Counting and Probability Sets and Counting Permutations & Combinations Probability.

Slides:



Advertisements
Similar presentations
Larson/Farber Ch. 3 Chapter 3: Probability StatisticsSpring2003/CourseHome/index.htm.
Advertisements

Chapter 2 Probability. 2.1 Sample Spaces and Events.
Chapter 4 Introduction to Probability
Chapter 3 Probability.
Counting Principles The Fundamental Counting Principle: If one event can occur m ways and another can occur n ways, then the number of ways the events.
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,
Today Today: Reading: –Read Chapter 1 by next Tuesday –Suggested problems (not to be handed in): 1.1, 1.2, 1.8, 1.10, 1.16, 1.20, 1.24, 1.28.
1 Probability Parts of life are uncertain. Using notions of probability provide a way to deal with the uncertainty.
Probability Chapter 3. § 3.1 Basic Concepts of Probability.
Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Chapter 2 Probability.
4. Counting 4.1 The Basic of Counting Basic Counting Principles Example 1 suppose that either a member of the faculty or a student in the department is.
Combinatorics 3/15 and 3/ Counting A restaurant offers the following menu: Main CourseVegetablesBeverage BeefPotatoesMilk HamGreen BeansCoffee.
Advanced Math Topics Chapters 4 and 5 Review. 1) A family plans to have 3 children. What is the probability that there will be at least 2 girls? (assume.
(13 – 1) The Counting Principle and Permutations Learning targets: To use the fundamental counting principle to count the number of ways an event can happen.
3.1 Probability Experiments Probability experiment: An action, or trial, through which specific results (counts, measurements, or responses) are obtained.
Math 409/409G History of Mathematics
Topics to be covered: Produce all combinations and permutations of sets. Calculate the number of combinations and permutations of sets of m items taken.
Chapter 12 Section 7 - Slide 1 Copyright © 2009 Pearson Education, Inc. AND.
1/31/2007 Pre-Calculus Chapter 9 Review a n = a 1 + (n – 1)d a n = a 1 r (n – 1)
3.1Set Notation Venn Diagrams Venn Diagram is used to illustrate the idea of sets and subsets. Example 1 X  U(b) A  B X U B A U.
Chapter 7 With Question/Answer Animations. Section 7.1.
Slide 7- 1 Copyright © 2006 Pearson Education, Inc. Publishing as Pearson Addison-Wesley.
Fall 2002CMSC Discrete Structures1 One, two, three, we’re… Counting.
Advanced Precalculus Advanced Precalculus Notes 12.2 Permutations and Combinations.
Section 10-3 Using Permutations and Combinations.
March 10, 2015Applied Discrete Mathematics Week 6: Counting 1 Permutations and Combinations How many different sets of 3 people can we pick from a group.
CSCI 115 Chapter 3 Counting. CSCI 115 §3.1 Permutations.
3. Counting Permutations Combinations Pigeonhole principle Elements of Probability Recurrence Relations.
Lesson Counting Techniques. Objectives Solve counting problems using the Multiplication Rule Solve counting problems using permutations Solve counting.
Quiz 10-1, Which of these are an example of a “descrete” set of data? 2.Make a “tree diagram” showing all the ways the letters ‘x’, ‘y’, and ‘z’
Larson/Farber Ch. 3 Weather forecast Psychology Games Sports 3 Elementary Statistics Larson Farber Business Medicine Probability.
1 Weather forecast Psychology Games Sports Chapter 3 Elementary Statistics Larson Farber Probability Business Medicine.
Introduction to Probability  Probability is a numerical measure of the likelihood that an event will occur.  Probability values are always assigned on.
March 10,  Counting  Fundamental Counting principle  Factorials  Permutations and combinations  Probability  Complementary events  Compound.
Sullivan Algebra and Trigonometry: Section 14.2 Objectives of this Section Solve Counting Problems Using the Multiplication Principle Solve Counting Problems.
Section 11.4 Tree Diagrams, Tables, and Sample Spaces Math in Our World.
3.4 Counting Principles I.The Fundamental Counting Principle: if one event can occur m ways and a second event can occur n ways, the number of ways the.
Chapter 4 Probability. Definitions A probability experiment is a chance process that leads to well-defined results called outcomes. An outcome is the.
Probability is a measure of the likelihood of a random phenomenon or chance behavior. Probability describes the long-term proportion with which a certain.
Chapter 12 Probability © 2008 Pearson Addison-Wesley. All rights reserved.
Dr. Fowler AFM Unit 7-8 Probability. Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall.
Discrete Mathematics, 1st Edition Kevin Ferland Chapter 6 Basic Counting 1.
SECTION 11-2 Events Involving “Not” and “Or” Slide
+ Chapter 5 Overview 5.1 Introducing Probability 5.2 Combining Events 5.3 Conditional Probability 5.4 Counting Methods 1.
Chapter 7: Probability Lesson 1: Basic Principles of Probability Mrs. Parziale.
 Counting  Fundamental Counting principle  Factorials  Permutations and combinations  Probability  Complementary events  Compound events  Independent.
College Algebra: Section 8.1 Sets and Counting Objectives of this Section Find All the Subsets of a Set Find All the Subsets of a Set Find the Intersection.
CSCI 115 Chapter 3 Counting. CSCI 115 §3.1 Permutations.
2/24/20161 One, two, three, we’re… Counting. 2/24/20162 Basic Counting Principles Counting problems are of the following kind: “How many different 8-letter.
Welcome to MM207 Unit 3 Seminar Dr. Bob Probability and Excel 1.
Sullivan Algebra and Trigonometry: Section 14.3 Objectives of this Section Construct Probability Models Compute Probabilities of Equally Likely Outcomes.
Sullivan Algebra and Trigonometry: Section 14.1 Objectives of this Section Find All the Subsets of a Set Find the Intersection and Union of Sets Find the.
1 2.3 Counting Sample Points (6)(6)(6)= 216 Example: How many outcome sequences are possible when a die is rolled three times?
Probability IIntroduction to Probability ASatisfactory outcomes vs. total outcomes BBasic Properties CTerminology IICombinatory Probability AThe Addition.
Chapter 4 Some basic Probability Concepts 1-1. Learning Objectives  To learn the concept of the sample space associated with a random experiment.  To.
12.3 Probability of Equally Likely Outcomes
11.6 SETS AND COUNTING.
Chapter 10 Counting Methods.
Introduction To Probability
The Pigeonhole Principle
Chapter 11 Probability.
Permutations and Combinations
COCS DISCRETE STRUCTURES
12.2 Permutations and Combinations
Chapter 5 Probability 5.5 Counting Techniques.
9. Counting and Probability 1 Summary
Elementary Statistics
Chapter 3 Probability.
Section 6.2 Probability Models
Presentation transcript:

