SAS DIPLOMA IN BUSINESS MANAGEMENT
Statistics (Individual Assignment)
Date of report submission: 27th April 2009
Name of student : Kua Siong Zhuang
NRIC : S8237534D
Statistics are used by all industries and businesses as a standardized unit of measurement for
presenting data in a useful and meaningful format. For managers and leaders, statistics provide
insight into how business units are performing relative to an organizations goals and objectives
Explain and illustrate with examples how statistics is used in organizations and analyse how it
has been able to contribute to Organizational Decision-making.
You may select any organization (public / private sector) or business domain and use it as a
basis for your discussion and reference.
Introduction of Statistics Page 3
Common Statistical Terminology with Applications Page 4 – 6
Overview of American Express Page 7
American Express Total Returns to Shareholder Page 8
Pie Chart on American Express cards members Page 9
Bar Chart on American Express card holder spending VS Age Group Page 10
Key Charateristics of Resident Labour Force, 1997 – 2007 Page 11
Most Favorable Spending of Retailer Merchant Categories Page 12
American Express Road Show Statistic for Year 2008 Page 13
American Express Out-door sales team performance for Year 2008 Page 14
American Express Market review during bad economies Page 15
List of references Page 16
Introduction of Statistics
Statistics is the scientific application of mathematical principles to the collection, analysis, and
presentation of numerical data
It is the study of data that includes descriptive statistics the study of methods and tools for
collecting data. Mathematical models to describe and interpret data and inferential statistics the
systems and techniques for making probability-based decisions and accurate predictions based on
incomplete sample ot data.
Statistics is a discipline which is concerned with:
• Use data to solve a wide variety of fields
• Designing experiments and other data collection,
• Summarizing information to aid understanding,
• Drawing conclusions from data, and
• Estimating the present or predicting the future.
In making predictions, Statistics uses the companion subject of Probability, which models chance
mathematically and enables calculations of chance in complicated cases.
Data are not just numbers, but numbers that carry information about a specific setting and need to
be interpreted in that setting. With the growth in the use of data comes a growing demand for the
services of statisticians, who are experts in the following:
• Producing trustworthy data
• Analyzing data to make their meaning clear
• Drawing practical conclusions from data
The main objective of Business Statistics is to make inferences (e.g., prediction, making decisions)
about certain characteristics of a population based on information contained in a random sample
from the entire population. The condition for randomness is essential to make sure the sample is
representative of the population.
Business Statistics is the science of good decision making in the face of uncertainty and is used in
many disciplines, such as financial analysis, econometrics, auditing, production and operations,
and marketing research. It provides knowledge and skills to interpret and use statistical techniques
in a variety of business applications.
Just like weather, if you cannot control something, you should learn how to measure and analyze
it, in order to predict it, effectively.
Common Statistical Terminology with Applications
Like all profession, also statisticians have their own keywords and phrases to ease a precise
communication. However, one must interpret the results of any decision making in a language that
is easy for the decision-maker to understand. Otherwise, he/she does not believe in what you
recommend, and therefore does not go into the implementation phase. This lack of communication
between statisticians and the managers is the major roadblock for using statistics.
Population: A population is any entire collection of people, animals, plants or things on which we
may collect data. It is the entire group of interest, which we wish to describe or about which we
wish to draw conclusions. In the above figure the life of the light bulbs manufactured say by GE, is
the concerned population.
Qualitative and Quantitative Variables: Any object or event, which can vary in successive
observations either in quantity or quality is called a"variable." Variables are classified accordingly
as quantitative or qualitative. A qualitative variable, unlike a quantitative variable does not vary in
magnitude in successive observations. The values of quantitative and qualitative variables are
called"Variates" and"Attributes", respectively.
Variable: A characteristic or phenomenon, which may take different values, such as weight,
gender since they are different from individual to individual.
Randomness: Randomness means unpredictability. The fascinating fact about inferential statistics
is that, although each random observation may not be predictable when taken alone, collectively
they follow a predictable pattern called its distribution function. For example, it is a fact that the
distribution of a sample average follows a normal distribution for sample size over 30. In other
words, an extreme value of the sample mean is less likely than an extreme value of a few raw data.
