74 1 3.3 3.3 10.0

12 1 3.3 3.3 13.331 1 3.3 3.3 16.739 2 6.7 6.7 23.342 1 3.3 3.3 26.743 1 3.3 3.3 30.056 1 3.3 3.3 33.358 1 3.3 3.3 36.760 1 3.3 3.3 40.072 1 3.3 3.3 43.381 1 3.3 3.3 46.784 1 3.3 3.3 50.091 1 3.3 3.3 53.393 1 3.3 3.3 56.795 1 3.3 3.3 60.0

104 1 3.3 3.3 63.3112 1 3.3 3.3 66.7113 1 3.3 3.3 70.0118 1 3.3 3.3 73.3121 1 3.3 3.3 76.7122 1 3.3 3.3 80.0128 1 3.3 3.3 83.3144 1 3.3 3.3 86.7150 1 3.3 3.3 90.0164 1 3.3 3.3 93.3167 1 3.3 3.3 96.7203 1 3.3 3.3 100.0

Total 30 100.0 100.0

Interpretation of Chart: If we look at the doughnut chart in the next page, we can

see that the highest percentage of new deposit accounts is opened in O R Nizam branch (8%)

followed by Motijheel Branch (7%). The lowest percentage belonged to English Road Branch

with 0% in the month of May 2008.

39

D. New Loan Accounts

The following is the frequency table of new loan accounts.

Frequency Table of New Loan AccountsValues Frequency Percent Valid Percent Cumulative

Percent0 4 13.3 13.3 13.31 1 3.3 3.3 16.72 2 6.7 6.7 23.33 1 3.3 3.3 26.74 4 13.3 13.3 40.05 1 3.3 3.3 43.36 3 10.0 10.0 53.37 1 3.3 3.3 56.78 1 3.3 3.3 60.09 2 6.7 6.7 66.7

10 1 3.3 3.3 70.011 1 3.3 3.3 73.312 2 6.7 6.7 80.014 1 3.3 3.3 83.316 1 3.3 3.3 86.717 1 3.3 3.3 90.023 1 3.3 3.3 93.325 1 3.3 3.3 96.7

40

42 1 3.3 3.3 100.0Total 30 100.0 100.0

Interpretation of Chart: If we look at the chart, we can see that the highest

percentage of new loan accounts is opened in Motijheel branch followed by Sonargaon Road

Branch. The lowest percentage belonged to Agrabad, Mirpur, Chouhatta and Khatunganj

Branches with 0% in the month of May 2008.

41

E. Direct Sales Teams

The following is the frequency table of direct sales team numbers in branches.

Frequency Table of Direct Sales TeamValues Frequency Percent Valid Percent Cumulative

Percent5 4 13.3 13.3 13.36 8 26.7 26.7 40.07 6 20.0 20.0 60.08 2 6.7 6.7 66.79 5 16.7 16.7 83.3

10 3 10.0 10.0 93.311 1 3.3 3.3 96.713 1 3.3 3.3 100.0

Total 30 100.0 100.0


percentage of direct sales team is in Gulshan branch. The lowest percentage belonged to

many Branches such as Chouhatta, Upashar etc. in the month of May 2008.

42

F. Deposit Growth Rate

The following is the frequency table of deposit growth rate in branches.

Frequency Table of Deposit Growth RateValues Frequency Percent Valid Percent Cumulative

Percent-.88 1 3.3 3.3 3.3-.43 1 3.3 3.3 6.7-.36 1 3.3 3.3 10.0.10 1 3.3 3.3 13.3.12 1 3.3 3.3 16.7.19 1 3.3 3.3 20.0.30 1 3.3 3.3 23.3.33 1 3.3 3.3 26.7.42 1 3.3 3.3 30.0.46 1 3.3 3.3 33.3.50 1 3.3 3.3 36.7.57 1 3.3 3.3 40.0.75 1 3.3 3.3 43.3

1.18 1 3.3 3.3 46.71.25 1 3.3 3.3 50.01.29 2 6.7 6.7 56.71.46 1 3.3 3.3 60.01.87 1 3.3 3.3 63.32.02 1 3.3 3.3 66.72.04 1 3.3 3.3 70.02.21 1 3.3 3.3 73.32.51 1 3.3 3.3 76.72.68 1 3.3 3.3 80.02.71 1 3.3 3.3 83.33.64 1 3.3 3.3 86.73.77 1 3.3 3.3 90.06.02 1 3.3 3.3 93.36.41 1 3.3 3.3 96.7

19.16 1 3.3 3.3 100.0Total 30 100.0 100.0


percentage of deposit growth rate is in Agrabad branch. The lowest percentage belonged to

many Branches such as O R Nizam, English Road etc. in the month of May 2008.

43

G. Loan Growth Rate

The following is the frequency table of loan growth rate in branches.

Frequency Table of Loan Growth RateValues Frequency Percent Valid Percent Cumulative

Percent-.77 1 3.3 3.3 3.3-.53 1 3.3 3.3 6.7-.24 1 3.3 3.3 10.0-.08 1 3.3 3.3 13.3.04 1 3.3 3.3 16.7.09 1 3.3 3.3 20.0.12 1 3.3 3.3 23.3.14 1 3.3 3.3 26.7.21 1 3.3 3.3 30.0.22 1 3.3 3.3 33.3.24 1 3.3 3.3 36.7.33 1 3.3 3.3 40.0.35 1 3.3 3.3 43.3.37 1 3.3 3.3 46.7.44 1 3.3 3.3 50.0.51 1 3.3 3.3 53.3

44

.52 1 3.3 3.3 56.7

.62 1 3.3 3.3 60.0

.63 1 3.3 3.3 63.3

.64 1 3.3 3.3 66.7

.65 1 3.3 3.3 70.0

.67 1 3.3 3.3 73.3

.77 1 3.3 3.3 76.7

.78 1 3.3 3.3 80.0

.92 2 6.7 6.7 86.71.03 1 3.3 3.3 90.01.28 1 3.3 3.3 93.31.56 1 3.3 3.3 96.72.28 1 3.3 3.3 100.0

Total 30 100.0 100.0


percentage of loan growth rate is in Sonargaon Road branch. The lowest percentage belonged

to Raozan Branch in the month of May 2008.

