Master Business Administration (MBA) Degree Guide

MASTERAL IN BUSINESS ADMINISTRATION (MBA
DEGREE)
Authored by: RODEL SY NAVARRO ISBN:xxxxxxx
Copyright No.:xxxxxxx
TABLE OF CONTENTS
1. Pre-MBA Courses
Business Communication
Applied Mathematics
Managerial Statistics
Financial Accounting
Methods of Research
2. Core Courses
Business Ethics
Leadership Effectiveness
Applied Management
Science
Operations Management
Managerial Accounting
Financial Management
Principles & Dynamics of
Management
Human Resource
Management
Management Concepts
for Information
Technology
Marketing Management
Economics for Managers
3. Electives
Brand Management
Business Intelligence
Business and

Professional Discourse
Controllership
Economic Development
Electronic Commerce
E-Marketing
Entrepreneurship
Financial Analysis for
Decision-making
Financial Engineering
Global Marketing
Information Security
Management
Investment Analysis and
Portfolio Management
Law in Business
Environment
Lean Six Sigma
Management of
Financial Institutions
Marketing
Communication
Personal Finance
Project Management
Supply Chain Management
4. Integrating Course
Strategic Management
Business communication
Business communication is the sharing of information between people within and outside the
organization that is performed for the commercial benefit of the organization. It can also be
defined as relaying of information within a business by its people.
Overview
Business communication (or simply "communication", in a business context) encompasses
topics such as marketing, brand management, customer relations, consumer behavior,
advertising, public relations, corporate communication, community engagement, reputation
management, interpersonal communication, employee engagement, and event management. It
is closely related to the fields of professional communication and technical communication.

Media channels for business communication include the Internet, print media, radio, television,
ambient media, and word of mouth.
Business communication can also refer to internal communication that takes place within an
organization.
Business communication is a common topic included in the curricula of Undergraduate and
Master programs of many colleges and universities.
There are several methods of business communication, including:
Web-based communication - for better and improved communication, anytime anywhere ...
video conferencing which allow people in different locations to hold interactive meetings;
Reports - important in documenting the activities of any department;
Presentations - very popular method of communication in all types of organizations, usually
involving audiovisual material, like copies of reports, or material prepared in Microsoft
PowerPoint or Adobe Flash;
telephone meetings, which allow for long distance speech;
forum boards, which allow people to instantly post information at a centralized location; and
face-to-face meetings, which are personal and should be succeeded by a written followup.
suggestion box: It is primarily used for upward communication, because some people may
hesitate to communicate with management directly, so they opt to give suggestions by drafting
one and putting it in the suggestion box.
Effective business communication
A two way information sharing process which involves one party sending a message that is
easily understood by the receiving party. Effective communication by business managers
facilitates information sharing between company employees and can substantially contribute to
its commercial success.

For business communication to be effective these qualities are essential :
Establish clear hierarchy
Use visual communication
Conflict Management
Consider Cultural Issues
Good Written communication
Face-to-face
Face-to-face communication helps to establish a personal connection and will help sell the
product or service to the customer. These interactions can portray a whole different message
than written communication as tone, pitch, and body language is observed. Information is
easier to access and delivered immediately with interactions rather than waiting for an email or
phone call. Conflicts are also easily resolved this way, as verbal and non-verbal cues are
observed and acted upon. Communicating professionally is very important as one is
representing the company. Speak clearly and ask questions to understand the needs and wants,
let the recipient respond as one resolves the issue. Decisions are made more confidently during
a face-to-face interaction as the recipient asks questions to understand and move forward with
their decision.
Email
When using email to communicate in the business world, it is important to be careful with the
choice of words. Miscommunication is very frequent as the reader doesn’t know what
non-verbal cues one is giving off, such as the pitch, tone, or expressions. Before beginning an
email, make sure the email address one is using is appropriate and professional as well as the
message one is going to send. Again, make sure the information is clear and to the point so the
recipient isn’t confused. Make sure one includes their signature, title, and other contact
information at the end

Telephone
When making a business call, make it clear who is on the line and where one is from as well as
one's message when on the phone. Smile and have a positive attitude as the recipient will be
able to read the caller and that will affect how they react. When leaving a message, make sure
one is clear and brief. One should state their name and who they are and the purpose for
contacting them. If replying to a voicemail, try to respond as soon as possible and take into
consideration the time of day. Don't call too early or too late, as it is important to respect
other's time. Also be mindful of where one is and the noise level as well as the people one is
around when trying to reach someone by phone.[4]
When making a sales call, hope for the person one are trying to connect to does not answer the
phone. Leave up to five enticing messages and one's target audience will be ready to speak
when one either gets a call back or one calls and reaches the person. The enticing message
prepares the person to speak to the representative. It may be that the person is not interested
based on what one had said in each voice message. Always be polite and accept that one may
have many more to call. If the individual is reached, one might ask if there might be someone
better suited for the advertised program.
If one is calling and leaving voice messages, include time of availability for callbacks. There is
nothing worse than a callback coming to one when one is not available. Use the telephone as a
great communication tool. Be polite and always put oneself in the other person's position.
Listening
When listening to another employee or customer speak it is very important to be an avid
listener. Here are some obstacles that you might have to overcome:
Filters and Assumptions
Biases and Prejudices

Inattention and Impatience
Surrounding Environment
A good way to overcome these factors is by using LOTS Better Communication method. This
method includes four steps in order to produce good listening skills and the ability to respond
with an educated statement. The four steps to this method are:
Listen
Observe
Think
Speak
Doing all of these things while showing good eye contact and body posture will assure the
speaker that he/she is getting full attention from the listeners.
Choice of Means and Mode of Communication - Choosing the right means and mode of
communication plays a vital role in the effectiveness of the message being communicated and
such choice depends on various factors such as:
Organization Size and Policy - If the organisation is small, probably more communication will be
oral, than in larger organizations where it may organizations where it may be in writing. The
policy for communication also would play a major role in influencing one's choice of mode of
communication.
Cost Factor - The main point to be considered here would be to evaluate wheather the cost
involved in sending the message would be commensurate with the results expected.
Nature of Message - Whether the message is confidential in nature, urgent or important etc.
and whether a matter would require hand delivery or be set by registered post etc. also

influences the choice of mode and means of communication.
Distance Involved - Whether the message to be sent is also another vital factor which could
influence the choice of means and modes of communication. For example, if a letter is to be
sent to a partner in a joint venture in Japan and it is urgent, you would not think of sending
someone to personally deliver it.
Resources - The resources available to both the sender and receiver would also influence your
choice. You can only send a fax if the other person/organization has a fax machine. Therefore
we can see that the choice of a particular mode and means of communication will depend on a
case to case basis and is influenced by various factors.
Choosing Communication Media
When choosing a media of communication, it is important to consider who are the respective
audience and the objective of the message itself. Rich media are more interactive than lean
media and provide the opportunity for two-way communication: the receiver can ask questions
and express opinions easily in person.[5] To help such decision, one may roughly refer to the
continuum shown below.
From Richer to Leaner
1.Face-to-Face Meeting
2.In-Person Oral Presentation
3.Online Meeting
4.Videoconferencing
5.Teleconferencing
6.Phone Call
7.Voice Message
8.Video

9.Blog
10.Report
11.Brochure
12.Newsletter
13.Flier
14.Email
15. Memo
Subliminal method of communication
Subliminal perception refers to the individual ability to perceive and respond to stimuli that are
below the threshold or level of consciousness, which proved to influence thoughts, feelings or
actions altogether or separately. There are four distinct methods of communicating
subliminally. These are visual stimuli in movies, accelerated speech, embedded images in a print
advertisement, and suggestiveness which is not normally seen at first glance.Focussing on
Subliminal Communication through visual stimuli, Marketing people have adopted this method
even incorporating it films and television shows.Subliminal method of communication first
made its debut in a 1957 advertisement, during which a brief message flashed, telling viewers
to eat popcorn and drink Coca-Cola. Since that time, subliminal communication has occupied a
controversial role in the advertising landscape, with some people claiming it's omnipresent,
while others emphasize it's not real. As of publication, there is still an ongoing scientific debate
about whether subliminal advertising works. Subliminal messaging is a form of advertising in
which a subtle message is inserted into a standard ad. This subtle message affects the
consumer's behavior, but the consumer does not know she's seen the message. For example, a
marketer might incorporate a single frame telling consumers to drink tea in a movie. In print
media, advertisers might put hidden images or coded messages into ad text.
Business Writing Process
The challenge of the communication process is for the sender and receiver to gain a mutual
understanding about the meaning of the message. A writer can put his or her words on paper,
but the reader may not react to the words as the writer intended. Most writers are much more
effective, successful, and productive if they spend time thinking about the communication
situation before beginning to write. Successful writers approach writing as a three- step process
that involves planning before starting to write, drafting with the audience (the reader) in mind,
and revising the document to determine if it meets the audience’s needs and if it represents the
organization well.

