Introductory Probability

Size: px
Start display at page:

Download "Introductory Probability"

Transcription

1 Introductory Probability Conditional Probability: Independent Events and Intersections Dr. Nguyen Department of Mathematics UK February 15, 2019

2 Agenda Independent Events and Intersections Two Events Examples Multiple Events Announcement: The fth homework is available. There is a quiz next Friday.

3 Independent Events Denition (4.1) Let E and F be two events. Then they are independent if either: P(E F ) = P(E) or P(F E) = P(F ). One or both events has probability zero.

4 Independent Events Denition (4.1) Let E and F be two events. Then they are independent if either: P(E F ) = P(E) or P(F E) = P(F ). One or both events has probability zero. Knowledge that E (or F ) occurred does not aect the probability of F (or E ).

5 Let E and F be two events. Then they are independent if either: P(E F ) = P(E) or P(F E) = P(F ). One or both events has probability zero. Knowledge that E (or F ) occurred does not aect the probability of F (or E ). Theorem (4.1) Two events E and F are independent if and only if P(E F ) = P(E)P(F ).

6 Four Statements Let E and F be events. The following four statements are either all true or all false: 1. The events E and F are independent. 2. P(E F ) = P(E) P(F ). 3. P(E F ) = P(E). 4. P(F E) = P(F ). To show whether E and F are independent or not, we need to compute P(E), P(F ), and at least one of P(E F ), P(E F ), and P(F E), and see whether the corresponding equality holds or not.

7 Sequences of 3 Tosses Overview Suppose we toss a fair coin 3 times. Let Ω be the 2 3 = 8 sequences of tosses. Let A 1 be the event The rst toss lands Heads. Let A 2 be the event There is 1 Head among the last two tosses. Let E be the event The rst toss lands Heads. Let F be the event There are 2 Heads total. We see if A 1 and A 2, and if E and F, are independent events.

8 Sequences of 3 Tosses 1a There are eight equally likely sequences of 3 tosses: HHH,HHT,HTH,THH, HTT,THT,TTH,TTT. Those in A 1 are in yellow, those in A 2 are blue, those in both are green. P(A 1 ) = 4/8 = 1/2 P(A 2 ) = 4/8 = 1/2 Let's compute P(A 1 A2 ) and compare it to P(A 1 ) P(A 2 ): P(A 1 A 2 ) = 2/8 = 1/4 P(A 1 A 2 ) = 1 4 = = P(A 1) P(A 2 ).

9 Sequences of 3 Tosses 1a There are eight equally likely sequences of 3 tosses: HHH,HHT,HTH,THH, HTT,THT,TTH,TTT. Those in A 1 are in yellow, those in A 2 are blue, those in both are green. P(A 1 ) = 4/8 = 1/2 P(A 2 ) = 4/8 = 1/2 Let's compute P(A 1 A2 ) and compare it to P(A 1 ) P(A 2 ): P(A 1 A 2 ) = 2/8 = 1/4 P(A 1 A 2 ) = 1 4 = = P(A 1) P(A 2 ).

10 Sequences of 3 Tosses 1a There are eight equally likely sequences of 3 tosses: HHH,HHT,HTH,THH, HTT,THT,TTH,TTT. Those in A 1 are in yellow, those in A 2 are blue, those in both are green. P(A 1 ) = 4/8 = 1/2 P(A 2 ) = 4/8 = 1/2 Let's compute P(A 1 A2 ) and compare it to P(A 1 ) P(A 2 ): P(A 1 A 2 ) = 2/8 = 1/4 P(A 1 A 2 ) = 1 4 = = P(A 1) P(A 2 ).

11 Sequences of 3 Tosses 1a There are eight equally likely sequences of 3 tosses: HHH,HHT,HTH,THH, HTT,THT,TTH,TTT. Those in A 1 are in yellow, those in A 2 are blue, those in both are green. P(A 1 ) = 4/8 = 1/2 P(A 2 ) = 4/8 = 1/2 Let's compute P(A 1 A2 ) and compare it to P(A 1 ) P(A 2 ): P(A 1 A 2 ) = 2/8 = 1/4 P(A 1 A 2 ) = 1 4 = = P(A 1) P(A 2 ).

12 There are eight equally likely sequences of 3 tosses: HHH,HHT,HTH,THH, HTT,THT,TTH,TTT. Those in A 1 are in yellow, those in A 2 are blue, those in both are green. P(A 1 ) = 4/8 = 1/2 P(A 2 ) = 4/8 = 1/2 Let's compute P(A 1 A2 ) and compare it to P(A 1 ) P(A 2 ): P(A 1 A 2 ) = 2/8 = 1/4 P(A 1 A 2 ) = 1 4 = = P(A 1) P(A 2 ). Since P(A 1 A2 ) = P(A 1 ) P(A 2 ), A 1 and A 2 are independent.

13 Sequences of 3 Tosses 1b There are eight equally likely sequences of 3 tosses: HHH,HHT,HTH,THH, HTT,THT,TTH,TTT. Those in A 1 are in yellow, those in A 2 are blue, those in both are green. Let's compute P(A 1 A 2 ) and compare it to P(A 1 ). We'll do this twice for practice. First, let's use our formula: P(A 1 A 2 ) = P(A 1 A 2 ) P(A 2 ) = 1/4 1/2 = 1 2.

14 There are eight equally likely sequences of 3 tosses: HHH,HHT,HTH,THH, HTT,THT,TTH,TTT. Those in A 1 are in yellow, those in A 2 are blue, those in both are green. Let's compute P(A 1 A 2 ) and compare it to P(A 1 ). We'll do this twice for practice. First, let's use our formula: P(A 1 A 2 ) = P(A 1 A 2 ) P(A 2 ) = 1/4 1/2 = 1 2. Let's compute again using A 2 as the sample space: P(A 1 A 2 ) 2 outcomes with H rst and 1 H among last 2 tosses = 4 outcomes with 1 H among last 2 tosses = 1 2.

15 Let's compute P(A 1 A 2 ) and compare it to P(A 1 ). We'll do this twice for practice. First, let's use our formula: P(A 1 A 2 ) = P(A 1 A 2 ) P(A 2 ) = 1/4 1/2 = 1 2. Let's compute again using A 2 as the sample space: P(A 1 A 2 ) 2 outcomes with H rst and 1 H among last 2 tosses = 4 outcomes with 1 H among last 2 tosses We see that P(A 1 A 2 ) = 1 2 = P(A 1), so since P(A 1 A 2 ) = P(A 1 ), A 1 and A 2 are independent. = 1 2.

