Ch. 12.1: Permutations

Size: px
Start display at page:

Download "Ch. 12.1: Permutations"

Transcription

1 Ch. 12.1: Permutations The Mathematics of Counting The field of mathematics concerned with counting is properly known as combinatorics. Whenever we ask a question such as how many different ways can we make this choice, or how many different arrangements are possible, we are asking a combinatoric question. The following is a very general rule that is foundational to the subject: The Fundamental Counting Principle Suppose an experiment consists of k sub-experiments, performed one after the other. If the 1st sub-experiment has n 1 possible outcomes, the 2nd sub-experiment has n 2 possible outcomes,... and the kth subexperiment has n k possible outcomes, then then the combined experiment has n 1 n 2... n k possible outcomes. The justification for this principle is due to tree diagrams (discussed in the book an in class). It may seem a bit vague, so it is best illustrated with an example: Example 1: Suppose we perform an experiment in which we first flip a coin twice, and then roll a dice. How many possible outcomes are there? ANSWER: This experiment consists of 3 sub-experiments: (1) flip a coin, (2) flip a coin, (3) roll a dice. 1st outcome: coin flip 2nd outcome: coin flip 3rd outcome: dice roll (1) The 1st sub-experiment has 2 possible outcomes: {H, T }. (2) The 2nd sub-experiment has 2 possible outcomes: {H, T }. (2) The 3rd sub-experiment has 6 possible outcomes: {,,,,, }. Thus, by the Fundamental Counting Principle, there are = 24 possible outcomes. 1

2 It is important to remember that when using the Fundamental Counting Principle, the order of the sub-experiments matters. For instance in the previous example, the outcome (H, T, ) is NOT the same as (T, H, ) because the order of the coin-flip outcomes is reversed. Example 2: Consider an ATM PIN. (a) How many different 4-digit PINs can be made using the digits 0,..., 9? (b) How many can be made if the 1st digit cannot be a zero? ANSWER: (a) Note that in this example, the order of the digits does matter: for instance, 1234 and 4321 are different PINs. We can think of this problem as an experiment in which we choose one digit after another. 1st digit 2nd digit 3rd digit 4rd digit For each of the 4 spaces, we can choose from 10 different digits. Thus, there are = 10 4 = 10, 000 different outcomes, each outcome giving us a different PIN. (b) Here our counting procedure is the same as above, except that for our 1st digit, we can only choose from 9 possible digits (since the digit 0 is not allowed). Thus, in this case, there are = = 9, 000 different ways to make a unique PIN. 2

3 Factorial Notation When working in combinatorics, we often run across very long products of numbers. The factorial notation is often helpful in simplifying such expressions, as we will shortly see. Factorial For any counting number, n, the factorial of n is defined by By convention, 0! = 1! = 1. n! = n(n 1)(n 2)... (3)(2)(1). Also note the useful property that n! = n (n 1)! for any n 1. Example 3: Evaluate the following: (a) 5! = = 120 (b) 5! 3! = = 114 (c) (5 3)! = 2! = 2 (d) 100! 98! = =

4 Permutations without Repetitions In the most general terms, a permutation is just an ordered list of elements selected from some set. In some usages, elements can be repeated, while in other usages this is not allowed. Example 4: Here are some example permutations: (3, 1, 2) and (2, 1, 3) are both distinct permutations of the set {1, 2, 3}, without repetitions allowed. (5, 6) is a 2-permutation of the set {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, without repetition allowed. The word abracadabra is an 11-permutation of the set of letters {a,b,c,d,r}, with repetition allowed. Note that elements of a permutation are often (though not always) listed between parentheses, (...), and that unlike set notation, the order of the list does matter. A common type of combinatoric problem is to determine the total number of possible permutations of a given set, when no repetitions are allowed. Example 5: Consider a club consisting of 6 members: { Tom, Calvin, Doug, Jane, Mary, Iris }. Suppose the club is giving a presentation in which 3 members have to give speeches, one after another, and no two speeches are given by the same person. How many different line-ups are possible? ANSWER: Let s consider the experiment in which we choose each speaker, one after the other, to form a particular line-up: Mary 1st speaker Doug 2nd speaker Iris 3rd speaker For the 1st speaker, we can choose among 5 members. Once that choice is made, however, we have only 4 members left to choose from for the 2nd speaker, and then only 3 members left to choose from for the 3rd speaker. Thus, by the Fundamental Counting Principle, there are = 60 ways to choose a distinct line up. 4

5 This type of problem occurs so often, that we use a special notation to describe the solution... Number of Permutations without Repetitions The total number of permutations of r elements selected from a set of n elements without repetition is given by the formula np r = n(n 1)(n 2)... (n r + 1) = By convention, np 0 = 1, for any n. n! (n r)! Example 6: Consider a lottery in which 6 balls are consecutively drawn at random from an urn containing 99 balls, each printed with a unique number 1,..., 99. What are the total number of possible outcomes of this draw? ANSWER: Each drawing is a permutation of 6 numbers chosen from a set of 99, without repetition. Thus, the total number of possible permutations is: 99P 6 = = 806, 781, 064, 320. This is a very large number, over 800 billion possible permutations. (As a prelude to the next chapter, think of trying to guess the exact permutation that is chosen. What would be your probability of guessing correctly?) 5