STEP1: Planning
You should spend more time planning and revising your document than you spend writing.
STEP2: Drafting
Once you have planned the purpose of your message, considered how your audience might
react to the message, gathered your information, decided on an order for your information, and
selected your medium for delivery, you are ready to compose your document. About 20
percent of your writing time should be spent drafting the document.
Do not be concerned with perfection as you draft your message. Write in a conversational tone,
without using slang; write as you would speak in a workplace environment. One guideline that
helps in the drafting stage is to write as though you are presenting the information to a friend.
Rather than thinking of the audience as just “someone out there,” think of the audience as a
specific person with whom you are building or maintaining a relationship. Thinking of a friend
helps you choose effective words and tone, helps you be clear, and helps you include
information helpful to the reader.
STEP3: Revising
Revising is more than checking your spelling and punctuation. Revising requires you to check
every part of your message to see if it is clear, concise, and correct and will take approximately
40 percent of your writing time. You want to look at every word to see if you selected the most
appropriate one, at every sentence to see whether the structure is the best it can be, and at
every paragraph to see whether it includes a well-developed argument. Finally review the
document design to look for an attractive, professional appearance that meets your employer’s
and your reader’s expectations.[1]
Corporate communication
Corporate communication is a set of activities involved in managing and orchestrating all
internal and external communications aimed at creating favourable point of view among
stakeholders on which the company depends. It is the messages issued by a corporate
organization, body, or institute to its audiences, such as employees, media, channel partners
and the general public. Organizations aim to communicate the same message to all its
stakeholders, to transmit coherence, credibility and ethic. Corporate Communications help

organizations explain their mission, combine its many visions and values into a cohesive
message to stakeholders. The concept of corporate communication could be seen as an
integrative communication structure linking stakeholders to the organization.
Methods and tactics
Three principal clusters of task-planning and communication form the backbone of business and
the activity of business organizations. These include management communication, marketing
communication, and organizational communication.
Management communication takes place between management and its internal and external
audiences. To support management communication, organizations rely heavily on specialists in
marketing communication and organizational communication.[citation needed]
Marketing communication gets the bulk of the budgets in most organizations, and consists of
product advertising, direct mail, personal selling, and sponsorship activities.
Organizational communication consist of specialists in public relations, public affairs, investor
relations, environmental communications, corporate advertising, and employee
communication.
The responsibilities of corporate communication are:
to promote the profile of the "company behind the brand" (corporate branding)
to minimize discrepancies between the company's desired identity and brand features
to delegate tasks in communication
to formulate and execute effective procedures to make decisions on communication matters
to mobilize internal and external support for corporate objectives
to coordinate with international business firms
Corporate branding
A corporate brand is the perception of a company that unites a group of products or services for

the public under a single name, a shared visual identity, and a common set of symbols. The
process of corporate branding consists creating favourable associations and positive reputation
with both internal and external stakeholders. The purpose of a corporate branding initiative is
to generate a positive halo over the products and businesses of the company, imparting more
favourable impressions of those products and businesses.
In more general terms, research suggests that corporate branding is an appropriate strategy for
companies to implement when:
there is significant "information asymmetry" between a company and its clients; That is to say
customers are much less informed about a company's products than the company itself is;
customers perceive a high degree of risk in purchasing the products or services of the
company;
features of the company behind the brand would be relevant to the product or service a
customer is considering purchasing.
Corporate and organizational identity
There are two approaches for identity:
Corporate identity is the reality and uniqueness of an organization, which is integrally related
to its external and internal image and reputation through corporate communication[6]
Organizational identity comprises those characteristics of an organization that its members
believe are central, distinctive and enduring. That is, organizational identity consists of those
attributes that members feel are fundamental to (central) and uniquely descriptive of
(distinctive) the organization and that persist within the organization over time (enduring)".
Corporate responsibility
Corporate responsibility (often referred to as corporate social responsibility), corporate
citizenship, sustainability, and even conscious capitalism are some of the terms bandied about
the news media and corporate marketing efforts as companies jockey to win the trust and

loyalty of constituents. Corporate responsibility (CR) constitutes an organization’s respect for
society’s interests, demonstrated by taking ownership of the effects its activities have on key
constituencies including customers, employees, shareholders, communities, and the
environment, in all parts of their operations. In short, CR prompts a corporation to look beyond
its traditional bottom line, to the social implications of its business.
Corporate reputation
Reputations are overall assessments of organizations by their stakeholders. They are aggregate
perceptions by stakeholders of an organization's ability to fulfill their expectations, whether
these stakeholders are interested in buying the company's products, working for the company,
or investing in the company's shares.
Crisis communications
Crisis communication is sometimes considered a sub-specialty of the public relations profession
that is designed to protect and defend an individual, company, or organization facing a public
challenge to its reputation. These challenges may come in the form of an investigation from a
government agency, a criminal allegation, a media inquiry, a shareholders lawsuit, a violation of
environmental regulations, or any of a number of other scenarios involving the legal, ethical, or
financial standing of the entity. The crisis for organizations can be defined as follows:
A crisis is a major catastrophe that may occur either naturally or as a result of human error,
intervention, or even malicious intent. It can include tangible devastation, such as the
destruction of lives or assets, or intangible devastation, such as the loss of an organization's
credibility or other reputational damage. The latter outcomes may be the result of
management's response to tangible devastation or the result of human error. A crisis usually
has significant actual or potential financial impact on a company, and it usually affects multiple
constituencies in more than one market.
Internal/employee communications
As the extent of communication grows, many companies create an employee relations (ER)
function with dedicated staff to manage the numerous media through which senior managers
can communicate among themselves and with the rest of the organization. Internal
communication in the 21st century is more than the memos, publications, and broadcasts that
comprise it; it’s about building a corporate culture on values that drive organizational
excellence. ER specialists are generally expected to fulfill one or more of the following four

roles:
Efficiency: Internal communication is used primarily to disseminate information about
corporate activities.
Shared meaning: Internal communication is used to build a shared understanding among
employees about corporate goals.
Connectivity: Internal communication is used mainly to clarify the connectedness of the
company's people and activities.
Satisfaction: Internal communication is used to improve job satisfaction throughout the
company.
Investor relations
The investor relations (IR) function is used by companies which publicly trade shares on a stock
exchange. In such companies, the purpose of the IR specialist is to interface with current and
potential financial stakeholders-namely retail investors, institutional investors, and financial
analysts.
The role of investor relations is to fulfill three principal functions:
comply with regulations;
Create a favorable relationship with key financial audiences;
contribute to building and maintaining the company's image and reputation.
Public relations: issues management and media relations
The role of the public relations specialist, in many ways, is to communicate with the general
public in ways that serve the interests of the company. PR therefore consists of numerous
specialty areas that convey information about the company to the public, including
sponsorships, events, issues management and media relations. When executing these types of
activities, the PR Specialist must incorporate broader corporate messages to convey the
company’s strategic positioning. This ensures the PR activities ultimately convey messages that
distinguish the company vis-à-vis its competitors and the overall marketplace, while also

communicating the company’s value to target audiences.
Issues management
A key role of the PR specialist is to make the company better known for traits and attributes
that build the company’s perceived distinctiveness and competitiveness with the public. In
recent years, PR specialists have become increasingly involved in helping companies manage
strategic issues – public concerns about their activities that are frequently magnified by special
interest groups and NGOs. The role of the PR specialist therefore also consists of issues
management, namely the “set of organizational procedures, routines, personnel, and issues”. A
strategic issue is one that compels a company to deal with it because there is “ a conflict
between two or more identifiable groups over procedural or substantive matters relating to the
distribution of positions or resources”.
Media relations
To build better relationships with the media, organizations must cultivate positive relations with
influential members of the media. This task might be handled by employees within the
company’s media relations department or handled by a public relations firm.
Company/spokesperson profiling
These "public faces" are considered authorities in their respective sector/field and ensure the
company/organization is in the limelight.
- Managing content of corporate websites and/or other external touch points
-Managing corporate publications - for the external world
Managing print media. [2]
Ethics in business communication
Communication is the process by which individuals exchange information between other
individuals or groups of people. Throughout the process, effective communicators try as clearly

