Bibliography. The Drunkard s Walk: How Randomness Rules Our Lives by Leonard Mlodinow. Duelling Idiots and Other Probability Puzzlers by Paul J.

Size: px
Start display at page:

Download "Bibliography. The Drunkard s Walk: How Randomness Rules Our Lives by Leonard Mlodinow. Duelling Idiots and Other Probability Puzzlers by Paul J."

Transcription

1

2

3 Bibliography The Drunkard s Walk: How Randomness Rules Our Lives by Leonard Mlodinow Duelling Idiots and Other Probability Puzzlers by Paul J. Nahin The History of mathematics by David M. Burton Is God a Mathematician? by Mario Livio Mathematical Analysis by Arya and Lardner Mathematical Ideas by Miller, Heeren & Hornsby

4 Introduction to Probability The Romans generally scorned mathematics. In the words of the Roman statesman Cicero ( BCE), The Greeks held the geometer in the highest honor; accordingly, nothing made more brilliant progress among them but mathematics. But we have established as the limit in that art its usefulness in measuring and counting. Indeed, whereas one might imagine a Greek textbook focused on the proof of congruences of abstract triangles, a typical Roman text focused on such issues as how to determine the width of a river when the enemy is occupying the other bank. In Roman culture it was comfort and war, not truth and beauty, that occupied center stage. because they focused on the practical, they did find value in probability; Cicero wrote probability is the very guide of life. [ Mlodinow, page 31.]

5 Obvious Probability. 1. A fair coin is flipped 3 times. If all three are heads, what is the probability of getting a heads on the fourth flip? 2. What is the probability of rolling a fair die and having a 6 show up? 3. In a family with a husband, a wife, and two children, what is the probability that the children are both girls? 4. In 1992 there were 4 million women in the U.S. who were battered by their husbands or boyfriends. In that same year, a total of 1,432 of those women were murdered. What is the probability that a battered women was killed by her husband or boyfriend?

6 Less Obvious Probability 1. A fair coin is flipped two times. The number of heads that can turn up is 0,1, or 2. What is the probability of getting 0 heads? 2. A family has two children and you know that one is a girl. What is the probability that both are girls? 3. In the above family, the girl you know is named Ruth. What is the probability that both are girls? 4. Three prisoners, A,B, and C are up for parole and each has an equal chance but only one of them will get it. In a conversation with the warden, A is told that now prisoner B, because of something he did, will definitely not get paroled. Prisoner A is excited by this and tells C the news. What is the probability that C will get paroled?

7

8 Blaise Pascal

9 Set Theory A Set is a collection of objects. The objects are called elements of the set. Particular sets can be defined by (i) a word description or (ii) a list of its elements or (iii) the use of set-builder notation. Example: (i) The set of counting numbers from 1 to 10 (ii) {1,2,3,4,5,6,7,8,9,10} or {1,2,3,...,10} (iii) { n n is a counting number from 1 to 10 } For a set to be useful, it must be well defined; that is, given any object, we must be able to tell whether or not it is an element of the given set.

10 An important set is the one with no elements; we call it the empty or null set and denote it by { } or by the symbol. The empty set can be described by words: The set of all green elephants in this room, or by a listing: { }, or by a set builder: {n n is a counting number between 3 and 4}. SYMBOLS: Sets are usually designated by capital letters and their elements by small letters. For example, Let A = {1,2,3} and B = {1,2,3,4,5} and C = {1,2,3,...}. To indicate an element of a set, we use the symbol. Thus, we can write 2 A or write n B to indicate that n is one of the numbers from 1 to 5. Note that the elements of C go on forever. To indicate an object is not an element of a set, we use. For example, 6 B and for any x whatsoever, x { }.

11 Venn Diagram U A A B C U is called the universal set A is called the complement of A and A = {x x A} We can observe that every element of B is also an element of A. In this case, we say B is a subset of A or B is contained in A and write B A. Is C a subset of A? Note: For any set A, A and A A

12 U A A C A union C is a new set: A C = {x x A or x C} Note: Union is like addition; and for any set A, A =A A intersect C is a new set; A C = {x x A and x C} A minus C is a new set; A C = {x x A and x C}

13

14 Now let s get back to the notion of a sample space. DEFINITIONS. RANDOM: non predictable. Experiment: observation of a random, repeatable happening. Outcome: the results of an experiment. Sample Space: a set S of all possible outcomes. Event: any subset of the sample space. Elements of an event are called successes.

15 Definition: To each outcome x in a sample space S, we assign a unique number P(x) (read p of x ) called the probability of x happening. P has the following properties: 1. 0 P(x) 1 for all x in S 2. P( ) = 0 3. The sum of the probabilities of all x in S is 1. For any event A S, P(A) is the probability that the event A happens and is the sum of P(x) for all outcomes x in A. Note: P(S) = 1 Important: (1) P(A ) = 1 - P(A) and (2) If the probability is uniform (each outcome has the same probability), then P(A) = the number of outcomes in A divided by the total number of outcomes.