45

H. Permanent Staff

The following is the frequency table of permanent staff in branches.

Frequency Table of Permanent StaffValues Frequency Percent Valid Percent Cumulative

Percent6 2 6.7 6.7 6.77 1 3.3 3.3 10.08 5 16.7 16.7 26.79 4 13.3 13.3 40.0

10 4 13.3 13.3 53.311 2 6.7 6.7 60.012 2 6.7 6.7 66.713 3 10.0 10.0 76.714 1 3.3 3.3 80.015 2 6.7 6.7 86.716 1 3.3 3.3 90.019 1 3.3 3.3 93.320 1 3.3 3.3 96.721 1 3.3 3.3 100.0

Total 30 100.0 100.0


percentage of permanent staff is in Agrabad branch. The lowest percentage belonged to

Biswanath Branch in the month of May 2008.

46

4.6 Estimation of Population Means

Since these data are only of the month of May 2008, I will be using estimation

methods to identify the population mean of the 8 variables that I am including in my

statistical analysis.

Justification of using T-test instead of Z-test: Since I do not know the variance of

my population, I will be using one-sample student’s t test instead of z-test to estimate the

population means using 90% confidence level. The student’s t-test is perfect over the z-test in

this case since I cannot simply assume that the population distribution is also normal.

1. Estimation of Credit Card Sales Population Mean

From the data sheet I have inserted the credit card sales data and with 90% confidence

interval level and using student’s t distribution, I have come up with the following statistical

output from SPSS.

One-Sample Test

Test Value = 140t df Sig. (2-tailed) Mean Difference

-1.802 29 .082 -19.80

Hypothesis Testing

Null Hypothesis H0 : Population Mean of Credit Card Sales is 140 (µ=140)

Alternative Hypothesis H1: Population Mean of Credit Card Sales is not 140 (µ≠140)

47

Decision: Since the p-value of the t-test is .082 which is less than α = .10, we will be rejecting the

null hypothesis and accept the alternative hypothesis that the population mean is not equal to 140.

90% Confidence Interval of the Difference

Lower Upper101.53 138.87

Estimation of Mean by using Confidence Interval: The results show that the population

mean of credit card sales is estimated to lie in the range of 101.53< µ < 138.87 with 90%

confidence level.

2. Estimation of Debit Card Sales Population Mean

Using 90% confidence interval level and using student’s t distribution, I have come up with

the following statistical output from SPSS by using the values from the data sheet.

One-Sample Test

Test Value = 60t df Sig. (2-tailed) Mean Difference

-.659 29 .515 -6.87

Hypothesis Testing

Null Hypothesis H0 : Population Mean of Debit Card Sales is 60 (µ=60)

Alternative Hypothesis H1: Population Mean of Debit Card Sales is not 60 (µ≠60)

Decision: Since the p-value of the t-test is .515 which is greater than α = .10, we will not be

rejecting the null hypothesis and discard the alternative hypothesis that the population mean

is not equal to 60.48



Estimation of Mean Using Confidence Interval: The results show that the population mean

of debit card sales is estimated to lie in the range of 35.42< µ < 70.85 with 90% confidence

level.

3. Estimation of New Deposit Accounts Population Mean

From the data sheet I have inserted the debit card sales data and with 90% confidence interval

level and using student’s t distribution, I have come up with the following statistical output

from SPSS.

One-Sample Test

Test Value = 90

t df Sig. (2-tailed)

Mean Difference

-.530 29 .600 -5.13

Hypothesis Testing

Null Hypothesis H0 : Population Mean of New Deposit Accounts is 90 (µ=90)

Alternative Hypothesis H1: Population Mean of New Deposit Accounts is not 90 (µ≠90)



is not equal to 90.

49



Estimation of Population Mean Using Confidence Interval: The results show that the

population mean of new deposit accounts is estimated to lie in the range of 68.4< µ < 101.34

with 90% confidence level.

4. Estimation of New Loan Accounts Population Mean



One-Sample Test

Test Value = 10


Mean Difference

-.767 29 .449 -1.27

Hypothesis Testing

Null Hypothesis H0 : Population Mean of New Loan Accounts is 10 (µ=10)

Alternative Hypothesis H1: Population Mean of New Loan Accounts is not 10 (µ≠10)



is not equal to 10.


50



population mean of new loan accounts is estimated to lie in the range of 5.93< µ < 11.54 with

90% confidence level.

5. Estimation of Direct Sales Team Population Mean



from SPSS.

One-Sample TestTest Value

= 10


Mean Difference

-6.803 29 .000 -2.50

Hypothesis Testing

Null Hypothesis H0 : Population Mean of Direct Sales Team is 10 (µ=10)

Alternative Hypothesis H1: Population Mean of Direct Sales Team is not 10 (µ≠10)

Decision: Since the p-value of the t-test is .0 which is less than α = .10, we will be rejecting the null

hypothesis and accept the alternative hypothesis that the population mean is not equal to 10.


Lower Upper

51

6.88 8.12


population mean of direct sales team is estimated to lie in the range of 6.88< µ < 8.12 with

90% confidence level.

6. Estimation of Deposit Growth Rate Population Mean



One-Sample Test

Test Value = 3


Mean Difference

-1.322 29 .196 -.8807

Hypothesis Testing

Null Hypothesis H0 : Population Mean of Deposit Growth Rate is 3% (µ=3)

Alternative Hypothesis H1: Population Mean of Deposit Growth Rate is not 3 (µ≠3)


rejecting the null hypothesis and discard the alternative hypothesis that the population mean is not

equal to 3.