16 Sequences of 3 Tosses 1c There are eight equally likely sequences of 3 tosses: HHH,HHT,HTH,THH, HTT,THT,TTH,TTT. Those in A 1 are in yellow, those in A 2 are blue, those in both are green. Let's compute P(A 2 A 1 ) and compare it to P(A 2 ). We'll do this twice for practice. First, let's use our formula: P(A 2 A 1 ) = P(A 1 A 2 ) P(A 1 ) = 1/4 1/2 = 1 2.

17 There are eight equally likely sequences of 3 tosses: HHH,HHT,HTH,THH, HTT,THT,TTH,TTT. Those in A 1 are in yellow, those in A 2 are blue, those in both are green. Let's compute P(A 2 A 1 ) and compare it to P(A 2 ). We'll do this twice for practice. First, let's use our formula: P(A 2 A 1 ) = P(A 1 A 2 ) P(A 1 ) = 1/4 1/2 = 1 2. Let's compute again using A 1 as the sample space: P(A 2 A 1 ) 2 outcomes with H rst and 1 H among last 2 tosses = 4 outcomes with H rst = 1 2.

18 Let's compute P(A 2 A 1 ) and compare it to P(A 2 ). We'll do this twice for practice. First, let's use our formula: P(A 2 A 1 ) = P(A 1 A 2 ) P(A 1 ) = 1/4 1/2 = 1 2. Let's compute again using A 1 as the sample space: P(A 2 A 1 ) 2 outcomes with H rst and 1 H among last 2 tosses = 4 outcomes with H rst We see that P(A 2 A 1 ) = 1 2 = P(A 2), so since P(A 2 A 1 ) = P(A 2 ), A 1 and A 2 are independent. = 1 2.

19 Sequences of 3 Tosses 1 Intuition: Events A 1 (rst toss is heads) and A 2 (one head among the last two tosses) involve dierent sets of coin tosses, so knowledge about one event should not tell us anything about the other event.

20 Sequences of 3 Tosses 2a There are eight equally likely sequences of 3 tosses: HHH,HHT,HTH,THH, HTT,THT,TTH,TTT. Let E be the event rst toss is heads and let F be the event there are exactly two heads. Those in E are yellow, those in F are blue, those in both are green. P(E) = 4/8 = 1/2 P(F ) = 3/8 Let's compute P(E F ) and compare it to P(E) P(F ): P(E F ) = 2/8 = 1/4 P(E F ) = = P(E) P(F ). 8

21 Sequences of 3 Tosses 2a There are eight equally likely sequences of 3 tosses: HHH,HHT,HTH,THH, HTT,THT,TTH,TTT. Let E be the event rst toss is heads and let F be the event there are exactly two heads. Those in E are yellow, those in F are blue, those in both are green. P(E) = 4/8 = 1/2 P(F ) = 3/8 Let's compute P(E F ) and compare it to P(E) P(F ): P(E F ) = 2/8 = 1/4 P(E F ) = = P(E) P(F ). 8

22 Sequences of 3 Tosses 2a There are eight equally likely sequences of 3 tosses: HHH,HHT,HTH,THH, HTT,THT,TTH,TTT. Let E be the event rst toss is heads and let F be the event there are exactly two heads. Those in E are yellow, those in F are blue, those in both are green. P(E) = 4/8 = 1/2 P(F ) = 3/8 Let's compute P(E F ) and compare it to P(E) P(F ): P(E F ) = 2/8 = 1/4 P(E F ) = = P(E) P(F ). 8

23 Sequences of 3 Tosses 2a There are eight equally likely sequences of 3 tosses: HHH,HHT,HTH,THH, HTT,THT,TTH,TTT. Let E be the event rst toss is heads and let F be the event there are exactly two heads. Those in E are yellow, those in F are blue, those in both are green. P(E) = 4/8 = 1/2 P(F ) = 3/8 Let's compute P(E F ) and compare it to P(E) P(F ): P(E F ) = 2/8 = 1/4 P(E F ) = = P(E) P(F ). 8

24 There are eight equally likely sequences of 3 tosses: HHH,HHT,HTH,THH, HTT,THT,TTH,TTT. Let E be the event rst toss is heads and let F be the event there are exactly two heads. Those in E are yellow, those in F are blue, those in both are green. P(E) = 4/8 = 1/2 P(F ) = 3/8 Let's compute P(E F ) and compare it to P(E) P(F ): P(E F ) = 2/8 = 1/4 P(E F ) = = P(E) P(F ). 8 Since P(E F ) P(E) P(F ), E and F are not independent.

25 Sequences of 3 Tosses 2b There are eight equally likely sequences of 3 tosses: HHH,HHT,HTH,THH, HTT,THT,TTH,TTT. Those in E are yellow, those in F are blue, those in both are green. Let's compute P(F E) and compare it to P(F ). We'll use E as the sample space: P(F E) 2 outcomes with H rst and 2 H's total = = 1 4 outcomes with H rst 2.

26 There are eight equally likely sequences of 3 tosses: HHH,HHT,HTH,THH, HTT,THT,TTH,TTT. Those in E are yellow, those in F are blue, those in both are green. Let's compute P(F E) and compare it to P(F ). We'll use E as the sample space: P(F E) = 2 outcomes with H rst and 2 H's total 4 outcomes with H rst = 1 2. This is not equal to P(F ), which was 3/8. Hence E and F are not independent.

27 Sequences of 3 Tosses 2c There are eight equally likely sequences of 3 tosses: HHH,HHT,HTH,THH, HTT,THT,TTH,TTT. Let's compute P(E F ) with F as the sample space: P(E F ) 2 outcomes with H rst and 2 H's total = = 2 3 outcomes with exactly 2 H's 3.

28 There are eight equally likely sequences of 3 tosses: HHH,HHT,HTH,THH, HTT,THT,TTH,TTT. Let's compute P(E F ) with F as the sample space: P(E F ) 2 outcomes with H rst and 2 H's total = = 2 3 outcomes with exactly 2 H's 3. This is not equal to P(E), which was 1/2. Hence E and F are not independent.

