Binomial Distributions

Slides:



Advertisements
Similar presentations
Presentation on Probability Distribution * Binomial * Chi-square
Advertisements

Note 6 of 5E Statistics with Economics and Business Applications Chapter 4 Useful Discrete Probability Distributions Binomial, Poisson and Hypergeometric.
Chapter 6 Some Special Discrete Distributions
ฟังก์ชั่นการแจกแจงความน่าจะเป็น แบบไม่ต่อเนื่อง Discrete Probability Distributions.
Dr. Engr. Sami ur Rahman Data Analysis Lecture 4: Binomial Distribution.
Larson/Farber Ch. 4 Elementary Statistics Larson Farber 4 x = number of on time arrivals x = number of points scored in a game x = number of employees.
Probability Distributions
Discrete Probability Distributions
Statistics Alan D. Smith.
Discrete Probability Distributions
Discrete Probability Distributions
Discrete Probability Distributions
Probability Models Binomial, Geometric, and Poisson Probability Models.
Discrete Probability Distributions Binomial Distribution Poisson Distribution Hypergeometric Distribution.
The Binomial Distribution
Chapter Discrete Probability Distributions 1 of 63 4 © 2012 Pearson Education, Inc. All rights reserved.
Binomial Distributions
McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 4 and 5 Probability and Discrete Random Variables.
Discrete Probability Distributions
Binomial Distributions
Chapter 4 Discrete Probability Distributions 1. Chapter Outline 4.1 Probability Distributions 4.2 Binomial Distributions 4.3 More Discrete Probability.
Probability Distribution
Discrete Probability Distributions
Copyright © 2015 The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1 C H A P T E R F I V E Discrete Probability Distributions.
4.1 Probability Distributions
PROBABILITY! Let’s learn about probability and chance!
Section 4.2 Binomial Distributions Larson/Farber 4th ed 1.
 5-1 Introduction  5-2 Probability Distributions  5-3 Mean, Variance, and Expectation  5-4 The Binomial Distribution.
Binomial Distributions 1 Section 4.2. Section 4.2 Objectives 2 Determine if a probability experiment is a binomial experiment Find binomial probabilities.
Introductory Statistics Lesson 4.2 A Objective: SSBAT determine if a probability experiment is a binomial experiment. SSBAT how to find binomial probabilities.
Chapter 5 Discrete Probability Distributions. Introduction Many decisions in real-life situations are made by assigning probabilities to all possible.
Copyright (C) 2002 Houghton Mifflin Company. All rights reserved. 1 Understandable Statistics Seventh Edition By Brase and Brase Prepared by: Mistah Flynn.
Probability Distribution
4.2 Binomial Distributions
Probability & Statistics
Chapter 5 Discrete Probability Distributions 1 Copyright © 2012 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
Chapter 5 Discrete Random Variables Probability Distributions
Objective: Objective: To solve multistep probability tasks with the concept of binomial distribution CHS Statistics.
DISCRETE PROBABILITY MODELS
Discrete Probability Distributions Chapter 4. § 4.2 Binomial Distributions.
The Binomial Probability Distribution. ● A binomial experiment has the following structure  The first test is performed … the result is either a success.
Chapter 4: Discrete Probability Distributions
Binomial Distributions. Objectives How to determine if a probability experiment is a binomial experiment How to find binomial probabilities using the.
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Chapter 5 Discrete Random Variables.
5-3 Binomial Distributions © 2012 Pearson Education, Inc. All rights reserved. 1 of 63.
Business Statistics, A First Course (4e) © 2006 Prentice-Hall, Inc. Chap 5-1 Chapter 5 Some Important Discrete Probability Distributions Business Statistics,
Section 4.2 Binomial Distributions © 2012 Pearson Education, Inc. All rights reserved. 1 of 29.
Probability Distributions. Constructing a Probability Distribution Definition: Consists of the values a random variable can assume and the corresponding.
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.
Copyright (C) 2002 Houghton Mifflin Company. All rights reserved. 1 Understandable Statistics Seventh Edition By Brase and Brase Prepared by: Lynn Smith.
Chapter 5 Discrete Probability Distributions 1. Chapter 5 Overview 2 Introduction  5-1 Probability Distributions  5-2 Mean, Variance, Standard Deviation,
Chapter Discrete Probability Distributions 1 of 63 4  2012 Pearson Education, Inc. All rights reserved.
Section 4.2 Binomial Distributions © 2012 Pearson Education, Inc. All rights reserved. 1 of 63.
CHAPTER 5 Discrete Probability Distributions. Chapter 5 Overview  Introduction  5-1 Probability Distributions  5-2 Mean, Variance, Standard Deviation,
Binomial Distributions
Chapter 4 Discrete Probability Distributions.
Binomial Distributions
Lesson 97 - Binomial Distributions
By Hatim Jaber MD MPH JBCM PhD
Chapter Five The Binomial Probability Distribution and Related Topics
Binomial Distributions
Discrete Probability Distributions
Chapter 4 Discrete Probability Distributions.
Discrete Probability Distributions
ENGR 201: Statistics for Engineers
By Hatim Jaber MD MPH JBCM PhD
Elementary Statistics: Picturing The World
Discrete Probability Distributions
4 Chapter Discrete Probability Distributions
Binomial Distributions
Presentation transcript:

Binomial Distributions

Binomial Experiments Probability experiments for which the results of each trial can be reduced to two outcomes: success and failure. When a basketball player attempts a free throw, he or she either makes the basket or does not. Probability experiments such as these are called binomial experiments.

Binomial Probability Distribution A Fixed Number of Observations (trials), n 15 tosses of a coin; 20 patients; 1000 people surveyed A Binary Outcome Head or tail in each toss of a coin; disease or no disease Generally called “success” and “failure” Probability of success is p, probability of failure is 1 – p Constant Probability for each observation Probability of getting a tail is the same each time we toss the coin

Notation for Binomial Experiments Symbol Description n The number of times a trial is repeated. p = P(S) The probability of success in a single trial. q = P(F) The probability of failure in a single trial (q = 1 – p) X The random variable represents a count of the number of successes in n trials: x = 0, 1, 2, 3, . . . n.

Example We pick a card from a standard deck of cards, and note whether it is a club or not, and replace the card. We repeat the experiment 5 times. n = 5 p = P(S) = ¼ q = P(F) = ¾ Possible values of the random variable are 0, 1, 2, 3, 4, and 5.

Binomial Experiments Decide whether the experiment is a binomial experiment: A binomial experiment specify the values of n (number of times a trial is repeated), p (Probability of Success), q (Probability of Failure) and list the possible values of the random variable, x.

Example A certain surgical procedure has an 85% chance of success. A doctor performs the procedure on eight patients. The random variable represents the number of successful surgeries.

Binomial Experiment p = 0.85 q = 1 – 0.85 = 0.15 Each surgery represents one trial. There are eight surgeries, and each surgery is independent of the others. Only two possible outcomes for each surgery - either the surgery is a success or it is a failure. n = 8 p = 0.85 q = 1 – 0.85 = 0.15 x = 0, 1, 2, 3, 4, 5, 6, 7, 8

Example A jar contains five red marbles, nine blue marbles and six green marbles. Select randomly three marbles from the jar, without replacement. The random variable represents the number of red marbles.

Not A Binomial Experiment Each marble selection represents one trial and selecting a red marble is a success. When selecting the first marble, the probability of success is 5/20. However because the marble is not replaced, the probability of further trials is no longer 5/20. Trials are not independent.

Binomial Probability Formula

Example: Binomial Probabilities A six sided die is rolled 3 times. Find the probability of rolling exactly one 6.

Roll 1 Roll 2 Roll 3 Frequency # of 6’s Probability (1)(1)(1) = 1 3 1/216 (1)(1)(5) = 5 2 5/216 (1)(5)(1) = 5 (1)(5)(5) = 25 1 25/216 (5)(1)(1) = 5 (5)(1)(5) = 25 (5)(5)(1) = 25 (5)(5)(5) = 125 125/216

Example: Binomial Probabilities Three outcomes that have exactly one six Each has a probability of 25/216 Probability of rolling exactly one six is 3(25/216) ≈ 0.347. Binomial Probability Formula (n = 3, p = 1/6, q = 5/6 and x = 1). Probability of rolling exactly one 6 is: Binomial Probability Formula

Example: Binomial Probabilities

Binomial Probability Distribution By listing the possible values of x with the corresponding probability of each, we can construct a Binomial Probability Distribution.

Constructing a Binomial Distribution In a survey, a company asked their workers and retirees to name their expected sources of retirement income. Seven workers who participated in the survey were asked whether they expect to rely on Pension for retirement income. 36% of the workers responded that they rely on Pension only. Create a binomial probability distribution.

Constructing a Binomial Distribution x P(x) 0.044 1 0.173 2 0.292 3 0.274 4 0.154 5 0.052 6 0.010 7 0.001 P(x) = 1 Notice all the probabilities are between 0 and 1 and that the sum of the probabilities is 1.