and accurately to convey their thoughts, intentions and objectives to their
receiver.Communication is successful only when both the sender and the receiver understand
the same information.In today's business environments, effective communication skills are
necessary due to the highly informational and technological era.
Regardless of context, communication involves choice, reflects values, and has consequences.
For better communication, understanding the obvious and the subtle issues relating to
communication is necessary. Any company that aims to be socially and ethically responsible
must make a priority of ethical communication both inside the company and in its interactions
with the public. In theory, many consumers prefer to do business with companies they believe
are ethical which gives those ethical businesses an advantage in the market. Ethical issues of
business communication are one such issue. Some of the vital characteristics of ethical
communication are discussed below.
Conveying the point without offending the audience:
While communicating to the audience, conveying the desired message to them in a significant
manner is of primary importance. For instance, the employees in a company can be asked to
increase their efficiency in a demanding manner whereas managers and executives will feel
offended if the same tone is used on them. There are different ways to explain the exact things
to them in a much smoother manner.
Maintain a relationship with the audience:
Maintaining the same wavelength with the audience is very important for a communicator to
ensure the audiences feel at home. Experienced communicators immediately build a
relationship based on trust with the audience as soon as they start speaking. Great orators such
as Winston Churchill and Mahatma Gandhi always were able to maintain a relationship with
their audience because they were masters at striking the same wavelength of the audience.
Avoid withholding crucial information:
In the modern era, information is vital for all decisions. Hence, it is vital for any organization to
be cautious when communicating with the public. The communicated information should be

absolute and all vital information must be conveyed appropriately. Purposely withholding
crucial information might result in the public conceiving a bad image.
Well organized value system:
In order to ensure that this concept is successfully practiced and understood in an organization,
a well-organized value system must be established throughout the organization by the top
management. If an organization functions on the base of value systems common to both the
top management and the employees, mutual respect between them will be present. A sound
and healthy value system can make way for ethical communication.
Accuracy of information is necessary:
Any information that is to be passed on must be true and accurate. Communicating without
checking the truth of the information can be highly dangerous for the organization.
Identification of the source and testing the information is necessary before communicating it.
Ways to overcome ethical dilemma
Message ahead of the person - Common good approach:
Most people in organization face ethical dilemma when they want to withhold crucial
information because of conflict with an individual or a group. In such situations, importance
should be given to the message to be communicated and not on the person or the group to
which the message is to be communicated. Hence people should give priority to the common
good of the organization rather than interpersonal or inter-group conflicts.
Decisions that produce more good and less harm – Utilitarian approach:

When in ethical dilemma consider the effects of various alternatives after a certain period of
time. Ethical decision is to choose the alternative which provides more good and less harm to
the organization.
Code of Ethics: The International Association of Business Communicators has developed a code
of ethics for business communication.The IABC code of ethics requires business communicators
to be truthful and accurate and to personally correct any inaccuracies they have the
opportunity to correct. They are also expected to support human rights, such as freedom of
speech and to respect and understand the values of different cultures and belief systems. They
must refuse to participate in any unethical business communication practices, follow all laws
and regulations affecting their industry, avoid plagiarism in communication, maintain
confidentiality except when it would be legally or ethically inappropriate to do so, avoid the
appearance of bribery or conflict of interest, avoid promising unrealistic results or benefits to
clients or customers and practice honesty with both self and others.[3]
Organizational communication
Organizational communication is a subfield of the larger discipline of communication studies.
Organizational communication, as a field, is the consideration, analysis, and criticism of the role
of communication in organizational contexts. Its main function is to inform, persuade and
promote goodwill. The flow of communication could be either formal or informal.
Communication flowing through formal channels are downward, horizontal and upward
whereas communication through informal channels are generally termed as grapevine.
Early underlying assumptions
Some of the main assumptions underlying much of the early organizational communication
research were:
Humans act rationally. Some people do not behave in rational ways, they generally have no
access to all of the information needed to make rational decisions they could articulate, and
therefore will make unrational decisions, unless there is some breakdown in the communication
process—which is common. Irrational people rationalize how they will rationalize their
communication measures whether or not it is rational.

Formal logic and empirically verifiable data ought to be the foundation upon which any theory
should rest. All we really need to understand communication in organizations is (a) observable
and replicable behaviors that can be transformed into variables by some form of measurement,
and (b) formally replicable syllogisms that can extend theory from observed data to other
groups and settings.
Communication is primarily a mechanical process, in which a message is constructed and
encoded by a sender, transmitted through some channel, then received and decoded by a
receiver. Distortion, represented as any differences between the original and the received
messages, can and ought to be identified and reduced or eliminated.
Organizations are mechanical things, in which the parts (including employees functioning in
defined roles) are interchangeable. What works in one organization will work in another similar
organization. Individual differences can be minimized or even eliminated with careful
management techniques.
Organizations function as a container within which communication takes place. Any differences
in form or function of communication between that occurring in an organization and in another
setting can be identified and studied as factors affecting the communicative activity.
Interorganization communication
Flow nomenclature
There is an emerging informal use of abbreviations to indicate the flow of information in
addition to other transactions. These share a common pattern of source and destination
separated by the numeral "2" in place of the word "to." This doesn't assume that the
communication only flows in one direction with these terms. duplex point-to-point
communication systems, computer networks, non-electronic telecommunications, and
meetings in person are all possible with the use of these terms. Example of terms:
In Business
B2B (business-to-business)
B2C (business-to-consumers)
B2E (business-to-employees)
B2G (business-to-government)

In Governance
G2G (government-to-government)
G2C (government-to-citizens)
G2E (government-to-employees)
G2B (government-to-business)
In Society
C2B (consumer-to-business)
C2C (consumer-to-consumer)
or (customer-to-customer)
or (citizen-to-citizen)
Interpersonal communication
Another fact of communication in the organization is the process of one-to-one or interpersonal
communication, between individuals. Such communication may take several forms. Messages
may be verbal (that is, expressed in words), or they may not involve words at all but consist of
gestures, facial expressions, and certain postures ("body language"). Nonverbal messages may
even stem from silence.
Managers do not need answers to operate a successful business; they need questions. Answers
can come from anyone, anytime, anywhere in the world thanks to the benefits of all the
electronic communication tools at our disposal. This has turned the real job of management
into determining what it is the business needs to know, along with the who/what/where/when
and how of learning it. To effectively solve problems, seize opportunities, and achieve
objectives, questions need to be asked by managers—these are the people responsible for the
operation of the enterprise as a whole.
Ideally, the meanings sent are the meanings received. This is most often the case when the
messages concern something that can be verified objectively. For example, "This piece of pipe
fits the threads on the coupling." In this case, the receiver of the message can check the
sender's words by actual trial, if necessary. However, when the sender's words describe a
feeling or an opinion about something that cannot be checked objectively, meanings can be
very unclear. "This work is too hard" or "Watergate was politically justified" are examples of

opinions or feelings that cannot be verified. Thus they are subject to interpretation and hence
to distorted meanings. The receiver's background of experience and learning may differ enough
from that of the sender to cause significantly different perceptions and evaluations of the topic
under discussion. As we shall see later, such differences form a basic barrier to communication.
A number of variables influence the effectiveness of communication. Some are found in the
environment in which communication takes place, some in the personalities of the sender and
the receiver, and some in the relationship that exists between sender and receiver. These
different variables suggest some of the difficulties of communicating with understanding
between two people. The sender wants to formulate an idea and communicate it to the
receiver. This desire to communicate may arise from his thoughts or feelings or it may have
been triggered by something in the environment. The communication may also be influenced
by the relationship between the sender and the receiver, such as status differences, a staff-line
relationship, or a learner-teacher relationship.
Physical and cognitive, including semantic filters (which decide the meaning of words) combine
to form a part of our memory system that helps us respond to reality. In this sense, March and
Simon compare a person to a data processing system. Behavior results from an interaction
between a person's internal state and environmental stimuli. What we have learned through
past experience becomes an inventory, or data bank, consisting of values or goals, sets of
expectations and preconceptions about the consequences of acting one way or another, and a
variety of possible ways of responding to the situation. This memory system determines what
things we will notice and respond to in the environment. At the same time, stimuli in the
environment help to determine what parts of the memory system will be activated. Hence, the
memory and the environment form an interactive system that causes our behavior. As this
interactive system responds to new experiences, new learnings occur which feed back into
memory and gradually change its content. This process is how people adapt to a changing
world.
Approaches
Informal and formal communication are used in an organization.
Informal communication, generally associated with interpersonal, horizontal communication,
was primarily seen as a potential hindrance to effective organizational performance. This is no
longer the case. Informal communication has become more important to ensuring the effective
conduct of work in modern organizations.
Top-down approach: This is also known as downward communication. This approach is used by
the Top Level Management to communicate to the lower levels. This is used to implement

policies, guidelines, etc. In this type of organizational communication, distortion of the actual
information occurs. This could be made effective by feedbacks.
Currently, some topics of research and theory in the field are:
Constitution, e.g.,
how communicative behaviors construct or modify organizing processes or products
how communication itself plays a constitutive role in organizations
how the organizations within which we interact affect our communicative behaviors, and
through these, our own identities
structures other than organizations which might be constituted through our communicative
activity (e.g., markets, cooperatives, tribes, political parties, social movements)
when does something "become" an organization? When does an organization become
(an)other thing(s)? Can one organization "house" another? Is the organization still a useful
entity/thing/concept, or has the social/political environment changed so much that what we
now call "organization" is so different from the organization of even a few decades ago that it
cannot be usefully tagged with the same word – "organization"?
Narrative, e.g.,
how do group members employ narrative to acculturate/initiate/indoctrinate new members?
do organizational stories act on different levels? Are different narratives purposively invoked
to achieve specific outcomes, or are there specific roles of "organizational storyteller"? If so, are
stories told by the storyteller received differently from those told by others in the organization?
in what ways does the organization attempt to influence storytelling about the organization?
under what conditions does the organization appear to be more or less effective in obtaining a
desired outcome?
when these stories conflict with one another or with official rules/policies, how are the
conflicts worked out? in situations in which alternative accounts are available, who or how or
why are some accepted and others rejected?