6 Permutations with Specific Repetitions In some problems, we need to deal with permutations in which some elements are repeated a specific number of times. For instance, we might want to know the number of ways of uniquely arranging 4 pictures on a wall (in a line) if 2 of the pictures are identical. Here s a simpler example Example 7: How many distinguishable permutations can be made using all letters in the word needle? ANSWER: In this case, we have a total of 6 letters, and 3 of them (the Es) are identical. Let the unknown N be the answer that we re looking for. If we temporarily assume that all 3 Es are distinguishable (say we denote them with different colors, E, E, E), then we have permutations with NO repetitions, and the total number possible is 6 P 6 = 6! Now, note that each of the N permutations in which the Es are identical correspond to 3! possible permutations in which the Es are distinguishable (since we can permute those Es in place without changing the identity of the word). For example: NEEDLE = { needle, needle, needle, needle, needle, needle. } Thus, N 3! = 6!, and so N = (6!)/(3!) = 120. The same reasoning leads us to the following general formula: Number of Permutaions with given Repetitions The total number of distinguishable permutations of k elements, in which the 1st element is repeated n 1 times, the 2nd element is repeated n 2 times,... and the kth element is repeated n k times, is given by the multinomial coefficient: ( n ) n 1, n 2,..., n k = n! n 1! n 2!... n k! where n = n 1 + n n k. 6

7 Example 8: How many distinguishable permutations of the word MISSISSIPPI are possible? ANSWER: We have n = 11 letters in total: n 1 = 1 M s, n 2 = 4 I s, n 3 = 4 S s, n 4 = 2 P s. Thus, the total number of permutations is... ( ) n 11! = n 1, n 2, n 3, n 4 1! 4! 4! 2! = 34, 650 7

Math 202-0 Quizzes Winter 2009

Math 202-0 Quizzes Winter 2009 Quiz : Basic Probability Ten Scrabble tiles are placed in a bag Four of the tiles have the letter printed on them, and there are two tiles each with the letters B, C and D on them (a) Suppose one tile

More information

Probability. Section 9. Probability. Probability of A = Number of outcomes for which A happens Total number of outcomes (sample space)

Probability. Section 9. Probability. Probability of A = Number of outcomes for which A happens Total number of outcomes (sample space) Probability Section 9 Probability Probability of A = Number of outcomes for which A happens Total number of outcomes (sample space) In this section we summarise the key issues in the basic probability

More information

6.3 Conditional Probability and Independence

6.3 Conditional Probability and Independence 222 CHAPTER 6. PROBABILITY 6.3 Conditional Probability and Independence Conditional Probability Two cubical dice each have a triangle painted on one side, a circle painted on two sides and a square painted

More information

Lesson 1. Basics of Probability. Principles of Mathematics 12: Explained! www.math12.com 314

Lesson 1. Basics of Probability. Principles of Mathematics 12: Explained! www.math12.com 314 Lesson 1 Basics of Probability www.math12.com 314 Sample Spaces: Probability Lesson 1 Part I: Basic Elements of Probability Consider the following situation: A six sided die is rolled The sample space

More information

2. How many ways can the letters in PHOENIX be rearranged? 7! = 5,040 ways.

2. How many ways can the letters in PHOENIX be rearranged? 7! = 5,040 ways. Math 142 September 27, 2011 1. How many ways can 9 people be arranged in order? 9! = 362,880 ways 2. How many ways can the letters in PHOENIX be rearranged? 7! = 5,040 ways. 3. The letters in MATH are

More information

Question of the Day. Key Concepts. Vocabulary. Mathematical Ideas. QuestionofDay

Question of the Day. Key Concepts. Vocabulary. Mathematical Ideas. QuestionofDay QuestionofDay Question of the Day What is the probability that in a family with two children, both are boys? What is the probability that in a family with two children, both are boys, if we already know

More information

Feb 7 Homework Solutions Math 151, Winter 2012. Chapter 4 Problems (pages 172-179)

Feb 7 Homework Solutions Math 151, Winter 2012. Chapter 4 Problems (pages 172-179) Feb 7 Homework Solutions Math 151, Winter 2012 Chapter Problems (pages 172-179) Problem 3 Three dice are rolled. By assuming that each of the 6 3 216 possible outcomes is equally likely, find the probabilities

More information

Lecture 1 Introduction Properties of Probability Methods of Enumeration Asrat Temesgen Stockholm University

Lecture 1 Introduction Properties of Probability Methods of Enumeration Asrat Temesgen Stockholm University Lecture 1 Introduction Properties of Probability Methods of Enumeration Asrat Temesgen Stockholm University 1 Chapter 1 Probability 1.1 Basic Concepts In the study of statistics, we consider experiments

