Basic Probability Models. PingShou Zhong


 Nathan Leon Simon
 3 years ago
 Views:
Transcription
1 asic Probability Models PingShou Zhong 1
2 Deterministic model n experiment that results in the same outcome for a given set of conditions Examples: law of gravity 2
3 Probabilistic model The outcome of the experiment can not be predicted with certainty. Examples: a: toss a coin b: number of people waiting in a bus stop c: drawing two cards from a set of 52 cards 3
4 asic probability concepts Sample spaces Events Probability measure 4
5 Sample space Definition: collection of all possible outcomes. sample space is discrete if it has finite or countable infinite number of outcomes. Example 1: Toss a coin (finite discrete) The sample space is S={H,T}, where H: head and T: tail Example 2: Number of people in a store (countable infinite discrete). S={0,1,2,3, } Example 3: Lifetime of a bulb: S={t: 0 t < } (continuous) 5
6 Example 4: toss two coins. Two possible sample spaces: S={HH,TH, HT, TT} each outcome being an ordered sequence of results; S={0,1,2} each outcome being a possible number of heads obtained. 6
7 Example 5: rolling a pair of dice The sample space can be: 7
8 The sample space could also be: 8
9 Events n event is a subset of a sample space. n event is said to happen if the outcome of an experiment is a member of. Example: rolling a die ={3}, ={3,4}, C={1,2,3} If the outcome is 3, we will say, and C happen. 9
10 ={at least one with 6 points} 10
11 Set operations: events combination x x c and event of }:The complement : { happens or either : happen and both : happens happens : 11
12 Properties of set operations c c c c c c d C C C C c C C C C b a ) (, ) ( Law : DeMorgan's ) ( ) ( ) ( ), ( ) ( ) ( Distributive Laws : ) ( ) (, ) ( ) ( ssociativity : :, Commutativity : : 12
13 Venn diagram: ( ) c = c c 13
14 Probability measure set function P defined on a set collection F that satisfies the following axioms (a) P() 0 for all F (b) P(S)=1 (c) If 1, 2, F are pairwise disjoint, then P i=1 i = i=1 P( i ) The triple S, F, P is called a probability space 14
15 Some properties of P 1. P = 0, P S = P 1 for F 3. P c = 1 P 4. If, then P P 5. P + P c = P 6. P = P + P P 7. P 1 2 n = n i=1 P i 1 i<j n P( i j ) n+1 n P( i=1 i ) 15
16 Discrete probability measure For a discrete sample space S = ω i with P({ω i }) 0 and P(S)=1. Define a measure P = ω i P({ω i }) for any F. Probability measure of equally likely outcomes. If P ω i = 1 where N(s) is the total number of outcomes, then N(S) Number of sample points in P = N() N(S) Total number of sample points in S 16
17 Example 1 Throwing a pair of dice. What is the probability of ={ at least one 6 }? Sample space (treat the dice distinguishable): S 1 ={11,12,13,14,15,16,21,22,23,24,25,26,31,32,33,34,35,36 41,42,43,44,45,46,51,52,53,54,55,56,61,62,63,64,65,66} Total number of sample points in the sample space are: N(S 1 ) = 6 6 = 36 Method 1: Listing all the outcomes in event ={16,26,36,46,56,66,61,62,63,64,65} N() = 11 P = 11/36 17
18 Question Why we need to treat the dice distinguishable? If not, the sample space is S 2 ={11, 22, 33, 44, 55,66, 12,13,14,15,16, 23,24,25,26, 34,35,36, 45,46, 56} ={16,26,36,46,56,66} P = N() N(S 2 ) = (Why?) Which one is right? 18
19 Reason For example {12} in sample space S 2 representing {12, 21} in sample space S 1 ccording to the sampling scheme, each sample point in S 1 is equal likely. ut this is not for S 2. P({12})= 2 36 in sample space S 2. 19
20 Method 2: Use the complement and multiply rule. ecause P = 1 P c, we calculate P c. c ={No 6 appears} N( c ) = 5 5 = 25 P c = Hence, P = 1 P c =11/36 20
21 Proportion of throws with at least one Proportion of throws with at least one Computer simulations Number of throws Number of throws 21
22 Example 2: Poker game 52card deck containing 13 ranks and each rank has 4 suits: Clubs, Spades, Hearts and Diamonds. 22
23 Categories Straight flush: 5 consecutive cards with the same suit Four of a kind: 4 cards are of one rank Full house: 3 cards of one rank and two of another Flush: 5 cards with the same suit Straight: 5 consecutive cards lways classified in the higher category if it satisfies the definition ces is counted as either low or high 23
24 Sample space Let D be the set of 52 cards Sample space S = {: D and N = 5} N S = 52 5 = =2,598,960 5! (Without replacement, unordered sample) 24
25 Probability of full house Count the number of possible full house Choosing ranks: firstly for rank with 3 cards, then for rank with 2 cards Number of ways=13 12 Note that the second rank can not be the same as the first one. Otherwise it will be classified as Fourofakind Choosing suits: Number of ways= 4 3 Total number of possible full house = =3744 P(Full House)=3744/ =1/
26 Probability of flush Choosing suits: 4 ways Choosing ranks: = 1277ways straight flush Total number of flush =4 1277=5108 P(flush)=5108/2,598,960=1/508 26
27 Example 3: Matching Problems n couples attend a dance party. The partners of the women are randomly assigned among the men. What is the probability that ={at least one woman dancing with her husband}? Let us label n couples by 1, 2, 3,, n and denote E i ={ Woman i dances with her husband}. = n i=1 E i 27
