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# HUMAN RESOURCE INFORMATION SYSTEM

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### HUMAN RESOURCE INFORMATION SYSTEM

1. 1. UNIVERSITY OF MYSORE TYPES OF PROBABILITY PRESENTED TO: D. JAYAPRASAD. FACULTY OF COMMERCE DEPT. UNIVERSITY OF MYSORE
2. 2. Contents:  Introduction  Concept of various terms  Approaches or types of probability  Theorems of probability  Discursion of problems  Conclusion
3. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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.
15. 15. HRIS - Development CONCIEVE & PLAN ANALYSE DESIGN TEST IMPLIMENT MAINTAIN
16. 16. HRIS - Implementation  Complete Business Solutions (CBS)  Build Your Own Integrated System Approach (BYOSIS)  Multiple Systems and Data Hub Approach (MS&DH) 16
17. 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. 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. 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. 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. 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
22. 22. PRESENTED BY • • • • BHARGAVI.B. III B. Com. M.M.W.A.C.C. Mysore University.
23. 23. THANK YOU