Lower Upper.9877 3.25

52


population mean of deposit growth rate is estimated to lie in the range of .9877< µ < 3.25


7. Estimation of Loan Growth Rate Population Mean



from SPSS.

One-Sample Test

Test Value = 3


Mean Difference

-23.066 29 .000 -2.5097

Hypothesis Testing

Null Hypothesis H0 : Population Mean of Loan Growth Rate is 3% (µ=3)

Alternative Hypothesis H1: Population Mean of Loan Growth Rate is not 3% (µ≠3)

Decision: Since the p-value of the t-test is .0 which is less than α = .10, we will be rejecting

the null hypothesis and accept the alternative hypothesis that the population mean is not equal

to 3%.


Lower Upper

53

.3055 3.6752


population mean of new loan accounts is estimated to lie in the range of .3055< µ < 3.6752


8. Estimation of Permanent Staff Population Mean



from SPSS.

One-Sample TestTest Value

= 15


Mean Difference

-5.083 29 .000 -3.67

Hypothesis Testing

Null Hypothesis H0 : Population Mean of Permanent Staff is 15 (µ=15)

Alternative Hypothesis H1: Population Mean of Loan Growth Rate is not 15 (µ≠15)

Decision: Since the p-value of the t-test is .0 which is less than α = .10, we will be rejecting the null

hypothesis and accept the alternative hypothesis that the population mean is not equal to 15.



54


population mean of new loan accounts is estimated to lie in the range of 10.11< µ < 12.56


4.7 Pearson Product-Moment Correlation

The following table shows the Pearson product-moment correlation coefficient

between all the variables included in my data sheet. In statistics, the Pearson product-moment

correlation coefficient (sometimes referred to as the MCV or PMCC, and typically denoted

by r) is a common measure of the correlation between two variables X and Y .However the

focus is only the correlation between the dependent variable “Credit Card Sales” and the

55

other 7 variables. We know that correlation values ranged between -1 to +1. Negative one

indicates perfectly negative correlation which means the variables move in opposite direction

and positive one indicates perfectly positive correlation which means the factors move in

same direction. A correlation of zero indicates no linear relationship between the two

variables but there might or might not be non-linear relationship.

Correlations between all variables

Credit Card

Sales

Debit Card

Sales

New Deposit

Accounts

New Loan

Accounts

Direct Sales Team

Deposit Growth

Rate

Loan Growth

Rate

Permanent Staff

Credit Card Sales

Pearson Correlation

1 .319 .335 -.200 .698** -.323 -.286 .879**

Sig. (2-tailed)

. .086 .070 .290 .000 .082 .126 .000

N 30 30 30 30 30 30 30 30Debit Card

SalesPearson

Correlation.319 1 -.257 .003 .393 .062 -.033 .230

Sig. (2-tailed)

.086 . .171 .989 .032 .747 .861 .222

N 30 30 30 30 30 30 30 30New

Deposit Accounts

Pearson Correlation

.335 -.257 1 .576 .174 .173 .555 .217

Sig. (2-tailed)

.070 .171 . .001 .357 .360 .001 .249

N 30 30 30 30 30 30 30 30New Loan Accounts

Pearson Correlation

-.200 .003 .576 1 .290 .125 .317 .202

Sig. (2-tailed)

.290 .989 .001 . .120 .509 .088 .283

N 30 30 30 30 30 30 30 30Direct Sales Team

Pearson Correlation

.698** .393 .174 .290 1 .064 .395 .650

Sig. (2-tailed)

.000 .032 .357 .120 . .739 .031 .000

N 30 30 30 30 30 30 30 30Deposit Growth

Rate

Pearson Correlation

-.323 .062 .173 .125 .064 1 .321 .465

Sig. (2-tailed)

.082 .747 .360 .509 .739 . .084 .010

N 30 30 30 30 30 30 30 30Loan

Growth Rate

Pearson Correlation

-.286 -.033 .555 .317 .395 .321 1 .345

Sig. (2-tailed)

.126 .861 .001 .088 .031 .084 . .062

N 30 30 30 30 30 30 30 30

56

Permanent Staff

Pearson Correlation

.879** .230 .217 .202 .650 .465 .345 1

Sig. (2-tailed)

.000 .222 .249 .283 .000 .010 .062 .

N 30 30 30 30 30 30 30 30** Correlation is significant at the 0.01 level (2-tailed).* Correlation is significant at the 0.05 level (2-tailed).

The following table is the most significant one and is extracted from the above table.

It shows the correlation between the only dependent variable, ‘credit card sales’ and the other

seven variables. This is important as the correlation will play a factor in the linear regression

model.

Correlations between Credit Card Sales & other variables

Credit

Card

Sales

Debit

Card

Sales

New

Deposit

Accounts

New Loan

Accounts

Direct

Sales

Team

Deposit

Growth

Rate

Loan

Growth

Rate

Permanent

Staff

Pearson

Correlation

1 .319 .335 -.200 .698** -.323 -.286 .879**

Sig. (2-

tailed)

. .086 .070 .290 .000 .082 .126 .000

N 30 30 30 30 30 30 30 30

Interpretation of Correlation: If we look at the correlation between the independent

variables and the dependent variables, we can see that debit card sales, new deposit accounts,

direct sales team and permanent staff have positive correlation with credit card sales and new

loan accounts, deposit growth rate and loan growth rate have negative correlation with credit

card sales. Only permanent staff and direct sales team have very high correlation with credit

card sales, which proves one point that these two variables play a very important role in the

sales of credit card figures. All the other variables have low to weak correlation, which means

they slightly or moderately influence the credit card sales figure in either positive or negative

way.

