The Multiplication Rule

Slides:



Advertisements
Similar presentations
9.5 Counting Subsets of a Set: Combinations
Advertisements

1 Counting Techniques: Possibility Trees, Multiplication Rule, Permutations.
Counting Chapter 6 With Question/Answer Animations.
Consider the possible arrangements of the letters a, b, and c. List the outcomes in the sample space. If the order is important, then each arrangement.
Counting and Probability The outcome of a random process is sure to occur, but impossible to predict. Examples: fair coin tossing, rolling a pair of dice,
Multiplication Rule. A tree structure is a useful tool for keeping systematic track of all possibilities in situations in which events happen in order.
Quit Permutations Combinations Pascal’s triangle Binomial Theorem.
ENM 207 Lecture 5. FACTORIAL NOTATION The product of positive integers from 1 to n is denoted by the special symbol n! and read “n factorial”. n!=1.2.3….(n-2).(n-1).n.
Combinations We should use permutation where order matters
1 More Counting Techniques Possibility trees Multiplication rule Permutations Combinations.
Copyright © Cengage Learning. All rights reserved. CHAPTER 9 COUNTING AND PROBABILITY.
Jessie Zhao Course page: 1.
1 Melikyan/DM/Fall09 Discrete Mathematics Ch. 6 Counting and Probability Instructor: Hayk Melikyan Today we will review sections 6.4,
Topics to be covered: Produce all combinations and permutations of sets. Calculate the number of combinations and permutations of sets of m items taken.
Generalized Permutations and Combinations
Chapter 6 With Question/Answer Animations 1. Chapter Summary The Basics of Counting The Pigeonhole Principle Permutations and Combinations Binomial Coefficients.
Permutations and Combinations
Methods of Counting Outcomes BUSA 2100, Section 4.1.
© The McGraw-Hill Companies, Inc., Chapter 4 Counting Techniques.
Simple Arrangements & Selections. Combinations & Permutations A permutation of n distinct objects is an arrangement, or ordering, of the n objects. An.
Counting Principles. Counting examples Ex 1: A small sandwich café has 4 different types of bread, 5 different types of meat and 3 different types of.
Discrete Mathematics Lecture # 25 Permutation & Combination.
Counting Principles Multiplication rule Permutations Combinations.
1 CS 140 Discrete Mathematics Combinatorics And Review Notes.
Copyright (C) 2002 Houghton Mifflin Company. All rights reserved. 1 Understandable Statistics Seventh Edition By Brase and Brase Prepared by: Lynn Smith.
Counting Techniques Tree Diagram Multiplication Rule Permutations Combinations.
© 2010 Pearson Prentice Hall. All rights reserved. CHAPTER 11 Counting Methods and Probability Theory.
Spring 2016 COMP 2300 Discrete Structures for Computation Donghyun (David) Kim Department of Mathematics and Physics North Carolina Central University.
Section 1.3 Each arrangement (ordering) of n distinguishable objects is called a permutation, and the number of permutations of n distinguishable objects.
Chapter 7 – Counting Techniques CSNB 143 Discrete Mathematical Structures.
Copyright © Peter Cappello 2011 Simple Arrangements & Selections.
Section The Pigeonhole Principle If a flock of 20 pigeons roosts in a set of 19 pigeonholes, one of the pigeonholes must have more than 1 pigeon.
Week 9 - Friday.  What did we talk about last time?  Partial orders  Total orders  Basic probability  Event  Sample space  Monty Hall  Multiplication.
Permutations and Combinations. Fundamental Counting Principle If there are r ways of performing one operation, s ways of performing a second operation,
Section 6.3. Section Summary Permutations Combinations.
Example A standard deck of 52 cards has 13 kinds of cards, with four cards of each of kind, one in each of the four suits, hearts, diamonds, spades, and.
The Pigeonhole Principle
Discrete Mathematics Lecture 8 Probability and Counting
4-1 Chapter 4 Counting Techniques.
Elementary Probability Theory
Permutations and Combinations
CSNB 143 Discrete Mathematical Structures
Counting Methods and Probability Theory
Generalized Permutations and Combinations
COCS DISCRETE STRUCTURES
Section 8.3 PROBABILITY.
Copyright © Cengage Learning. All rights reserved.
Permutations and Combinations
3! ways, and 2! ways, so Permutations where some objects are repeated
Chapter 0.4 Counting Techniques.
4-1 Chapter 4 Counting Techniques.
4-1 Chapter 4 Counting Techniques.
CS100: Discrete structures
Lesson 11.6 – 11.7 Permutations and Combinations
COUNTING AND PROBABILITY
8.3 Counting Apply the fundamental counting principle
COUNTING AND PROBABILITY
Permutations and Combinations
Lesson 11-1 Permutations and Combinations
COUNTING AND PROBABILITY
COUNTING AND PROBABILITY
Permutations and Combinations
How many possible outcomes can you make with the accessories?
Discrete Structures Counting.
Chapter 10 Counting Methods.
Counting Methods and Probability Theory
4-1 Chapter 4 Counting Techniques.
Using Permutations and Combinations
Applied Combinatorics, 4th Ed. Alan Tucker
10.3 – Using Permutations and Combinations
Presentation transcript:

The Multiplication Rule We are going to draw a colored symbol. The color can be red, blue, green, or yellow, and the shape can be circle, triangle, or square. How many different resulting symbols can we get?

The Multiplication Rule We generate a positive integer with each digit in the set {1,2,3,4}. How many different 3-digit integers can be generated?

The Multiplication Rule We generate a positive integer with each digit in the set {1,2,3,4}. How many different 3-digit integers with no repeated digits can be generated?

The Multiplication Rule We generate a positive integer with each digit in the set {1,2,3,4}. How many different 3-digit even integers with no repeated digits can be generated?

The Addition Rule We generate a positive integer with each digit in the set {1,2,3,4}. How many different such integers that are less than 1000 can be generated?

The Difference Rule We generate a positive integer with each digit in the set {1,2,3,4}. How many different 3-digit integers with some repeated digits can be generated?

The Inclusion/Exclusion Rule We generate a positive integer with each digit in the set {1,2,3,4}. How many different 3-digit integers where the highest digit is 1 or the lowest digit is 4 can be generated?

The Inclusion/Exclusion Rule We generate a positive integer with each digit in the set {1,2,3,4}. How many different 3-digit integers where the highest digit is 1 or the middle digit is 2 or the lowest digit is 4 can be generated?

Permutations A permutation of a set of objects is an ordering of the objects in a row. For example, the set of elements a, b, and c has six permutations. abc acb cba bac bca cab In general, given a set of n objects, how many permutations does the set have? Solution: There are n  k + 1 choices for the k-th component of the permutation. Hence, by the multiplication rule, there are n(n – 1)(n – 2) · · · 2  1 = n! ways to perform the entire operation.

Permutations In other words, there are n! permutations of a set of n elements.

Permutations of Selected Elements Given the set {a, b, c}, there are six ways to select two letters from the set and write them in order. ab ac ba bc ca cb Each such ordering of two elements of {a, b, c} is called a 2-permutation of {a, b, c}.

Permutations of Selected Elements

Example 8 – Permutations of the Letters in a Word How many ways can the letters in the word COMPUTER be arranged in a row? Solution: All the eight letters in the word COMPUTER are distinct, so the number of ways in which we can arrange the letters equals the number of permutations of a set of eight elements. This equals 8! = 40,320.

Example 8 – Permutations of the Letters in a Word How many ways can the letters in the word COMPUTER be arranged if the letters CO must remain next to each other (in order) as a unit? Solution: If the letter group CO is treated as a unit, then there are effectively only seven objects that are to be arranged in a row. Hence there are as many ways to write the letters as there are permutations of a set of seven elements, namely 7! = 5,040.

