Chapter 3 Probability Distribution.  A probability function is a function which assigns probabilities to the values of a random variable.  Individual.

Slides:



Advertisements
Similar presentations
DISCRETE RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS
Advertisements

Introduction to Probability and Statistics Chapter 5 Discrete Distributions.
Note 6 of 5E Statistics with Economics and Business Applications Chapter 4 Useful Discrete Probability Distributions Binomial, Poisson and Hypergeometric.
HAWKES LEARNING SYSTEMS math courseware specialists Copyright © 2010 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Chapter 7 Probability.
1. (f) Use continuity corrections for discrete random variable LEARNING OUTCOMES At the end of the lesson, students will be able to (g) Use the normal.
Chapter 3 Probability Distribution. Chapter 3, Part A Probability Distributions n Random Variables n Discrete Probability Distributions n Binomial Probability.
1 1 Slide © 2013 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
Discrete Probability Distributions
Chapter 5 Discrete Random Variables and Probability Distributions
Statistics Alan D. Smith.
Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 2000 LIND MASON MARCHAL 1-1 Chapter Five Discrete Probability Distributions GOALS When you have completed.
McGraw-Hill Ryerson Copyright © 2011 McGraw-Hill Ryerson Limited. Adapted by Peter Au, George Brown College.
Chapter 5 Several Discrete Distributions General Objectives: Discrete random variables are used in many practical applications. These random variables.
Chapter 5 Discrete Probability Distribution I. Basic Definitions II. Summary Measures for Discrete Random Variable Expected Value (Mean) Variance and Standard.
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.
1 1 Slide Discrete Probability Distributions (Random Variables and Discrete Probability Distributions) Chapter 5 BA 201.
PROBABILITY DISTRIBUTIONS
©The McGraw-Hill Companies, Inc. 2008McGraw-Hill/Irwin Probability Distributions Chapter 6.
©The McGraw-Hill Companies, Inc. 2008McGraw-Hill/Irwin Probability Distributions Chapter 6.
HAWKES LEARNING SYSTEMS math courseware specialists Copyright © 2010 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Chapter 8 Continuous.
Introduction Discrete random variables take on only a finite or countable number of values. Three discrete probability distributions serve as models for.
Chapter 5 Discrete Random Variables Statistics for Business 1.
 A probability function is a function which assigns probabilities to the values of a random variable.  Individual probability values may be denoted by.
 A probability function is a function which assigns probabilities to the values of a random variable.  Individual probability values may be denoted by.
Copyright ©2011 Nelson Education Limited The Binomial Experiment n identical trials. 1.The experiment consists of n identical trials. one of two outcomes.
Introduction to Probability and Statistics Thirteenth Edition Chapter 5 Several Useful Discrete Distributions.
MATB344 Applied Statistics Chapter 5 Several Useful Discrete Distributions.
Free Powerpoint Templates ROHANA BINTI ABDUL HAMID INSTITUT E FOR ENGINEERING MATHEMATICS (IMK) UNIVERSITI MALAYSIA PERLIS ROHANA BINTI ABDUL HAMID INSTITUT.
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}.
 A probability function is a function which assigns probabilities to the values of a random variable.  Individual probability values may be denoted by.
Binomial Experiment A binomial experiment (also known as a Bernoulli trial) is a statistical experiment that has the following properties:
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.
Introduction to Probability and Statistics Thirteenth Edition Chapter 5 Several Useful Discrete Distributions.
King Saud University Women Students
Free Powerpoint Templates ROHANA BINTI ABDUL HAMID INSTITUT E FOR ENGINEERING MATHEMATICS (IMK) UNIVERSITI MALAYSIA PERLIS ROHANA BINTI ABDUL HAMID INSTITUT.
1 Since everything is a reflection of our minds, everything can be changed by our minds.
 A probability function is a function which assigns probabilities to the values of a random variable.  Individual probability values may be denoted.
Math b (Discrete) Random Variables, Binomial Distribution.
Definition A random variable is a variable whose value is determined by the outcome of a random experiment/chance situation.
Your 3rd quiz will cover sections a){HHH,HTT,THT,TTH,THH,HTH,HHT,TTT} {0,1,2,3} b) {1/8,3/8,3/8,1/8} d) P(x=2 or x=3)= P(x=2)+P(x=3)=3/8+1/8=1/2.
Business Statistics (BQT 173) ІМ ќ INSTITUT MATEMATIK K E J U R U T E R A A N U N I M A P Discrete Probability Distribution: Binomial Distribution.
 A probability function is a function which assigns probabilities to the values of a random variable.  Individual probability values may be denoted.
Module 5: Discrete Distributions
Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Introduction to Probability and Statistics Twelfth Edition Robert J. Beaver Barbara M.
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin 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.
 A probability function - function when probability values are assigned to all possible numerical values of a random variable (X).  Individual probability.
Random Variables Lecture Lecturer : FATEN AL-HUSSAIN.
1. 2 At the end of the lesson, students will be able to (c)Understand the Binomial distribution B(n,p) (d) find the mean and variance of Binomial distribution.
12.1 Discrete Probability Distributions (Poisson Distribution)
Copyright (C) 2002 Houghton Mifflin Company. All rights reserved. 1 Understandable Statistics Seventh Edition By Brase and Brase Prepared by: Lynn Smith.
Chapter5 Statistical and probabilistic concepts, Implementation to Insurance Subjects of the Unit 1.Counting 2.Probability concepts 3.Random Variables.
©The McGraw-Hill Companies, Inc. 2008McGraw-Hill/Irwin Probability Distributions Chapter 6.
Probability Distributions  A variable (A, B, x, y, etc.) can take any of a specified set of values.  When the value of a variable is the outcome of a.
Chapter 6 – Continuous Probability Distribution Introduction A probability distribution is obtained when probability values are assigned to all possible.
Probability Distributions
Chapter Five The Binomial Probability Distribution and Related Topics
Discrete Probability Distributions
CHAPTER 2 RANDOM VARIABLES.
Chapter 1 ІМќ Discrete Probability Distribution: Binomial Distribution
ENGR 201: Statistics for Engineers
Introduction to Probability and Statistics
Probability distributions
DISCRETE RANDOM VARIABLES AND THEIR PROBABILITY DISTRIBUTIONS
Lecture 11: Binomial and Poisson Distributions
Introduction to Probability and Statistics
Elementary Statistics
Presentation transcript:

Chapter 3 Probability Distribution

 A probability function is a function which assigns probabilities to the values of a random variable.  Individual probability values may be denoted by the symbol P(X=x), in the discrete case, which indicates that the random variable can have various specific values.  All the probabilities must be between 0 and 1; 0≤ P(X=x)≤ 1.  The sum of the probabilities of the outcomes must be 1. ∑ P(X=x)=1  It may also be denoted by the symbol f(x), in the continuous, which indicates that a mathematical function is involved. Probability Distributions

Continuous Probability Distributions Continuous Probability Distributions Binomial Poisson Probability Distributions Discrete Probability Distributions Discrete Probability Distributions Normal

Binomial Distribution An experiment in which satisfied the following characteristic is called a binomial experiment: 1. The random experiment consists of n identical trials. 2. Each trial can result in one of two outcomes, which we denote by success, S or failure, F. 3. The trials are independent. 4. The probability of success is constant from trial to trial, we denote the probability of success by p and the probability of failure is equal to (1 - p) = q. Examples: 1.No. of getting a head in tossing a coin 10 times. 2.A firm bidding for contracts will either get a contract or not Binomial distribution is written as X ~ B(n,p)

A binomial experiment consist of n identical trial with probability of success, p in each trial. The probability of x success in n trials is given by The Mean and Variance of X if X ~ B(n,p) are Mean : Variance : Std Deviation : where n is the total number of trials, p is the probability of success and q is the probability of failure.