16 Counting Principal: Suppose that a random process can have m outcomes and another random process can have n outcomes. Then the number of possible outcomes (i.e., the number of points in the sample space) of performing the processes in sequence is mn. This can be extended to any number of processes. Examples: (1) Toss a fair coin two times. (2) Family with three children. (3) Roll a pair of dice. (4) Deal out 5 cards from a 52 card deck. (311,875,200 points in the sample space.) Note: These are finite sample spaces. A slightly different case: (5) Toss a coin until the first head appears. This sample space is infinite.

17 1. A fair coin is flipped 3 times. If all three are heads, what is the probability of getting a heads on the fourth flip? What is a sample space for this problem? 2. What is the probability of rolling a fair die and having a 6 show up? What is a sample space for this problem? 3. In a family with a husband, a wife, and two children, what is the probability that the children are both girls? 4. In 1992 there were 4 million women in the U.S. who were battered by their husbands or boyfriends. In that same year, a total of 1,432 of those women were murdered. What is the probability that a battered women was killed by her husband or boyfriend?

18 . A fair coin is flipped two times. The number of heads that can turn up is 0,1, or 2. What is the probability of getting 0 heads? 2. A family has two children and you know that one is a girl. What is the probability that both are girls? 3. In the above family, the girl you know is named Ruth. What is the probability that both are girls? 4. Three prisoners, A,B, and C are up for parole and each has an equal chance but only one of them will get it. In a conversation with the warden, A is told that prisoner B will definitely not get paroled. Prisoner A is excited by this and tells C the news. What is the probability that C will get paroled?

19 No class next week

MA : Introductory Probability

MA : Introductory Probability MA 320-001: Introductory Probability David Murrugarra Department of Mathematics, University of Kentucky http://www.math.uky.edu/~dmu228/ma320/ Spring 2017 David Murrugarra (University of Kentucky) MA 320:

More information

Probability- describes the pattern of chance outcomes

Probability- describes the pattern of chance outcomes Chapter 6 Probability the study of randomness Probability- describes the pattern of chance outcomes Chance behavior is unpredictable in the short run, but has a regular and predictable pattern in the long

More information

Lecture 1. Chapter 1. (Part I) Material Covered in This Lecture: Chapter 1, Chapter 2 ( ). 1. What is Statistics?

Lecture 1. Chapter 1. (Part I) Material Covered in This Lecture: Chapter 1, Chapter 2 ( ). 1. What is Statistics? Lecture 1 (Part I) Material Covered in This Lecture: Chapter 1, Chapter 2 (2.1 --- 2.6). Chapter 1 1. What is Statistics? 2. Two definitions. (1). Population (2). Sample 3. The objective of statistics.

More information

Chapter 6: Probability The Study of Randomness

Chapter 6: Probability The Study of Randomness Chapter 6: Probability The Study of Randomness 6.1 The Idea of Probability 6.2 Probability Models 6.3 General Probability Rules 1 Simple Question: If tossing a coin, what is the probability of the coin

More information

The probability of an event is viewed as a numerical measure of the chance that the event will occur.

The probability of an event is viewed as a numerical measure of the chance that the event will occur. Chapter 5 This chapter introduces probability to quantify randomness. Section 5.1: How Can Probability Quantify Randomness? The probability of an event is viewed as a numerical measure of the chance that

More information

Econ 325: Introduction to Empirical Economics

Econ 325: Introduction to Empirical Economics Econ 325: Introduction to Empirical Economics Lecture 2 Probability Copyright 2010 Pearson Education, Inc. Publishing as Prentice Hall Ch. 3-1 3.1 Definition Random Experiment a process leading to an uncertain

More information

STAT 430/510 Probability

STAT 430/510 Probability STAT 430/510 Probability Hui Nie Lecture 3 May 28th, 2009 Review We have discussed counting techniques in Chapter 1. Introduce the concept of the probability of an event. Compute probabilities in certain

More information

Probabilistic models

Probabilistic models Probabilistic models Kolmogorov (Andrei Nikolaevich, 1903 1987) put forward an axiomatic system for probability theory. Foundations of the Calculus of Probabilities, published in 1933, immediately became

More information

Compound Events. The event E = E c (the complement of E) is the event consisting of those outcomes which are not in E.

Compound Events. The event E = E c (the complement of E) is the event consisting of those outcomes which are not in E. Compound Events Because we are using the framework of set theory to analyze probability, we can use unions, intersections and complements to break complex events into compositions of events for which it

More information

Probabilistic models

Probabilistic models Kolmogorov (Andrei Nikolaevich, 1903 1987) put forward an axiomatic system for probability theory. Foundations of the Calculus of Probabilities, published in 1933, immediately became the definitive formulation

More information

Axioms of Probability

Axioms of Probability Sample Space (denoted by S) The set of all possible outcomes of a random experiment is called the Sample Space of the experiment, and is denoted by S. Example 1.10 If the experiment consists of tossing

More information

Properties of Probability

Properties of Probability Econ 325 Notes on Probability 1 By Hiro Kasahara Properties of Probability In statistics, we consider random experiments, experiments for which the outcome is random, i.e., cannot be predicted with certainty.