More information

Chapter 3. Distribution Problems. 3.1 The idea of a distribution. 3.1.1 The twenty-fold way

Chapter 3. Distribution Problems. 3.1 The idea of a distribution. 3.1.1 The twenty-fold way Chapter 3 Distribution Problems 3.1 The idea of a distribution Many of the problems we solved in Chapter 1 may be thought of as problems of distributing objects (such as pieces of fruit or ping-pong balls)

More information

1.4 Compound Inequalities

1.4 Compound Inequalities Section 1.4 Compound Inequalities 53 1.4 Compound Inequalities This section discusses a technique that is used to solve compound inequalities, which is a phrase that usually refers to a pair of inequalities

More information

Question: What is the probability that a five-card poker hand contains a flush, that is, five cards of the same suit?

Question: What is the probability that a five-card poker hand contains a flush, that is, five cards of the same suit? ECS20 Discrete Mathematics Quarter: Spring 2007 Instructor: John Steinberger Assistant: Sophie Engle (prepared by Sophie Engle) Homework 8 Hints Due Wednesday June 6 th 2007 Section 6.1 #16 What is the

More information

SECTION 10-5 Multiplication Principle, Permutations, and Combinations

SECTION 10-5 Multiplication Principle, Permutations, and Combinations 10-5 Multiplication Principle, Permutations, and Combinations 761 54. Can you guess what the next two rows in Pascal s triangle, shown at right, are? Compare the numbers in the triangle with the binomial

More information

(b) You draw two balls from an urn and track the colors. When you start, it contains three blue balls and one red ball.

(b) You draw two balls from an urn and track the colors. When you start, it contains three blue balls and one red ball. Examples for Chapter 3 Probability Math 1040-1 Section 3.1 1. Draw a tree diagram for each of the following situations. State the size of the sample space. (a) You flip a coin three times. (b) You draw

More information

Lecture 2 Binomial and Poisson Probability Distributions

Lecture 2 Binomial and Poisson Probability Distributions Lecture 2 Binomial and Poisson Probability Distributions Binomial Probability Distribution l Consider a situation where there are only two possible outcomes (a Bernoulli trial) H Example: u flipping a

More information

The Binomial Probability Distribution

The Binomial Probability Distribution The Binomial Probability Distribution MATH 130, Elements of Statistics I J. Robert Buchanan Department of Mathematics Fall 2015 Objectives After this lesson we will be able to: determine whether a probability

More information

Definition and Calculus of Probability

Definition and Calculus of Probability In experiments with multivariate outcome variable, knowledge of the value of one variable may help predict another. For now, the word prediction will mean update the probabilities of events regarding the

More information

Fundamentals of Probability

Fundamentals of Probability Fundamentals of Probability Introduction Probability is the likelihood that an event will occur under a set of given conditions. The probability of an event occurring has a value between 0 and 1. An impossible

More information

1.2 Solving a System of Linear Equations

1.2 Solving a System of Linear Equations 1.. SOLVING A SYSTEM OF LINEAR EQUATIONS 1. Solving a System of Linear Equations 1..1 Simple Systems - Basic De nitions As noticed above, the general form of a linear system of m equations in n variables

More information

3. Mathematical Induction

3. Mathematical Induction 3. MATHEMATICAL INDUCTION 83 3. Mathematical Induction 3.1. First Principle of Mathematical Induction. Let P (n) be a predicate with domain of discourse (over) the natural numbers N = {0, 1,,...}. If (1)

More information

Math 55: Discrete Mathematics

Math 55: Discrete Mathematics Math 55: Discrete Mathematics UC Berkeley, Fall 2011 Homework # 7, due Wedneday, March 14 Happy Pi Day! (If any errors are spotted, please email them to morrison at math dot berkeley dot edu..5.10 A croissant

More information

Find the indicated probability. 1) If a single fair die is rolled, find the probability of a 4 given that the number rolled is odd.

Find the indicated probability. 1) If a single fair die is rolled, find the probability of a 4 given that the number rolled is odd. Math 0 Practice Test 3 Fall 2009 Covers 7.5, 8.-8.3 MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Find the indicated probability. ) If a single

More information

5.1 Radical Notation and Rational Exponents

5.1 Radical Notation and Rational Exponents Section 5.1 Radical Notation and Rational Exponents 1 5.1 Radical Notation and Rational Exponents We now review how exponents can be used to describe not only powers (such as 5 2 and 2 3 ), but also roots

More information

Methods Used for Counting

Methods Used for Counting COUNTING METHODS From our preliminary work in probability, we often found ourselves wondering how many different scenarios there were in a given situation. In the beginning of that chapter, we merely tried