Identity, e.g.,
who do we see ourselves to be, in terms of our organizational affiliations?
do communicative behaviors or occurrences in one or more of the organizations in which we
participate effect changes in us? To what extent do we consist of the organizations to which we
belong?
is it possible for individuals to successfully resist organizational identity? what would that look
like?
do people who define themselves by their work-organizational membership communicate
differently within the organizational setting than people who define themselves more by an
avocational (non-vocational) set of relationships?
Interrelatedness of organizational experiences, e.g.,
how do our communicative interactions in one organizational setting affect our
communicative actions in other organizational settings?
how do the phenomenological experiences of participants in a particular organizational
setting effect changes in other areas of their lives?
when the organizational status of a member is significantly changed (e.g., by promotion or
expulsion) how are their other organizational memberships affected?
what kind of future relationship between business and society does organizational
communication seem to predict?
Power e.g.,
How does the use of particular communicative practices within an organizational setting
reinforce or alter the various interrelated power relationships within the setting? Are the
potential responses of those within or around these organizational settings constrained by
factors or processes either within or outside of the organization – (assuming there is an
"outside")?

Do taken-for-granted organizational practices work to fortify the dominant hegemonic
narrative? Do individuals resist/confront these practices, through what actions/agencies, and to
what effects?
Do status changes in an organization (e.g., promotions, demotions, restructuring,
financial/social strata changes) change communicative behavior? Are there criteria employed
by organizational members to differentiate between "legitimate" (i.e., endorsed by the formal
organizational structure) and "illegitimate" (i.e., opposed by or unknown to the formal power
structure) behaviors? When are they successful, and what do we mean by "successful" when
there are "pretenders" or "usurpers" who employ these communicative means? [4]
Professional communication
Professional communication encompasses written, oral, visual and digital communication
within a workplace context. This discipline blends together pedagogical principles of rhetoric,
technology, software, and learning theory to improve and deliver communication in a variety of
settings ranging from technical writing to usability and digital media design. It is a new discipline
that focuses on the study of information and the ways it is created, managed, distributed, and
consumed. Since communication in modern society is a rapidly changing area, the progress of
technologies seems to often outpace the number of available expert practitioners. This creates
a demand for skilled communicators which continues to exceed the supply of trained
professionals.
The field of professional communication is closely related to that of technical communication,
though professional communication encompasses a wider variety of skills. Professional
communicators use strategies, learning theory, and technologies to more effectively
communicate in the business world.
Successful communication skills are critical to a business because all businesses, though to
varying degrees, involve the following: writing, reading, editing, speaking, listening, software
applications, computer graphics, and Internet research. Job candidates with professional
communication backgrounds are more likely to bring to the organization sophisticated
perspectives on society, culture, science, and technology.[5]
Applied mathematics
Applied mathematics is a branch of mathematics that deals with mathematical methods that
find use in science, engineering, business, computer science, and industry. Thus, applied

mathematics is a combination of mathematical science and specialized knowledge. The term
"applied mathematics" also describes the professional specialty in which mathematicians work
on practical problems by formulating and studying mathematical models. In the past, practical
applications have motivated the development of mathematical theories, which then became
the subject of study in pure mathematics where abstract concepts are studied for their own
sake. The activity of applied mathematics is thus intimately connected with research in pure
mathematics.
Historically, applied mathematics consisted principally of applied analysis, most notably
differential equations; approximation theory (broadly construed, to include representations,
asymptotic methods, variational methods, and numerical analysis); and applied probability.
These areas of mathematics related directly to the development of Newtonian physics, and in
fact, the distinction between mathematicians and physicists was not sharply drawn before the
mid-19th century. This history left a pedagogical legacy in the United States: until the early 20th
century, subjects such as classical mechanics were often taught in applied mathematics
departments at American universities rather than in physics departments, and fluid mechanics
may still be taught in applied mathematics departments. Quantitative finance is now taught in
mathematics departments across universities and mathematical finance is considered a full
branch of applied mathematics. Engineering and computer science departments have
traditionally made use of applied mathematics.
Today, the term "applied mathematics" is used in a broader sense. It includes the classical areas
noted above as well as other areas that have become increasingly important in applications.
Even fields such as number theory that are part of pure mathematics are now important in
applications (such as cryptography), though they are not generally considered to be part of the
field of applied mathematics per se. Sometimes, the term "applicable mathematics" is used to
distinguish between the traditional applied mathematics that developed alongside physics and
the many areas of mathematics that are applicable to real-world problems today.
There is no consensus as to what the various branches of applied mathematics are. Such
categorizations are made difficult by the way mathematics and science change over time, and
also by the way universities organize departments, courses, and degrees.
The success of modern numerical mathematical methods and software has led to the
emergence of computational mathematics, computational science, and computational
engineering, which use high-performance computing for the simulation of phenomena and the
solution of problems in the sciences and engineering. These are often considered
interdisciplinary.
Historically, mathematics was most important in the natural sciences and engineering.
However, since World War II, fields outside of the physical sciences have spawned the creation
of new areas of mathematics, such as game theory and social choice theory, which grew out of
economic considerations.

The advent of the computer has enabled new applications: studying and using the new
computer technology itself (computer science) to study problems arising in other areas of
science (computational science) as well as the mathematics of computation (for example,
theoretical computer science, computer algebra, numerical analysis). Statistics is probably the
most widespread mathematical science used in the social sciences, but other areas of
mathematics, most notably economics, are proving increasingly useful in these disciplines.
Academic institutions are not consistent in the way they group and label courses, programs, and
degrees in applied mathematics. At some schools, there is a single mathematics department,
whereas others have separate departments for Applied Mathematics and (Pure) Mathematics.
It is very common for Statistics departments to be separated at schools with graduate
programs, but many undergraduate-only institutions include statistics under the mathematics
department.
Many applied mathematics programs (as opposed to departments) consist of primarily
cross-listed courses and jointly appointed faculty in departments representing applications.
Some Ph.D. programs in applied mathematics require little or no coursework outside of
mathematics, while others require substantial coursework in a specific area of application. In
some respects this difference reflects the distinction between "application of mathematics" and
"applied mathematics".
Applied mathematics has substantial overlap with the discipline of statistics. Statistical theorists
study and improve statistical procedures with mathematics, and statistical research often raises
mathematical questions. Statistical theory relies on probability and decision theory, and makes
extensive use of scientific computing, analysis, and optimization; for the design of experiments,
statisticians use algebra and combinatorial design. Applied mathematicians and statisticians
often work in a department of mathematical sciences (particularly at colleges and small
universities).
Mathematical economics is the application mathematical methods to represent theories and
analyze problems in economics. The applied methods usually refer to nontrivial mathematical
techniques or approaches. Mathematical economics is based on statistics, probability,
mathematical programming (as well as other computational methods), operations research,
game theory, and some methods from mathematical analysis. In this regard, it resembles (but is
distinct from) financial mathematics, another part of applied mathematics.[6]
Applied mathematics is a branch of mathematics that concerns itself with the application of
mathematical knowledge to other domains. Such applications include numerical analysis,
mathematics of engineering, linear programming, optimization and operations research,
continuous modelling, mathematical biology and bioinformatics, information theory, game
theory, probability and statistics, financial mathematics, actuarial science, cryptography and
hence combinatorics and even finite geometry to some extent, graph theory as applied to
network analysis, and a great deal of what is called computer science.[7]

Business statistics
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
including services improvement, and marketing research.
These sources feature regular repetitive publication of series of data. This makes the topic of
time series especially important for business statistics. It is also a branch of applied statistics
working mostly on data collected as a by-product of doing business or by government agencies.
It provides knowledge and skills to interpret and use statistical techniques in a variety of
business applications.
A typical business statistics course is intended for business majors, and covers statistical study,
descriptive statistics (collection, description, analysis, and summary of data), probability, and
the binomial and normal distributions, test of hypotheses and confidence intervals, linear
regression, and correlation. [8]
Statistics
Statistics is the study of the collection, analysis, interpretation, presentation, and organization
of data.[1] In applying statistics to, e.g., a scientific, industrial, or social problem, it is
conventional to begin with a statistical population or a statistical model process to be studied.
Populations can be diverse topics such as "all people living in a country" or "every atom
composing a crystal". Statistics deals with all aspects of data including the planning of data
collection in terms of the design of surveys and experiments.
Some popular definitions are:
Merriam-Webster dictionary defines statistics as "classified facts representing the conditions
of a people in a state – especially the facts that can be stated in numbers or any other tabular or
classified arrangement
When census data cannot be collected, statisticians collect data by developing specific
experiment designs and survey samples. Representative sampling assures that inferences and
conclusions can safely extend from the sample to the population as a whole. An experimental
study involves taking measurements of the system under study, manipulating the system, and
then taking additional measurements using the same procedure to determine if the
manipulation has modified the values of the measurements. In contrast, an observational study
does not involve experimental manipulation.