Example 8 – Permutations of the Letters in a Word If letters of the word COMPUTER are randomly arranged in a row, what is the probability that the letters CO remain next to each other (in order) as a unit? Solution: When the letters are arranged randomly in a row, the total number of arrangements is 40,320, and the number of arrangements with the letters CO next to each other (in order) as a unit is 5,040. Thus the probability is

Example 10 – Evaluating r-Permutations How many 4-permutations are there of a set of seven objects? Solution:

Example 12 – Proving a Property of P (n, r) Prove that for all integers n  2, P(n, 2) + P(n, 1) = n2. Solution: Suppose n is an integer that is greater than or equal to 2. By Theorem 9.2.3,

Example 12 – Solution and Hence which is what we needed to show. cont’d and Hence which is what we needed to show.

Combinations Given a set S with n elements, how many subsets of size r can be chosen from S? The number of subsets of size r that can be chosen from S equals the number of subsets of size r that S has. Each individual subset of size r is called an r-combination of the set.

Combinations We have known that calculators generally use symbols like C(n, r), nCr , Cn,r , or nCr instead of .

Permutations v.s. Combinations r-permutation and r-combination are two distinct methods that can be used to select r objects from a set of n elements. r-permutation is an ordered selection, it is not only what elements are chosen but also the order in which they are chosen that matters. r-combination is an unordered selection, on the other hand, it is only the identity of the chosen elements that matters.

Permutations v.s. Combinations r-permutation: in our class, we randomly select r students to stand in a line in front of me. How many different lines can we get? P(n,r)= n! n−r ! Step 1: choose r students to come to the front n r Step 2: choose an order of the r students just chosen P(r,r)=r!

Permutations v.s. Combinations

Example 10 – Permutations of a Set with Repeated Elements Consider various ways of ordering the letters in the word MISSISSIPPI: IIMSSPISSIP, ISSSPMIIPIS, PIMISSSSIIP, and so on. How many distinguishable orderings are there?

Example 10 – Solution Imagine placing the 11 letters of MISSISSIPPI one after another into 11 positions. Because copies of the same letter cannot be distinguished from one another, once the positions for a certain letter are known, then all copies of the letter can go into the positions in any order.

Example 10 – Solution cont’d It follows that constructing an ordering for the letters can be thought of as a four-step process: Step 1: Choose a subset of four positions for the S’s. Step 2: Choose a subset of four positions for the I’s. Step 3: Choose a subset of two positions for the P’s. Step 4: Choose a subset of one position for the M. Since there are 11 positions in all, there are 11 4 subsets of four positions for the S’s.

Example 10 – Solution cont’d Once the four S’s are in place, there are seven positions that remain empty, so there are 7 4 subsets of four positions for the I’s. After the I’s are in place, there are three positions left empty, so there are 3 2 subsets of two positions for the P’s. That leaves just 1 1 position for the M. Hence by the multiplication rule,

Counting Subsets of a Set: Combinations The reasoning used in this example can be used to derive the following general theorem.

Excercise Consider various ways of ordering the letters in the word STUDENT: How many distinguishable orderings are there? Consider various ways of ordering the letters in the word BANANA: How many distinguishable orderings are there?

Common Incorrect Solutions: Double Counting Be sure not to double-count! Example: A group consists of five men and seven women. How many teams of five contain at least one man? Incorrect Solution Imagine constructing the team as a two-step process: Step 1: Choose a subset of one man from the five men. Step 2: Choose a subset of four others from the remaining eleven people.

Common Incorrect Solutions: Double Counting cont’d Hence, by the multiplication rule, there are  = 1,650 five-person teams that contain at least one man. Analysis of the Incorrect Solution The problem with the solution is that some teams are counted more than once. Suppose the men are Anwar, Ben, Carlos, Dwayne, and Ed and the women are Fumiko, Gail, Hui-Fan, Inez, Jill, Kim, and Laura. According to the method described previously, one possible outcome of the two-step process is as follows: Outcome of step 1: Anwar Outcome of step 2: Ben, Gail, Inez, and Jill.

Common Incorrect Solutions: Double Counting cont’d In this case the team would be {Anwar, Ben, Gail, Inez, Jill}. But another possible outcome is Outcome of step 1: Ben Outcome of step 2: Anwar, Gail, Inez, and Jill, which also gives the team {Anwar, Ben, Gail, Inez, Jill}. Thus this one team is given by two different branches of the possibility tree, and so it is counted twice.