More information

9.4. The Scalar Product. Introduction. Prerequisites. Learning Style. Learning Outcomes

9.4. The Scalar Product. Introduction. Prerequisites. Learning Style. Learning Outcomes The Scalar Product 9.4 Introduction There are two kinds of multiplication involving vectors. The first is known as the scalar product or dot product. This is so-called because when the scalar product of

More information

Vieta s Formulas and the Identity Theorem

Vieta s Formulas and the Identity Theorem Vieta s Formulas and the Identity Theorem This worksheet will work through the material from our class on 3/21/2013 with some examples that should help you with the homework The topic of our discussion

More information

Chapter 20: chance error in sampling

Chapter 20: chance error in sampling Chapter 20: chance error in sampling Context 2 Overview................................................................ 3 Population and parameter..................................................... 4

More information

Statistics 100A Homework 2 Solutions

Statistics 100A Homework 2 Solutions Statistics Homework Solutions Ryan Rosario Chapter 9. retail establishment accepts either the merican Express or the VIS credit card. total of percent of its customers carry an merican Express card, 6

More information

Discrete mathematics

Discrete mathematics Discrete mathematics Petr Kovář petr.kovar@vsb.cz VŠB Technical University of Ostrava DiM 470-2301/01, Winter term 2015/2016 About this file This file is meant to be a guideline for the lecturer. Many

More information

3.2 Roulette and Markov Chains

3.2 Roulette and Markov Chains 238 CHAPTER 3. DISCRETE DYNAMICAL SYSTEMS WITH MANY VARIABLES 3.2 Roulette and Markov Chains In this section we will be discussing an application of systems of recursion equations called Markov Chains.

More information

Mathematical goals. Starting points. Materials required. Time needed

Mathematical goals. Starting points. Materials required. Time needed Level S2 of challenge: B/C S2 Mathematical goals Starting points Materials required Time needed Evaluating probability statements To help learners to: discuss and clarify some common misconceptions about

More information

1 Combinations, Permutations, and Elementary Probability

1 Combinations, Permutations, and Elementary Probability 1 Combinations, Permutations, and Elementary Probability Roughly speaking, Permutations are ways of grouping things where the order is important. Combinations are ways of grouping things where the order

More information

You flip a fair coin four times, what is the probability that you obtain three heads.

You flip a fair coin four times, what is the probability that you obtain three heads. Handout 4: Binomial Distribution Reading Assignment: Chapter 5 In the previous handout, we looked at continuous random variables and calculating probabilities and percentiles for those type of variables.

More information

EXAM. Exam #3. Math 1430, Spring 2002. April 21, 2001 ANSWERS

EXAM. Exam #3. Math 1430, Spring 2002. April 21, 2001 ANSWERS EXAM Exam #3 Math 1430, Spring 2002 April 21, 2001 ANSWERS i 60 pts. Problem 1. A city has two newspapers, the Gazette and the Journal. In a survey of 1, 200 residents, 500 read the Journal, 700 read the

More information

Lesson Plans for (9 th Grade Main Lesson) Possibility & Probability (including Permutations and Combinations)

Lesson Plans for (9 th Grade Main Lesson) Possibility & Probability (including Permutations and Combinations) Lesson Plans for (9 th Grade Main Lesson) Possibility & Probability (including Permutations and Combinations) Note: At my school, there is only room for one math main lesson block in ninth grade. Therefore,

More information

ALGEBRA. sequence, term, nth term, consecutive, rule, relationship, generate, predict, continue increase, decrease finite, infinite

ALGEBRA. sequence, term, nth term, consecutive, rule, relationship, generate, predict, continue increase, decrease finite, infinite ALGEBRA Pupils should be taught to: Generate and describe sequences As outcomes, Year 7 pupils should, for example: Use, read and write, spelling correctly: sequence, term, nth term, consecutive, rule,

More information

Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 10

Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 10 CS 70 Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 10 Introduction to Discrete Probability Probability theory has its origins in gambling analyzing card games, dice,

More information

Maths class 11 Chapter 7. Permutations and Combinations

Maths class 11 Chapter 7. Permutations and Combinations 1 P a g e Maths class 11 Chapter 7. Permutations and Combinations Fundamental Principles of Counting 1. Multiplication Principle If first operation can be performed in m ways and then a second operation

More information

Sequences. A sequence is a list of numbers, or a pattern, which obeys a rule.

