03C_09_noHyper (1)

# 03C_09_noHyper (1) - ISOM 111 Tutorial Set 3 Random...

This preview shows pages 1–3. Sign up to view the full content.

Random Variable A random variable x is de ned as the numerical outcome of an experiment, which corresponding to the various outcomes of this experiment, are chance or random events. Random variable need not be a number originally. For example, the outcome when a coin is tossed can be “head” or “tail”. However, we often want to represent outcomes as numbers. Arandomvar iab le x can be discrete random variable: a countable number of values continuous random variable: the in nitely large number of values corresponding to the points on a line interval Probability Distribution The probability distribution for a discrete random variable can be represented by a formula, a table or a graph that provides the probability P ( x ) associated with each value of the random variable x . Example In table format, Outcome Tail Head x 0 1 P ( x ) 0 . 5 0 . 5 Note P all x P ( x )=1 1

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Expected Value μ The expected value of a discrete random variable x is a weighted
This is the end of the preview. Sign up to access the rest of the document.

## This note was uploaded on 01/01/2010 for the course ISOM ISOM111 taught by Professor Anthonychan during the Spring '09 term at HKUST.

### Page1 / 4

03C_09_noHyper (1) - ISOM 111 Tutorial Set 3 Random...

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online