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Update README.md
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‎README.md‎

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@@ -358,11 +358,54 @@ P (E) = No. of favourable outcome / Total no. of outcomes
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P (E) + P (E’) = 1
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### Terminologies of Probability
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##### (1) Random Experiment
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##### (2) Sample Space
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##### (3) Event
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##### (4) Union of Event
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##### (5) Intersection of Event
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##### (1) Random Experiment:
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An operation which can produce some well-defined outcomes is called an experiment. Each outcome is called an event
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For example; throwing a die or tossing a coin etc.
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In an experiment where all possible outcomes are known and in advance if the exact outcome cannot be predicted, is called a random experiment.
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##### (3) Trial:
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By a trial, we mean performing a random experiment.
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##### (4) Sample Space
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The sample space for a probability experiment is the set of all possible outcomes. This is usually written with set notation (curly brackets). For example, going back to a regular 6-sided die the sample space would be:
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S={1,2,3,4,5,6}
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##### (5) Event
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Out of the total results obtained from a certain experiment, the set of those results which are in favor of a definite result is called the event and it is denoted as E.
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#### (6) Equally Likely Events:
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When there is no reason to expect the happening of one event in preference to the other, then the events are known equally likely events.
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For example; when an unbiased coin is tossed the chances of getting a head or a tail are the same.
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#### (7) Exhaustive Events:
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All the possible outcomes of the experiments are known as exhaustive events.
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For example; in throwing a die there are 6 exhaustive events in a trial.
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#### (8) Favorable Events:
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The outcomes which make necessary the happening of an event in a trial are called favorable events.
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For example; if two dice are thrown, the number of favorable events of getting a sum 5 is four,
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i.e., (1, 4), (2, 3), (3, 2) and (4, 1).
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#### (9) Mutually Exclusive Events:
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If there be no element common between two or more events, i.e., between two or more subsets of the sample space, then these events are called mutually exclusive events.
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If E1 and E2 are two mutually exclusive events, then E1 ∩ E2 = ∅
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#### (10) Complementary Event:
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An event which consists in the negation of another event is called complementary event of the er event. In case of throwing a die, ‘even face’ and ‘odd face’ are complementary to each other. "Multiple of 3" ant "Not multiple of 3" are complementary events of each other.
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##### (11) Union of Event
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##### (12) Intersection of Event
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# 8. Baye's Theorem
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