Exponential Distribution (Chapter 14) M.I.G. McEachern High School.

Slides:



Advertisements
Similar presentations
Exponential and Poisson Chapter 5 Material. 2 Poisson Distribution [Discrete] Poisson distribution describes many random processes quite well and is mathematically.
Advertisements

Many useful applications, especially in queueing systems, inventory management, and reliability analysis. A connection between discrete time Markov chains.
Exponential Distribution. = mean interval between consequent events = rate = mean number of counts in the unit interval > 0 X = distance between events.
JMB Chapter 6 Part 1 v2 EGR 252 Spring 2009 Slide 1 Continuous Probability Distributions Many continuous probability distributions, including: Uniform.
1 Discrete Probability Distributions. 2 Random Variable Random experiment is an experiment with random outcome. Random variable is a variable related.
Continuous Random Variables. L. Wang, Department of Statistics University of South Carolina; Slide 2 Continuous Random Variable A continuous random variable.
Reliability 1. Probability a product will perform as promoted for a given time period under given conditions Functional Failure: does not operate as designed.
BCOR 1020 Business Statistics Lecture 14 – March 4, 2008.
QBM117 Business Statistics
More Discrete Probability Distributions
Introduction Before… Next…
7/3/2015© 2007 Raymond P. Jefferis III1 Queuing Systems.
Reliability Chapter 4S.
Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 2000 LIND MASON MARCHAL 1-1 Chapter Five Discrete Probability Distributions GOALS When you have completed.
This is a discrete distribution. Poisson is French for fish… It was named due to one of its uses. For example, if a fish tank had 260L of water and 13.
© 2010 Pearson Education Inc.Goldstein/Schneider/Lay/Asmar, CALCULUS AND ITS APPLICATIONS, 12e – Slide 1 of 15 Chapter 12 Probability and Calculus.
Applications of Poisson Process
McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 4 and 5 Probability and Discrete Random Variables.
McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. Discrete Random Variables Chapter 4.
6- 1 Chapter Six McGraw-Hill/Irwin © 2005 The McGraw-Hill Companies, Inc., All Rights Reserved.
The Poisson Process. A stochastic process { N ( t ), t ≥ 0} is said to be a counting process if N ( t ) represents the total number of “events” that occur.
CHAPTER 5 Binomial and Poisson Probability Distributions.
Probabilistic and Statistical Techniques 1 Lecture 19 Eng. Ismail Zakaria El Daour 2010.
M16 Poisson Distribution 1  Department of ISM, University of Alabama, Lesson Objectives  Learn when to use the Poisson distribution.  Learn.
Random Variables and Probability Models
Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Chapter 5 Discrete Random Variables.
McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 5 Discrete Random Variables.
Free Powerpoint Templates ROHANA BINTI ABDUL HAMID INSTITUT E FOR ENGINEERING MATHEMATICS (IMK) UNIVERSITI MALAYSIA PERLIS ROHANA BINTI ABDUL HAMID INSTITUT.
1 Topic 3 - Discrete distributions Basics of discrete distributions Mean and variance of a discrete distribution Binomial distribution Poisson distribution.
Definition A random variable is a variable whose value is determined by the outcome of a random experiment/chance situation.
Distributions.ppt - © Aki Taanila1 Discrete Probability Distributions.
Topic 3 - Discrete distributions Basics of discrete distributions - pages Mean and variance of a discrete distribution - pages ,
Elementary Statistics Discrete Probability Distributions.
4.3 More Discrete Probability Distributions NOTES Coach Bridges.
4.3 Discrete Probability Distributions Binomial Distribution Success or Failure Probability of EXACTLY x successes in n trials P(x) = nCx(p)˄x(q)˄(n-x)
1 Ardavan Asef-Vaziri Sep-09Operations Management: Waiting Lines3 Example: The arrival rate to a GAP store is 6 customers per hour and has Poisson distribution.
THE POISSON DISTRIBUTION
TRAFFIC MODELS. MPEG2 (sport) Voice Data MPEG2 (news)
 Recall your experience when you take an elevator.  Think about usually how long it takes for the elevator to arrive.  Most likely, the experience.
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Chapter 5 Discrete Random Variables.
Chap 5-1 Chapter 5 Discrete Random Variables and Probability Distributions Statistics for Business and Economics 6 th Edition.
12.1 Discrete Probability Distributions (Poisson Distribution)
Lesson Poisson Probability Distribution. Objectives Understand when a probability experiment follows a Poisson process Compute probabilities of.
President UniversityErwin SitompulPBST 9/1 Lecture 9 Probability and Statistics Dr.-Ing. Erwin Sitompul President University
Chapter 6 – Continuous Probability Distribution Introduction A probability distribution is obtained when probability values are assigned to all possible.
Chapter Five The Binomial Probability Distribution and Related Topics
Chapter Six McGraw-Hill/Irwin
Chapter 5 Created by Bethany Stubbe and Stephan Kogitz.
Probability Distributions: a review
Continuous Probability Distributions Part 2
Models of Traffic Flow 1.
Continuous Random Variables
Probability Distributions
Discrete Random Variables
Probability Distributions
Solutions Queueing Theory 1
Problem 6.15: A manufacturer wishes to maintain a process average of 0.5% nonconforming product or less less. 1,500 units are produced per day, and 2 days’
Probability distributions
LESSON 11: THE POISSON DISTRIBUTION
STATISTICAL MODELS.
Continuous Probability Distributions Part 2
Chapter 4 Discrete Probability Distributions.
Continuous Probability Distributions Part 2
Discrete Probability Distributions
Continuous Probability Distributions Part 2
Reliability.
Continuous Probability Distributions Part 2
Elementary Statistics
Continuous Probability Distributions Part 2
Uniform Probability Distribution
Presentation transcript:

Exponential Distribution (Chapter 14) M.I.G. McEachern High School

Exponential Distribution Exponential distributions are directly related to Poisson distributions If we have a discrete (a number we can count) number of events within a specific time, then there is a continuous (a number measured) time interval between the events. Exponential distributions calculate the probability of a certain time interval between events.

Exponential Distributions

Exponential Distributiond

Exponential Distribution Example 1: An electric motor's constant failure rate is failures/hr. Calculate the probability of failure for a 150 hr mission.

Example 1: If the electric motor's constant failure rate is failures/hr. Calculate probability of complete success for a 150 hr mission. Exponential Distribution

Example 1: An electric motor's constant failure rate is failures/hr. What is the Mean life expectancy E(x) = ? Exponential Distribution

Exponential Distribution Practice 1.If two customers arrive every 30 seconds on average, what is the probability of waiting less than or equal to 30 seconds for the next customer. What is the mean time between customers? 2.Accidents occur with a Poisson distribution at an average of 4 per week. Calculate the probability that at least two days will elapse between accidents? What is the mean time between accidents?

T.O.D. A CD player has an average record of successfully operating and providing listening enjoyment for more than 5,000 hours on the average before requiring repairs. A customer is planning on buying a CD player for installation in a boat that will be taking an extended cruise that will demand 4,000 hours of play before being able to obtain repairs or routine maintenance. How reliable will the CD player be for the customer? (Assume an exponential distribution)