57

In short, the correlation just shows the strength and direction but does not actually

quantify by how much those variables will influence the dependent variable. For that we have

to go now to multiple linear regression model. But for now, correlation shows that direct

sales team and permanent staff have statistically strong influence on credit card sales.

4.7.1 Hypothesis Testing of Population Correlations

1. Null Hypothesis H0 : Population Correlation between Credit Card & Debit Card is

zero (ρ=0)

Alternative Hypothesis H1: Population Correlation between Credit Card & Debit Card is not

zero (ρ≠0)

Decision: Since the p-value of the correlation test is .086 which is greater than α =.05, the

population correlation is statistically insignificant and hence we do not reject the null

hypothesis and discard the alternative hypothesis that the population correlation is not zero.

2. Null Hypothesis H0 : Population Correlation between Credit Card & New Deposit

Accounts is zero (ρ=0)

Alternative Hypothesis H1: Population Correlation between Credit Card & New Deposit

Accounts is not zero (ρ≠0)

58




3. Null Hypothesis H0 : Population Correlation between Credit Card & New Loan

Accounts is zero (ρ=0)

Alternative Hypothesis H1: Population Correlation between Credit Card & New Loan

Accounts is not zero (ρ≠0)




4. Null Hypothesis H0: Population Correlation between Credit Card & Direct Sales

Team is zero (ρ=0)

Alternative Hypothesis H1: Population Correlation between Credit Card & Direct Sales Team

is not zero (ρ≠0)

Decision: Since the p-value of the correlation test is .00 which is less than α =.05, the

population correlation is statistically significant and hence we reject the null hypothesis and

accept the alternative hypothesis that the population correlation is not zero.

5. Null Hypothesis H0 : Population Correlation between Credit Card & Deposit Growth

Rate is zero (ρ=0)

59

Alternative Hypothesis H1: Population Correlation between Credit Card & Deposit Growth

Rate is not zero (ρ≠0)




6. Null Hypothesis H0 : Population Correlation between Credit Card & Loan Growth

Rate is zero (ρ=0)

Alternative Hypothesis H1: Population Correlation between Credit Card & Loan Growth

Rate is not zero (ρ≠0)




7. Null Hypothesis H0 : Population Correlation between Credit Card & Permanent Staff

is zero (ρ=0)

Alternative Hypothesis H1: Population Correlation between Credit Card & Permanent Staff is

not zero (ρ≠0)

60

Decision: Since the p-value of the correlation test is .00 which is less than α =.05, the

population correlation is statistically significant and hence we reject the null hypothesis and

accept the alternative hypothesis that the population correlation is not zero.

4.8 Multiple Linear Regression Model

In this part, I will focus on creating a multiple linear regression model in which the

credit card sales will be dependent model and the rest will be independent model. I have

already shown in the correlation part to determine in what way each of the independent

61

variables affect the dependent variable. In this regression model, I will exactly quantify by

much the independent variables will affect the dependent variable.

4.8.1 Model Summary

Model Summary

R R Square Adjusted

R Square

Std. Error

of the

Estimate

Durbin-

Watson

.938 .880 .842 23.938 2.362

a Predictors: (Constant), Permanent Staff, New Loan Accounts, Debit Card Sales, Loan Growth Rate, Deposit Growth Rate, New Deposit Accounts, Direct Sales Teamb Dependent Variable: Credit Card Sales

Interpretation of R square: The model summary shows some important indicators

of the explaining power of the model. The R-square value shows how much change in the

dependent variable is caused by the independent variables. In this case a r-square value of .88

or 88% means 88% of the change in dependent variable is caused by the seven independent

variables.

Interpretation of Adjusted R-square: On the other hand, the adjusted r-square value

shows how much change in the dependent variable is caused by statistically significant

variables. So in this case, an adjusted r-square value of .842 or 84.2% means that 84.2% of

the change in dependent variable is caused by statistically significant variables, which will be

found later in the report. The standard error of the estimate measures the accuracy of the

predictions within the regression line, which is around 23.938.

4.8.2 Durbin-Watson Test

62

According to Wikipedia.com, the Durbin-Watson statistic is a test statistic used to

detect the presence of autocorrelation in the residuals from a regression analysis. In statistics,

the autocorrelation function (ACF) of a random process describes the correlation between the

processes at different points in time. The Durbin Watson value ranges from 0 to 4. A value of

2 indicates there appears to be no autocorrelation. If the Durbin-Watson statistic is

substantially less than 2, there is evidence of positive serial correlation. As a rough rule of

thumb, if Durbin-Watson is less than 1.0, there may be cause for alarm. Small values of d

indicate successive error terms are, on average, close in value to one another, or positively

correlated. Large values of d indicate successive error terms are, on average, much different

in value to one another, or negatively correlated.

The test statistic of Durbin-Watson model is and hence

we see we use error values according to time.

R R Square Adjusted

R Square

Std. Error

of the

Estimate

Durbin-

Watson

.938 .880 .842 23.938 2.362

The Durbin-Watson test statistic of my model is 2.362, which means there is very little

negative correlation among the variable. However this is not alarming as the value is close of

2, which signals it is close to no autocorrelation.

4.8.3 Analysis of Variance (ANOVA)

63

The ANOVA table shows the total variation, the explained variation and the

unexplained variation (residual) due to the error. The explained variation is denoted as SSR

and the unexplained variation due to residuals is denoted by SSE. The total variation (SST) is

104986.8 and variation explained by regression (SSR) is 92380.427 and variation explained

by regression error SSE is 12606.373. So the explaining power of the regression model is

good since SSR is greater than SSE. If we divide the SSR with SST, we get the value of r-

square which we have already found out to be 88%.

ANOVA

Sum of

Squares

df Mean

Square

F Sig.