Finding a Binomial Probability Using a Table Fifty percent of working adults spend less than 20 minutes commuting to their jobs. If you randomly select six working adults, what is the probability that exactly three of them spend less than 20 minutes commuting to work? Using the distribution for n = 6 and p = 0.5, we can find the probability that x = 3

Population Parameters of a Binomial Distribution Mean:  = np Variance: 2 = npq Standard Deviation:  = √npq

Example In Murree, 57% of the days in a year are cloudy. Find the mean, variance, and standard deviation for the number of cloudy days during the month of June. Mean:  = np = 30(0.57) = 17.1 Variance: 2 = npq = 30(0.57)(0.43) = 7.353 Standard Deviation:  = √npq = √7.353 ≈2.71

Problem 1 Four fair coins are tossed simultaneously. Find the probability function of the random variable X = Number of Heads and compute the probabilities of obtaining no heads, precisely 1 head, at least 1 head, not more than 3 heads.

Problem 2 If the Probability of hitting a target in a single shot is 10% and 10 shots are fired independently. What is the probability that the target will be hit at least once?

Problem 3 If the Probability of hitting a target in a single shot is 5% and 20 shots are fired independently. What is the probability that the target will be hit at least once?

Problem 5 Let X be the number of cars per minute passing a certain point of some road between 8 A.M and 10 A.M on a Sunday. Assume that X has a Poisson distribution with mean 5. Find the probability of observing 3 or fewer cars during any given minute.

Problem 7 In 1910, E. Rutherford and H. Geiger showed experimentally that number of alpha particles emitted per second in a radioactive process is random variable X having a Poisson distribution. If X has mean 0.5. What is the probability of observing 2 or more particles during any given second?

Problem 9 Suppose that in the production of 50л resistors, non-defective items are those that have a resistance between 45л and 55л and the probability of being defective is 0.2%. The resistors are sold in a lot of 100, with the guarantee that all resistors are non-defective. What is the probability that a given lot will violate this guarantee?

Problem 11 Let P = 1% be the probability that a certain type of light bulb will fail in 24 hours test. Find the probability that a sign consisting of 100 such bulbs will burn 24 hours with no bulb failures.

Problem 13 Suppose that a test for extrasensory perception consists of naming (in any order) 3 card randomly drawn from a deck of 13 cards. Find the probability that by chance alone, the person will correctly name (a) no cards, (b) 1 Card, (c) 2 Cards, and (d) 3 cards.

Ex. 6: Finding Binomial Probabilities A survey indicates that 41% of American women consider reading as their favorite leisure time activity. You randomly select four women and ask them if reading is their favorite leisure-time activity. Find the probability that (1) exactly two of them respond yes, (2) at least two of them respond yes, and (3) fewer than two of them respond yes.

Ex. 6: Finding Binomial Probabilities #1--Using n = 4, p = 0.41, q = 0.59 and x =2, the probability that exactly two women will respond yes is: Calculator or look it up on pg. A10

Ex. 6: Finding Binomial Probabilities #2--To find the probability that at least two women will respond yes, you can find the sum of P(2), P(3), and P(4). Using n = 4, p = 0.41, q = 0.59 and x =2, the probability that at least two women will respond yes is: Calculator or look it up on pg. A10

Ex. 6: Finding Binomial Probabilities #3--To find the probability that fewer than two women will respond yes, you can find the sum of P(0) and P(1). Using n = 4, p = 0.41, q = 0.59 and x =2, the probability that at least two women will respond yes is: Calculator or look it up on pg. A10

Ex. 7: Constructing and Graphing a Binomial Distribution 65% of American households subscribe to cable TV. You randomly select six households and ask each if they subscribe to cable TV. Construct a probability distribution for the random variable, x. Then graph the distribution. Calculator or look it up on pg. A10

Ex. 7: Constructing and Graphing a Binomial Distribution 65% of American households subscribe to cable TV. You randomly select six households and ask each if they subscribe to cable TV. Construct a probability distribution for the random variable, x. Then graph the distribution. x 1 2 3 4 5 6 P(x) 0.002 0.020 0.095 0.235 0.328 0.244 0.075 Because each probability is a relative frequency, you can graph the probability using a relative frequency histogram as shown on the next slide.

Ex. 7: Constructing and Graphing a Binomial Distribution Then graph the distribution. x 1 2 3 4 5 6 P(x) 0.002 0.020 0.095 0.235 0.328 0.244 0.075 Relative Frequency NOTE: that the histogram is skewed left. The graph of a binomial distribution with p > .05 is skewed left, while the graph of a binomial distribution with p < .05 is skewed right. The graph of a binomial distribution with p = .05 is symmetric. Households

Mean, Variance and Standard Deviation Although you can use the formulas learned in 4.1 for mean, variance and standard deviation of a probability distribution, the properties of a binomial distribution enable you to use much simpler formulas. They are on the next slide.

Quiz # 3 32 CE(B) – 12 NOV 2012 Let P = 1% be the probability that a certain type of light bulb will fail in 24 hours test. Find the probability that a sign consisting of 10 such bulbs will burn 24 hours with no bulb failures. (3 Marks) Write Probability Distribution Function for Multinomial and Hypergeometric distributions. (2 Marks)