Example

Solution

Cumulative Binomial Distribution When the sample is relatively large, tables of Binomial are often used. Since the probabilities provided in the tables are in the cumulative form the following guidelines can be used:

Cumulative Binomial Distribution In a Binomial Distribution, n =12 and p = 0.3. Find the following probabilities. a) b) c) d) e)

Exercise 1.Let X be a Binomial random variable with parameters n = 20, p = 0.4. By using cumulative binomial distribution table, find : a) b) c) 2 Let X be a Binomial random variable with n = 5 and p = 0.6 to find the probabilities below: a) b)

Example The probability that a boards purchased by a cabinet manufacturer are unusable for building cabinets is The cabinet manufacturer bought eleven boards, what is the probability that i.Four or more of the eleven boards are unusable for building cabinets? ii.At most two of the eleven boards are unusable for building cabinets? iii.None of the eleven boards are unusable for building cabinets?

Solution

Exercise 1.Suppose you will be attending 6 hockey games. If each game will go to overtime with probability 0.10, find the probability that i.At least 1 of the games will go to overtime. ii.At most 1 of the games will go to overtime. 2. Statistics indicate that alcohol is a factor in 50 percent of fatal automobile accidents. Of the 3 fatal automobile accidents, find the probability that alcohol is a factor in i.Exactly two ii.At least 1

Poisson Distribution  Poisson distribution is the probability distribution of the number of successes in a given space*. *space can be dimensions, place or time or combination of them  Examples: 1.No. of cars passing a toll booth in one hour. 2.No. defects in a square meter of fabric 3.No. of network error experienced in a day.

A random variable X has a Poisson distribution and it is referred to as a Poisson random variable if and only if its probability distribution is given by A random variable X having a Poisson distribution can also be written as

Example Consider a Poisson random variable with mean equal to three. Calculate the following probabilities : i.Write the distribution of Poisson ii.P(X=0) iii.P(X=1) iv.P(X >1)

Solution

Example The average number of traffic accidents on a certain section of highway is two per week. Assume that the number of accidents follows a Poisson distribution with mean is 2. i)Find the probability of no accidents on this section of highway during a 1-week period ii)Find the probability of three accidents on this section of highway during a 2-week period.

Solution (i)

Solution (ii)

Example

Exercise 1.The demand for car rental by AMN Travel and Tours can be modelled using Poisson distribution. It is known that on average 4 cars are being rented per day. Find the probability that is randomly choosing day, the demand of car is: a)Exactly two cars b)More than three cars 2. Overflow of flood results in the closure of a causeway. From past records, the road is closed for this reason on 8 days during a 20-year period. At a village, the villagers were concern about the closure of the causeway because the causeway provides the only access to another village nearby. a)Determine the probability that the road is closed less than 5 days in 20 years period. b)Determine the probability that the road is closed between 2 and 6 days in five years period.

Poisson Approximation of Binomial Probabilities The Poisson distribution is suitable as an approximation of Binomial probabilities when n is large and p is small. Approximation can be made when, and either or Example 3.6: Given that, find : a) b)

Solution

Exercise 1.Given that Find (ans: 0.36, 0.16, 1.0, 0.64, 0.8, 0.48). 2.In Kuala Lumpur, 30% of workers take public transportation. In a sample of 10 workers, i) what is the probability that exactly three workers take public transportation daily? (ans: ) ii) what is the probability that at least three workers take public transportation daily? (ans: )

3. Let Using Poisson distribution table, find i) (ans: , ) ii) (ans: , ) iii) (ans: ) 4. Last month ABC company sold 1000 new watches. Past experience indicates that the probability that a new watch will need repair during its warranty period is Compute the probability that: i) At least 5 watches will need to warranty work. (ans: ) ii) At most 3 watches will need warranty work. (ans: ) iii) Less than 7 watches will need warranty work. (ans: )