29 Independence with More Events Denition (4.2) Let {A 1,...,A n } be a set of n events. Then the set is mutually independent if for any subset {A i,...,a m } of 2, 3, 4,..., n events, P(A i... A m ) = P(A i )... P(A m ). We have to test intersections of any 2, 3, 4,..., n events at a time, not just 2 at a time!

30 Suits and Ranks Let us draw a card from a deck of 52 cards. P(E 1 ) = P(card has rank A) = 4 aces 52 cards = P(E 2 ) = P(card is red) = P(E 3 ) = P(card is a heart) = 26 red cards = 1 52 cards hearts 52 cards = 1 4.

31 Suits and Ranks Let us draw a card from a deck of 52 cards. P(E 1 ) = P(card has rank A) = 4 aces 52 cards = P(E 2 ) = P(card is red) = P(E 3 ) = P(card is a heart) = P(E 1 E 2 ) = P(card is red A) = 26 red cards = 1 52 cards hearts 52 cards = : A or A = 1 52 cards 26.

32 Suits and Ranks Let us draw a card from a deck of 52 cards. P(E 1 ) = P(card has rank A) = 4 aces 52 cards = P(E 2 ) = P(card is red) = P(E 3 ) = P(card is a heart) = P(E 1 E 2 ) = P(card is red A) = 26 red cards = 1 52 cards hearts 52 cards = : A or A 52 cards P(E 1 E 3 ) = P(card is A ) = 1 A 52 cards = = 1 26.

33 Let us draw a card from a deck of 52 cards. P(E 1 ) = P(card has rank A) = 4 aces 52 cards = P(E 2 ) = P(card is red) = P(E 3 ) = P(card is a heart) = P(E 1 E 2 ) = P(card is red A) = 26 red cards = 1 52 cards hearts 52 cards = : A or A 52 cards P(E 1 E 3 ) = P(card is A ) = 1 A 52 cards = P(E 2 E 3 ) = P(card is a heart) = 13 hearts 52 cards = 1 4. = 1 26.

34 P(E 1 ) = P(card has rank A) = 4 aces 52 cards = P(E 2 ) = P(card is red) = P(E 3 ) = P(card is a heart) = P(E 1 E 2 ) = P(card is red A) = 26 red cards = 1 52 cards hearts 52 cards = : A or A 52 cards P(E 1 E 3 ) = P(card is A ) = 1 A 52 cards = P(E 2 E 3 ) = P(card is a heart) = 13 hearts 52 cards = 1 4. P(E 1 E 2 E 3 ) = P(card is A ) = 1 A 52 cards = = 1 26.

35 Suits and Ranks E 1 and E 2 are independent: P(E 1 )P(E 2 ) = P(E 1 E 2 ) = 1 26 E 1 and E 3 are independent: P(E 1 )P(E 3 ) = P(E 1 E 3 ) = 1 52 E 2 and E 3 are not independent: P(E 2 )P(E 3 ) P(E 2 E 3 )

36 Suits and Ranks E 1 and E 2 are independent: P(E 1 )P(E 2 ) = P(E 1 E 2 ) = 1 26 E 1 and E 3 are independent: P(E 1 )P(E 3 ) = P(E 1 E 3 ) = 1 52 E 2 and E 3 are not independent: P(E 2 )P(E 3 ) P(E 2 E 3 )

37 Suits and Ranks E 1 and E 2 are independent: P(E 1 )P(E 2 ) = P(E 1 E 2 ) = 1 26 E 1 and E 3 are independent: P(E 1 )P(E 3 ) = P(E 1 E 3 ) = 1 52 E 2 and E 3 are not independent: P(E 2 )P(E 3 ) P(E 2 E 3 )

38 E 1 and E 3 are independent: P(E 1 )P(E 3 ) = P(E 1 E 3 ) = 1 52 E 2 and E 3 are not independent: P(E 2 )P(E 3 ) P(E 2 E 3 ) E 1, E 2, and E 3 are not mutually independent for two reasons: P(E 2 )P(E 3 ) P(E 2 E 3 ), P(E 1 )P(E 2 )P(E 3 ) P(E 1 E 2 E 3 )

39 Suits and Ranks Intuition Knowledge of a card's color (suit) gives information about which suit (color) it could be. For example, A red card must be a heart or diamond. A heart must be a red card.

40 @Home: Reading Note: page numbers refer to printed version. Add 8 to get page numbers in a PDF reader. You should look at the urn example on pages Please look at Examples 4-7 to 4.9 on pages Try Exercise 7 on page 151 to see an example of three events where any pair of them are independent, but the three events are not mutually independent. Note that there are 4 equally likely sequences of 2 tosses: HH, HT, TH, TT: A = {HH, HT }, B = {HH, TH}, C = {HH, TT }.

41 Next Time Please read Section 4.1 (you can skip the historical remarks). Look at joint distributions. You may want to brush up on multivariable functions (functions with more than one input). The fth homework is due February 27.

Introductory Probability

Introductory Probability Introductory Probability Combinations Nicholas Nguyen nicholas.nguyen@uky.edu Department of Mathematics UK Agenda Assigning Objects to Identical Positions Denitions Committee Card Hands Coin Toss Counts

More information

7.1 Experiments, Sample Spaces, and Events

7.1 Experiments, Sample Spaces, and Events 7.1 Experiments, Sample Spaces, and Events An experiment is an activity that has observable results. Examples: Tossing a coin, rolling dice, picking marbles out of a jar, etc. The result of an experiment

More information

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

Probability and Statistics. Copyright Cengage Learning. All rights reserved. Probability and Statistics Copyright Cengage Learning. All rights reserved. 14.2 Probability Copyright Cengage Learning. All rights reserved. Objectives What Is Probability? Calculating Probability by

More information

Probability. Ms. Weinstein Probability & Statistics

Probability. Ms. Weinstein Probability & Statistics Probability Ms. Weinstein Probability & Statistics Definitions Sample Space The sample space, S, of a random phenomenon is the set of all possible outcomes. Event An event is a set of outcomes of a random

More information

The probability set-up

The probability set-up CHAPTER 2 The probability set-up 2.1. Introduction and basic theory We will have a sample space, denoted S (sometimes Ω) that consists of all possible outcomes. For example, if we roll two dice, the sample

More information

The Coin Toss Experiment

The Coin Toss Experiment Experiments p. 1/1 The Coin Toss Experiment Perhaps the simplest probability experiment is the coin toss experiment. Experiments p. 1/1 The Coin Toss Experiment Perhaps the simplest probability experiment