Sequences. A sequence is a list of numbers, or a pattern, which obeys a rule. Sequences A sequence is a list of numbers, or a pattern, which obeys a rule. Each number in a sequence is called a term. ie the fourth term of the sequence 2, 4, 6, 8, 10, 12... is 8, because it is the

More information

Math 408, Actuarial Statistics I, Spring 2008. Solutions to combinatorial problems

Math 408, Actuarial Statistics I, Spring 2008. Solutions to combinatorial problems , Spring 2008 Word counting problems 1. Find the number of possible character passwords under the following restrictions: Note there are 26 letters in the alphabet. a All characters must be lower case

More information

10.2 Series and Convergence

10.2 Series and Convergence 10.2 Series and Convergence Write sums using sigma notation Find the partial sums of series and determine convergence or divergence of infinite series Find the N th partial sums of geometric series and

More information

Math 3C Homework 3 Solutions

Math 3C Homework 3 Solutions Math 3C Homework 3 s Ilhwan Jo and Akemi Kashiwada ilhwanjo@math.ucla.edu, akashiwada@ucla.edu Assignment: Section 2.3 Problems 2, 7, 8, 9,, 3, 5, 8, 2, 22, 29, 3, 32 2. You draw three cards from a standard

More information

Random variables, probability distributions, binomial random variable

Random variables, probability distributions, binomial random variable Week 4 lecture notes. WEEK 4 page 1 Random variables, probability distributions, binomial random variable Eample 1 : Consider the eperiment of flipping a fair coin three times. The number of tails that

More information

Chapter 6: Probability

Chapter 6: Probability Chapter 6: Probability In a more mathematically oriented statistics course, you would spend a lot of time talking about colored balls in urns. We will skip over such detailed examinations of probability,

More information

Lecture Note 1 Set and Probability Theory. MIT 14.30 Spring 2006 Herman Bennett

Lecture Note 1 Set and Probability Theory. MIT 14.30 Spring 2006 Herman Bennett Lecture Note 1 Set and Probability Theory MIT 14.30 Spring 2006 Herman Bennett 1 Set Theory 1.1 Definitions and Theorems 1. Experiment: any action or process whose outcome is subject to uncertainty. 2.

More information

Combinatorics. Chapter 1. 1.1 Factorials

Combinatorics. Chapter 1. 1.1 Factorials Chapter 1 Combinatorics Copyright 2009 by David Morin, morin@physics.harvard.edu (Version 4, August 30, 2009) This file contains the first three chapters (plus some appendices) of a potential book on Probability

More information

A Little Set Theory (Never Hurt Anybody)

A Little Set Theory (Never Hurt Anybody) A Little Set Theory (Never Hurt Anybody) Matthew Saltzman Department of Mathematical Sciences Clemson University Draft: August 21, 2013 1 Introduction The fundamental ideas of set theory and the algebra

More information

Math 55: Discrete Mathematics

Math 55: Discrete Mathematics Math 55: Discrete Mathematics UC Berkeley, Spring 2012 Homework # 9, due Wednesday, April 11 8.1.5 How many ways are there to pay a bill of 17 pesos using a currency with coins of values of 1 peso, 2 pesos,

More information

Introduction to Probability

Introduction to Probability 3 Introduction to Probability Given a fair coin, what can we expect to be the frequency of tails in a sequence of 10 coin tosses? Tossing a coin is an example of a chance experiment, namely a process which

More information

Statistics 100A Homework 4 Solutions

Statistics 100A Homework 4 Solutions Chapter 4 Statistics 00A Homework 4 Solutions Ryan Rosario 39. A ball is drawn from an urn containing 3 white and 3 black balls. After the ball is drawn, it is then replaced and another ball is drawn.

More information

Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 13. Random Variables: Distribution and Expectation

Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 13. Random Variables: Distribution and Expectation CS 70 Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 3 Random Variables: Distribution and Expectation Random Variables Question: The homeworks of 20 students are collected

More information

Math/Stats 425 Introduction to Probability. 1. Uncertainty and the axioms of probability

Math/Stats 425 Introduction to Probability. 1. Uncertainty and the axioms of probability Math/Stats 425 Introduction to Probability 1. Uncertainty and the axioms of probability Processes in the real world are random if outcomes cannot be predicted with certainty. Example: coin tossing, stock

More information

Session 29 Scientific Notation and Laws of Exponents. If you have ever taken a Chemistry class, you may have encountered the following numbers:

Session 29 Scientific Notation and Laws of Exponents. If you have ever taken a Chemistry class, you may have encountered the following numbers: Session 9 Scientific Notation and Laws of Exponents If you have ever taken a Chemistry class, you may have encountered the following numbers: There are approximately 60,4,79,00,000,000,000,000 molecules

More information

5. Probability Calculus

5. Probability Calculus 5. Probability Calculus So far we have concentrated on descriptive statistics (deskriptiivinen eli kuvaileva tilastotiede), that is methods for organizing and summarizing data. As was already indicated

More information

Lottery Combinatorics

Lottery Combinatorics Published by the Applied Probability Trust Applied Probability Trust 2009 110 Lottery Combinatorics IAN MCPHERSON and DEREK HODSON The chance of landing the National Lottery jackpot (or a share of it)

More information

Probability, statistics and football Franka Miriam Bru ckler Paris, 2015.

