Unit 4 Bernoulli and Binomial Distributions Week #6 - Practice Problems. SOLUTIONS Revised (enhanced for q4)

Similar documents
Unit 6 Bernoulli and Binomial Distributions Homework SOLUTIONS

Chapter 3 - Lecture 5 The Binomial Probability Distribution

Binomial Random Variable - The count X of successes in a binomial setting

MA : Introductory Probability

Math 14 Lecture Notes Ch. 4.3

Chapter 4 Discrete Random variables

5.4 Normal Approximation of the Binomial Distribution Lesson MDM4U Jensen

guessing Bluman, Chapter 5 2

MATH 112 Section 7.3: Understanding Chance

5.4 Normal Approximation of the Binomial Distribution

Chapter 6: Random Variables. Ch. 6-3: Binomial and Geometric Random Variables

Chapter 4 Discrete Random variables

Probability & Statistics Chapter 5: Binomial Distribution

What do you think "Binomial" involves?

Random Variables CHAPTER 6.3 BINOMIAL AND GEOMETRIC RANDOM VARIABLES

Lecture 9. Probability Distributions. Outline. Outline

Math 361. Day 8 Binomial Random Variables pages 27 and 28 Inv Do you have ESP? Inv. 1.3 Tim or Bob?

Lecture 9. Probability Distributions

CHAPTER 6 Random Variables

Section 8.4 The Binomial Distribution

Determine whether the given procedure results in a binomial distribution. If not, state the reason why.

Unit 4 The Bernoulli and Binomial Distributions

A random variable (r. v.) is a variable whose value is a numerical outcome of a random phenomenon.

List of Online Quizzes: Quiz7: Basic Probability Quiz 8: Expectation and sigma. Quiz 9: Binomial Introduction Quiz 10: Binomial Probability

Section 6.3 Binomial and Geometric Random Variables

Chapter 6: Random Variables

Central Limit Theorem 11/08/2005

Chapter 8: Binomial and Geometric Distributions

BINOMIAL EXPERIMENT SUPPLEMENT

Central Limit Theorem (cont d) 7/28/2006

The Binomial Distribution

MA 1125 Lecture 12 - Mean and Standard Deviation for the Binomial Distribution. Objectives: Mean and standard deviation for the binomial distribution.

CHAPTER 6 Random Variables

Stat 20: Intro to Probability and Statistics


VIDEO 1. A random variable is a quantity whose value depends on chance, for example, the outcome when a die is rolled.

***SECTION 8.1*** The Binomial Distributions

The normal distribution is a theoretical model derived mathematically and not empirically.

Simple Random Sample

Part 10: The Binomial Distribution

Probability Distributions. Definitions Discrete vs. Continuous Mean and Standard Deviation TI 83/84 Calculator Binomial Distribution

CHAPTER 6 Random Variables

Probability and Statistics. Copyright Cengage Learning. All rights reserved.

OCR Statistics 1. Discrete random variables. Section 2: The binomial and geometric distributions. When to use the binomial distribution

STOR 155 Introductory Statistics (Chap 5) Lecture 14: Sampling Distributions for Counts and Proportions

Lecture 7 Random Variables

Experimental Probability - probability measured by performing an experiment for a number of n trials and recording the number of outcomes

Math 14 Lecture Notes Ch The Normal Approximation to the Binomial Distribution. P (X ) = nc X p X q n X =

Example - Let X be the number of boys in a 4 child family. Find the probability distribution table:

4 Random Variables and Distributions

work to get full credit.

Chapter 6 Section 3: Binomial and Geometric Random Variables

Probability and Sample space

Probability mass function; cumulative distribution function

Binomial Distributions

chapter 13: Binomial Distribution Exercises (binomial)13.6, 13.12, 13.22, 13.43

Chapter 5: Discrete Probability Distributions

Essential Question: What is a probability distribution for a discrete random variable, and how can it be displayed?

Section Distributions of Random Variables

Binomial and Geometric Distributions

MA 1125 Lecture 18 - Normal Approximations to Binomial Distributions. Objectives: Compute probabilities for a binomial as a normal distribution.

4.1 Probability Distributions

Math 243 Section 4.3 The Binomial Distribution

Statistical Methods in Practice STAT/MATH 3379

MATH 118 Class Notes For Chapter 5 By: Maan Omran

4.2 Bernoulli Trials and Binomial Distributions

Example. Chapter 8 Probability Distributions and Statistics Section 8.1 Distributions of Random Variables

Name: Show all your work! Mathematical Concepts Joysheet 1 MAT 117, Spring 2013 D. Ivanšić

If X = the different scores you could get on the quiz, what values could X be?

Calculating probabilities

The binomial distribution

MATH CALCULUS & STATISTICS/BUSN - PRACTICE EXAM #2 - SUMMER DR. DAVID BRIDGE

Binomial Distributions

PROBABILITY DISTRIBUTIONS

The binomial distribution p314

ME3620. Theory of Engineering Experimentation. Spring Chapter III. Random Variables and Probability Distributions.

Event p351 An event is an outcome or a set of outcomes of a random phenomenon. That is, an event is a subset of the sample space.

Discrete Probability Distributions

GOALS. Discrete Probability Distributions. A Distribution. What is a Probability Distribution? Probability for Dice Toss. A Probability Distribution

Chapter Five. The Binomial Distribution and Related Topics

Discrete Probability Distributions Chapter 6 Dr. Richard Jerz

MA 1125 Lecture 14 - Expected Values. Wednesday, October 4, Objectives: Introduce expected values.

