Poisson Distribution.

Slides:



Advertisements
Similar presentations
Think about the following random variables… The number of dandelions in a square metre of open ground The number of errors in a page of a typed manuscript.
Advertisements

Acknowledgement: Thanks to Professor Pagano
Section 2.6 Consider a random variable X = the number of occurrences in a “unit” interval. Let = E(X) = expected number of occurrences in a “unit” interval.
The Poisson Probability Distribution
THE POISSON RANDOM VARIABLE. POISSON DISTRIBUTION ASSUMPTIONS Can be used to model situations where: –No two events occur simultaneously –The probabilities.
QBM117 Business Statistics
More Discrete Probability Distributions
Chapter 21 Random Variables Discrete: Bernoulli, Binomial, Geometric, Poisson Continuous: Uniform, Exponential, Gamma, Normal Expectation & Variance, Joint.
The Poisson Probability Distribution The Poisson probability distribution provides a good model for the probability distribution of the number of “rare.
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.
Poisson Distribution The Poisson Distribution is used for Discrete events (those you can count) In a continuous but finite interval of time and space The.
McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. Discrete Random Variables Chapter 4.
Poisson Distribution.
Poisson Random Variable Provides model for data that represent the number of occurrences of a specified event in a given unit of time X represents the.
Geometric Distribution
M16 Poisson Distribution 1  Department of ISM, University of Alabama, Lesson Objectives  Learn when to use the Poisson distribution.  Learn.
Section Copyright © 2014, 2012, 2010 Pearson Education, Inc. Lecture Slides Elementary Statistics Twelfth Edition and the Triola Statistics Series.
Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Chapter 5 Discrete Random Variables.
Chapter 12 Probability. Chapter 12 The probability of an occurrence is written as P(A) and is equal to.
1 Poisson Probability Models The Poisson experiment typically models situations where rare events occur over a fixed amount of time or within a specified.
Section 3.2 Notes Conditional Probability. Conditional probability is the probability of an event occurring, given that another event has already occurred.
§ 5.3 Normal Distributions: Finding Values. Probability and Normal Distributions If a random variable, x, is normally distributed, you can find the probability.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Section 5-5 Poisson Probability Distributions.
Methodology Solving problems with known distributions 1.
1 Lecture 9: The Poisson Random Variable and its PMF Devore, Ch. 3.6.
The final exam solutions. Part I, #1, Central limit theorem Let X1,X2, …, Xn be a sequence of i.i.d. random variables each having mean μ and variance.
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)
AP STATISTICS Section 7.1 Random Variables. Objective: To be able to recognize discrete and continuous random variables and calculate probabilities using.
THE POISSON DISTRIBUTION
TRAFFIC MODELS. MPEG2 (sport) Voice Data MPEG2 (news)
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Chapter 5 Discrete Random Variables.
Discrete Probability Distributions Chapter 4. § 4.3 More Discrete Probability Distributions.
Distributions GeometricPoisson Probability Distribution Review.
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)
Discrete Probability Distributions Chapter 4. § 4.3 More Discrete Probability Distributions.
Lesson Poisson Probability Distribution. Objectives Understand when a probability experiment follows a Poisson process Compute probabilities of.
1 5.6 Poisson Distribution and the Poisson Process Some experiments result in counting the numbers of particular events occur in given times or on given.
Created by Tom Wegleitner, Centreville, Virginia Section 4-5 The Poisson Distribution.
The Pure Birth Process Derivation of the Poisson Probability Distribution Assumptions events occur completely at random the probability of an event occurring.
SWBAT: -Calculate probabilities using the geometric distribution -Calculate probabilities using the Poisson distribution Agenda: -Review homework -Notes:
Chapter Five The Binomial Probability Distribution and Related Topics
The Poisson probability distribution
Applications of the Poisson Distribution
Discrete Random Variables
The Poisson Probability Distribution
Section 6.3 The Poisson Probability Distribution
Probability Distributions: a review
Continuous Probability Distributions Part 2
Discrete Random Variables
Elementary Statistics
S2 Poisson Distribution.
Multinomial Distribution
STATISTICAL MODELS.
Continuous Probability Distributions Part 2
Chapter 4 Discrete Probability Distributions.
Continuous Probability Distributions Part 2
Discrete Probability Distributions
Calculating probabilities for a normal distribution
Continuous Probability Distributions Part 2
Continuous Probability Distributions Part 2
Elementary Statistics
Theorem 5.3: The mean and the variance of the hypergeometric distribution h(x;N,n,K) are:  = 2 = Example 5.10: In Example 5.9, find the expected value.
The Geometric Distributions
Continuous Probability Distributions Part 2
Uniform Probability Distribution
Consider the following problem
District Random Variables and Probability Distribution
Presentation transcript:

Poisson Distribution

Consider the following: X= no. of flaws in a length of material X= no. of lightening strikes in a particular area within a specified time.

We use the Poisson because: Events occur independently of each other. Events occur singly. Events occur at a constant rate over a time interval. Events tend to be random or rare. Mean should be around the same as the poisson.

How do we calculate Poisson probabilities? The Poisson dist is applied using a parameter,λ, lambda. This is the mean number of occurrences of an event over a time interval. X~Po(λ)

Examples Using X~Po(λ=3), find P(X=0) P(X=4) P(X≤4) P(X>7)

Ex 1A page 7