More information

Probability and Sample space

Probability and Sample space Probability and Sample space We call a phenomenon random if individual outcomes are uncertain but there is a regular distribution of outcomes in a large number of repetitions. The probability of any outcome

More information

STAT 111 Recitation 1

STAT 111 Recitation 1 STAT 111 Recitation 1 Linjun Zhang January 20, 2017 What s in the recitation This class, and the exam of this class, is a mix of statistical concepts and calculations. We are going to do a little bit of

More information

ORF 245 Fundamentals of Statistics Chapter 5 Probability

ORF 245 Fundamentals of Statistics Chapter 5 Probability ORF 245 Fundamentals of Statistics Chapter 5 Probability Robert Vanderbei Oct 2015 Slides last edited on October 14, 2015 http://www.princeton.edu/ rvdb Sample Spaces (aka Populations) and Events When

More information

Fundamentals of Probability CE 311S

Fundamentals of Probability CE 311S Fundamentals of Probability CE 311S OUTLINE Review Elementary set theory Probability fundamentals: outcomes, sample spaces, events Outline ELEMENTARY SET THEORY Basic probability concepts can be cast in

More information

(6, 1), (5, 2), (4, 3), (3, 4), (2, 5), (1, 6)

(6, 1), (5, 2), (4, 3), (3, 4), (2, 5), (1, 6) Section 7.3: Compound Events Because we are using the framework of set theory to analyze probability, we can use unions, intersections and complements to break complex events into compositions of events

More information

Statistics 251: Statistical Methods

Statistics 251: Statistical Methods Statistics 251: Statistical Methods Probability Module 3 2018 file:///volumes/users/r/renaes/documents/classes/lectures/251301/renae/markdown/master%20versions/module3.html#1 1/33 Terminology probability:

More information

Chapter 14. From Randomness to Probability. Copyright 2012, 2008, 2005 Pearson Education, Inc.

Chapter 14. From Randomness to Probability. Copyright 2012, 2008, 2005 Pearson Education, Inc. Chapter 14 From Randomness to Probability Copyright 2012, 2008, 2005 Pearson Education, Inc. Dealing with Random Phenomena A random phenomenon is a situation in which we know what outcomes could happen,

More information

Probability: Sets, Sample Spaces, Events

Probability: Sets, Sample Spaces, Events Probability: Sets, Sample Spaces, Events Engineering Statistics Section 2.1 Josh Engwer TTU 01 February 2016 Josh Engwer (TTU) Probability: Sets, Sample Spaces, Events 01 February 2016 1 / 29 The Need

More information

Outline. Probability. Math 143. Department of Mathematics and Statistics Calvin College. Spring 2010

Outline. Probability. Math 143. Department of Mathematics and Statistics Calvin College. Spring 2010 Outline Math 143 Department of Mathematics and Statistics Calvin College Spring 2010 Outline Outline 1 Review Basics Random Variables Mean, Variance and Standard Deviation of Random Variables 2 More Review

More information

Probability Theory Review

Probability Theory Review Cogsci 118A: Natural Computation I Lecture 2 (01/07/10) Lecturer: Angela Yu Probability Theory Review Scribe: Joseph Schilz Lecture Summary 1. Set theory: terms and operators In this section, we provide

More information

3.2 Probability Rules

3.2 Probability Rules 3.2 Probability Rules The idea of probability rests on the fact that chance behavior is predictable in the long run. In the last section, we used simulation to imitate chance behavior. Do we always need

More information

ECE353: Probability and Random Processes. Lecture 2 - Set Theory

ECE353: Probability and Random Processes. Lecture 2 - Set Theory ECE353: Probability and Random Processes Lecture 2 - Set Theory Xiao Fu School of Electrical Engineering and Computer Science Oregon State University E-mail: xiao.fu@oregonstate.edu January 10, 2018 Set

More information

Week 2. Section Texas A& M University. Department of Mathematics Texas A& M University, College Station 22 January-24 January 2019

Week 2. Section Texas A& M University. Department of Mathematics Texas A& M University, College Station 22 January-24 January 2019 Week 2 Section 1.2-1.4 Texas A& M University Department of Mathematics Texas A& M University, College Station 22 January-24 January 2019 Oğuz Gezmiş (TAMU) Topics in Contemporary Mathematics II Week2 1

More information

STA Module 4 Probability Concepts. Rev.F08 1

STA Module 4 Probability Concepts. Rev.F08 1 STA 2023 Module 4 Probability Concepts Rev.F08 1 Learning Objectives Upon completing this module, you should be able to: 1. Compute probabilities for experiments having equally likely outcomes. 2. Interpret

