Probability Expected Value

As its humble beginnings are at the gambling tables in the seventeenth century, the probability theory has been introduced and employed to treat & solve many weighty problems. It is the foundation of classical decision & the process of estimation and testing. The Probability models can be very useful for making the predictions. The term 'probability' or 'chance' is very widely used in day-to-day conversation and normally people have a vague idea about its meaning. For e.g., we come across statements such as ''it is likely that Mr. z may not come for taking his class today '', ''probably it may rain today'',  "the chances of teams a & b winning in  a certain match are same'', ''probably you are right'', " it is possible that I may not be able to join you at the tea party'' all these terms-possible, probably , likely, etc.. convey the similar sense i.e. the event is not certain it take place or, in another words these are uncertainty about the happening of the event in question. In layman's terminology the term ''probability'' thus connotes that there is an uncertainty about the happening of the event. Though, in mathematics and statistics we try to present situations under which we can make sensible numerical statements about uncertainty and apply certain methods of calculating numerical values of probabilities and expectations. In the statistical sense the word probability is thus established by the definition and is not connected with beliefs or any form of wishful thinking.

Some of its main important topics are:

1. Bayes' theorem

2. Counting rules

3. Elementary set theory

4. Mathematical expectation

5. Probability and expected value

6. Probability defined

7. Calculation of probability

8. Probability events

9. Conditional probability

10. Random variable

11. Probability sampling methods