More information

Introduction to probability

Introduction to probability Introduction to probability Suppose an experiment has a finite set X = {x 1,x 2,...,x n } of n possible outcomes. Each time the experiment is performed exactly one on the n outcomes happens. Assign each

More information

The probability set-up

The probability set-up CHAPTER The probability set-up.1. Introduction and basic theory We will have a sample space, denoted S sometimes Ω that consists of all possible outcomes. For example, if we roll two dice, the sample space

More information

Unit 6: Probability. Marius Ionescu 10/06/2011. Marius Ionescu () Unit 6: Probability 10/06/ / 22

Unit 6: Probability. Marius Ionescu 10/06/2011. Marius Ionescu () Unit 6: Probability 10/06/ / 22 Unit 6: Probability Marius Ionescu 10/06/2011 Marius Ionescu () Unit 6: Probability 10/06/2011 1 / 22 Chapter 13: What is a probability Denition The probability that an event happens is the percentage

More information

Unit 6: Probability. Marius Ionescu 10/06/2011. Marius Ionescu () Unit 6: Probability 10/06/ / 22

Unit 6: Probability. Marius Ionescu 10/06/2011. Marius Ionescu () Unit 6: Probability 10/06/ / 22 Unit 6: Probability Marius Ionescu 10/06/2011 Marius Ionescu () Unit 6: Probability 10/06/2011 1 / 22 Chapter 13: What is a probability Denition The probability that an event happens is the percentage

More information

Simple Probability. Arthur White. 28th September 2016

Simple Probability. Arthur White. 28th September 2016 Simple Probability Arthur White 28th September 2016 Probabilities are a mathematical way to describe an uncertain outcome. For eample, suppose a physicist disintegrates 10,000 atoms of an element A, and

More information

Discussion : Independence 1.6: Counting. Qingyang Xue based on slides from Zack While February 7, University of Massachusetts Amherst

Discussion : Independence 1.6: Counting. Qingyang Xue based on slides from Zack While February 7, University of Massachusetts Amherst Discussion 2 1.5: Independence 1.6: Counting Qingyang Xue based on slides from Zack While February 7, 2019 University of Massachusetts Amherst 1 Table of Contents 1. Preliminaries 2. Quiz 1 Review 3. Practice

More information

I. WHAT IS PROBABILITY?

I. WHAT IS PROBABILITY? C HAPTER 3 PROAILITY Random Experiments I. WHAT IS PROAILITY? The weatherman on 10 o clock news program states that there is a 20% chance that it will snow tomorrow, a 65% chance that it will rain and

More information

Probability. Dr. Zhang Fordham Univ.

Probability. Dr. Zhang Fordham Univ. Probability! Dr. Zhang Fordham Univ. 1 Probability: outline Introduction! Experiment, event, sample space! Probability of events! Calculate Probability! Through counting! Sum rule and general sum rule!

More information

Intermediate Math Circles November 1, 2017 Probability I

Intermediate Math Circles November 1, 2017 Probability I Intermediate Math Circles November 1, 2017 Probability I Probability is the study of uncertain events or outcomes. Games of chance that involve rolling dice or dealing cards are one obvious area of application.

More information

Independent and Mutually Exclusive Events

Independent and Mutually Exclusive Events Independent and Mutually Exclusive Events By: OpenStaxCollege Independent and mutually exclusive do not mean the same thing. Independent Events Two events are independent if the following are true: P(A

More information

Before giving a formal definition of probability, we explain some terms related to probability.

Before giving a formal definition of probability, we explain some terms related to probability. probability 22 INTRODUCTION In our day-to-day life, we come across statements such as: (i) It may rain today. (ii) Probably Rajesh will top his class. (iii) I doubt she will pass the test. (iv) It is unlikely

More information

Probability: Terminology and Examples Spring January 1, / 22

Probability: Terminology and Examples Spring January 1, / 22 Probability: Terminology and Examples 18.05 Spring 2014 January 1, 2017 1 / 22 Board Question Deck of 52 cards 13 ranks: 2, 3,..., 9, 10, J, Q, K, A 4 suits:,,,, Poker hands Consists of 5 cards A one-pair

More information

Contents 2.1 Basic Concepts of Probability Methods of Assigning Probabilities Principle of Counting - Permutation and Combination 39

Contents 2.1 Basic Concepts of Probability Methods of Assigning Probabilities Principle of Counting - Permutation and Combination 39 CHAPTER 2 PROBABILITY Contents 2.1 Basic Concepts of Probability 38 2.2 Probability of an Event 39 2.3 Methods of Assigning Probabilities 39 2.4 Principle of Counting - Permutation and Combination 39 2.5

More information

CSE 312: Foundations of Computing II Quiz Section #2: Inclusion-Exclusion, Pigeonhole, Introduction to Probability

CSE 312: Foundations of Computing II Quiz Section #2: Inclusion-Exclusion, Pigeonhole, Introduction to Probability CSE 312: Foundations of Computing II Quiz Section #2: Inclusion-Exclusion, Pigeonhole, Introduction to Probability Review: Main Theorems and Concepts Binomial Theorem: Principle of Inclusion-Exclusion

More information

RANDOM EXPERIMENTS AND EVENTS

RANDOM EXPERIMENTS AND EVENTS Random Experiments and Events 18 RANDOM EXPERIMENTS AND EVENTS In day-to-day life we see that before commencement of a cricket match two captains go for a toss. Tossing of a coin is an activity and getting

More information

Important Distributions 7/17/2006

Important Distributions 7/17/2006 Important Distributions 7/17/2006 Discrete Uniform Distribution All outcomes of an experiment are equally likely. If X is a random variable which represents the outcome of an experiment of this type, then

More information

Section 6.5 Conditional Probability

Section 6.5 Conditional Probability Section 6.5 Conditional Probability Example 1: An urn contains 5 green marbles and 7 black marbles. Two marbles are drawn in succession and without replacement from the urn. a) What is the probability

More information

Tree and Venn Diagrams

Tree and Venn Diagrams OpenStax-CNX module: m46944 1 Tree and Venn Diagrams OpenStax College This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 4.0 Sometimes, when the probability

More information

Such a description is the basis for a probability model. Here is the basic vocabulary we use.

Such a description is the basis for a probability model. Here is the basic vocabulary we use. 5.2.1 Probability Models When we toss a coin, we can t know the outcome in advance. What do we know? We are willing to say that the outcome will be either heads or tails. We believe that each of these

More information

Raise your hand if you rode a bus within the past month. Record the number of raised hands.