More information

UNIT 5 ~ Probability: What Are the Chances? 1

UNIT 5 ~ Probability: What Are the Chances? 1 UNIT 5 ~ Probability: What Are the Chances? 1 6.1: Simulation Simulation: The of chance behavior, based on a that accurately reflects the phenomenon under consideration. (ex 1) Suppose we are interested

More information

Chapter 5 : Probability. Exercise Sheet. SHilal. 1 P a g e

Chapter 5 : Probability. Exercise Sheet. SHilal. 1 P a g e 1 P a g e experiment ( observing / measuring ) outcomes = results sample space = set of all outcomes events = subset of outcomes If we collect all outcomes we are forming a sample space If we collect some

More information

ECE 340 Probabilistic Methods in Engineering M/W 3-4:15. Lecture 2: Random Experiments. Prof. Vince Calhoun

ECE 340 Probabilistic Methods in Engineering M/W 3-4:15. Lecture 2: Random Experiments. Prof. Vince Calhoun ECE 340 Probabilistic Methods in Engineering M/W 3-4:15 Lecture 2: Random Experiments Prof. Vince Calhoun Reading This class: Section 2.1-2.2 Next class: Section 2.3-2.4 Homework: Assignment 1: From the

More information

Notes 1 Autumn Sample space, events. S is the number of elements in the set S.)

Notes 1 Autumn Sample space, events. S is the number of elements in the set S.) MAS 108 Probability I Notes 1 Autumn 2005 Sample space, events The general setting is: We perform an experiment which can have a number of different outcomes. The sample space is the set of all possible

More information

Lecture 1. ABC of Probability

Lecture 1. ABC of Probability Math 408 - Mathematical Statistics Lecture 1. ABC of Probability January 16, 2013 Konstantin Zuev (USC) Math 408, Lecture 1 January 16, 2013 1 / 9 Agenda Sample Spaces Realizations, Events Axioms of Probability

More information

What is the probability of getting a heads when flipping a coin

What is the probability of getting a heads when flipping a coin Chapter 2 Probability Probability theory is a branch of mathematics dealing with chance phenomena. The origins of the subject date back to the Italian mathematician Cardano about 1550, and French mathematicians

More information

MATH 10 INTRODUCTORY STATISTICS

MATH 10 INTRODUCTORY STATISTICS MATH 10 INTRODUCTORY STATISTICS Ramesh Yapalparvi Week 2 Chapter 4 Bivariate Data Data with two/paired variables, Pearson correlation coefficient and its properties, general variance sum law Chapter 6

More information

Lecture 1 Introduction to Probability and Set Theory Text: A Course in Probability by Weiss

Lecture 1 Introduction to Probability and Set Theory Text: A Course in Probability by Weiss Lecture 1 to and Set Theory Text: A Course in by Weiss 1.2 2.3 STAT 225 to Models January 13, 2014 to and Whitney Huang Purdue University 1.1 Agenda to and 1 2 3 1.2 Motivation Uncertainty/Randomness in

More information

Lecture 6 Probability

Lecture 6 Probability Lecture 6 Probability Example: When you toss a coin, there are only two possible outcomes, heads and tails. What if we toss a coin 4 times? Figure below shows the results of tossing a coin 5000 times twice.

More information

Example. What is the sample space for flipping a fair coin? Rolling a 6-sided die? Find the event E where E = {x x has exactly one head}

Example. What is the sample space for flipping a fair coin? Rolling a 6-sided die? Find the event E where E = {x x has exactly one head} Chapter 7 Notes 1 (c) Epstein, 2013 CHAPTER 7: PROBABILITY 7.1: Experiments, Sample Spaces and Events Chapter 7 Notes 2 (c) Epstein, 2013 What is the sample space for flipping a fair coin three times?

More information

Probability Theory and Applications

Probability Theory and Applications Probability Theory and Applications Videos of the topics covered in this manual are available at the following links: Lesson 4 Probability I http://faculty.citadel.edu/silver/ba205/online course/lesson

More information

Probability Year 9. Terminology

Probability Year 9. Terminology Probability Year 9 Terminology Probability measures the chance something happens. Formally, we say it measures how likely is the outcome of an event. We write P(result) as a shorthand. An event is some

More information

Chapter 2 PROBABILITY SAMPLE SPACE

Chapter 2 PROBABILITY SAMPLE SPACE Chapter 2 PROBABILITY Key words: Sample space, sample point, tree diagram, events, complement, union and intersection of an event, mutually exclusive events; Counting techniques: multiplication rule, permutation,

More information

I - Probability. What is Probability? the chance of an event occuring. 1classical probability. 2empirical probability. 3subjective probability

I - Probability. What is Probability? the chance of an event occuring. 1classical probability. 2empirical probability. 3subjective probability What is Probability? the chance of an event occuring eg 1classical probability 2empirical probability 3subjective probability Section 2 - Probability (1) Probability - Terminology random (probability)