Regression 92380.427 7 13197.204 23.031 .000

Residual 12606.373 22 573.017

Total 104986.80

0

29

a Predictors: (Constant), Permanent Staff, New Loan Accounts, Debit Card Sales, Loan Growth Rate, Deposit Growth Rate, New Deposit Accounts, Direct Sales Team

b Dependent Variable: Credit Card Sales

4.8.4 Validity of the Model by using F-Test:

The F-test, or an analysis of variance, is used to test the magnitudes of explained

variation (SSR) and unexplained variation (SSE) with their appropriate degrees of freedom.

The hypotheses for the test are as follows:

Null Hypothesis H0 : Beta coefficients of all independent variables has zero value

64

Alternative Hypothesis H1: At least 1 Beta coefficient of independent variable has non-zero

value

Decision: The F-test shows that the p-value of the test is 0%. Since p-value is less than α =

10%, we will reject the null hypothesis that all of the beta coefficients have zero value and

accept the alternate hypothesis that at least 1 beta coefficient has non-zero value.

4.8.5 Coefficients (β) of Independent Variables

Coefficients

Unstandardized Coefficients

Standardized Coefficients

t Sig.

B Std. Error Beta(Constant) -82.284 19.546 -4.210 .000Debit Card

Sales.168 .092 .159 1.825 .082

New Deposit Accounts

.451 .125 .398 3.601 .002

New Loan Accounts

-1.161 .629 -.174 -1.846 .078

Direct Sales Team

6.914 3.689 .231 1.874 .074

Deposit Growth Rate

-.286 1.573 -.017 -.182 .857

Loan Growth Rate

-21.106 10.206 -.209 -2.068 .051

Permanent Staff

10.986 1.816 .721 6.048 .000

4.8.6 Unstandardized Multiple Linear Regression Equation before

Significance Test:

Y (Credit Card Sales) = -82.284 + .168X1 (Debit Card Sales) + .451X2 (New Deposit Accounts) –

1.161X3 (New Loan Accounts) + 6.914X4 (Direct Sales Team) - .286X5 (Deposit Growth Rate) –

21.106X6 (Loan Growth Rate) + 10.986X7 (Permanent Staff)

65

4.8.7 Interpretation of Unstandardized Beta Coefficients

The Beta (β) coefficients are the estimated coefficients of population variables.

Unstandardized coefficients mean that they include the y-intercept in the regression equation,

which is often meaningless. Where else, standardized coefficients are used in standardized

regression equation which has no y-intercept.

Debit Card Sales has an unstandardized beta of .168 which means that if other

variables are held constant, then for every unit of increase in debit card sales the credit card

sales will rise by .168 units.

New Deposit Accounts has an unstandardized beta of .451 which means that if other

variables are held constant, then for every unit of increase in new deposit accounts, the credit

card sales will rise by .451 units.

New Loan Accounts has an unstandardized beta of -1.161 which means that if other

variables are held constant, then for every unit of increase in new loan accounts, the credit

card sales will fall by 1.161 units.

Direct Sales Team has an unstandardized beta of 6.914 which means that if other

variables are held constant, then for every unit of increase in direct sales team, the credit card

sales will rise by 6.914 units.

Deposit Growth Rate has an unstandardized beta of .-.286 which means that if other

variables are held constant, then for every unit of increase in deposit growth rate, the credit

card sales will fall by .286 units.

66

Loan Growth Rate has an unstandardized beta of -21.106 which means that if other

variables are held constant, then for every unit of increase in loan growth rate, the credit card

sales will fall by 21.106 units.

Permanent Staff has an unstandardized beta of 10.986 which means that if other

variables are held constant, then for every unit of increase in permanent staff, the credit card


Overall we can see from unstandardized equation that direct sales team and

permanent staff heavily influence credit card sales.

4.8.8 Standardized Multiple Linear Regression Equation before

Significance Test:

Y (Credit Card Sales) = .159X1 (Debit Card Sales) + .398X2 (New Deposit Accounts) – .174X3 (New

Loan Accounts) + .231X4 (Direct Sales Team) - .017X5 (Deposit Growth Rate) – .209X6 (Loan Growth

Rate) + .721X7 (Permanent Staff)

4.8.9 Interpretation of Standardized Beta Coefficients:

Standardized coefficients are used in standardized regression equation which has no y-

intercept, which is often meaningless.

Debit Card Sales has a standardized beta of .159 which means that if other variables

are held constant, then for every unit of increase in debit card sales the credit card sales will

rise by .159 units.

67

New Deposit Accounts has a standardized beta of .398 which means that if other



New Loan Accounts has a standardized beta of -.174 which means that if other



Direct Sales Team has a standardized beta of .231 which means that if other variables

are held constant, then for every unit of increase in direct sales team, the credit card sales will

rise by .231 units.

Deposit Growth Rate has a standardized beta of -.017 which means that if other

variables are held constant, then for every unit of increase in deposit growth rate, the credit


Loan Growth Rate has a standardized beta of -.209 which means that if other


sales will fall by .209 units.

Permanent Staff has a standardized beta of .721 which means that if other variables

are held constant, then for every unit of increase in permanent staff, the credit card sales will

rise by .721 units.

Overall we can see from standardized coefficient that new deposit accounts, direct

sales team and permanent staff heavily influence credit card sales.

68

4.8.10 T-test for statistical significance of individual independent

variables:

Coefficients



t Sig.