Raise your hand if you rode a bus within the past month. Record the number of raised hands. 166 CHAPTER 3 PROBABILITY TOPICS Raise your hand if you rode a bus within the past month. Record the number of raised hands. Raise your hand if you answered "yes" to BOTH of the first two questions. Record

More information

Section Introduction to Sets

Section Introduction to Sets Section 1.1 - Introduction to Sets Definition: A set is a well-defined collection of objects usually denoted by uppercase letters. Definition: The elements, or members, of a set are denoted by lowercase

More information

4.1 Sample Spaces and Events

4.1 Sample Spaces and Events 4.1 Sample Spaces and Events An experiment is an activity that has observable results. Examples: Tossing a coin, rolling dice, picking marbles out of a jar, etc. The result of an experiment is called an

More information

Section 6.1 #16. Question: What is the probability that a five-card poker hand contains a flush, that is, five cards of the same suit?

Section 6.1 #16. Question: What is the probability that a five-card poker hand contains a flush, that is, five cards of the same suit? Section 6.1 #16 What is the probability that a five-card poker hand contains a flush, that is, five cards of the same suit? page 1 Section 6.1 #38 Two events E 1 and E 2 are called independent if p(e 1

More information

Probability. The MEnTe Program Math Enrichment through Technology. Title V East Los Angeles College

Probability. The MEnTe Program Math Enrichment through Technology. Title V East Los Angeles College Probability The MEnTe Program Math Enrichment through Technology Title V East Los Angeles College 2003 East Los Angeles College. All rights reserved. Topics Introduction Empirical Probability Theoretical

More information

PROBABILITY. 1. Introduction. Candidates should able to:

PROBABILITY. 1. Introduction. Candidates should able to: PROBABILITY Candidates should able to: evaluate probabilities in simple cases by means of enumeration of equiprobable elementary events (e.g for the total score when two fair dice are thrown), or by calculation

More information

ECON 214 Elements of Statistics for Economists

ECON 214 Elements of Statistics for Economists ECON 214 Elements of Statistics for Economists Session 4 Probability Lecturer: Dr. Bernardin Senadza, Dept. of Economics Contact Information: bsenadza@ug.edu.gh College of Education School of Continuing

More information

Applied Statistics I

Applied Statistics I Applied Statistics I Liang Zhang Department of Mathematics, University of Utah June 12, 2008 Liang Zhang (UofU) Applied Statistics I June 12, 2008 1 / 29 In Probability, our main focus is to determine

More information

Exam III Review Problems

Exam III Review Problems c Kathryn Bollinger and Benjamin Aurispa, November 10, 2011 1 Exam III Review Problems Fall 2011 Note: Not every topic is covered in this review. Please also take a look at the previous Week-in-Reviews

More information

Probability and Randomness. Day 1

Probability and Randomness. Day 1 Probability and Randomness Day 1 Randomness and Probability The mathematics of chance is called. The probability of any outcome of a chance process is a number between that describes the proportion of

More information

Exercise Class XI Chapter 16 Probability Maths

Exercise Class XI Chapter 16 Probability Maths Exercise 16.1 Question 1: Describe the sample space for the indicated experiment: A coin is tossed three times. A coin has two faces: head (H) and tail (T). When a coin is tossed three times, the total

More information

Textbook: pp Chapter 2: Probability Concepts and Applications

Textbook: pp Chapter 2: Probability Concepts and Applications 1 Textbook: pp. 39-80 Chapter 2: Probability Concepts and Applications 2 Learning Objectives After completing this chapter, students will be able to: Understand the basic foundations of probability analysis.

More information

Chapter 1. Probability

Chapter 1. Probability Chapter 1. Probability 1.1 Basic Concepts Scientific method a. For a given problem, we define measures that explains the problem well. b. Data is collected with observation and the measures are calculated.

More information

Applications of Probability

Applications of Probability Applications of Probability CK-12 Kaitlyn Spong Say Thanks to the Authors Click http://www.ck12.org/saythanks (No sign in required) To access a customizable version of this book, as well as other interactive

More information

Unit 9: Probability Assignments

Unit 9: Probability Assignments Unit 9: Probability Assignments #1: Basic Probability In each of exercises 1 & 2, find the probability that the spinner shown would land on (a) red, (b) yellow, (c) blue. 1. 2. Y B B Y B R Y Y B R 3. Suppose

More information

Probability (Devore Chapter Two)

Probability (Devore Chapter Two) Probability (Devore Chapter Two) 1016-351-01 Probability Winter 2011-2012 Contents 1 Axiomatic Probability 2 1.1 Outcomes and Events............................... 2 1.2 Rules of Probability................................

More information

Math 227 Elementary Statistics. Bluman 5 th edition

Math 227 Elementary Statistics. Bluman 5 th edition Math 227 Elementary Statistics Bluman 5 th edition CHAPTER 4 Probability and Counting Rules 2 Objectives Determine sample spaces and find the probability of an event using classical probability or empirical

More information

Chapter 1. Probability

Chapter 1. Probability Chapter 1. Probability 1.1 Basic Concepts Scientific method a. For a given problem, we define measures that explains the problem well. b. Data is collected with observation and the measures are calculated.

More information

Mathacle. Name: Date:

Mathacle. Name: Date: Quiz Probability 1.) A telemarketer knows from past experience that when she makes a call, the probability that someone will answer the phone is 0.20. What is probability that the next two phone calls

More information

Basic Concepts * David Lane. 1 Probability of a Single Event

Basic Concepts * David Lane. 1 Probability of a Single Event OpenStax-CNX module: m11169 1 Basic Concepts * David Lane This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 1.0 1 Probability of a Single Event If you roll

More information

Compound Probability. Set Theory. Basic Definitions

Compound Probability. Set Theory. Basic Definitions Compound Probability Set Theory A probability measure P is a function that maps subsets of the state space Ω to numbers in the interval [0, 1]. In order to study these functions, we need to know some basic

More information

Math 14 Lecture Notes Ch. 3.3

Math 14 Lecture Notes Ch. 3.3 3.3 Two Basic Rules of Probability If we want to know the probability of drawing a 2 on the first card and a 3 on the 2 nd card from a standard 52-card deck, the diagram would be very large and tedious

More information

Class XII Chapter 13 Probability Maths. Exercise 13.1

Class XII Chapter 13 Probability Maths. Exercise 13.1 Exercise 13.1 Question 1: Given that E and F are events such that P(E) = 0.6, P(F) = 0.3 and P(E F) = 0.2, find P (E F) and P(F E). It is given that P(E) = 0.6, P(F) = 0.3, and P(E F) = 0.2 Question 2:

More information

Discrete Mathematics and Probability Theory Spring 2016 Rao and Walrand Note 13

Discrete Mathematics and Probability Theory Spring 2016 Rao and Walrand Note 13 CS 70 Discrete Mathematics and Probability Theory Spring 2016 Rao and Walrand Note 13 Introduction to Discrete Probability In the last note we considered the probabilistic experiment where we flipped a

More information

Fall (b) Find the event, E, that a number less than 3 is rolled. (c) Find the event, F, that a green marble is selected.