Probability, statistics and football Franka Miriam Bru ckler Paris, 2015. Probability, statistics and football Franka Miriam Bru ckler Paris, 2015 Please read this before starting! Although each activity can be performed by one person only, it is suggested that you work in groups

More information

Ch. 13.2: Mathematical Expectation

Ch. 13.2: Mathematical Expectation Ch. 13.2: Mathematical Expectation Random Variables Very often, we are interested in sample spaces in which the outcomes are distinct real numbers. For example, in the experiment of rolling two dice, we

More information

Section 1.5 Exponents, Square Roots, and the Order of Operations

Section 1.5 Exponents, Square Roots, and the Order of Operations Section 1.5 Exponents, Square Roots, and the Order of Operations Objectives In this section, you will learn to: To successfully complete this section, you need to understand: Identify perfect squares.

More information

ECE302 Spring 2006 HW1 Solutions January 16, 2006 1

ECE302 Spring 2006 HW1 Solutions January 16, 2006 1 ECE302 Spring 2006 HW1 Solutions January 16, 2006 1 Solutions to HW1 Note: These solutions were generated by R. D. Yates and D. J. Goodman, the authors of our textbook. I have added comments in italics

More information

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS Systems of Equations and Matrices Representation of a linear system The general system of m equations in n unknowns can be written a x + a 2 x 2 + + a n x n b a

More information

Solving Rational Equations

Solving Rational Equations Lesson M Lesson : Student Outcomes Students solve rational equations, monitoring for the creation of extraneous solutions. Lesson Notes In the preceding lessons, students learned to add, subtract, multiply,

More information

Greatest Common Factor and Least Common Multiple

Greatest Common Factor and Least Common Multiple Greatest Common Factor and Least Common Multiple Intro In order to understand the concepts of Greatest Common Factor (GCF) and Least Common Multiple (LCM), we need to define two key terms: Multiple: Multiples

More information

Basics of Counting. The product rule. Product rule example. 22C:19, Chapter 6 Hantao Zhang. Sample question. Total is 18 * 325 = 5850

Basics of Counting. The product rule. Product rule example. 22C:19, Chapter 6 Hantao Zhang. Sample question. Total is 18 * 325 = 5850 Basics of Counting 22C:19, Chapter 6 Hantao Zhang 1 The product rule Also called the multiplication rule If there are n 1 ways to do task 1, and n 2 ways to do task 2 Then there are n 1 n 2 ways to do

More information

Section 5-3 Binomial Probability Distributions

Section 5-3 Binomial Probability Distributions Section 5-3 Binomial Probability Distributions Key Concept This section presents a basic definition of a binomial distribution along with notation, and methods for finding probability values. Binomial

More information

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 2. x n. a 11 a 12 a 1n b 1 a 21 a 22 a 2n b 2 a 31 a 32 a 3n b 3. a m1 a m2 a mn b m

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 2. x n. a 11 a 12 a 1n b 1 a 21 a 22 a 2n b 2 a 31 a 32 a 3n b 3. a m1 a m2 a mn b m MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS 1. SYSTEMS OF EQUATIONS AND MATRICES 1.1. Representation of a linear system. The general system of m equations in n unknowns can be written a 11 x 1 + a 12 x 2 +

More information

Section 6.4: Counting Subsets of a Set: Combinations

Section 6.4: Counting Subsets of a Set: Combinations Section 6.4: Counting Subsets of a Set: Combinations In section 6.2, we learnt how to count the number of r-permutations from an n-element set (recall that an r-permutation is an ordered selection of r

More information

Section 6-5 Sample Spaces and Probability

Section 6-5 Sample Spaces and Probability 492 6 SEQUENCES, SERIES, AND PROBABILITY 52. How many committees of 4 people are possible from a group of 9 people if (A) There are no restrictions? (B) Both Juan and Mary must be on the committee? (C)

More information

Probability. Sample space: all the possible outcomes of a probability experiment, i.e., the population of outcomes

Probability. Sample space: all the possible outcomes of a probability experiment, i.e., the population of outcomes Probability Basic Concepts: Probability experiment: process that leads to welldefined results, called outcomes Outcome: result of a single trial of a probability experiment (a datum) Sample space: all

More information

Solving Linear Equations - General Equations

Solving Linear Equations - General Equations 1.3 Solving Linear Equations - General Equations Objective: Solve general linear equations with variables on both sides. Often as we are solving linear equations we will need to do some work to set them

More information

ST 371 (IV): Discrete Random Variables

ST 371 (IV): Discrete Random Variables ST 371 (IV): Discrete Random Variables 1 Random Variables A random variable (rv) is a function that is defined on the sample space of the experiment and that assigns a numerical variable to each possible

More information

Ch. 13.3: More about Probability

Ch. 13.3: More about Probability Ch. 13.3: More about Probability Complementary Probabilities Given any event, E, of some sample space, U, of a random experiment, we can always talk about the complement, E, of that event: this is the

More information

Formal Languages and Automata Theory - Regular Expressions and Finite Automata -

Formal Languages and Automata Theory - Regular Expressions and Finite Automata - Formal Languages and Automata Theory - Regular Expressions and Finite Automata - Samarjit Chakraborty Computer Engineering and Networks Laboratory Swiss Federal Institute of Technology (ETH) Zürich March