Counting and Probability Sets and Counting Permutations & Combinations Probability

A set is a well-defined collection of distinct objects. Well-defined means there is a rule that enables us to determine whether a given object is an element of the set. If a set has no elements, it is called the empty set, or null set, and is denoted by the symbol Sets and Counting

If two sets A and B have precisely the same elements, then A and B are said to be equal and write A = B.

U A B

U BA

U BA

U B A

U A A

Theorem: Counting Formula

In survey of 50 people, 21 said they owned stocks, 32 said they owned bonds and 12 said they owned both stocks and bonds. How many of the 50 people owned stocks or bonds? How many owned neither? A: person owns stockB: person owns bonds = = = 9 owned neither

Universe is 50 people. In A = 21 owned stocks. In B = 32 owned bonds. In A  B = 12 owned both stocks and bonds. In A  B = = 41 owned stocks or bonds. In (A  B)= = 9 owned neither. A 21 B 32 (A  B) _ _ 9 A  B _12

Permutations and Combinations This is a tricky subject where even the text author makes mistakes. The next three slides are to distinguish some of the subtleties. After these are the slides from the text set and that help to elaborate some cases. Some situations can be very difficult to evaluate.

Counting Permutation Cases I A permutation is an ordered arrangement of r objects from n objects. To find the total number of possible cases, the easy types of situations are: 1.Multiplication of p,q,r,s,... ways of selection => Total number of cases is N = pqrs... 2.The number of permutations of r distinct objects with allowed repetition from n distinct objects (order is important) => N = n r 3.The number of permutations of r distinct objects with no repetition from n distinct objects (order is important) => N = P(n, r) = n!/(n - r)!