More information

Chap 4 Probability p227 The probability of any outcome in a random phenomenon is the proportion of times the outcome would occur in a long series of

Chap 4 Probability p227 The probability of any outcome in a random phenomenon is the proportion of times the outcome would occur in a long series of Chap 4 Probability p227 The probability of any outcome in a random phenomenon is the proportion of times the outcome would occur in a long series of repetitions. (p229) That is, probability is a long-term

More information

The enumeration of all possible outcomes of an experiment is called the sample space, denoted S. E.g.: S={head, tail}

The enumeration of all possible outcomes of an experiment is called the sample space, denoted S. E.g.: S={head, tail} Random Experiment In random experiments, the result is unpredictable, unknown prior to its conduct, and can be one of several choices. Examples: The Experiment of tossing a coin (head, tail) The Experiment

More information

Basic Probability. Introduction

Basic Probability. Introduction Basic Probability Introduction The world is an uncertain place. Making predictions about something as seemingly mundane as tomorrow s weather, for example, is actually quite a difficult task. Even with

More information

HW MATH425/525 Lecture Notes 1

HW MATH425/525 Lecture Notes 1 HW MATH425/525 Lecture Notes 1 Definition 4.1 If an experiment can be repeated under the same condition, its outcome cannot be predicted with certainty, and the collection of its every possible outcome

More information

Probability Year 10. Terminology

Probability Year 10. Terminology Probability Year 10 Terminology Probability measures the chance something happens. Formally, we say it measures how likely is the outcome of an event. We write P(result) as a shorthand. An event is some

More information

ORF 245 Fundamentals of Statistics Chapter 1 Probability

ORF 245 Fundamentals of Statistics Chapter 1 Probability ORF 245 Fundamentals of Statistics Chapter 1 Probability Robert Vanderbei Sept 2014 Slides last edited on September 19, 2014 http://www.princeton.edu/ rvdb Course Info Prereqs: Textbook: Three semesters

More information

18.600: Lecture 3 What is probability?

18.600: Lecture 3 What is probability? 18.600: Lecture 3 What is probability? Scott Sheffield MIT Outline Formalizing probability Sample space DeMorgan s laws Axioms of probability Outline Formalizing probability Sample space DeMorgan s laws

More information

= 2 5 Note how we need to be somewhat careful with how we define the total number of outcomes in b) and d). We will return to this later.

= 2 5 Note how we need to be somewhat careful with how we define the total number of outcomes in b) and d). We will return to this later. PROBABILITY MATH CIRCLE (ADVANCED /27/203 The likelyhood of something (usually called an event happening is called the probability. Probability (informal: We can calculate probability using a ratio: want

More information

Lecture Lecture 5

Lecture Lecture 5 Lecture 4 --- Lecture 5 A. Basic Concepts (4.1-4.2) 1. Experiment: A process of observing a phenomenon that has variation in its outcome. Examples: (E1). Rolling a die, (E2). Drawing a card form a shuffled

More information

Lecture 1 : The Mathematical Theory of Probability

Lecture 1 : The Mathematical Theory of Probability Lecture 1 : The Mathematical Theory of Probability 0/ 30 1. Introduction Today we will do 2.1 and 2.2. We will skip Chapter 1. We all have an intuitive notion of probability. Let s see. What is the probability

More information

Notes Week 2 Chapter 3 Probability WEEK 2 page 1

Notes Week 2 Chapter 3 Probability WEEK 2 page 1 Notes Week 2 Chapter 3 Probability WEEK 2 page 1 The sample space of an experiment, sometimes denoted S or in probability theory, is the set that consists of all possible elementary outcomes of that experiment

More information

MATH 3C: MIDTERM 1 REVIEW. 1. Counting

MATH 3C: MIDTERM 1 REVIEW. 1. Counting MATH 3C: MIDTERM REVIEW JOE HUGHES. Counting. Imagine that a sports betting pool is run in the following way: there are 20 teams, 2 weeks, and each week you pick a team to win. However, you can t pick

More information

Mathematical Probability

Mathematical Probability Mathematical Probability STA 281 Fall 2011 1 Introduction Engineers and scientists are always exposed to data, both in their professional capacities and in everyday activities. The discipline of statistics

More information

Section 13.3 Probability

Section 13.3 Probability 288 Section 13.3 Probability Probability is a measure of how likely an event will occur. When the weather forecaster says that there will be a 50% chance of rain this afternoon, the probability that it

More information

ELEG 3143 Probability & Stochastic Process Ch. 1 Probability

ELEG 3143 Probability & Stochastic Process Ch. 1 Probability Department of Electrical Engineering University of Arkansas ELEG 3143 Probability & Stochastic Process Ch. 1 Probability Dr. Jingxian Wu wuj@uark.edu OUTLINE 2 Applications Elementary Set Theory Random

More information

Venn Diagrams; Probability Laws. Notes. Set Operations and Relations. Venn Diagram 2.1. Venn Diagrams; Probability Laws. Notes