More information

How to Calculate the Probabilities of Winning the Eight LUCKY MONEY Prize Levels:

How to Calculate the Probabilities of Winning the Eight LUCKY MONEY Prize Levels: How to Calculate the Probabilities of Winning the Eight LUCKY MONEY Prize Levels: LUCKY MONEY numbers are drawn from two sets of numbers. Four numbers are drawn from one set of 47 numbered white balls

More information

For two disjoint subsets A and B of Ω, say that A and B are disjoint events. For disjoint events A and B we take an axiom P(A B) = P(A) + P(B)

For two disjoint subsets A and B of Ω, say that A and B are disjoint events. For disjoint events A and B we take an axiom P(A B) = P(A) + P(B) Basic probability A probability space or event space is a set Ω together with a probability measure P on it. This means that to each subset A Ω we associate the probability P(A) = probability of A with

More information

3.1. Solving linear equations. Introduction. Prerequisites. Learning Outcomes. Learning Style

3.1. Solving linear equations. Introduction. Prerequisites. Learning Outcomes. Learning Style Solving linear equations 3.1 Introduction Many problems in engineering reduce to the solution of an equation or a set of equations. An equation is a type of mathematical expression which contains one or

More information

Permutation Groups. Tom Davis tomrdavis@earthlink.net http://www.geometer.org/mathcircles April 2, 2003

Permutation Groups. Tom Davis tomrdavis@earthlink.net http://www.geometer.org/mathcircles April 2, 2003 Permutation Groups Tom Davis tomrdavis@earthlink.net http://www.geometer.org/mathcircles April 2, 2003 Abstract This paper describes permutations (rearrangements of objects): how to combine them, and how

More information

Second Order Linear Nonhomogeneous Differential Equations; Method of Undetermined Coefficients. y + p(t) y + q(t) y = g(t), g(t) 0.

Second Order Linear Nonhomogeneous Differential Equations; Method of Undetermined Coefficients. y + p(t) y + q(t) y = g(t), g(t) 0. Second Order Linear Nonhomogeneous Differential Equations; Method of Undetermined Coefficients We will now turn our attention to nonhomogeneous second order linear equations, equations with the standard

More information

6.2. Discrete Probability Distributions

6.2. Discrete Probability Distributions 6.2. Discrete Probability Distributions Discrete Uniform distribution (diskreetti tasajakauma) A random variable X follows the dicrete uniform distribution on the interval [a, a+1,..., b], if it may attain

More information

2x + y = 3. Since the second equation is precisely the same as the first equation, it is enough to find x and y satisfying the system

2x + y = 3. Since the second equation is precisely the same as the first equation, it is enough to find x and y satisfying the system 1. Systems of linear equations We are interested in the solutions to systems of linear equations. A linear equation is of the form 3x 5y + 2z + w = 3. The key thing is that we don t multiply the variables

More information

Click on the links below to jump directly to the relevant section

Click on the links below to jump directly to the relevant section Click on the links below to jump directly to the relevant section What is algebra? Operations with algebraic terms Mathematical properties of real numbers Order of operations What is Algebra? Algebra is

More information

Probability: Terminology and Examples Class 2, 18.05, Spring 2014 Jeremy Orloff and Jonathan Bloom

Probability: Terminology and Examples Class 2, 18.05, Spring 2014 Jeremy Orloff and Jonathan Bloom Probability: Terminology and Examples Class 2, 18.05, Spring 2014 Jeremy Orloff and Jonathan Bloom 1 Learning Goals 1. Know the definitions of sample space, event and probability function. 2. Be able to

More information

Hoover High School Math League. Counting and Probability

Hoover High School Math League. Counting and Probability Hoover High School Math League Counting and Probability Problems. At a sandwich shop there are 2 kinds of bread, 5 kinds of cold cuts, 3 kinds of cheese, and 2 kinds of dressing. How many different sandwiches

More information

IEOR 6711: Stochastic Models I Fall 2012, Professor Whitt, Tuesday, September 11 Normal Approximations and the Central Limit Theorem

IEOR 6711: Stochastic Models I Fall 2012, Professor Whitt, Tuesday, September 11 Normal Approximations and the Central Limit Theorem IEOR 6711: Stochastic Models I Fall 2012, Professor Whitt, Tuesday, September 11 Normal Approximations and the Central Limit Theorem Time on my hands: Coin tosses. Problem Formulation: Suppose that I have

More information

Ch. 11.2: Installment Buying

Ch. 11.2: Installment Buying Ch. 11.2: Installment Buying When people take out a loan to make a big purchase, they don t often pay it back all at once in one lump-sum. Instead, they usually pay it back back gradually over time, in

More information

2.2 Derivative as a Function

2.2 Derivative as a Function 2.2 Derivative as a Function Recall that we defined the derivative as f (a) = lim h 0 f(a + h) f(a) h But since a is really just an arbitrary number that represents an x-value, why don t we just use x

More information

Probabilistic Strategies: Solutions

Probabilistic Strategies: Solutions Probability Victor Xu Probabilistic Strategies: Solutions Western PA ARML Practice April 3, 2016 1 Problems 1. You roll two 6-sided dice. What s the probability of rolling at least one 6? There is a 1

More information

Pure Math 30: Explained!