Sample: A subset of a population or universe.
An Experiment: An experiment is a process whose outcome is not known in advance with
Statistical Experiment: An experiment in general is an operation in which one chooses the values
of some variables and measures the values of other variables, as in physics. A statistical
experiment, in contrast is an operation in which one take a random sample from a population and
infers the values of some variables.
In order to make any generalization about a population, a random sample from the entire
population; that is meant to be representative of the population, is often studied. For each
population, there are many possible samples. A sample statistic gives information about a
corresponding population parameter. For example, the sample mean for a set of data would give
information about the overall population mean m .
It is important that the investigator carefully and completely defines the population before
collecting the sample, including a description of the members to be included.
An experiment is any process or study which results in the collection of data, the outcome of
which is unknown. In statistics, the term is usually restricted to situations in which the researcher
has control over some of the conditions under which the experiment takes place.
Design of experiments is a key tool for increasing the rate of acquiring new knowledge.
Knowledge in turn can be used to gain competitive advantage, shorten the product development
cycle, and produce new products and processes which will meet and exceed your customer's
Primary data and Secondary data sets: If the data are from a planned experiment relevant to the
objective(s) of the statistical investigation, collected by the analyst, it is called a Primary Data set.
However, if some condensed records are given to the analyst, it is called a Secondary Data set.
Random Variable: A random variable is a function that assigns a numerical value to each simple
event. For example, in sampling for quality control an item could be defective or non-defective,
therefore, one may assign X=1, and X = 0 for a defective and non-defective item, respectively.
You may assign any other two distinct real numbers, as you wish; however, non-negative integer
random variables are easy to work with. Random variables are needed since one cannot do
arithmetic operations on words; the random variable enables us to compute statistics, such as
average and variance. Any random variable has a distribution of probabilities associated with it.
Probability: Probability (i.e., probing for the unknown) is the tool used for anticipating what the
distribution of data should look like under a given model. Random phenomena are not haphazard:
they display an order that emerges only in the long run and is described by a distribution. The
mathematical description of variation is central to statistics. The probability required for statistical
inference is not primarily axiomatic or combinatorial, but is oriented toward describing data
Sampling Unit: A unit is a person, animal, plant or thing which is actually studied by a researcher;
the basic objects upon which the study or experiment is executed. For example, a person; a sample
of soil; a zip code area
Parameter: A parameter is an unknown value, and therefore it has to be estimated. Parameters are
used to represent a certain population characteristic. For example, the population mean m is a
parameter that is often used to indicate the average value of a quantity.
Within a population, a parameter is a fixed value that does not vary. Each sample drawn from the
population has its own value of any statistic that is used to estimate this parameter. For example,
the mean of the data in a sample is used to give information about the overall mean min the
population from which that sample was drawn.
Statistic: A statistic is a quantity that is calculated from a sample of data. It is used to give
information about unknown values in the corresponding population. For example, the average of
the data in a sample is used to give information about the overall average in the population from
which that sample was drawn.
A statistic is a function of an observable random sample. It is therefore an observable random
variable. Notice that, while a statistic is a"function" of observations, unfortunately, it is commonly
called a random"variable" not a function.
It is possible to draw more than one sample from the same population, and the value of a statistic
will in general vary from sample to sample. For example, the average value in a sample is a
statistic. The average values in more than one sample, drawn from the same population, will not
necessarily be equal.
Statistics are often assigned Roman letters (e.g. and s), whereas the equivalent unknown values in
the population (parameters ) are assigned Greek letters (e.g., µ, s).
The word estimate means to esteem, that is giving a value to something. A statistical estimate is an
indication of the value of an unknown quantity based on observed data.
More formally, an estimate is the particular value of an estimator that is obtained from a particular
sample of data and used to indicate the value of a parameter.
Overview of American Express
American Express is a world class prestige brand and well recognizes Multi-National Corporation.
She was established since year 1850 in United States and evolved from a successful express
delivery business into a company that created and sold money orders and travler’s cheques.
American Express is the largest global card issuer by spend volume. One of the to its success has
been offering card members a choice of products designed to meet their spending
In addition to the many cards AMEX issued, they had partnered with more than 100 banks and
other institutions in more than 120 countries to issue cards accepted on the American Express
At year end 2008, American Express had 92.4 million cards-in-force (including cards issuing by
partners), issued in more than 40 currencies with a total billed business of $683 billion.