Venn Diagrams; Probability Laws. Notes. Set Operations and Relations. Venn Diagram 2.1. Venn Diagrams; Probability Laws. Notes Lecture 2 s; Text: A Course in Probability by Weiss 2.4 STAT 225 Introduction to Probability Models January 8, 2014 s; Whitney Huang Purdue University 2.1 Agenda s; 1 2 2.2 Intersection: the intersection

More information

Recap. The study of randomness and uncertainty Chances, odds, likelihood, expected, probably, on average,... PROBABILITY INFERENTIAL STATISTICS

Recap. The study of randomness and uncertainty Chances, odds, likelihood, expected, probably, on average,... PROBABILITY INFERENTIAL STATISTICS Recap. Probability (section 1.1) The study of randomness and uncertainty Chances, odds, likelihood, expected, probably, on average,... PROBABILITY Population Sample INFERENTIAL STATISTICS Today. Formulation

More information

Exclusive Disjunction

Exclusive Disjunction Exclusive Disjunction Recall A statement is a declarative sentence that is either true or false, but not both. If we have a declarative sentence s, p: s is true, and q: s is false, can we rewrite s is

More information

Random Variable. Discrete Random Variable. Continuous Random Variable. Discrete Random Variable. Discrete Probability Distribution

Random Variable. Discrete Random Variable. Continuous Random Variable. Discrete Random Variable. Discrete Probability Distribution Random Variable Theoretical Probability Distribution Random Variable Discrete Probability Distributions A variable that assumes a numerical description for the outcome of a random eperiment (by chance).

More information

Intermediate Math Circles November 8, 2017 Probability II

Intermediate Math Circles November 8, 2017 Probability II Intersection of Events and Independence Consider two groups of pairs of events Intermediate Math Circles November 8, 017 Probability II Group 1 (Dependent Events) A = {a sales associate has training} B

More information

AMS7: WEEK 2. CLASS 2

AMS7: WEEK 2. CLASS 2 AMS7: WEEK 2. CLASS 2 Introduction to Probability. Probability Rules. Independence and Conditional Probability. Bayes Theorem. Risk and Odds Ratio Friday April 10, 2015 Probability: Introduction Probability:

More information

Basic Set Concepts (2.1)

Basic Set Concepts (2.1) 1 Basic Set Concepts (2.1) I. Set A collection of objects whose contents can be clearly determined. Capitol letters usually name a set. Elements are the contents in a set. Sets can be described using words,

More information

STAT 430/510 Probability Lecture 7: Random Variable and Expectation

STAT 430/510 Probability Lecture 7: Random Variable and Expectation STAT 430/510 Probability Lecture 7: Random Variable and Expectation Pengyuan (Penelope) Wang June 2, 2011 Review Properties of Probability Conditional Probability The Law of Total Probability Bayes Formula

More information

STAT Chapter 3: Probability

STAT Chapter 3: Probability Basic Definitions STAT 515 --- Chapter 3: Probability Experiment: A process which leads to a single outcome (called a sample point) that cannot be predicted with certainty. Sample Space (of an experiment):

More information

Probability (Devore Chapter Two)

Probability (Devore Chapter Two) Probability (Devore Chapter Two) 1016-345-01: Probability and Statistics for Engineers Fall 2012 Contents 0 Administrata 2 0.1 Outline....................................... 3 1 Axiomatic Probability 3

More information

Math 1313 Experiments, Events and Sample Spaces

Math 1313 Experiments, Events and Sample Spaces Math 1313 Experiments, Events and Sample Spaces At the end of this recording, you should be able to define and use the basic terminology used in defining experiments. Terminology The next main topic in

More information

Binomial Probability. Permutations and Combinations. Review. History Note. Discuss Quizzes/Answer Questions. 9.0 Lesson Plan

Binomial Probability. Permutations and Combinations. Review. History Note. Discuss Quizzes/Answer Questions. 9.0 Lesson Plan 9.0 Lesson Plan Discuss Quizzes/Answer Questions History Note Review Permutations and Combinations Binomial Probability 1 9.1 History Note Pascal and Fermat laid out the basic rules of probability in a

More information

4. Probability of an event A for equally likely outcomes:

4. Probability of an event A for equally likely outcomes: University of California, Los Angeles Department of Statistics Statistics 110A Instructor: Nicolas Christou Probability Probability: A measure of the chance that something will occur. 1. Random experiment:

More information

Chapter 13, Probability from Applied Finite Mathematics by Rupinder Sekhon was developed by OpenStax College, licensed by Rice University, and is

Chapter 13, Probability from Applied Finite Mathematics by Rupinder Sekhon was developed by OpenStax College, licensed by Rice University, and is Chapter 13, Probability from Applied Finite Mathematics by Rupinder Sekhon was developed by OpenStax College, licensed by Rice University, and is available on the Connexions website. It is used under a

