Lecture-5-Summer2010-1

# Lecture-5-Summer2010-1 - LECTURE 5 Discrete Probability...

This preview shows pages 1–2. Sign up to view the full content.

LECTURE 5 Discrete Probability Distributions This lecture covers material on Random Variables, discrete probability distributions, and computing the expected value, variance and standard deviation for discrete probability distributions. Read: Chapter 5, Sections 5.1, 5.2, and 5.3. Experiment: Any process that generates well-defined outcomes. For example the process of selling shares of a particular company is an experiment if it has clearly defined outcomes - sell at a gain, sell at a loss, or sell with no change in the share price. Random Variable: A random variable is a rule or function that assigns one (and only) numerical value to the outcome of an experiment. For example, x can be a random variable whose value indicates the number of shares sold at a loss. Note: A random variable can be either discrete or continuous. Discrete Random Variable: Random variables that can assume a finite number or an infinite sequence of numerical values such as 0, 1, 2. ... . If a brokerage house has four traders, and y is a discrete random variable whose value indicates the number of traders who sold shares of ABC company at a loss in one day, then y can assume only one of the values: 0, 1, 2, 3, 4. (In this case

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

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

## This note was uploaded on 09/21/2011 for the course OM 210 taught by Professor Singer during the Summer '08 term at George Mason.

### Page1 / 5

Lecture-5-Summer2010-1 - LECTURE 5 Discrete Probability...

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online