B Std. Error Beta(Constant) -82.284 19.546 -4.210 .000Debit Card

Sales.168 .092 .159 1.825 .082


.451 .125 .398 3.601 .002

New Loan Accounts

-1.161 .629 -.174 -1.846 .078

Direct Sales Team

6.914 3.689 .231 1.874 .074

Deposit Growth Rate

-.286 1.573 -.017 -.182 .857

Loan Growth Rate

-21.106 10.206 -.209 -2.068 .051

Permanent Staff

10.986 1.816 .721 6.048 .000

A. The first hypothesis test for coefficient of debit card sales is:

Null Hypothesis H0: Beta coefficient of debit card sales has zero value

Alternative Hypothesis H1: Beta coefficient of debit card sales has non-zero value

Decision for significance of Coefficients: The null hypotheses (Ho) is rejected and

alternative hypothesis is accepted, since p-value (.082) < α (here, α=.10 is the significance

level). So the beta coefficient is statistically significant.

B. The second hypothesis test for coefficient of new deposit accounts is:

Null Hypothesis: H0 : Beta coefficient of new deposit accounts has zero value69

Alternative Hypothesis: H1 : Beta coefficient of new deposit accounts has non-zero value




C. The third hypothesis test for coefficient of new loan accounts is:

Null Hypothesis: H0 : Beta coefficient of new loan accounts has zero value

Alternative Hypothesis: H1 : Beta coefficient of new loan accounts has non-zero value




D. The fourth hypothesis test for coefficient of direct sales team is:

Null Hypothesis: H0 : Beta coefficient of direct sales team has zero value

Alternative Hypothesis: H1 : Beta coefficient of direct sales team has non-zero value




E. The fifth hypothesis test for coefficient of deposit growth rate is:

Null Hypothesis: H0 : Beta coefficient of deposit growth rate has zero value

70

Alternative Hypothesis: H1 : Beta coefficient of deposit growth rate has non-zero value

Decision for significance of Coefficients: The null hypotheses (Ho) is not rejected and

alternative hypothesis is not accepted, since p-value (.857) > α (here, α=.10 is the significance

level). So the beta coefficient is statistically insignificant.

F. The sixth hypothesis test for coefficient of loan growth rate is:

Null Hypothesis: H0 : Beta coefficient of loan growth rate has zero value

Alternative Hypothesis: H1 : Beta coefficient of loan growth rate has non-zero value




G. The seventh hypothesis test for coefficient of permanent staff is:

Null Hypothesis: H0 : Beta coefficient of permanent staff has zero value

Alternative Hypothesis: H1 : Beta coefficient of permanent staff has non-zero value




4.8.11 Unstandardized Multiple Linear Regression Equation After

Significance Test:

71


1.161X3 (New Loan Accounts) + 6.914X4 (Direct Sales Team) – 21.106X6 (Loan Growth Rate) +

10.986X7 (Permanent Staff)

4.8.12 Standardized Multiple Linear Regression Equation After

Significance Test:

Y (Credit Card Sales) = .159X1 (Debit Card Sales) + .398X2 (New Deposit Accounts) – .174X3 (New

Loan Accounts) + .231X4 (Direct Sales Team) – .209X6 (Loan Growth Rate) + .721X7 (Permanent

Staff)

4.8.13 Residual Statistics

The following table shows the residual statistics.

Residuals Statistics

Minimum Maximum Mean Std. Deviation

N

Predicted Value

34.10 243.54 120.20 56.441 30

Residual -44.97 41.73 .00 20.850 30Std.

Predicted Value

-1.525 2.185 .000 1.000 30

Std. Residual

-1.879 1.743 .000 .871 30

a Dependent Variable: Credit Card Sales

Interpretation of Residual Statistics: The above table shows the maximum, minimum,

mean and standard deviation of residual for both standardized and unstandardized equation.

As expected the mean of the residuals have turned out to be zero.

The following table shows the residuals for both standardized and unstandardized

regression model. The residuals have been shown for each of the branches.

72

Unstandardized Residuals Standardized Residuals-0.87151 -0.03641-0.72963 -0.0304815.93813 0.66582

-32.26526 -1.347881.85073 0.07731

10.70635 0.4472622.45799 0.9381820.13994 0.84135-7.43496 -0.310612.84659 0.53667-3.47981 -0.1453729.64442 1.2384

3.80956 0.1591416.1706 0.67553

-31.13149 -1.3005241.73325 1.74341

-26.07014 -1.0890816.58349 0.69277-6.70038 -0.27991-8.03108 -0.3355

-19.93262 -0.83268-11.86801 -0.49579-33.29588 -1.39093

6.03458 0.25209-18.70001 -0.7811916.66203 0.6960626.89832 1.12368-3.27414 -0.136787.27745 0.30402

-44.96853 -1.87856

4.8.14 Normal Probability Plot

73

The following is the normal probability plot of the regression model. From the

diagram, we can clearly see that the model fits nicely along the straight line. This means that

the data set is normally distributed and that the model fulfills the normality assumption.

4.9 Revised Model after Deducting Insignificant variables

74

The revised model excludes the deposit growth rate variable as it has been found to be

statistically insignificant. Hence the model is re-run is SPSS without using the independent

variable in the equation.

4.9.1 Revised Model Summary

Model Summary

Model R R Square Adjusted R Square

Std. Error of the

Estimate

Durbin-Watson

1 .938 .880 .848 23.429 2.357

a Predictors: (Constant), Permanent Staff, New Loan Accounts, Debit Card Sales, Loan Growth Rate, Direct Sales Team, New Deposit Accounts


Interpretation of Revised R square: The model summary shows some important

indicators of the explaining power of the model. The R-square value shows how much

change in the dependent variable is caused by the independent variables. In this case a r-

square value of .88 or 88% means 88% of the change in dependent variable is caused by the

seven independent variables.

Interpretation of Revised Adjusted R-square: On the other hand, the adjusted r-

square value shows how much change in the dependent variable is caused by statistically

significant variables. So in this case, an adjusted r-square value of .848 or 84.8% means that

84.2% of the change in dependent variable is caused by statistically significant variables,

which will be found later in the report. The standard error of the estimate measures the

accuracy of the predictions within the regression line, which is around 23.938.