More information

Business Statistics. Lecture 3: Random Variables and the Normal Distribution

Business Statistics. Lecture 3: Random Variables and the Normal Distribution Business Statistics Lecture 3: Random Variables and the Normal Distribution 1 Goals for this Lecture A little bit of probability Random variables The normal distribution 2 Probability vs. Statistics Probability:

More information

1 INFO Sep 05

1 INFO Sep 05 Events A 1,...A n are said to be mutually independent if for all subsets S {1,..., n}, p( i S A i ) = p(a i ). (For example, flip a coin N times, then the events {A i = i th flip is heads} are mutually

More information

Announcements. Topics: To Do:

Announcements. Topics: To Do: Announcements Topics: In the Probability and Statistics module: - Sections 1 + 2: Introduction to Stochastic Models - Section 3: Basics of Probability Theory - Section 4: Conditional Probability; Law of

More information

P [(E and F )] P [F ]

P [(E and F )] P [F ] CONDITIONAL PROBABILITY AND INDEPENDENCE WORKSHEET MTH 1210 This worksheet supplements our textbook material on the concepts of conditional probability and independence. The exercises at the end of each

More information

CMPSCI 240: Reasoning about Uncertainty

CMPSCI 240: Reasoning about Uncertainty CMPSCI 240: Reasoning about Uncertainty Lecture 2: Sets and Events Andrew McGregor University of Massachusetts Last Compiled: January 27, 2017 Outline 1 Recap 2 Experiments and Events 3 Probabilistic Models

More information

Lecture #13 Tuesday, October 4, 2016 Textbook: Sections 7.3, 7.4, 8.1, 8.2, 8.3

Lecture #13 Tuesday, October 4, 2016 Textbook: Sections 7.3, 7.4, 8.1, 8.2, 8.3 STATISTICS 200 Lecture #13 Tuesday, October 4, 2016 Textbook: Sections 7.3, 7.4, 8.1, 8.2, 8.3 Objectives: Identify, and resist the temptation to fall for, the gambler s fallacy Define random variable

More information

2. Probability. Chris Piech and Mehran Sahami. Oct 2017

2. Probability. Chris Piech and Mehran Sahami. Oct 2017 2. Probability Chris Piech and Mehran Sahami Oct 2017 1 Introduction It is that time in the quarter (it is still week one) when we get to talk about probability. Again we are going to build up from first

More information

Formalizing Probability. Choosing the Sample Space. Probability Measures

Formalizing Probability. Choosing the Sample Space. Probability Measures Formalizing Probability Choosing the Sample Space What do we assign probability to? Intuitively, we assign them to possible events (things that might happen, outcomes of an experiment) Formally, we take

More information

PERMUTATIONS, COMBINATIONS AND DISCRETE PROBABILITY

PERMUTATIONS, COMBINATIONS AND DISCRETE PROBABILITY Friends, we continue the discussion with fundamentals of discrete probability in the second session of third chapter of our course in Discrete Mathematics. The conditional probability and Baye s theorem

More information

Problems from Probability and Statistical Inference (9th ed.) by Hogg, Tanis and Zimmerman.

Problems from Probability and Statistical Inference (9th ed.) by Hogg, Tanis and Zimmerman. Math 224 Fall 2017 Homework 1 Drew Armstrong Problems from Probability and Statistical Inference (9th ed.) by Hogg, Tanis and Zimmerman. Section 1.1, Exercises 4,5,6,7,9,12. Solutions to Book Problems.

More information

RULES OF PROBABILITY

RULES OF PROBABILITY RULES OF PROBABILITY COMPLEMENTARY EVENTS: Consider any event A. Let p(a) be the probability that A happens and let p(a ) read as the probability of A prime or A c (A Complement), be the probability that

More information

5.5 PROBABILITY AS A THEORETICAL CONCEPT

5.5 PROBABILITY AS A THEORETICAL CONCEPT 5.5 PROAILIY AS A EOREICAL CONCEP So far, we have solved probability problems by estimating the required probability after conducting some sort of experiment and collecting data. ut probability may be

More information

STA 247 Solutions to Assignment #1

STA 247 Solutions to Assignment #1 STA 247 Solutions to Assignment #1 Question 1: Suppose you throw three six-sided dice (coloured red, green, and blue) repeatedly, until the three dice all show different numbers. Assuming that these dice

More information

Term Definition Example Random Phenomena

Term Definition Example Random Phenomena UNIT VI STUDY GUIDE Probabilities Course Learning Outcomes for Unit VI Upon completion of this unit, students should be able to: 1. Apply mathematical principles used in real-world situations. 1.1 Demonstrate

More information

Statistics for Engineers

Statistics for Engineers Statistics for Engineers Antony Lewis http://cosmologist.info/teaching/stat/ Starter question Have you previously done any statistics? 1. Yes 2. No 54% 46% 1 2 BOOKS Chatfield C, 1989. Statistics for