Two main statistical methodologies are used in data analysis: descriptive statistics, which
summarizes data from a sample using indexes such as the mean or standard deviation, and
inferential statistics, which draws conclusions from data that are subject to random variation
(e.g., observational errors, sampling variation). Descriptive statistics are most often concerned
with two sets of properties of a distribution (sample or population): central tendency (or
location) seeks to characterize the distribution's central or typical value, while dispersion (or
variability) characterizes the extent to which members of the distribution depart from its center
and each other. Inferences on mathematical statistics are made under the framework of
probability theory, which deals with the analysis of random phenomena.
A standard statistical procedure involves the test of the relationship between two statistical
data sets, or a data set and a synthetic data drawn from idealized model. An hypothesis is
proposed for the statistical relationship between the two data sets, and this is compared as an
alternative to an idealized null hypothesis of no relationship between two data sets. Rejecting
or disproving the null hypothesis is done using statistical tests that quantify the sense in which
the null can be proven false, given the data that are used in the test. Working from a null
hypothesis, two basic forms of error are recognized: Type I errors (null hypothesis is falsely
rejected giving a "false positive") and Type II errors (null hypothesis fails to be rejected and an
actual difference between populations is missed giving a "false negative"). Multiple problems
have come to be associated with this framework: ranging from obtaining a sufficient sample
size to specifying an adequate null hypothesis.
Measurement processes that generate statistical data are also subject to error. Many of these
errors are classified as random (noise) or systematic (bias), but other types of errors (e.g.,
blunder, such as when an analyst reports incorrect units) can also be important. The presence
of missing data and/or censoring may result in biased estimates and specific techniques have
been developed to address these problems.
Statistics can be said to have begun in ancient civilization, going back at least to the 5th century
BC, but it was not until the 18th century that it started to draw more heavily from calculus and
probability theory. Statistics continues to be an area of active research, for example on the
problem of how to analyze Big data.
Statistics is a mathematical body of science that pertains to the collection, analysis,
interpretation or explanation, and presentation of data, or as a branch of mathematics. Some
consider statistics to be a distinct mathematical science rather than a branch of mathematics.
While many scientific investigations make use of data, statistics is concerned with the use of
data in the context of uncertainty and decision making in the face of uncertainty.
Mathematical statistics is the application of mathematics to statistics, which was originally
conceived as the science of the state — the collection and analysis of facts about a country: its
economy, land, military, population, and so forth. Mathematical techniques used for this
include mathematical analysis, linear algebra, stochastic analysis, differential equations, and

measure-theoretic probability theory.
In applying statistics to a problem, it is common practice to start with a population or process to
be studied. Populations can be diverse topics such as "all persons living in a country" or "every
atom composing a crystal".
Ideally, statisticians compile data about the entire population (an operation called census). This
may be organized by governmental statistical institutes. Descriptive statistics can be used to
summarize the population data. Numerical descriptors include mean and standard deviation for
continuous data types (like income), while frequency and percentage are more useful in terms
of describing categorical data (like race).
When a census is not feasible, a chosen subset of the population called a sample is studied.
Once a sample that is representative of the population is determined, data is collected for the
sample members in an observational or experimental setting. Again, descriptive statistics can be
used to summarize the sample data. However, the drawing of the sample has been subject to
an element of randomness, hence the established numerical descriptors from the sample are
also due to uncertainty. To still draw meaningful conclusions about the entire population,
inferential statistics is needed. It uses patterns in the sample data to draw inferences about the
population represented, accounting for randomness. These inferences may take the form of:
answering yes/no questions about the data (hypothesis testing), estimating numerical
characteristics of the data (estimation), describing associations within the data (correlation) and
modeling relationships within the data (for example, using regression analysis). Inference can
extend to forecasting, prediction and estimation of unobserved values either in or associated
with the population being studied; it can include extrapolation and interpolation of time series
or spatial data, and can also include data mining.
Sampling
When full census data cannot be collected, statisticians collect sample data by developing
specific experiment designs and survey samples. Statistics itself also provides tools for
prediction and forecasting the use of data through statistical models. To use a sample as a guide
to an entire population, it is important that it truly represents the overall population.
Representative sampling assures that inferences and conclusions can safely extend from the
sample to the population as a whole. A major problem lies in determining the extent that the
sample chosen is actually representative. Statistics offers methods to estimate and correct for
any bias within the sample and data collection procedures. There are also methods of
experimental design for experiments that can lessen these issues at the outset of a study,
strengthening its capability to discern truths about the population.
Sampling theory is part of the mathematical discipline of probability theory. Probability is used
in mathematical statistics to study the sampling distributions of sample statistics and, more
generally, the properties of statistical procedures. The use of any statistical method is valid

when the system or population under consideration satisfies the assumptions of the method.
The difference in point of view between classic probability theory and sampling theory is,
roughly, that probability theory starts from the given parameters of a total population to
deduce probabilities that pertain to samples. Statistical inference, however, moves in the
opposite direction—inductively inferring from samples to the parameters of a larger or total
population.
A common goal for a statistical research project is to investigate causality, and in particular to
draw a conclusion on the effect of changes in the values of predictors or independent variables
on dependent variables. There are two major types of causal statistical studies: experimental
studies and observational studies. In both types of studies, the effect of differences of an
independent variable (or variables) on the behavior of the dependent variable are observed.
The difference between the two types lies in how the study is actually conducted. Each can be
very effective. An experimental study involves taking measurements of the system under study,
manipulating the system, and then taking additional measurements using the same procedure
to determine if the manipulation has modified the values of the measurements. In contrast, an
observational study does not involve experimental manipulation. Instead, data are gathered
and correlations between predictors and response are investigated. While the tools of data
analysis work best on data from randomized studies, they are also applied to other kinds of data
– like natural experiments and observational studies – for which a statistician would use a
modified, more structured estimation method (e.g., Difference in differences estimation and
instrumental variables, among many others) that produce consistent estimators.
Experiments
The basic steps of a statistical experiment are:
Planning the research, including finding the number of replicates of the study, using the
following information: preliminary estimates regarding the size of treatment effects, alternative
hypotheses, and the estimated experimental variability. Consideration of the selection of
experimental subjects and the ethics of research is necessary. Statisticians recommend that
experiments compare (at least) one new treatment with a standard treatment or control, to
allow an unbiased estimate of the difference in treatment effects.
Design of experiments, using blocking to reduce the influence of confounding variables, and
randomized assignment of treatments to subjects to allow unbiased estimates of treatment
effects and experimental error. At this stage, the experimenters and statisticians write the
experimental protocol that will guide the performance of the experiment and which specifies
the primary analysis of the experimental data.
Performing the experiment following the experimental protocol and analyzing the data

following the experimental protocol.
Further examining the data set in secondary analyses, to suggest new hypotheses for future
study.
Documenting and presenting the results of the study.
An example of an observational study is one that explores the association between smoking and
lung cancer. This type of study typically uses a survey to collect observations about the area of
interest and then performs statistical analysis. In this case, the researchers would collect
observations of both smokers and non-smokers, perhaps through a case-control study, and
then look for the number of cases of lung cancer in each group.
Types of data
Because variables conforming only to nominal or ordinal measurements cannot be reasonably
measured numerically, sometimes they are grouped together as categorical variables, whereas
ratio and interval measurements are grouped together as quantitative variables, which can be
either discrete or continuous, due to their numerical nature. Such distinctions can often be
loosely correlated with data type in computer science, in that dichotomous categorical variables
may be represented with the Boolean data type, polytomous categorical variables with
arbitrarily assigned integers in the integral data type, and continuous variables with the real
data type involving floating point computation. But the mapping of computer science data types
to statistical data types depends on which categorization of the latter is being implemented.
The issue of whether or not it is appropriate to apply different kinds of statistical methods to
data obtained from different kinds of measurement procedures is complicated by issues
concerning the transformation of variables and the precise interpretation of research questions.
"The relationship between the data and what they describe merely reflects the fact that certain
kinds of statistical statements may have truth values which are not invariant under some
transformations. Whether or not a transformation is sensible to contemplate depends on the
question one is trying to answer" .
Statistics, estimators and pivotal quantities
Consider independent identically distributed (IID) random variables with a given probability
distribution: standard statistical inference and estimation theory defines a random sample as
the random vector given by the column vector of these IID variables.[19] The population being
examined is described by a probability distribution that may have unknown parameters.