4.9.2 Revised Analysis of Variance (ANOVA)

75

The ANOVA table shows the total variation, the explained variation and the

unexplained variation (residual) due to the error. The explained variation is denoted as SSR

and the unexplained variation due to residuals is denoted by SSE. The total variation (SST) is

104986.8 and variation explained by regression (SSR) is 92361.437 and variation explained

by regression error SSE is 12625.363. So the explaining power of the regression model is

good since SSR is greater than SSE. If we divide the SSR with SST, we get the value of r-

square which we have already found out to be 88%.

ANOVA

Sum of Squares

df Mean Square

F Sig.

Regression

92361.437 6 15393.573 28.043 .000

Residual 12625.363 23 548.929 Total 104986.80

029

a Predictors: (Constant), Permanent Staff, New Loan Accounts, Debit Card Sales, Loan Growth Rate, Direct Sales Team, New Deposit Accounts


4.9.3 Revised Validity of the Model by using F-Test:

The F-test, or an analysis of variance, is used to test the magnitudes of explained

variation (SSR) and unexplained variation (SSE) with their appropriate degrees of freedom.

The hypotheses for the test are as follows:

Null Hypothesis H0 : Beta coefficients of all independent variables has zero value

Alternative Hypothesis H1: At least 1 Beta coefficient of independent variable has non-zero

value

76

Decision: The F-test value of 28.043 shows that the p-value of the test is 0%. Since p-value is

less than α = 10%, we will reject the null hypothesis that all of the beta coefficients have zero

value and accept the alternate hypothesis that at least 1 beta coefficient has non-zero value.

4.9.4 Revised Beta Coefficients

Coefficients



t Sig.

B Std. Error Beta (Constant) -82.705 18.997 -4.354 .000 Debit Card

Sales.166 .089 .157 1.856 .076


.453 .122 .400 3.704 .001

New Loan Accounts

-1.172 .613 -.176 -1.913 .068

Direct Sales Team

7.217 3.222 .241 2.240 .035

Loan Growth Rate

-21.686 9.491 -.215 -2.285 .032

Permanent Staff

10.800 1.470 .709 7.346 .000


4.9.5 Revised Unstandardized Multiple Linear Regression Equation

before Significance Test:


1.172X3 (New Loan Accounts) + 7.217X4 (Direct Sales Team) – 21.686X6 (Loan Growth Rate) + 10.8X7

(Permanent Staff)

77

4.9.6 Revised Interpretation of Unstandardized Beta Coefficients:

The Beta (β) coefficients are the estimated coefficients of population variables.

Unstandardized coefficients mean that they include the y-intercept in the regression equation,

which is often meaningless. Where else, standardized coefficients are used in standardized

regression equation which has no y-intercept.

Constant Beta Coefficient of -82.705 has no meaningful interpretation and hence it is

not useful in the model.

Debit Card Sales has an unstandardized beta of .166 which means that if other

variables are held constant, then for every unit of increase in debit card sales the credit card

sales will rise by .166 units.

New Deposit Accounts has an unstandardized beta of .453 which means that if other



New Loan Accounts has an unstandardized beta of -1.172 which means that if other


card sales will fall by 1.172 units.

Direct Sales Team has an unstandardized beta of 7.217 which means that if other

variables are held constant, then for every unit of increase in direct sales team, the credit card


Loan Growth Rate has an unstandardized beta of -21.686 which means that if other


sales will fall by 21.686 units.

78

Permanent Staff has an unstandardized beta of 10.8 which means that if other

variables are held constant, then for every unit of increase in permanent staff, the credit card


Overall we can see from unstandardized equation that loan growth rate, direct sales

team and permanent staff statistically heavily influence credit card sales.

4.9.7 Revised Standardized Multiple Linear Regression Equation before

Significance Test:

Y (Credit Card Sales) = .157X1 (Debit Card Sales) + .4X2 (New Deposit Accounts) – .176X3 (New Loan

Accounts) + .241X4 (Direct Sales Team) - .215X6 (Loan Growth Rate) + .709X7 (Permanent Staff)

4.9.8 Revised Interpretation of Standardized Beta Coefficients:

Standardized coefficients are used in standardized regression equation which has no y-

intercept, which is often meaningless.

Debit Card Sales has a standardized beta of .157 which means that if other variables

are held constant, then for every unit of increase in debit card sales the credit card sales will

rise by .157 units.

New Deposit Accounts has a standardized beta of .4 which means that if other



79

New Loan Accounts has a standardized beta of -.176 which means that if other



Direct Sales Team has a standardized beta of .241 which means that if other variables

are held constant, then for every unit of increase in direct sales team, the credit card sales will

rise by .241 units.

Loan Growth Rate has a standardized beta of -.215 which means that if other


sales will fall by .215 units.

Permanent Staff has a standardized beta of .709 which means that if other variables

are held constant, then for every unit of increase in permanent staff, the credit card sales will

rise by .709 units.

Overall we can see from standardized coefficient that new deposit accounts, direct

sales team and permanent staff statistically influence credit card sales.

4.9.9 Revised T-test for statistical significance of individual independent

variables:

Coefficients



t Sig.