Top 10 Customer satisfaction (J.D. Power and Associates 2008 Credit Card Satisfaction Study
(Based on a 1,000-point scale)
1. American Express -- 783
2. Discover Card -- 751
3. National City -- 721
4. Chase -- 719
5. U.S. Bank -- 716
6. Washington Mutual -- 712
7. Citi -- 710
8. Wells Fargo -- 709
9. Bank of America -- 692
10. GE Money -- 683
(Source: J.D. Power and Associates, September 2008) – references: www.creditcards.com.sg
American Express Total Returns to Shareholder
This chart compare the cumulative total shareholder return on American Express common shares
with the total return on the S&P 500 Financial index for the last five years. It shoes the growth of a
$100 investment on December 21,2003, including the reinvestment of all diviends.
Pie Chart on American Express cards members
Credit cards are personal credit card that gives all segmentation of consumer to enjoy the
benefits discounts and promotions when they pay the bill with American Express merchant.
The following surveys, statistics and analyst reports will show the data of different segments
to help the management and marketing team to identify their most groups of consumer and
which campaign will best reach out to the targeted group.
Sample Data of active American Express card holders in year 2008
Year 2008 American Express Active Card Holders
40 yrs old & above ,
24,000 21 to 25 yrs old, 3,000
25 to 30 yrs old , 8,000
18% 31 to 35 yrs old , 18,000
25% 21 to 25 yrs old
40 to 45 yrs old, 25,000 25 to 30 yrs old
31 to 35 yrs old
36 to 40 yrs old , 21,000 36 to 40 yrs old
40 to 45 yrs old
40 yrs old & above
Age group Percentage Total no of active card holder
21 to 25 years old 3% 3,000 members
26 to 30 years old 9% 9,000 members
31 to 25 years old 18% 18,000 members
36 to 40 years old 21% 21,000 members
40 to 45 years old 25% 25,000 members
45 & above years old 24% 24,000 members
Total No. of active card holders 100,000 members
Bar Chart on American Express card holder spending VS Age Group
In this chart, it’s clearly shown that the higher the age group, the spending is higher.
Therefore, the management shall focus their target market more on mature age group from 30
years old has got more spending power or willing to spend more than the younger age group.
Year 2008 Spending VS Age Group
21 to 25 yrs old
$1,000,000.00 26 to 30 yrs old
$800,000.00 31 to 35 yrs old
$600,000.00 36 to 40 yrs old
$400,000.00 40 to 45 yrs old
45 yrs old & above
Age group Spending Total no of active card holder
21 to 25 years old $ 450,000.00 3,000 members
26 to 30 years old $ 800,000.00 9,000 members
31 to 25 years old $ 1,192,000.00 18,000 members
36 to 40 years old $ 1,238,000.00 21,000 members
40 to 45 years old $ 1,380,000.00 25,000 members
45 & above years old $ 1,180,000.00 24,000 members
Total Spending &
$ 6,240,000.00 100,000 members
Key Charateristics of Resident Labour Force, 1997 – 2007
In this tables, we’ll be able to view the analyst between year 1997 to 2007 on the key
characteristics of resident labour force on employment aged between 35 to 64 against the median
The median monthly income shown on recent year 2007 was $ 2,330 on the total. Males median
income was $ 2,500 & Females was $ 2,170.
Based on this report, the conclusions of more males monthly incomes is greater than the females
for a decade. Literally, this report could not conclude the spending of males are higher than
females even though the earnings are greating
Singapore Yearbook of manpower Statistics in Singapore
Most Favorable Spending of Retailer Merchant Categories
The credit cards spending with the retailer merchant is predominately the most revenue based on
records from the number of cards swiped each month.
These data however will need more survey given by card holders as there might be probability of
exploring new or potential merchant categories (e.g. internet purchases) that would be more
favorable & most willingly spend by new and card holders. It can also maximize the spending of
new & existing card holders.
In this questionnaire survey, out of 100,000 new and existing card holders, American Express took
a sample of 60,000 members to give their survey by getting a free pair of movie tickets.