A statistic is a random variable that is a function of the random sample, but not a function of
unknown parameters. The probability distribution of the statistic, though, may have unknown
parameters.
Consider now a function of the unknown parameter: an estimator is a statistic used to estimate
such function. Commonly used estimators include sample mean, unbiased sample variance and
sample covariance.
A random variable that is a function of the random sample and of the unknown parameter, but
whose probability distribution does not depend on the unknown parameter is called a pivotal
quantity or pivot. Widely used pivots include the z-score, the chi square statistic and Student's
t-value.
Between two estimators of a given parameter, the one with lower mean squared error is said to
be more efficient. Furthermore, an estimator is said to be unbiased if its expected value is equal
to the true value of the unknown parameter being estimated, and asymptotically unbiased if its
expected value converges at the limit to the true value of such parameter.
Other desirable properties for estimators include: UMVUE estimators that have the lowest
variance for all possible values of the parameter to be estimated (this is usually an easier
property to verify than efficiency) and consistent estimators which converges in probability to
the true value of such parameter.
This still leaves the question of how to obtain estimators in a given situation and carry the
computation, several methods have been proposed: the method of moments, the maximum
likelihood method, the least squares method and the more recent method of estimating
equations.
Error
Working from a null hypothesis, two basic forms of error are recognized:
Type I errors where the null hypothesis is falsely rejected giving a "false positive".
Type II errors where the null hypothesis fails to be rejected and an actual difference between
populations is missed giving a "false negative".
Standard deviation refers to the extent to which individual observations in a sample differ from

a central value, such as the sample or population mean, while Standard error refers to an
estimate of difference between sample mean and population mean.
A statistical error is the amount by which an observation differs from its expected value, a
residual is the amount an observation differs from the value the estimator of the expected
value assumes on a given sample (also called prediction).
Mean squared error is used for obtaining efficient estimators, a widely used class of estimators.
Root mean square error is simply the square root of mean squared error.
Many statistical methods seek to minimize the residual sum of squares, and these are called
"methods of least squares" in contrast to Least absolute deviations. The latter gives equal
weight to small and big errors, while the former gives more weight to large errors. Residual sum
of squares is also differentiable, which provides a handy property for doing regression. Least
squares applied to linear regression is called ordinary least squares method and least squares
applied to nonlinear regression is called non-linear least squares. Also in a linear regression
model the non deterministic part of the model is called error term, disturbance or more simply
noise. Both linear regression and non-linear regression are addressed in polynomial least
squares, which also describes the variance in a prediction of the dependent variable (y axis) as a
function of the independent variable (x axis) and the deviations (errors, noise, disturbances)
from the estimated (fitted) curve.
Measurement processes that generate statistical data are also subject to error. Many of these
errors are classified as random (noise) or systematic (bias), but other types of errors (e.g.,
blunder, such as when an analyst reports incorrect units) can also be important. The presence
of missing data and/or censoring may result in biased estimates and specific techniques have
been developed to address these problems.
Interval estimation
Most studies only sample part of a population, so results don't fully represent the whole
population. Any estimates obtained from the sample only approximate the population value.
Confidence intervals allow statisticians to express how closely the sample estimate matches the
true value in the whole population. Often they are expressed as 95% confidence intervals.
Formally, a 95% confidence interval for a value is a range where, if the sampling and analysis
were repeated under the same conditions (yielding a different dataset), the interval would
include the true (population) value in 95% of all possible cases. This does not imply that the
probability that the true value is in the confidence interval is 95%. From the frequentist
perspective, such a claim does not even make sense, as the true value is not a random variable.
Either the true value is or is not within the given interval. However, it is true that, before any

data are sampled and given a plan for how to construct the confidence interval, the probability
is 95% that the yet-to-be-calculated interval will cover the true value: at this point, the limits of
the interval are yet-to-be-observed random variables. One approach that does yield an interval
that can be interpreted as having a given probability of containing the true value is to use a
credible interval from Bayesian statistics: this approach depends on a different way of
interpreting what is meant by "probability", that is as a Bayesian probability.
In principle confidence intervals can be symmetrical or asymmetrical. An interval can be
asymmetrical because it works as lower or upper bound for a parameter (left-sided interval or
right sided interval), but it can also be asymmetrical because the two sided interval is built
violating symmetry around the estimate. Sometimes the bounds for a confidence interval are
reached asymptotically and these are used to approximate the true bounds.
Statistics rarely give a simple Yes/No type answer to the question under analysis. Interpretation
often comes down to the level of statistical significance applied to the numbers and often refers
to the probability of a value accurately rejecting the null hypothesis (sometimes referred to as
the p-value).
The standard approach is to test a null hypothesis against an alternative hypothesis. A critical
region is the set of values of the estimator that leads to refuting the null hypothesis. The
probability of type I error is therefore the probability that the estimator belongs to the critical
region given that null hypothesis is true (statistical significance) and the probability of type II
error is the probability that the estimator doesn't belong to the critical region given that the
alternative hypothesis is true. The statistical power of a test is the probability that it correctly
rejects the null hypothesis when the null hypothesis is false.
Referring to statistical significance does not necessarily mean that the overall result is
significant in real world terms. For example, in a large study of a drug it may be shown that the
drug has a statistically significant but very small beneficial effect, such that the drug is unlikely
to help the patient noticeably.
While in principle the acceptable level of statistical significance may be subject to debate, the
p-value is the smallest significance level that allows the test to reject the null hypothesis. This is
logically equivalent to saying that the p-value is the probability, assuming the null hypothesis is
true, of observing a result at least as extreme as the test statistic. Therefore, the smaller the
p-value, the lower the probability of committing type I error.
Some problems are usually associated with this framework (See criticism of hypothesis testing):
A difference that is highly statistically significant can still be of no practical significance, but it
is possible to properly formulate tests to account for this. One response involves going beyond
reporting only the significance level to include the p-value when reporting whether a hypothesis

is rejected or accepted. The p-value, however, does not indicate the size or importance of the
observed effect and can also seem to exaggerate the importance of minor differences in large
studies. A better and increasingly common approach is to report confidence intervals. Although
these are produced from the same calculations as those of hypothesis tests or p-values, they
describe both the size of the effect and the uncertainty surrounding it.
Fallacy of the transposed conditional, aka prosecutor's fallacy: criticisms arise because the
hypothesis testing approach forces one hypothesis (the null hypothesis) to be favored, since
what is being evaluated is probability of the observed result given the null hypothesis and not
probability of the null hypothesis given the observed result. An alternative to this approach is
offered by Bayesian inference, although it requires establishing a prior probability.
Rejecting the null hypothesis does not automatically prove the alternative hypothesis.
As everything in inferential statistics it relies on sample size, and therefore under fat tails
p-values may be seriously mis-computed.
Some well-known statistical tests and procedures are:
Analysis of variance (ANOVA)
Chi-squared test
Correlation
Factor analysis
Mann–Whitney U
Mean square weighted deviation (MSWD)
Pearson product-moment correlation coefficient
Regression analysis
Spearman's rank correlation coefficient
Student's t-test
Time series analysis
Conjoint Analysis
Misuse of statistics can produce subtle, but serious errors in description and
interpretation—subtle in the sense that even experienced professionals make such errors, and
serious in the sense that they can lead to devastating decision errors. For instance, social policy,

medical practice, and the reliability of structures like bridges all rely on the proper use of
statistics.
Even when statistical techniques are correctly applied, the results can be difficult to interpret
for those lacking expertise. The statistical significance of a trend in the data—which measures
the extent to which a trend could be caused by random variation in the sample—may or may
not agree with an intuitive sense of its significance. The set of basic statistical skills (and
skepticism) that people need to deal with information in their everyday lives properly is
referred to as statistical literacy.
Ways to avoid misuse of statistics include using proper diagrams and avoiding bias.Misuse can
occur when conclusions are overgeneralized and claimed to be representative of more than
they really are, often by either deliberately or unconsciously overlooking sampling bias.Bar
graphs are arguably the easiest diagrams to use and understand, and they can be made either
by hand or with simple computer programs. Unfortunately, most people do not look for bias or
errors, so they are not noticed. Thus, people may often believe that something is true even if it
is not well represented. To make data gathered from statistics believable and accurate, the
sample taken must be representative of the whole.
The concept of correlation is particularly noteworthy for the potential confusion it can cause.
Statistical analysis of a data set often reveals that two variables (properties) of the population
under consideration tend to vary together, as if they were connected. For example, a study of
annual income that also looks at age of death might find that poor people tend to have shorter
lives than affluent people. The two variables are said to be correlated; however, they may or
may not be the cause of one another. The correlation phenomena could be caused by a third,
previously unconsidered phenomenon, called a lurking variable or confounding variable. For this
reason, there is no way to immediately infer the existence of a causal relationship between the
two variables.
"Applied statistics" comprises descriptive statistics and the application of inferential
statistics.Theoretical statistics concerns both the logical arguments underlying justification of
approaches to statistical inference, as well encompassing mathematical statistics. Mathematical
statistics includes not only the manipulation of probability distributions necessary for deriving
results related to methods of estimation and inference, but also various aspects of
computational statistics and the design of experiments.
There are two applications for machine learning and data mining: data management and data
analysis. Statistics tools are necessary for the data analysis.
Statistics is applicable to a wide variety of academic disciplines, including natural and social
sciences, government, and business. Statistical consultants can help organizations and