B Std. Error Beta (Constant) -82.705 18.997 -4.354 .000 Debit Card

Sales.166 .089 .157 1.856 .076


.453 .122 .400 3.704 .001

New Loan Accounts

-1.172 .613 -.176 -1.913 .068

80

Direct Sales Team

7.217 3.222 .241 2.240 .035

Loan Growth Rate

-21.686 9.491 -.215 -2.285 .032

Permanent Staff

10.800 1.470 .709 7.346 .000


A. The first hypothesis test for coefficient of debit card sales is:

Null Hypothesis H0: Beta coefficient of debit card sales has zero value

Alternative Hypothesis H1: Beta coefficient of debit card sales has non-zero value




B. The second hypothesis test for coefficient of new deposit accounts is:

Null Hypothesis: H0 : Beta coefficient of new deposit accounts has zero value

Alternative Hypothesis: H1 : Beta coefficient of new deposit accounts has non-zero value




C. The third hypothesis test for coefficient of new loan accounts is:

Null Hypothesis: H0 : Beta coefficient of new loan accounts has zero value

81

Alternative Hypothesis: H1 : Beta coefficient of new loan accounts has non-zero value




D. The fourth hypothesis test for coefficient of direct sales team is:

Null Hypothesis: H0 : Beta coefficient of direct sales team has zero value

Alternative Hypothesis: H1 : Beta coefficient of direct sales team has non-zero value




E. The sixth hypothesis test for coefficient of loan growth rate is:

Null Hypothesis: H0 : Beta coefficient of loan growth rate has zero value

Alternative Hypothesis: H1 : Beta coefficient of loan growth rate has non-zero value




F. The seventh hypothesis test for coefficient of permanent staff is:

Null Hypothesis: H0 : Beta coefficient of permanent staff has zero value

82

Alternative Hypothesis: H1 : Beta coefficient of permanent staff has non-zero value




4.9.10 Revised Unstandardized Multiple Linear Regression Equation

After Significance Test:


1.172X3 (New Loan Accounts) + 7.217X4 (Direct Sales Team) – 21.686X6 (Loan Growth Rate) + 10.8X7

(Permanent Staff)

4.9.11 Revised Standardized Multiple Linear Regression Equation After

Significance Test:

Y (Credit Card Sales) = .157X1 (Debit Card Sales) + .4X2 (New Deposit Accounts) – .176X3 (New Loan

Accounts) + .241X4 (Direct Sales Team) - .215X6 (Loan Growth Rate) + .709X7 (Permanent Staff)

4.9.12 Revised Residual Statistics

The following table shows the residual statistics.

Residuals Statistics

Minimum Maximum Mean Std. Deviation

N

Predicted Value

34.32 242.08 120.20 56.435 30

Residual -45.09 41.39 .00 20.865 30 Std.

Predicted Value

-1.522 2.160 .000 1.000 30

Std. Residual

-1.925 1.767 .000 .891 30


83

Interpretation of Residual Statistics: The above table shows the maximum, minimum,

mean and standard deviation of residual for both standardized and unstandardized equation.

As expected the mean of the residuals have turned out to be zero.

4.9.13 Revised Normal Probability Plot

The following is the normal probability plot of the revised regression model. From the

diagram, we can clearly see that the model fits nicely along the straight line. This means that

the data set is normally distributed and that the model fulfills the normality assumption.

5.0 Findings & Conclusion

84

The findings are the following:

The most important factors that play a significant role in credit card sales are

permanent staff and direct sales team. Other than that, new deposit accounts also have

impact on credit card sales.

The original unstandardized equation, with statistically significant & insignificant

variables, is:

Y (Credit Card Sales) = -82.284 + .168X1 (Debit Card Sales) + .451X2 (New Deposit Accounts)

– 1.161X3 (New Loan Accounts) + 6.914X4 (Direct Sales Team) - .286X5 (Deposit Growth

Rate) – 21.106X6 (Loan Growth Rate) + 10.986X7 (Permanent Staff)

The original standardized equation, with statistically significant & insignificant

variables, is:

Y (Credit Card Sales) = .159X1 (Debit Card Sales) + .398X2 (New Deposit Accounts) – .174X3

(New Loan Accounts) + .231X4 (Direct Sales Team) - .017X5 (Deposit Growth Rate) – .209X6

(Loan Growth Rate) + .721X7 (Permanent Staff)

The revised unstandardized equation, without statistically insignificant variables, is:

Y (Credit Card Sales) = -82.705 + .166X1 (Debit Card Sales) + .453X2 (New Deposit Accounts)

– 1.172X3 (New Loan Accounts) + 7.217X4 (Direct Sales Team) – 21.686X6 (Loan Growth

Rate) + 10.8X7 (Permanent Staff)

The revised standardized equation, without statistically insignificant variables, is:

Y (Credit Card Sales) = .157X1 (Debit Card Sales) + .4X2 (New Deposit Accounts) – .176X3

(New Loan Accounts) + .241X4 (Direct Sales Team) - .215X6 (Loan Growth Rate) + .709X7

(Permanent Staff)

6.0 Recommendations

The recommendations are as follows:85

Management should focus on direct sales team and permanent staff quality to ensure

good credit card sales, as this has been statistically proven.

EBL’s management should be aware of negative influence of new loan accounts and

loan growth rate on sales of credit cards as they are competing products.

Management should also focus on cross selling of credit cards to new deposit account

holders, as this was also statistically proven.

Statistically, factors such as debit card sales or loan accounts play little role in credit

card sales and hence cross selling in such cases must be practiced in limited versions.

Other factors might also be relevant in credit card sales. So this report should not be

considered as a bible for increasing credit card sales. Marketing strategy and

environmental factors also play a key role in the performance of credit cards.

Bibliography

1. EBL Consumer Performeter May 2008, published date: July 2008, EBL Consumer

Banking Division, Dhaka.

86

2. Durbin Watson, Wikipedia.com, <http://en.wikipedia.org/wiki/Durbin-

Watson_statistic>, access date: 20th August, 2008.

3. Durbin Watson Table, <http://hadm.sph.sc.edu/courses/J716/Dw.html>, access date:

20th August, 2008.

4. Linear Regression, California State University website,

<www.math.csusb.edu/faculty/stanton/m262/regress/regress.html>, access date: 20th

August, 2008.

5. Linear Regression, York University website,

<www.stat.yale.edu/Courses/1997-98/101/linreg.htm>, access date: 20th August, 2008.

87

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