probability formula

An experiment repeated under essentially homogeneous and similar conditions results in an outcome, which is unique or not unique but may be one of the several possible outcomes. When the result is unique then the experiment is called a deterministic experiment . Any experiment whose ..

Although the probability distribution of a continuous random variable cannot be presented in tabular form, it can have a formula in the form of a function represented by f(x) usually called the probability density functio..

Introduction - In our day to day life, we come across many uncertainty of events. We wake up in the morning and check the weather report. The statement could be 'there is 60% chance of rain today'. This statement infers that the chance of rain is more than that having a dry weather. We decide upon ..

Let A and B be any two events associated with a random experiment. The probability of occurrence of event A when the event B has already occurred is called the conditional probability of A when B is given and is denoted as P(A/B..

If a trial results in n-exhaustive, mutually exclusive and equally likely cases and m of them are favourable to the occurrence of an event A, then the probability of the happening of A, denoted by P(A), is given by: P(A) = m/n. Important terms are 1. Statistical or Empirical P..

Probability of an Event - So far, we have introduced the sample of an experiment and used it to describe events. In this section, we introduce probabilities associated to the events. If a trial results in n-exhaustive, mutually exclusive and equally likely cases and m of them ar..

Let us consider the random experiment of throwing a die. Let A be the event of getting an odd number on the die. \ S = {1, 2, 3, 4, 5, 6} and A = {1, 3, 5} Let B = {2, 3, 4, 5, 6}. If, after the die is thrown, we are given the information, that the event B has occurred, then the probability..

Probability (continued)..

1. Addition Rule of Probability: If A and B are any two events, then 2. P(A C ) = 1 - P(A). 3. P( f ) = ..

If x is a discrete random variable assuming the values x 1 , x 2 , x 3 ,….,x n with probabilities p 1 , p 2 , p 3 ,…., p n respectively then (x 1 ,p 1 ), (x 2 , p 2 ),…(x n , p n ) defines a probability distribution of X. Mathematical Expectation of X ..