companies that don't have in-house expertise relevant to their particular questions.
Statistical computing
The rapid and sustained increases in computing power starting from the second half of the 20th
century have had a substantial impact on the practice of statistical science. Early statistical
models were almost always from the class of linear models, but powerful computers, coupled
with suitable numerical algorithms, caused an increased interest in nonlinear models (such as
neural networks) as well as the creation of new types, such as generalized linear models and
multilevel models.
Traditionally, statistics was concerned with drawing inferences using a semi-standardized
methodology that was "required learning" in most sciences. This has changed with use of
statistics in non-inferential contexts. What was once considered a dry subject, taken in many
fields as a degree-requirement, is now viewed enthusiastically. Initially derided by some
mathematical purists, it is now considered essential methodology in certain areas.
In number theory, scatter plots of data generated by a distribution function may be
transformed with familiar tools used in statistics to reveal underlying patterns, which may then
lead to hypotheses.
Methods of statistics including predictive methods in forecasting are combined with chaos
theory and fractal geometry to create video works that are considered to have great beauty.
The process art of Jackson Pollock relied on artistic experiments whereby underlying
distributions in nature were artistically revealed.[citation needed] With the advent of
computers, statistical methods were applied to formalize such distribution-driven natural
processes to make and analyze moving video art.
Methods of statistics may be used predicatively in performance art, as in a card trick based on
a Markov process that only works some of the time, the occasion of which can be predicted
using statistical methodology.[9]
Statistic
A statistic (singular) or sample statistic is a single measure of some attribute of a sample (e.g.,
its arithmetic mean value). It is calculated by applying a function (statistical algorithm) to the
values of the items of the sample, which are known together as a set of data.
More formally, statistical theory defines a statistic as a function of a sample where the function
itself is independent of the sample's distribution; that is, the function can be stated before
realization of the data. The term statistic is used both for the function and for the value of the

function on a given sample.
A statistic is distinct from a statistical parameter, which is not computable because often the
population is much too large to examine and measure all its items. However, a statistic, when
used to estimate a population parameter, is called an estimator. For instance, the sample mean
is a statistic that estimates the population mean, which is a parameter.
When a statistic (a function) is being used for a specific purpose, it may be referred to by a
name indicating its purpose: in descriptive statistics, a descriptive statistic is used to describe
the data; in estimation theory, an estimator is used to estimate a parameter of the distribution
(population); in statistical hypothesis testing, a test statistic is used to test a hypothesis.
However, a single statistic can be used for multiple purposes – for example the sample mean
can be used to describe a data set, to estimate the population mean, or to test a hypothesis.
In calculating the arithmetic mean of a sample, for example, the algorithm works by summing
all the data values observed in the sample and then dividing this sum by the number of data
items. This single measure, the mean of the sample, is called a statistic; its value is frequently
used as an estimate of the mean value of all items comprising the population from which the
sample is drawn. The population mean is also a single measure; however, it is not called a
statistic, because it is not obtained from a sample; instead it is called a population parameter,
because it is obtained from the whole population.
Other examples of statistics include
Sample mean discussed in the example above and sample median
Sample variance and sample standard deviation
Sample quantiles besides the median, e.g., quartiles and percentiles
Test statistics, such as t statistics, chi-squared statistics, f statistics
Order statistics, including sample maximum and minimum
Sample moments and functions thereof, including kurtosis and skewness
Various functionals of the empirical distribution function
A statistic is an observable random variable, which differentiates it both from a parameter that
is a generally unobservable quantity describing a property of a statistical population, and from
an unobservable random variable, such as the difference between an observed measurement
and a population average. A parameter can only be computed exactly if the entire population
can be observed without error; for instance, in a perfect census or for a population of

standardized test takers.
Statisticians often contemplate a parameterized family of probability distributions, any member
of which could be the distribution of some measurable aspect of each member of a population,
from which a sample is drawn randomly. For example, the parameter may be the average
height of 25-year-old men in North America. The height of the members of a sample of 100
such men are measured; the average of those 100 numbers is a statistic. The average of the
heights of all members of the population is not a statistic unless that has somehow also been
ascertained (such as by measuring every member of the population). The average height that
would be calculated using the all of the individual heights of all 25-year-old North American
men is a parameter, and not a statistic. [10]
Financial accounting
Financial accounting (or financial accountancy) is the field of accounting concerned with the
summary, analysis and reporting of financial transactions pertaining to a business. This involves
the preparation of financial statements available for public consumption. Stockholders,
suppliers, banks, employees, government agencies, business owners, and other stakeholders
are examples of people interested in receiving such information for decision making purposes.
Financial accountancy is governed by both local and international accounting standards.
Generally Accepted Accounting Principles (GAAP) is the standard framework of guidelines for
financial accounting used in any given jurisdiction. It includes the standards, conventions and
rules that accountants follow in recording and summarising and in the preparation of financial
statements. On the other hand, International Financial Reporting Standards (IFRS) is a set of
international accounting standards stating how particular types of transactions and other
events should be reported in financial statements. IFRS are issued by the International
Accounting Standards Board (IASB). With IFRS becoming more widespread on the international
scene, consistency in financial reporting has become more prevalent between global
organisations.
While financial accounting is used to prepare accounting information for people outside the
organisation or not involved in the day-to-day running of the company, management
accounting provides accounting information to help managers make decisions to manage the
business.
Objectives of financial accounting

Financial accounting and financial reporting are often used as synonyms.
1. According to International Financial Reporting Standards, the objective of financial reporting
is:
To provide financial information about the reporting entity that is useful to existing and
potential investors, lenders and other creditors in making decisions about providing resources
to the entity.[4]
2. According to the European Accounting Association:
Capital maintenance is a competing objective of financial reporting.
Qualities of financial accounting
Financial accounting is the preparation of financial statements that can be consumed by the
public and the relevant stakeholders using either Historical Cost Accounting (HCA) or Constant
Purchasing Power Accounting (CPPA). When producing financial statements, they must comply
to the following:
Relevance: Financial accounting which is decision-specific. It must be possible for accounting
information to influence decisions. Unless this characteristic is present, there is no point in
cluttering statements.
Materiality: information is material if its omission or misstatement could influence the
economic decisions of users taken on the basis of the financial statements.
Reliability: accounting must be free from significant error or bias. It should be easily relied
upon by managers. Often information that is highly relevant isn’t very reliable, and vice versa.
Understandability: accounting reports should be expressed as clearly as possible and should
be understood by those to whom the information is relevant.

Comparability: financial reports from different periods should be comparable with one
another in order to derive meaningful conclusions about the trends in an entity’s financial
performance and position over time. Comparability can be ensured by applying the same
accounting policies over time.
Three components of financial statements
Statement of Cash Flows
The Statement of Cash Flows considers the inputs and outputs in concrete cash within a stated
period. The general template of a cash flow statement is as follows: Cash Inflow - Cash Outflow
+ Opening Balance = Closing Balance
Example 1: in the beginning of September, Ellen started out with $5 in her bank account. During
that same month, Ellen borrowed $20 from Tom. At the end of the month, Ellen bought a pair
of shoes for $7. Ellen's cash flow statement for the month of September looks like this:
Cash inflow: $20
Cash outflow: $7
Opening balance: $5
Closing balance: $20 – $7 + $5 = $18
Example 2: in the beginning of June, WikiTables, a company that buys and resells tables, sold 2
tables. They'd originally bought the tables for $25 each, and sold them at a price of $50 per
table. The first table was paid out in cash however the second one was bought in credit terms.
WikiTables' cash flow statement for the month of June looks like this:
Cash inflow: $50 - How much WikiTables received in cash for the first table. They didn't
receive cash for the second table (sold in credit terms).
Cash outflow: $50 - How much they'd originally bought the 2 tables for.