Pure Math 30: Explained! www.puremath30.com 323 Lesson 1, Part One: The Fundamental Counting Principle The Fundamental Counting Principle: This is an easy way to determine how many ways you can arrange items. The following examples

More information

23. RATIONAL EXPONENTS

23. RATIONAL EXPONENTS 23. RATIONAL EXPONENTS renaming radicals rational numbers writing radicals with rational exponents When serious work needs to be done with radicals, they are usually changed to a name that uses exponents,

More information

How to Verify Performance Specifications

How to Verify Performance Specifications How to Verify Performance Specifications VERIFICATION OF PERFORMANCE SPECIFICATIONS In 2003, the Centers for Medicare and Medicaid Services (CMS) updated the CLIA 88 regulations. As a result of the updated

More information

Section 6.2 Definition of Probability

Section 6.2 Definition of Probability Section 6.2 Definition of Probability Probability is a measure of the likelihood that an event occurs. For example, if there is a 20% chance of rain tomorrow, that means that the probability that it will

More information

6th Grade Lesson Plan: Probably Probability

6th Grade Lesson Plan: Probably Probability 6th Grade Lesson Plan: Probably Probability Overview This series of lessons was designed to meet the needs of gifted children for extension beyond the standard curriculum with the greatest ease of use

More information

DETERMINE whether the conditions for a binomial setting are met. COMPUTE and INTERPRET probabilities involving binomial random variables

DETERMINE whether the conditions for a binomial setting are met. COMPUTE and INTERPRET probabilities involving binomial random variables 1 Section 7.B Learning Objectives After this section, you should be able to DETERMINE whether the conditions for a binomial setting are met COMPUTE and INTERPRET probabilities involving binomial random

More information

E3: PROBABILITY AND STATISTICS lecture notes

E3: PROBABILITY AND STATISTICS lecture notes E3: PROBABILITY AND STATISTICS lecture notes 2 Contents 1 PROBABILITY THEORY 7 1.1 Experiments and random events............................ 7 1.2 Certain event. Impossible event............................

More information

Economics 1011a: Intermediate Microeconomics

Economics 1011a: Intermediate Microeconomics Lecture 12: More Uncertainty Economics 1011a: Intermediate Microeconomics Lecture 12: More on Uncertainty Thursday, October 23, 2008 Last class we introduced choice under uncertainty. Today we will explore

More information

What is the winning probability of each prize in Mark Six? Actually how small is the winning probability?

What is the winning probability of each prize in Mark Six? Actually how small is the winning probability? Mark Six A legal lottery game in Hong Kong What is the winning probability of each prize in Mark Six? Actually how small is the winning probability? Describe non-certain proposition In terms of a numerical

More information

AP Stats - Probability Review

AP Stats - Probability Review AP Stats - Probability Review Multiple Choice Identify the choice that best completes the statement or answers the question. 1. I toss a penny and observe whether it lands heads up or tails up. Suppose

More information

Introductory Probability. MATH 107: Finite Mathematics University of Louisville. March 5, 2014

Introductory Probability. MATH 107: Finite Mathematics University of Louisville. March 5, 2014 Introductory Probability MATH 07: Finite Mathematics University of Louisville March 5, 204 What is probability? Counting and probability 2 / 3 Probability in our daily lives We see chances, odds, and probabilities

More information

Regions in a circle. 7 points 57 regions

Regions in a circle. 7 points 57 regions Regions in a circle 1 point 1 region points regions 3 points 4 regions 4 points 8 regions 5 points 16 regions The question is, what is the next picture? How many regions will 6 points give? There's an

More information

6.042/18.062J Mathematics for Computer Science. Expected Value I

6.042/18.062J Mathematics for Computer Science. Expected Value I 6.42/8.62J Mathematics for Computer Science Srini Devadas and Eric Lehman May 3, 25 Lecture otes Expected Value I The expectation or expected value of a random variable is a single number that tells you

More information

MATH 140 Lab 4: Probability and the Standard Normal Distribution

MATH 140 Lab 4: Probability and the Standard Normal Distribution MATH 140 Lab 4: Probability and the Standard Normal Distribution Problem 1. Flipping a Coin Problem In this problem, we want to simualte the process of flipping a fair coin 1000 times. Note that the outcomes

More information

2013 MBA Jump Start Program

2013 MBA Jump Start Program 2013 MBA Jump Start Program Module 2: Mathematics Thomas Gilbert Mathematics Module Algebra Review Calculus Permutations and Combinations [Online Appendix: Basic Mathematical Concepts] 2 1 Equation of

More information