Fall (b) Find the event, E, that a number less than 3 is rolled. (c) Find the event, F, that a green marble is selected. Fall 2018 Math 140 Week-in-Review #6 Exam 2 Review courtesy: Kendra Kilmer (covering Sections 3.1-3.4, 4.1-4.4) (Please note that this review is not all inclusive) 1. An experiment consists of rolling

More information

Chapter 5 - Elementary Probability Theory

Chapter 5 - Elementary Probability Theory Chapter 5 - Elementary Probability Theory Historical Background Much of the early work in probability concerned games and gambling. One of the first to apply probability to matters other than gambling

More information

CSE 312: Foundations of Computing II Quiz Section #2: Inclusion-Exclusion, Pigeonhole, Introduction to Probability (solutions)

CSE 312: Foundations of Computing II Quiz Section #2: Inclusion-Exclusion, Pigeonhole, Introduction to Probability (solutions) CSE 31: Foundations of Computing II Quiz Section #: Inclusion-Exclusion, Pigeonhole, Introduction to Probability (solutions) Review: Main Theorems and Concepts Binomial Theorem: x, y R, n N: (x + y) n

More information

Due Friday February 17th before noon in the TA drop box, basement, AP&M. HOMEWORK 3 : HAND IN ONLY QUESTIONS: 2, 4, 8, 11, 13, 15, 21, 24, 27

Due Friday February 17th before noon in the TA drop box, basement, AP&M. HOMEWORK 3 : HAND IN ONLY QUESTIONS: 2, 4, 8, 11, 13, 15, 21, 24, 27 Exercise Sheet 3 jacques@ucsd.edu Due Friday February 17th before noon in the TA drop box, basement, AP&M. HOMEWORK 3 : HAND IN ONLY QUESTIONS: 2, 4, 8, 11, 13, 15, 21, 24, 27 1. A six-sided die is tossed.

More information

Bell Work. Warm-Up Exercises. Two six-sided dice are rolled. Find the probability of each sum or 7

Bell Work. Warm-Up Exercises. Two six-sided dice are rolled. Find the probability of each sum or 7 Warm-Up Exercises Two six-sided dice are rolled. Find the probability of each sum. 1. 7 Bell Work 2. 5 or 7 3. You toss a coin 3 times. What is the probability of getting 3 heads? Warm-Up Notes Exercises

More information

7 5 Compound Events. March 23, Alg2 7.5B Notes on Monday.notebook

7 5 Compound Events. March 23, Alg2 7.5B Notes on Monday.notebook 7 5 Compound Events At a juice bottling factory, quality control technicians randomly select bottles and mark them pass or fail. The manager randomly selects the results of 50 tests and organizes the data

More information

Math Exam 2 Review. NOTE: For reviews of the other sections on Exam 2, refer to the first page of WIR #4 and #5.

Math Exam 2 Review. NOTE: For reviews of the other sections on Exam 2, refer to the first page of WIR #4 and #5. Math 166 Spring 2007 c Heather Ramsey Page 1 Math 166 - Exam 2 Review NOTE: For reviews of the other sections on Exam 2, refer to the first page of WIR #4 and #5. Section 7.1 - Experiments, Sample Spaces,

More information

Math Exam 2 Review. NOTE: For reviews of the other sections on Exam 2, refer to the first page of WIR #4 and #5.

Math Exam 2 Review. NOTE: For reviews of the other sections on Exam 2, refer to the first page of WIR #4 and #5. Math 166 Spring 2007 c Heather Ramsey Page 1 Math 166 - Exam 2 Review NOTE: For reviews of the other sections on Exam 2, refer to the first page of WIR #4 and #5. Section 7.1 - Experiments, Sample Spaces,

More information

Section : Combinations and Permutations

Section : Combinations and Permutations Section 11.1-11.2: Combinations and Permutations Diana Pell A construction crew has three members. A team of two must be chosen for a particular job. In how many ways can the team be chosen? How many words

More information

Probability, Continued

Probability, Continued Probability, Continued 12 February 2014 Probability II 12 February 2014 1/21 Last time we conducted several probability experiments. We ll do one more before starting to look at how to compute theoretical

More information

Topic : ADDITION OF PROBABILITIES (MUTUALLY EXCLUSIVE EVENTS) TIME : 4 X 45 minutes

Topic : ADDITION OF PROBABILITIES (MUTUALLY EXCLUSIVE EVENTS) TIME : 4 X 45 minutes Worksheet 6 th Topic : ADDITION OF PROBABILITIES (MUTUALLY EXCLUSIVE EVENTS) TIME : 4 X 45 minutes STANDARD COMPETENCY : 1. To use the statistics rules, the rules of counting, and the characteristic of

More information

The topic for the third and final major portion of the course is Probability. We will aim to make sense of statements such as the following:

The topic for the third and final major portion of the course is Probability. We will aim to make sense of statements such as the following: CS 70 Discrete Mathematics for CS Spring 2006 Vazirani Lecture 17 Introduction to Probability The topic for the third and final major portion of the course is Probability. We will aim to make sense of

More information

CIS 2033 Lecture 6, Spring 2017

CIS 2033 Lecture 6, Spring 2017 CIS 2033 Lecture 6, Spring 2017 Instructor: David Dobor February 2, 2017 In this lecture, we introduce the basic principle of counting, use it to count subsets, permutations, combinations, and partitions,

More information

Rosen, Discrete Mathematics and Its Applications, 6th edition Extra Examples

Rosen, Discrete Mathematics and Its Applications, 6th edition Extra Examples Rosen, Discrete Mathematics and Its Applications, 6th edition Extra Examples Section 6.1 An Introduction to Discrete Probability Page references correspond to locations of Extra Examples icons in the textbook.