Opening balance: $0
Closing balance: $50 – $50 + $0 = $0 - Indeed, the cash flow for the month of June for
WikiTables amounts to $0 and not $50.
Important: the cash flow statement only considers the exchange of actual cash, and ignores
what the person in question owes or is owed.
Profit and Loss Statement (also called Statement of Comprehensive Income)
In case of service organisations they are called as profit & loss a/c as income statement.
the profit or loss is determined by:
Sales (revenue) – Cost of Sales – total expenses + total income – tax paid = profit/loss
If there's a negative balance, it's a loss
if there's a positive balance, it's a profit
Statement of Financial Position (also called Balance Sheet)[edit]
The balance sheet is the statement showing assets & liabilities. As per the proforma, on its right
it shows assets and on its left side it shows liabilities. It helps know the status of a company. The
difference between current assets and current liabilities is called working capital. The assets are
mainly divided into 2 types:
fixed assets and
current assets

The liabilities are
long term liabilities and
short term liabilities or current liabilities.
The statements assist detailed study and analysis in each segment. For suppose in case of if you
analyse the income or profit and loss statement that means you analyse the real meaning to
how much earned or sustained loss when compare to last financial year to this year.
Basic accounting concepts[edit]
THE STABLE MEASURING UNIT ASSUMPTION One of the basic principles in accounting is “The
Measuring Unit principle:
The unit of measure in accounting shall be the base money unit of the most relevant
currency. This principle also assumes the unit of measure is stable; that is, changes in its general
purchasing power are not considered sufficiently important to require adjustments to the basic
financial statements.”
Historical Cost Accounting, i.e., financial capital maintenance in nominal monetary units, is
based on the stable measuring unit assumption under which accountants simply assume that
money, the monetary unit of measure, is perfectly stable in real value for the purpose of
measuring (1) monetary items not inflation-indexed daily in terms of the Daily CPI and (2)
constant real value non-monetary items not updated daily in terms of the Daily CPI during low
and high inflation and deflation.
UNITS OF CONSTANT PURCHASING POWER The stable measuring unit assumption is not applied
during hyperinflation. IFRS requires entities to implement capital maintenance in units of
constant purchasing power in terms of IAS 29 Financial Reporting in Hyperinflationary
Economies.

Financial accountants produce financial statements based on the accounting standards in a
given jurisdiction. These standards may be the Generally Accepted Accounting Principles of a
respective country, which are typically issued by a national standard setter, or International
Financial Reporting Standards (IFRS), which are issued by the International Accounting
Standards Board (IASB).
Financial accounting serves the following purposes:
producing general purpose financial statements
producing information used by the management of a business entity for decision making,
planning and performance evaluation
producing financial statements for meeting regulatory requirements.
Objectives of Financial Accounting
Systematic recording of transactions: basic objective of accounting is to systematically record
the financial aspects of business transactions (i.e. book-keeping). These recorded transactions
are later on classified and summarized logically for the preparation of financial statements and
for their analysis and interpretation.
Ascertainment of result of above recorded transactions: accountant prepares profit and loss
account to know the result of business operations for a particular period of time. If expenses
exceed revenue then it is said that business running under loss. The profit and loss account
helps the management and different stakeholders in taking rational decisions. For example, if
business is not proved to be remunerative or profitable, the cause of such a state of affair can
be investigated by the management for taking remedial steps.
Ascertainment of the financial position of business: businessman is not only interested in
knowing the result of the business in terms of profits or loss for a particular period but is also
anxious to know that what he owes (liability) to the outsiders and what he owns (assets) on a
certain date. To know this, accountant prepares a financial position statement of assets and
liabilities of the business at a particular point of time and helps in ascertaining the financial
health of the business.

Providing information to the users for rational decision-making: accounting as a ‘language of
business’ communicates the financial result of an enterprise to various stakeholders by means
of financial statements. Accounting aims to meet the financial information needs of the
decision-makers and helps them in rational decision-making.
To know the solvency position: by preparing the balance sheet, management not only reveals
what is owned and owed by the enterprise, but also it gives the information regarding concern’s
ability to meet its liabilities in the short run (liquidity position) and also in the long-run (solvency
position) as and when they fall due.
Financial accounting vs cost accounting
Financial accounting aims at finding out results of accounting year in the form of Profit and
Loss Account and Balance Sheet. Cost Accounting aims at computing cost of production/service
in a scientific manner and facilitate cost control and cost reduction.
Financial accounting reports the results and position of business to government, creditors,
investors, and external parties.
Cost Accounting is an internal reporting system for an organization’s own management for
decision making.
In financial accounting, cost classification based on type of transactions, e.g. salaries, repairs,
insurance, stores etc. In cost accounting, classification is basically on the basis of functions,
activities, products, process and on internal planning and control and information needs of the
organization.
Financial accounting aims at presenting ‘true and fair’ view of transactions, profit and loss for
a period and Statement of financial position (Balance Sheet) on a given date. It aims at
computing ‘true and fair’ view of the cost of production/services offered by the firm. [11]
Research
Research comprises "creative work undertaken on a systematic basis in order to increase the
stock of knowledge, including knowledge of humans, culture and society, and the use of this
stock of knowledge to devise new applications."[1] It is used to establish or confirm facts,
reaffirm the results of previous work, solve new or existing problems, support theorems, or
develop new theories. A research project may also be an expansion on past work in the field. To
test the validity of instruments, procedures, or experiments, research may replicate elements of

prior projects, or the project as a whole. The primary purposes of basic research (as opposed to
applied research) are documentation, discovery, interpretation, or the research and
development (R&D) of methods and systems for the advancement of human knowledge.
Approaches to research depend on epistemologies, which vary considerably both within and
between humanities and sciences. There are several forms of research: scientific, humanities,
artistic, economic, social, business, marketing, practitioner research, life,technological,etc.
Forms of research
Scientific research is a systematic way of gathering data and harnessing curiosity. This research
provides scientific information and theories for the explanation of the nature and the properties
of the world. It makes practical applications possible. Scientific research is funded by public
authorities, by charitable organizations and by private groups, including many companies.
Scientific research can be subdivided into different classifications according to their academic
and application disciplines. Scientific research is a widely used criterion for judging the standing
of an academic institution, such as business schools, but some argue that such is an inaccurate
assessment of the institution, because the quality of research does not tell about the quality of
teaching (these do not necessarily correlate).
Research in the humanities involves different methods such as for example hermeneutics and
semiotics, and a different, more relativist epistemology. Humanities scholars usually do not
search for the ultimate correct answer to a question, but instead explore the issues and details
that surround it. Context is always important, and context can be social, historical, political,
cultural, or ethnic. An example of research in the humanities is historical research, which is
embodied in historical method. Historians use primary sources and other evidence to
systematically investigate a topic, and then to write histories in the form of accounts of the
past.
Artistic research, also seen as 'practice-based research', can take form when creative works are
considered both the research and the object of research itself. It is the debatable body of
thought which offers an alternative to purely scientific methods in research in its search for
knowledge and truth.
The Merriam-Webster Online Dictionary defines research in more detail as "a studious inquiry
or examination; especially investigation or experimentation aimed at the discovery and
interpretation of facts, revision of accepted theories or laws in the light of new facts, or
practical application of such new or revised theories or laws".
Steps in conducting research
Research is often conducted using the hourglass model structure of research. The hourglass
model starts with a broad spectrum for research, focusing in on the required information
through the method of the project (like the neck of the hourglass), then expands the research in

the form of discussion and results. The major steps in conducting research are:
Identification of research problem
Literature review
Specifying the purpose of research
Determine specific research questions
Specification of a conceptual framework, usually a set of hypotheses[9]
Choice of a methodology (for data collection)
Data collection
Verify data
Analyzing and interpreting the data
Reporting and evaluating research
Communicating the research findings and, possibly, recommendations
The steps generally represent the overall process; however, they should be viewed as an
ever-changing iterative process rather than a fixed set of steps. Most research begins with a
general statement of the problem, or rather, the purpose for engaging in the study. The
literature review identifies flaws or holes in previous research which provides justification for
the study. Often, a literature review is conducted in a given subject area before a research
question is identified. A gap in the current literature, as identified by a researcher, then
engenders a research question. The research question may be parallel to the hypothesis. The
hypothesis is the supposition to be tested. The researcher(s) collects data to test the
hypothesis. The researcher(s) then analyzes and interprets the data via a variety of statistical
methods, engaging in what is known as empirical research. The results of the data analysis in
confirming or failing to reject the Null hypothesis are then reported and evaluated. At the end,
the researcher may discuss avenues for further research. However, some researchers advocate
for the flip approach: starting with articulating findings and discussion of them, moving "up" to
identification research problem that emerging in the findings and literature review introducing
the findings. The flip approach is justified by the transactional nature of the research endeavor
where research inquiry, research questions, research method, relevant research literature, and
so on are not fully known until the findings fully emerged and interpreted.
Plato in Meno talks about an inherent difficulty, if not a paradox, of doing research that can be

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