Chapter 8 Binomial and Geometric Distribu7ons

A random variable (r. v.) is a variable whose value is a numerical outcome of a random phenomenon.

8.4: The Binomial Distribution

MATH 446/546 Homework 1:

Chapter 8. Binomial and Geometric Distributions

Statistics Chapter 8

Module 4: Probability

Random variables. Discrete random variables. Continuous random variables.

The Binomial Distribution

Math 160 Professor Busken Chapter 5 Worksheets

Section 8.4 The Binomial Distribution. (a) Rolling a fair die 20 times and observing how many heads appear. s

Chapter 8.1.notebook. December 12, Jan 17 7:08 PM. Jan 17 7:10 PM. Jan 17 7:17 PM. Pop Quiz Results. Chapter 8 Section 8.1 Binomial Distribution

Problem Set 07 Discrete Random Variables

Chapter Five. The Binomial Probability Distribution and Related Topics

Chapter 3 Discrete Random Variables and Probability Distributions

2. Modeling Uncertainty

Statistics Class 15 3/21/2012

Section Random Variables

Transcription:

PubHlth 540 Introductory Biostatistics Page 1 of 6 Unit 4 Bernoulli and Binomial Distributions Week #6 - Practice Problems SOLUTIONS Revised (enhanced for q4) 10-29-2008 1. This exercise gives you practice in calculating number of ways to choose. Suppose my 2008 BE540 class that meets in class in Worcester, MA has 10 students. a. I wish to pair up students to work on homework together. How many pairs of 2 students could I form? Answer: 45 10 10! (10)(9) 8! 90 10 choose 2 = = = = = 45 2 2! 8! (2)( 1) 8! 2 b. Next, I wish to form project groups of size 5. How many groups of 5 students could I form? Answer: 252 10 10! (10)(9)(8)(7)(6) 5! 10 choose 5 = = = = 252 5 5! 5! (5)(4)(3)(2)(1) 5! 2. This exercise is a straightforward application of a binomial probability calculation. A die will be rolled six times. What are the chances that, over all six rolls, the die lands neither ace nor deuce exactly 2 times? Answer:.08 Success on roll of die occurs for event neither ace nor deuce. This has probability 4/6=.67 Outcome of interest is exactly 2 successes and 4 failures. Define random variable X = # successes on six rolls of one die Binomial number of trials, N = 6 Event probability π=.67 Want Pr [ X = 2 ] 6 2 4 Pr [ X=2 N=6 and π =.67 ] = [.67] [ 1.67] =.07985 2 2 times 4 times Probability[one scenario of 2 success and 4 failure] = [ 4/6] [ 2/6 ] =.0055 6 6! (6)(5) 4! Number of scenarios yielding exactly 2 success and 4 failure] = = = = 15 2 2!4! (2)(1) 4!

PubHlth 540 Introductory Biostatistics Page 2 of 6 You can also get this using the binomial calculator on line. From the course website, click on the Bernoulli and Binomial unit from the left navigation bar. From there, scroll down and click on Vassar Statistics Binomial Calculator. Scroll down and fill in the following values. Calculate After you have done that, scroll down to read off the required calculation. 3. This is also an application of a binomial probability calculation. Suppose that, in the general population, there is a 2% chance that a child will be born with a genetic anomaly. What is the probability that no congenital anomaly will be found among four random births? Answer:.92 Success occurs for event no congenital anomaly. This has probability =.98 Outcome of interest is exactly 4 successes and 0 failures. Define random variable X = # successes among four random births Binomial number of trials, N = 4 Event probability π=.98 Want Pr [ X = 4 ]

PubHlth 540 Introductory Biostatistics Page 3 of 6 4. This is a slightly harder application of a binomial probability calculation. Suppose it is known that, for a given couple, there is a 25% chance that a child of theirs will have a particular recessive disease. If they have three children, what are the chances that at least one of them will be affected? Answer:.58 The event being investigated is that of a particular recessive disease Event probability π=.25 The number of trials considered is N=3 There is more than one way to reason out the solution. One approach: chances that at least one will be affected = 1 chances that zero will be affected = 1 Probability [ X=0 ] for X distributed Binomial (N=3, π=.25 ) = 1 -.421775 =.58

PubHlth 540 Introductory Biostatistics Page 4 of 6 Another approach: chances that at least one will be affected = chances that one or two or three will be affected = Probability [ X=1 ] + Probability [ X=2 ] + Probability [ X=3 ] for X distributed Binomial (N=3, π=.25 ) =.58

PubHlth 540 Introductory Biostatistics Page 5 of 6 Yet another approach: chances that at least one will be affected = 1 - chances that zero are affected = 1 chances that ALL 3 are DISEASE FREE so we consider the event of being disease free which occurs with probability =.75 = 1 Probability [ X=3 ] for X distributed Binomial (N=3, π=.75 ) = 1 -.421875 =.58

PubHlth 540 Introductory Biostatistics Page 6 of 6 5. This exercise is the most involved. Suppose a quiz contains 20 true/false questions. You know the correct answer to the first 10 questions. You have no idea of the correct answer to questions 11 through 20 and decide to answer each using the coin toss method. Calculate the probability of obtaining a total quiz score of at least 85%. Answer:.17 As there are 20 questions, each is worth 5 points. Having answered the first 10 questions correctly, you already have 50 points. Remaining to get are 35 points or greater. This corresponds to 7 or more correct answers among the last 10 questions. Define random variable X = # correct answers among questions #11-20 Binomial number of trials, N = 10 Event probability π=.50 because you are using the coin toss method. Want Pr [ X > 7 ]