Probability Mathematics

Probability is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1, where, loosely speaking,0 indicates impossibility and 1 indicates certainty. The higher the probability of an event, the more certain that the event will occur. A simple example is the tossing of a fair (unbiased) coin. Since the coin is fair, the two outcomes ("heads" and "tails") are both equally probable; the probability of "heads" equals the probability of "tails"; and since no other outcomes are possible, the probability of either "heads" or "tails" is 1/2 (which could also be written as 0.5 or 50%).
Probability Mathematics for all level of student’s beginner to advance
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Probability Mathematics covers following topics of Probability Mathematics

PROBABILITY
INTRODUCTION
DETERMINISTIC EXPERIMENT
PROBABILISTIC OR RANDOM EXPERIMENT
CLASSICAL APPROACH TO PROBABILITY
TRIAL AND ELEMENTARY EVENTS
COMPOUND EVENTS
EXHAUSTIC NUMBER OF CASES
MUTUALLY EXCLUSIVE EVENTS
EQUALLY LIKELY EVENTS
FAVOURABLE NUMBER OF CASES
INDEPENDENT EVENTS
CLASSICAL DEFINITION OF PROBABILITY OF AN EVENT
AXIOMATIC APPROACH TO PROBABILITY
SAMPLE SPACEEVENT
ELEMENTARY EVENTS
COMPOUND EVENTS
IMPOSSIBLE AND CERTAIN EVENTS
OCCURRENCE OR HAPPENING OF AN EVENT
MUTUALLY EXCLUSIVE EVENTS
MUTUALLY EXCLUSIVE AND EXHAUSTIVE SYSTEM OF EVENTS
FAVOURABLE EVENTS
AXIOMATIC DEFINITION OF PROBABILITY
ADDITION THEOREMS ON PROBABILITY
CONDITIONAL PROBABILITY
MULTIPLICATION THEOREMS ON PROBABILITY
INDEPENDENT EVENTS
MULTIPLICATON THEOREM FOR INDEPENDENT EVENTS
ADDITION THEOREM FOR INDEPENDENT EVENTS
THE LAW OF TOTAL PROBABILITY BAYE’S RULE
RANDOM VARIABLE AND ITS PROBABILITY DISTRIBUTION
RANDOM VARIABLE BINOMIAL DISTRIBUTION
MEAN AND VARIANCE OF BINOMIAL DISTRIBUTION
MEAN OF BINOMIAL DISTRIBUTION
VARIANCE OF BINOMIAL DISTRIBUTION
RELATION BETWEEN MEAN AND VARIANCE.

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