Apparently, only 50,000 samples participated in this survey.
The survey data as follows:
Age Group & Sex
21 to 30 yrs 31 to 40 yrs 41 to 50 yrs 50 & above yrs
Flavorite Merchant old old old old
Restaurant / Dining 7312 8421 7120 4260
Holiday 850 690 961 795
Beauty / Spa / fitness centre 902 833 740 425
Electronics / IT gadget 1002 910 502 382
Automotive 563 390 295 55
Fashion 800 463 200 104
Super Market 984 3284 2063 1252
Entertainment / Movie /
Concert 490 392 300 153
Internet Purchases 192 185 120 78
Others 462 230 340 500
This survey shows that America Express should focus its promotions on restaurant/dining category
& rewards the existing / new clients.
American Express can also reach out to more dining restaurant to add on the group of their
Secondary will be for electronics / IT Gadget segments. PC show & road shows on IT mall will be
more advantages to bring the awareness that purchases with American Express gives the benefits
& freedom to decide for a wide range of merchant. Shopping will be much fun for AMEX card
American Express Road Show Statistic for Year 2008
This Statistics determines & assist management to identify the best location on the road show &
the sales team effort. The summary will tells the management that which sales team had done the
best sales performance.
*No of No of % of
Sales No of No of new rejected successful successful
Road show Venue Team Roadshow sign ups application sign ups Sign ups
In house American
Express Branch A 24 580 19 561 97%
Club house or pubs A 24 440 50 390 89%
CBD Area Road
show B 24 630 53 577 92%
Door to Door to
offices with free gifts B 24 560 42 518 93%
Shopping malls in
town area C 24 610 280 330 54%
Heartlands malls C 24 320 140 180 56%
Total 3 teams 144 3140 584 2773 80%
AMEX Road Show Statistics for Year 2008
Express house or Road with free town Heartlands
Door to Shopping
American Club CBD Area offices malls in
No of Roadshow
B No of new sign ups
*No of rejected application
0 200 400 600 800 1000
American Express Out-door sales team performance for Year 2008
Important Notes : Each sales agent will get an incentive of $10 upon signs up with full
application forms filled. It’s a new system implemented on 1st Jan 2008.
Management would like to find out if this rewarding scheme will motivate the drive for each sales
Conclusions: The management should go into further investigation on sales pitching for Team C as
the % of successful signs up are much lesser compared to the rest of the 2 teams. They might be
abusing the incentive system to convince client to sign up even though the customers’ profile are
Total no of Total no of
Total no of % of successful
Sales Team rejected successful new
new sign up sign up
applicants sign up
Team A 1020 69 951 93%
Team B 1190 95 1095 92%
Team C 930 420 510 55%
Total 3140 584 2556 80%
Sales Team Peformance for Year 2008
Total no of new sign up
1500 Total no of rejected applicants
Total no of successful new
1000 sign up
new sign up
Total no of
Team B Team A Team C
new sign up
Total no of
American Express Market review during bad economies
NEW YORK (Fortune) -- In a strange turn of events, American Express is trying to run off
some of the customers it went out of its way to attract earlier this decade.
The credit card company was a major beneficiary of the economic boom earlier this decade.
AmEx (AXP, Fortune 500) card use grew at a remarkable 15% annually on average between
2002 and 2007, as American Express spent heavily to attract new users and customers of all
stripes purchased more. Peers such as Discover (DFS) struggled to grow half as fast.
But with the global economic bust well into its second year and more borrowers slipping
behind on their payments, AmEx's rapid growth is being viewed in a less flattering light.
Shares recently hit a 14-year low, prompting billionaire investor Warren Buffett -- whose
Berkshire Hathaway (BRKA, Fortune 500) owns 13% of the New York-based company -- to
pronounce the stock "a hell of a buy at $10."
"American Express is going to be around forever," Buffett said in an interview with CNBC
Monday. "They've got the cream of cardholders. Unfortunately, they have some cardholders
that aren't the cream, too."
AmEx isn't the only credit card issuer to be reducing credit lines and closing unused accounts,
Colin Barr, senior writer
Last Updated: March 16, 2009: 6:40 AM ET
List of references