Counting Permutation Cases II A permutation is an ordered arrangement of r objects from n objects. An important harder situation of finding the total number of possible cases is when there are k distinct types each of non-distinct objects, with n 1 of the 1 st type, n 2 of the 2 nd type,... n k of the k th type, and n = n 1 + n 2 + n n k. 4.Permutation of n, some non-distinct, objects with allowed repetition from k distinct types of objects (order is important) => N = n!/[n 1 ! n 2 !... n k !].

Counting Combination Cases A combination is an arrangement with no regard to order of r distinct objects without repetition from n distinct objects (r < n). For finding the total number of possible cases, the easy type of situation is: The number of combinations of r distinct objects without repetition from n distinct objects (order is not important) => N = C(n, r) = n!/[r!(n - r)!].

Theorem: Multiplication Principle of Counting If a task consists of a sequence of choices in which there are p selections for the first choice, q selections for the second choice, r selections for the third choice, and so on, then the task of making these selections can be done in different ways. Permutations and Combinations

If a license plate consists of a letter, then 5 numbers, how many different types of license plates are possible?

A permutation is an ordered arrangement of n distinct objects without repetitions. The symbol P(n, r) represents the number of permutations of n distinct objects, taken r at a time, where r < n.

Theorem: Number of Permutations of n Distinct Objects Taken r at a Time The number of different arrangements from selecting r objects from a set of n objects (r < n), in which 1. the n objects are distinct 2. once an object is used, it cannot be repeated 3. order is important is given by the formula

A combination is an arrangement, without regard to order, of n distinct objects without repetitions. The symbol C(n, r) represents the number of combinations of n distinct objects taken r at a time, where r < n.

Theorem: Number of Combinations of n Distinct Objects Taken r at a Time The number of different arrangements from selecting r objects from a set of n objects (r < n), in which 1. the n objects are distinct 2. once an object is used, it cannot be repeated 3. order is not important is given by the formula

An event is an outcome from an experiment. Its probability gives the likelihood it occurs. A probability model lists the different outcomes from an experiment and their corresponding probabilities. To construct probability models, we need to know the sample space of the experiment. This is the set S that lists all the possible outcomes of the experiment. Probability

Determine the sample space resulting from the experiment of rolling a die. S = {1, 2, 3, 4, 5, 6}

Properties of Probabilities

Determine which of the following are probability models from rolling a single die. Not a probability model. The sum of all probabilities is not 1.

All probabilities between 0 and 1 inclusive and the sum of all probabilities is 1.

Not a probability model. The event “roll a 6” has a negative probability.

Theorem: Probability for Equally Likely Outcomes If an experiment has n equally likely outcomes, and if the number of ways an event E can occur is m, then the probability of E is

A classroom contains 20 students: 7 Freshman, 5 Sophomores, 6 Juniors, and 2 Seniors. A student is selected at random. Construct a probability model for this experiment.

Theorem: Additive Rule of P(E  F)

What is the probability of selecting an Ace or Diamond from a standard deck of cards?

Let S denote the sample space of an experiment and let E denote an event. The complement of E, denoted E, is the set of all outcomes in the sample space S that are not outcomes in the event E.

Theorem: Computing Probabilities of Complementary Events If E represents any event and E represents the complement of E, then

The probability of having 4 boys in a four child family is What is the probability of having at least one girl? Sample Space: {4 boys; 3 boys, 1 girl, 2 boys, 2 girls; 1 boy, 3 girls; 4 girls} E = “at least one girl” E = “4 boys” P(E) = 1 - P(E) = =

What is the probability of obtaining 3 of a kind when 5 cards are drawn from a standard 52-card deck? P(3 of a kind) This answer from the text slides is just wrong. For the correct value of 2.11% and similar examples see either of the poker sites:

What is the probability of obtaining 3 of a kind when 5 cards are drawn from a standard 52-card deck? P(3 of a kind) Done correctly, one has Why?