More information

Math 42, Discrete Mathematics

Math 42, Discrete Mathematics c Fall 2018 last updated 10/29/2018 at 18:22:13 For use by students in this class only; all rights reserved. Note: some prose & some tables are taken directly from Kenneth R. Rosen, and Its Applications,

More information

Diamond ( ) (Black coloured) (Black coloured) (Red coloured) ILLUSTRATIVE EXAMPLES

Diamond ( ) (Black coloured) (Black coloured) (Red coloured) ILLUSTRATIVE EXAMPLES CHAPTER 15 PROBABILITY Points to Remember : 1. In the experimental approach to probability, we find the probability of the occurence of an event by actually performing the experiment a number of times

More information

1324 Test 1 Review Page 1 of 10

1324 Test 1 Review Page 1 of 10 1324 Test 1 Review Page 1 of 10 Review for Exam 1 Math 1324 TTh Chapters 7, 8 Problems 1-10: Determine whether the statement is true or false. 1. {5} {4,5, 7}. 2. {4,5,7}. 3. {4,5} {4,5,7}. 4. {4,5} {4,5,7}

More information

Total. STAT/MATH 394 A - Autumn Quarter Midterm. Name: Student ID Number: Directions. Complete all questions.

Total. STAT/MATH 394 A - Autumn Quarter Midterm. Name: Student ID Number: Directions. Complete all questions. STAT/MATH 9 A - Autumn Quarter 015 - Midterm Name: Student ID Number: Problem 1 5 Total Points Directions. Complete all questions. You may use a scientific calculator during this examination; graphing

More information

Basic Probability Models. Ping-Shou Zhong

Basic Probability Models. Ping-Shou Zhong asic Probability Models Ping-Shou Zhong 1 Deterministic model n experiment that results in the same outcome for a given set of conditions Examples: law of gravity 2 Probabilistic model The outcome of the

More information

Probability Rules. 2) The probability, P, of any event ranges from which of the following?

Probability Rules. 2) The probability, P, of any event ranges from which of the following? Name: WORKSHEET : Date: Answer the following questions. 1) Probability of event E occurring is... P(E) = Number of ways to get E/Total number of outcomes possible in S, the sample space....if. 2) The probability,

More information

Block 1 - Sets and Basic Combinatorics. Main Topics in Block 1:

Block 1 - Sets and Basic Combinatorics. Main Topics in Block 1: Block 1 - Sets and Basic Combinatorics Main Topics in Block 1: A short revision of some set theory Sets and subsets. Venn diagrams to represent sets. Describing sets using rules of inclusion. Set operations.

More information

Week 1: Probability models and counting

Week 1: Probability models and counting Week 1: Probability models and counting Part 1: Probability model Probability theory is the mathematical toolbox to describe phenomena or experiments where randomness occur. To have a probability model

More information

Key Concept Probability of Independent Events. Key Concept Probability of Mutually Exclusive Events. Key Concept Probability of Overlapping Events

Key Concept Probability of Independent Events. Key Concept Probability of Mutually Exclusive Events. Key Concept Probability of Overlapping Events 15-4 Compound Probability TEKS FOCUS TEKS (1)(E) Apply independence in contextual problems. TEKS (1)(B) Use a problemsolving model that incorporates analyzing given information, formulating a plan or strategy,

More information

Theory of Probability - Brett Bernstein

Theory of Probability - Brett Bernstein Theory of Probability - Brett Bernstein Lecture 3 Finishing Basic Probability Review Exercises 1. Model flipping two fair coins using a sample space and a probability measure. Compute the probability of

More information

INDEPENDENT AND DEPENDENT EVENTS UNIT 6: PROBABILITY DAY 2

INDEPENDENT AND DEPENDENT EVENTS UNIT 6: PROBABILITY DAY 2 INDEPENDENT AND DEPENDENT EVENTS UNIT 6: PROBABILITY DAY 2 WARM UP Students in a mathematics class pick a card from a standard deck of 52 cards, record the suit, and return the card to the deck. The results

More information

Probability I Sample spaces, outcomes, and events.

Probability I Sample spaces, outcomes, and events. Probability I Sample spaces, outcomes, and events. When we perform an experiment, the result is called the outcome. The set of possible outcomes is the sample space and any subset of the sample space is

More information

Combinations. April 14, 2006

Combinations. April 14, 2006 Combinations April 14, 2006 Combinations (cont'd), April 14, 2006 Inclusion-Exclusion Principle Theorem. Let P be a probability distribution on a sample space Ω, and let {A 1, A 2,..., A n } be a nite

More information

STATISTICS. Instructor: Prof. Dr. Doğan Nadi LEBLEBİCİ

STATISTICS. Instructor: Prof. Dr. Doğan Nadi LEBLEBİCİ STATISTICS INTRODUCTION TO PROBABILITY Instructor: Prof. Dr. Doğan Nadi Source: Kaplan, Robert M. Basic Statistics for the Behavioral Sciences, Allyn and Bacon, Inc., Boston, 1987. SENTENCES IN THIS POWER

More information

Probability. Probabilty Impossibe Unlikely Equally Likely Likely Certain

Probability. Probabilty Impossibe Unlikely Equally Likely Likely Certain PROBABILITY Probability The likelihood or chance of an event occurring If an event is IMPOSSIBLE its probability is ZERO If an event is CERTAIN its probability is ONE So all probabilities lie between 0

More information

Axiomatic Probability

Axiomatic Probability Axiomatic Probability The objective of probability is to assign to each event A a number P(A), called the probability of the event A, which will give a precise measure of the chance thtat A will occur.