More information

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

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

More information

LECTURE 1. 1 Introduction. 1.1 Sample spaces and events

LECTURE 1. 1 Introduction. 1.1 Sample spaces and events LECTURE 1 1 Introduction The first part of our adventure is a highly selective review of probability theory, focusing especially on things that are most useful in statistics. 1.1 Sample spaces and events

More information

MATH2206 Prob Stat/20.Jan Weekly Review 1-2

MATH2206 Prob Stat/20.Jan Weekly Review 1-2 MATH2206 Prob Stat/20.Jan.2017 Weekly Review 1-2 This week I explained the idea behind the formula of the well-known statistic standard deviation so that it is clear now why it is a measure of dispersion

More information

AP Statistics Ch 6 Probability: The Study of Randomness

AP Statistics Ch 6 Probability: The Study of Randomness Ch 6.1 The Idea of Probability Chance behavior is unpredictable in the short run but has a regular and predictable pattern in the long run. We call a phenomenon random if individual outcomes are uncertain

More information

Event A: at least one tail observed A:

Event A: at least one tail observed A: Chapter 3 Probability 3.1 Events, sample space, and probability Basic definitions: An is an act of observation that leads to a single outcome that cannot be predicted with certainty. A (or simple event)

More information

A brief review of basics of probabilities

A brief review of basics of probabilities brief review of basics of probabilities Milos Hauskrecht milos@pitt.edu 5329 Sennott Square robability theory Studies and describes random processes and their outcomes Random processes may result in multiple

More information

5.3 Conditional Probability and Independence

5.3 Conditional Probability and Independence 28 CHAPTER 5. PROBABILITY 5. Conditional Probability and Independence 5.. 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

Discrete Mathematics and Probability Theory Spring 2016 Rao and Walrand Note 14

Discrete Mathematics and Probability Theory Spring 2016 Rao and Walrand Note 14 CS 70 Discrete Mathematics and Probability Theory Spring 2016 Rao and Walrand Note 14 Introduction One of the key properties of coin flips is independence: if you flip a fair coin ten times and get ten

More information

Chapter 8 Sequences, Series, and Probability

Chapter 8 Sequences, Series, and Probability Chapter 8 Sequences, Series, and Probability Overview 8.1 Sequences and Series 8.2 Arithmetic Sequences and Partial Sums 8.3 Geometric Sequences and Partial Sums 8.5 The Binomial Theorem 8.6 Counting Principles

More information

STP 226 ELEMENTARY STATISTICS

STP 226 ELEMENTARY STATISTICS STP 226 ELEMENTARY STATISTICS CHAPTER 5 Probability Theory - science of uncertainty 5.1 Probability Basics Equal-Likelihood Model Suppose an experiment has N possible outcomes, all equally likely. Then

More information

4/17/2012. NE ( ) # of ways an event can happen NS ( ) # of events in the sample space

4/17/2012. NE ( ) # of ways an event can happen NS ( ) # of events in the sample space I. Vocabulary: A. Outcomes: the things that can happen in a probability experiment B. Sample Space (S): all possible outcomes C. Event (E): one outcome D. Probability of an Event (P(E)): the likelihood

More information

Probability: Part 1 Naima Hammoud

Probability: Part 1 Naima Hammoud Probability: Part 1 Naima ammoud Feb 7, 2017 Motivation ossing a coin Rolling a die Outcomes: eads or ails Outcomes: 1, 2, 3, 4, 5 or 6 Defining Probability If I toss a coin, there is a 50% chance I will

More information

Independence. P(A) = P(B) = 3 6 = 1 2, and P(C) = 4 6 = 2 3.

Independence. P(A) = P(B) = 3 6 = 1 2, and P(C) = 4 6 = 2 3. Example: A fair die is tossed and we want to guess the outcome. The outcomes will be 1, 2, 3, 4, 5, 6 with equal probability 1 6 each. If we are interested in getting the following results: A = {1, 3,

More information

Lecture 8: Probability

Lecture 8: Probability Lecture 8: Probability The idea of probability is well-known The flipping of a balanced coin can produce one of two outcomes: T (tail) and H (head) and the symmetry between the two outcomes means, of course,

More information

Lecture 10: Probability distributions TUESDAY, FEBRUARY 19, 2019

Lecture 10: Probability distributions TUESDAY, FEBRUARY 19, 2019 Lecture 10: Probability distributions DANIEL WELLER TUESDAY, FEBRUARY 19, 2019 Agenda What is probability? (again) Describing probabilities (distributions) Understanding probabilities (expectation) Partial

More information

If the objects are replaced there are n choices each time yielding n r ways. n C r and in the textbook by g(n, r).

If the objects are replaced there are n choices each time yielding n r ways. n C r and in the textbook by g(n, r). Caveat: Not proof read. Corrections appreciated. Combinatorics In the following, n, n 1, r, etc. will denote non-negative integers. Rule 1 The number of ways of ordering n distinguishable objects (also

More information