By: M.Hashaaam Allama Iqbal Open University

2. TOPIC:
PROBABILITY &
APPLICATION IN
BUSINESS
Presented To: Mr. Shahzad Babar
Presented By: M.Hashaam
Roll No. : AM552381
Class : MBA (B&F) 2nd Semester

3. ACKNOWLEDGMENT
First of all thanks of Allah who is most
beneficent and the most merciful
whose blessings are abundant and
favors are unlimited.
It is my pleasure to acknowledge the
guidance and support of my subject
Teacher: Shahzad Babar for their
guidance.

4. AN ABSTRACT
Probability theory is an important
part of Statistical theory. It is
classified in three ways.
In business, probability theory is
used in the calculation of long-
term gains & losses and also for
many other business related
works.

5. INTRODUCTION TO PROBABILITY
Probability theory is an important part of
statistical theory that bridges descriptive and
inferential statistics. It is the science of
uncertainty or chance, or likelihood.
A probability value ranges between 0 and 1
inclusive and represents the likelihood that a
particular event will happen.
A probability value of 0 means there is no
chance that an will happen and a value of 1
means there is 100 percent chance that the
event will happen.
Understanding probability is helpful for
decision-making.

6. INTRODUCTION.....(CONT.)
Conducting an experiment or sample test provides an
outcome that can be used to compute the chance of
events occurring in the future.
An experiment is the observation of some activity or
the act of taking some measurement. Whereas, an
outcome is a particular result of an experiment. The
collection of one or more outcomes of an
experiment is known as an event.
For example, a market testing of a sample of new
breakfast cereal, new drink, new shoes, new
magazine, etc. gives the Director of Production or
Director of Marketing a company a preliminary idea
(outcome) whether consumers would like the
product if it is produced and distributed in bulk.

7. PROBABILITY
Classification of Probability:
1) Classical Probability
2) Empirical Probability
3) Subjective Probability

8. CLASSICAL PROBABILITY
“When there are n equally likely outcomes to an
experiment”.
The probability of certain events is already known or
the resulting probabilities are definitive. For
example: (1)The chance that a woman gives birth
to a male or female baby (p = 0.50 or ½), (2)The
chance that tail or head appears in a toss of coin (p
= 0.50 or ½), and (3)The chance that one spot will
appear in die-rolling (p = 0.16 or 1/6).

9. EMPIRICAL PROBABILITY
The second one is empirical probability that is based on
past experience. The empirical probability, also known
as relative frequency, or experimental probability.
For example:
(1) 383 of 751 business graduates were employed in the
past. The probability that a particular graduate will be
employed in his or her major area is 383/751 = 0.51 or
51%.
(2) The probability that your income tax return will be
audited if there are two million mailed to your district
office and 2,400 are to be audited is 2,400/2,000,000 =
0.0012 or 0.12%.

10. SUBJECTIVE PROBABILITY
Subjective probability is a probability assigned to
an event based on whatever evidence is
available. It is an educated guess. Unlike
empirical probability, it is not based on past
experience. Subjective probability is obtained
by evaluating the available options and by
assigning the probability. Examples of events
that require computing subjective probability:
(1) Estimating the probability that a person wins a
lottery.
(2) Estimating the probability that the GM will lose
its first ranking in the car sales.

11. PROBABILITY DISTRIBUTION
Listing of probabilities of all the possible outcomes
that could result if the experiment were done.
Discrete Probability Distribution: describes a finite set of
possible occurrences, for discrete “count data.” For
example, the number of successful treatments out of
2 patients is discrete, because the random variable
represent the number of success can be only 0, 1, or
2. The probability of all possible occurrences—Pr(0
successes), Pr(1 success), Pr(2 successes)—
constitutes the probability distribution for this discrete
random variable. There are 2 types for further depth,
1. Binomial Distribution
2. Poisson Distribution

12. PROBABILITY DISTRIBUTION
Continuous probability distributions: describe an
“unbroken” continuum of possible occurrences. For
example, the probability of a given birth weight can be
anything from, say, half a pound to more than 12
pounds (or something like that). Thus, the random
variable of birth weight is continuous, with an infinite
number of possible points between any two values.
Normal Distribution: The variable flows without a break
and is thus continuous, with no limit to the number of
individuals with different measurements. Such
measurements are distributed in any of a number of
ways. We will consider it, the normal distribution.

13. APPLICATION IN BUSINESS
 In business, probability theory is used in the
calculation of long-term gains and losses. This is how
a company whose business is based on risk
calculates "probability of profitability" within acceptable
margins.
 Every decision made in the business world has risk to
it. So, in business, you would use probability to take a
close look at the company's financial risks. Even the
decisions that come down from management all have
a probability of success and a probability to fail.

14. APPLICATION IN BUSINESS
 Probability in Manufacturing
Manufacturing businesses can use probability to
determine the cost-benefit ratio or the transfer of a new
manufacturing technology process by addressing the
likelihood of improved profits. In other instances,
manufacturing firms use probability to determine the
possibility of financial success of a new product when
considering competition from other manufacturers,
market demand, market value and manufacturing costs.
Other instances of probability in manufacturing include
determining the likelihood of producing defective
products, and regional need and capacity for certain
fields of manufacturing.

15. APPLICATION IN BUSINESS
 Scenario Analysis
Probability distributions can be used to create scenario analyses. For
example, a business might create three scenarios: worst-case, likely
and best-case. The worst-case scenario would contain some value
from the lower end of the probability distribution; the likely scenario
would contain a value towards the middle of the distribution; and the
best-case scenario would contain a value in the upper end of the
scenario.
 Risk Evaluation
In addition to predicting future sales levels, probability distribution can
be a useful tool for evaluating risk. Consider, for example, a
company considering entering a new business line. If the company
needs to generate $500,000 in revenue in order to break even and
their probability distribution tells them that there is a 10 percent
chance that revenues will be less than $500,000, the company
knows roughly what level of risk it is facing if it decides to pursue
that new business line.

16. APPLICATION IN BUSINESS
 Sales Forecasting
One practical use for probability distributions and
scenario analysis in business is to predict future levels
of sales. It is essentially impossible to predict the
precise value of a future sales level;
however, businesses still need to be able to plan for
future events. Using a scenario analysis based on a
probability distribution can help a company frame its
possible future values in terms of a likely sales level
and a worst-case and best-case scenario. By doing
so, the company can base its business plans on the
likely scenario but still be aware of the alternative
possibilities.