International Business Environments and Operations 16th Global Edition test b...
HUMAN RESOURCE INFORMATION SYSTEM
1. UNIVERSITY OF MYSORE
TYPES OF PROBABILITY
PRESENTED TO:
D. JAYAPRASAD.
FACULTY OF
COMMERCE DEPT.
UNIVERSITY OF MYSORE
2. Contents:
Introduction
Concept
of various terms
Approaches or types of probability
Theorems of probability
Discursion of problems
Conclusion
3. Introduction
Probability theory is a very fascinating subject which
can be studied at various mathematical levels. Probability
is the foundation of statistical theory and applications.
Several mathematicians like Pascal, James Bernoulli,
De-Moivre, Bayes applied the theory of permutations and
combinations to quantify or calculate probability. Today
the probability theory has become one of the fundamental
technique in the development of Statistics.
The term “probability” in Statistics refers to the
chances of occurrence of an event among a large number of
possibilities.
4. TERMINOLOGIES
Random Experiment:
If an experiment or trial is repeated under the same
conditions for any number of times and it is possible to
count the total number of outcomes is called as “Random
Experiment”.
Sample Space:
The set of all possible outcomes of a random
experiment is known as “Sample Space” and denoted by
set S. [this is similar to Universal set in Set Theory] The
outcomes of the random experiment are called sample
points or outcomes.
5. Event:
An ‘event’ is an outcome of a trial meeting a
specified set of conditions other words, event is
a subset of the sample space S.
Events are usually denoted by capital letters.
There are different types of events.
1.
Null or impossible event is an event which
contains no outcomes.
2.
Elementary event is an event which contains
only one outcomes.
3.
Composite event is an event which contains
two or more outcomes.
4.
Sure or certain event is an event which
contains all the outcomes of a sample space.
6. • Exhaustive Events:
The total number of all possible elementary outcomes
in a random experiment is known as ‘exhaustive events’. In
other words, a set is said to be exhaustive, when no other
possibilities exists.
• Favourable
Events:
The elementary outcomes which entail or favour the
happening of an event is known as ‘favourable events’ i.e.,
the outcomes which help in the occurrence of that event.
• Mutually Exclusive Events:
Events are said to be ‘mutually exclusive’ if the
occurrence of an event totally prevents occurrence of all
other events in a trial. In other words, two events A and B
cannot occur simultaneously.
7. • Equally likely or Equi-probable Events:
Outcomes are said to be ‘equally likely’ if there is no reason
to expect one outcome to occur in preference to another. i.e.,
among all exhaustive outcomes, each of them has equal chance
of occurrence.
• Complementary Events:
Let E denote occurrence of event. The complement of E
denotes the non occurrence of event E. Complement of E is
denoted by ‘Ē’.
• Independent Events:
Two or more events are said to be ‘independent’, in a
series of a trials if the outcome of one event is does not affect the
outcome of the other event or vise versa.
8. In other words, several events are said to be
‘dependents’ if the occurrence of an event is affected by the
occurrence of any number of remaining events, in a series of
trials.
Measurement of Probability:
There are three approaches to construct a
measure of probability of occurrence of an event.
They are:
Classical Approach,
Frequency Approach and
Axiomatic Approach.
9. Classical or Mathematical
Approach:
In this approach we assume that an experiment or
trial results in any one of many possible outcomes, each
outcome being Equi-probable or equally-likely.
Definition: If a trial results in ‘n’ exhaustive, mutually
exclusive, equally likely and independent outcomes, and if
‘m’ of them are favourable for the happening of the event
E, then the probability ‘P’ of occurrence of the event ‘E’ is
given by-
P(E) =
Number of outcomes favourable to event E
Exhaustive number of outcomes
=
m
n
10. Empirical or Statistical
Approach:
This approach is also called the ‘frequency’ approach
to probability. Here the probability is obtained by actually
performing the experiment large number of times. As the
number of trials n increases, we get more accurate result.
Definition: Consider a random experiment which is
repeated large number of times under essentially
homogeneous and identical conditions. If ‘n’ denotes the
number of trials and ‘m’ denotes the number of times an
event A has occurred, then, probability of event A is the
limiting value of the relative frequency m .
n
11. Axiomatic Approach:
This approach was proposed by Russian
Mathematician A.N.Kolmogorov in1933.
‘Axioms’ are statements which are reasonably true and
are accepted as such, without seeking any proof.
Definition: Let S be the sample space associated with a
random experiment. Let A be any event in S. then P(A) is
the probability of occurrence of A if the following axioms
are satisfied.
1.
2.
3.
P(A)>0, where A is any event.
P(S)=1.
P(AUB) = P(A) + P(B), when event A and B are
mutually exclusive.
12. – HRIS system is able to provide us various
benefits like speedy retrieval and
processing of data, its easy classification.
– It helps in better analysis and more
effective decisions making .
– Provides us with accurate information,
quality reports and overall better work
culture.
– Eliminates personal biasness, brings
13. What are the problems faced
by HR people while using the
system? the system is efficient, but
Although
sometimes they face the problems
like system slowdown or higher
downtimes and if there is some
particular limitation in module than
work suffers, some HR people are
not comfortable in using system
efficiently so time is to be given in
14. What are the uses of HRIS in different
functions of HR?
HRIS system is helping out in all the functions
and activities related to HR like payroll processing,
training and development , job evaluation process
and appraisals, recruitments etc. by providing
accurate and timely information and helping in better
analysis of information.
16. HRIS - Implementation
Complete Business Solutions (CBS)
Build Your Own Integrated System
Approach (BYOSIS)
Multiple Systems and Data Hub Approach
(MS&DH)
16
17. HRIS – Example
Oracle/PeopleSoft HRMS (ver.
12)
Automates the entire recruit-to-retire process.
A single integrated application includes the
following HR activities:
Recruitment
Performance management
Learning
Compensation and benefits
Payroll
Workforce scheduling
Time management and real time
analytics.
18. HRIS - Benefits
Higher Speed of retrieval and processing of data.
Reduction in duplication of efforts leading to reduced cost.
Ease in classifying and reclassifying data.
Better analysis leading to more effective decision making.
Higher accuracy of information/report generated.
Fast response to answer queries.
Improved quality of reports.
Better work culture.
Establishing of streamlined and systematic procedure.
More transparency in the system.
Employee – Self Management
19. HRIS - Disadvantages
It can be expensive in terms of finance and
manpower.
It can be threatening and inconvenient.
Thorough understanding of what constitutes
quality information for the user.
Computer cannot substitute human beings.
20. Conclusion
“We are becoming
the servants in
thought, as in
action, of the
machines.
Evidently, we
actually have
created them to
serve us”.
21. References
Management Information Systems: New
Approaches to Organization and Technology –
Upper Saddle River
Integrated HR Systems – Linda Stroh
Web References:
www.google.com
www.wekipedia.com
