discrete random variable and its probability distribution

A discrete random variable assumes each of its values with a certain probability, Let X be a discrete random variable which takes values x 1 , x 2 , x 3 ,x n where p i = P{X = x i } Then X : x 1 x 2 x 3 .. x n P(X): p 1 p..

Let S be a sample space associated with a given random experiment. A real valued function X which assigns to each w i S, a unique real number, X( w i ) = x i is called a random variable . Two types of random variables are 1. Co..

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..

In this chapter we have studied the method of evaluating probabilities of events relating to independent events and conditional events. We have also studied about random variables and their probability distributions, namely binomial distributio..

>If A, B and C are mutually exclusive then Total Probability: P(A) = P(E 1 ) P(A|E 1 ) + P(E 2 ) P(A|E 2 )+ +P(E n ) P(A|E n ) Random variable: A real valued function 'X' defined on the sample space is called a random variable. Discrete ra..

Random variables which can assume only a finite number, or a countable infinity number of values, are called discrete random variables..

Let X be a discrete random variable which can assume values x 1 , x 2 , x 3 ,x n with probabilities p 1 , p 2 , p 3 .. p n respectively then (a) Mean of X or expectation of X denoted by E(X) or m is given by (b) Variance of X denoted by s 2 is given ..

Random Variable and Probability Distribution - If is often very important to allocate a numerical value to an outcome of a random experiment. For example consider an experiment of tossing a coin twice and note the number of heads (x) obtained. Outco..

If is often very important to allocate a numerical value to an outcome of a random experiment. For example consider an experiment of tossing a coin twice and note the number of heads (x) obtained. Outcome : HH HT TH TT No. of heads (x) : 2 1 1 0 x is called a random variabl..

where l is a finite number and is equal to np. The sum of the probabilities P(X = r) or simply P(r) for r = 0, 1, 2, is 1. This can be seen by putting r = 0, 1, 2, in (4) and adding all the probabilities. Also, each of the probabilities is a non-negative fraction. This..