More information

Chapter 5: Probability: What are the Chances? Section 5.2 Probability Rules

Chapter 5: Probability: What are the Chances? Section 5.2 Probability Rules + Chapter 5: Probability: What are the Chances? Section 5.2 + Two-Way Tables and Probability When finding probabilities involving two events, a two-way table can display the sample space in a way that

More information

Section The Multiplication Principle and Permutations

Section The Multiplication Principle and Permutations Section 2.1 - The Multiplication Principle and Permutations Example 1: A yogurt shop has 4 flavors (chocolate, vanilla, strawberry, and blueberry) and three sizes (small, medium, and large). How many different

More information

Math 141 Exam 3 Review with Key. 1. P(E)=0.5, P(F)=0.6 P(E F)=0.9 Find ) b) P( E F ) c) P( E F )

Math 141 Exam 3 Review with Key. 1. P(E)=0.5, P(F)=0.6 P(E F)=0.9 Find ) b) P( E F ) c) P( E F ) Math 141 Exam 3 Review with Key 1. P(E)=0.5, P(F)=0.6 P(E F)=0.9 Find C C C a) P( E F) ) b) P( E F ) c) P( E F ) 2. A fair coin is tossed times and the sequence of heads and tails is recorded. Find a)

More information

Grade 7/8 Math Circles February 25/26, Probability

Grade 7/8 Math Circles February 25/26, Probability Faculty of Mathematics Waterloo, Ontario N2L 3G1 Probability Grade 7/8 Math Circles February 25/26, 2014 Probability Centre for Education in Mathematics and Computing Probability is the study of how likely

More information

ABC High School, Kathmandu, Nepal. Topic : Probability

ABC High School, Kathmandu, Nepal. Topic : Probability BC High School, athmandu, Nepal Topic : Probability Grade 0 Teacher: Shyam Prasad charya. Objective of the Module: t the end of this lesson, students will be able to define and say formula of. define Mutually

More information

Probability and Counting Rules. Chapter 3

Probability and Counting Rules. Chapter 3 Probability and Counting Rules Chapter 3 Probability as a general concept can be defined as the chance of an event occurring. Many people are familiar with probability from observing or playing games of

More information

Independent Events. 1. Given that the second baby is a girl, what is the. e.g. 2 The probability of bearing a boy baby is 2

Independent Events. 1. Given that the second baby is a girl, what is the. e.g. 2 The probability of bearing a boy baby is 2 Independent Events 7. Introduction Consider the following examples e.g. E throw a die twice A first thrown is "" second thrown is "" o find P( A) Solution: Since the occurrence of Udoes not dependu on

More information

Non-overlapping permutation patterns

Non-overlapping permutation patterns PU. M. A. Vol. 22 (2011), No.2, pp. 99 105 Non-overlapping permutation patterns Miklós Bóna Department of Mathematics University of Florida 358 Little Hall, PO Box 118105 Gainesville, FL 326118105 (USA)

More information

Probability MAT230. Fall Discrete Mathematics. MAT230 (Discrete Math) Probability Fall / 37

Probability MAT230. Fall Discrete Mathematics. MAT230 (Discrete Math) Probability Fall / 37 Probability MAT230 Discrete Mathematics Fall 2018 MAT230 (Discrete Math) Probability Fall 2018 1 / 37 Outline 1 Discrete Probability 2 Sum and Product Rules for Probability 3 Expected Value MAT230 (Discrete

More information

(a) Suppose you flip a coin and roll a die. Are the events obtain a head and roll a 5 dependent or independent events?

(a) Suppose you flip a coin and roll a die. Are the events obtain a head and roll a 5 dependent or independent events? Unit 6 Probability Name: Date: Hour: Multiplication Rule of Probability By the end of this lesson, you will be able to Understand Independence Use the Multiplication Rule for independent events Independent

More information

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Study Guide for Test III (MATH 1630) Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Find the number of subsets of the set. 1) {x x is an even

More information

Probability Rules 3.3 & 3.4. Cathy Poliak, Ph.D. (Department of Mathematics 3.3 & 3.4 University of Houston )

Probability Rules 3.3 & 3.4. Cathy Poliak, Ph.D. (Department of Mathematics 3.3 & 3.4 University of Houston ) Probability Rules 3.3 & 3.4 Cathy Poliak, Ph.D. cathy@math.uh.edu Department of Mathematics University of Houston Lecture 3: 3339 Lecture 3: 3339 1 / 23 Outline 1 Probability 2 Probability Rules Lecture

More information

Math 1313 Section 6.2 Definition of Probability

Math 1313 Section 6.2 Definition of Probability Math 1313 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

More information

Probability. Chapter-13

Probability. Chapter-13 Chapter-3 Probability The definition of probability was given b Pierre Simon Laplace in 795 J.Cardan, an Italian physician and mathematician wrote the first book on probability named the book of games

More information

2. How many different three-member teams can be formed from six students?

2. How many different three-member teams can be formed from six students? KCATM 2011 Probability & Statistics 1. A fair coin is thrown in the air four times. If the coin lands with the head up on the first three tosses, what is the probability that the coin will land with the

More information

4.1 What is Probability?

4.1 What is Probability? 4.1 What is Probability? between 0 and 1 to indicate the likelihood of an event. We use event is to occur. 1 use three major methods: 1) Intuition 3) Equally Likely Outcomes Intuition - prediction based

More information

[Independent Probability, Conditional Probability, Tree Diagrams]

[Independent Probability, Conditional Probability, Tree Diagrams] Name: Year 1 Review 11-9 Topic: Probability Day 2 Use your formula booklet! Page 5 Lesson 11-8: Probability Day 1 [Independent Probability, Conditional Probability, Tree Diagrams] Read and Highlight Station

More information

Probability - Chapter 4

Probability - Chapter 4 Probability - Chapter 4 In this chapter, you will learn about probability its meaning, how it is computed, and how to evaluate it in terms of the likelihood of an event actually happening. A cynical person

More information

CSE 21 Mathematics for Algorithm and System Analysis

CSE 21 Mathematics for Algorithm and System Analysis CSE 21 Mathematics for Algorithm and System Analysis Unit 1: Basic Count and List Section 3: Set CSE21: Lecture 3 1 Reminder Piazza forum address: http://piazza.com/ucsd/summer2013/cse21/hom e Notes on

More information

The next several lectures will be concerned with probability theory. We will aim to make sense of statements such as the following:

The next several lectures will be concerned with probability theory. We will aim to make sense of statements such as the following: CS 70 Discrete Mathematics for CS Fall 2004 Rao Lecture 14 Introduction to Probability The next several lectures will be concerned with probability theory. We will aim to make sense of statements such

More information

1MA01: Probability. Sinéad Ryan. November 12, 2013 TCD

1MA01: Probability. Sinéad Ryan. November 12, 2013 TCD 1MA01: Probability Sinéad Ryan TCD November 12, 2013 Definitions and Notation EVENT: a set possible outcomes of an experiment. Eg flipping a coin is the experiment, landing on heads is the event If an

More information