4.3 More Discrete Probability Distributions NOTES Coach Bridges.

Slides:

Advertisements

Similar presentations

Chapter 8 The shape of data: probability distributions

Advertisements

Discrete Uniform Distribution

Geometric Distribution & Poisson Distribution Week # 8.

Probability and Statistics for Engineers (ENGC 6310) Review.

The Binomial Probability Distribution and Related Topics

QBM117 Business Statistics

A random variable that has the following pmf is said to be a binomial random variable with parameters n, p The Binomial random variable.

More Discrete Probability Distributions

Class notes for ISE 201 San Jose State University

Discrete Probability Distributions

Discrete Probability Distributions

The Poisson Probability Distribution The Poisson probability distribution provides a good model for the probability distribution of the number of “rare.

T HE G EOMETRIC AND P OISSON D ISTRIBUTIONS. G EOMETRIC D ISTRIBUTION – A GEOMETRIC DISTRIBUTION SHOWS THE NUMBER OF TRIALS NEEDED UNTIL A SUCCESS IS.

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.

4.3 More Discrete Probability Distributions Statistics Mrs. Spitz Fall 2008.

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.

The Negative Binomial Distribution An experiment is called a negative binomial experiment if it satisfies the following conditions: 1.The experiment of.

381 Discrete Probability Distributions (The Poisson and Exponential Distributions) QSCI 381 – Lecture 15 (Larson and Farber, Sect 4.3)

Geometric Distribution

Random Variables. A random variable X is a real valued function defined on the sample space, X : S  R. The set { s  S : X ( s )  [ a, b ] is an event}.

Discrete Probability Distributions. Random Variable Random variable is a variable whose value is subject to variations due to chance. A random variable.

Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Chapter 5 Discrete Random Variables.

Copyright © 2009 Pearson Education, Inc. Chapter 17 Probability Models.

McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 5 Discrete Random Variables.

Probability Definitions Dr. Dan Gilbert Associate Professor Tennessee Wesleyan College.

4.1 Probability Distributions NOTES Coach Bridges.

Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Section 5-5 Poisson Probability Distributions.

Elementary Statistics Discrete Probability Distributions.

Random Variables Example:

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)

Chapter 6: Continuous Probability Distributions A visual comparison.

Special Discrete Distributions: Geometric Distributions.

Discrete Probability Distributions Chapter 4. § 4.3 More Discrete Probability Distributions.

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.

Discrete Probability Distributions Chapter 4. § 4.3 More Discrete Probability Distributions.

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.

AP Statistics Chapter 8 Section 2. If you want to know the number of successes in a fixed number of trials, then we have a binomial setting. If you want.

SWBAT: -Calculate probabilities using the geometric distribution -Calculate probabilities using the Poisson distribution Agenda: -Review homework -Notes:

Discrete Probability Distributions

Chapter Five The Binomial Probability Distribution and Related Topics

Negative Binomial Experiment

Math 4030 – 4a More Discrete Distributions

The Poisson Probability Distribution

Poisson Distribution.

CH 23: Discrete random variables

Discrete Probability Distributions

Business Statistics Topic 4

Discrete Probability Distributions

Discrete Random Variables

Elementary Statistics

Discrete Probability Distributions

Multinomial Distribution

Probability & Statistics Probability Theory Mathematical Probability Models Event Relationships Distributions of Random Variables Continuous Random.

Some Discrete Probability Distributions

III. More Discrete Probability Distributions

Chapter 4 Discrete Probability Distributions.

Discrete Probability Distributions

Introduction to Probability Distributions

Elementary Statistics

Introduction to Probability Distributions

The Geometric Distributions

Uniform Probability Distribution

The Geometric Distribution

District Random Variables and Probability Distribution

Presentation transcript:

4.3 More Discrete Probability Distributions NOTES Coach Bridges

Geometric Distribution A discrete probability distribution of a random variable x that satisfies the following conditions: 1.A trial is repeated until a success occurs 2.The repeated trials are independent of each other 3.The probability of success p is constant

Poisson Distribution A discrete probability distribution of a random variable x that satisfies the following conditions: 1.The experiment consists of counting the number of times, x, an event occurs in a given interval. The interval can be an interval of time, area, or volume

Poisson Distribution Cont. 2.The probability of the event occurring is the same for each interval 3.The number of occurrences in one interval is independent of the number of occurrences in other intervals.