28 pply inclusionexclusion principle n n P = P E i = i=1 P E i 1 i<j n P(E i E j ) n+1 P(E 1 E 2 E n ) i=1 P E i = n 1! n! P E i E j = n 2! n! P E 1 E 2 E 3 E r = n r! n! Number of summands n n 2 n r 28
29 P n i=1 E i = n n 1! n! n 2 n 2! n! + n n 3! + 3 n! = 1 1 2! + 1 3! + 1 n+1 1 n! = e 1 when n is large This method can also be applied to calculate the probability of exactly k (k n) letters and envelops matching each other. 29
30 Example 4: Coupon Problem company randomly places one of a set of 4 cards into each packet of its product. nyone who collect a complete set of 4 cards can claim a prize. What is the probability that you will able to ={claim at least one prize if you buy 10 packets}? What is the complement event to? c ={can not claim prize} ={at least one card in the set can not be found in 10 packets} 30
31 Define E i ={can not find card i (i=1,2,3,4) in 10 packets} c = 4 i=1 E i P = 1 P c 4 = 1 P( i=1 E i ) 4 = 1 i=1 P E i + i<j P E i E j i<j<k P E i E j E k + P(E 1 E 2 E 3 E 4 ) 31
32 P E i =( 3 4 )10 P E i E j =( 2 4 )10 P E i E j E k =( 1 4 )10 P(E 1 E 2 E 3 E 4 )=0 P =14 ( 3 4 )10 +6 ( 2 4 )10 4 ( 1 4 )10 =
The probability setup
CHAPTER 2 The probability setup 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 informationThe probability setup
CHAPTER The probability setup.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 informationNovember 6, Chapter 8: Probability: The Mathematics of Chance
Chapter 8: Probability: The Mathematics of Chance November 6, 2013 Last Time Crystallographic notation Groups Crystallographic notation The first symbol is always a p, which indicates that the pattern
More informationIntermediate 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 informationProbability: 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 onepair
More information2.5 Sample Spaces Having Equally Likely Outcomes
Sample Spaces Having Equally Likely Outcomes 3 Sample Spaces Having Equally Likely Outcomes Recall that we had a simple example (fair dice) before on equallylikely sample spaces Since they will appear
More informationChapter 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 informationSTAT 430/510 Probability Lecture 3: Space and Event; Sample Spaces with Equally Likely Outcomes
STAT 430/510 Probability Lecture 3: Space and Event; Sample Spaces with Equally Likely Outcomes Pengyuan (Penelope) Wang May 25, 2011 Review We have discussed counting techniques in Chapter 1. (Principle
More informationNovember 8, Chapter 8: Probability: The Mathematics of Chance
Chapter 8: Probability: The Mathematics of Chance November 8, 2013 Last Time Probability Models and Rules Discrete Probability Models Equally Likely Outcomes Crystallographic notation The first symbol
More informationCompound 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 informationProbability. 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 information3. Discrete Probability. CSE 312 Spring 2015 W.L. Ruzzo
3. Discrete Probability CSE 312 Spring 2015 W.L. Ruzzo 2 Probability theory: an aberration of the intellect and ignorance coined into science John Stuart Mill 3 sample spaces Sample space: S is a set of
More informationChapter 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 information4.1 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 informationThe 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 informationSection Introduction to Sets
Section 1.1  Introduction to Sets Definition: A set is a welldefined collection of objects usually denoted by uppercase letters. Definition: The elements, or members, of a set are denoted by lowercase
More informationToday s Topics. Next week: Conditional Probability
Today s Topics 2 Last time: Combinations Permutations Group Assignment TODAY: Probability! Sample Spaces and Event Spaces Axioms of Probability Lots of Examples Next week: Conditional Probability Sets
More informationI. 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 informationThe 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 informationExercise 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 informationMore Probability: Poker Hands and some issues in Counting
More Probability: Poker Hands and some issues in Counting Data From Thursday Everybody flipped a pair of coins and recorded how many times they got two heads, two tails, or one of each. We saw that the
More information7.1 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 informationCounting Poker Hands
Counting Poker Hands George Ballinger In a standard deck of cards there are kinds of cards: ce (),,,,,,,,,, ack (), ueen () and ing (). Each of these kinds comes in four suits: Spade (), Heart (), Diamond
More information4.3 Rules of Probability
4.3 Rules of Probability If a probability distribution is not uniform, to find the probability of a given event, add up the probabilities of all the individual outcomes that make up the event. Example:
More informationProbability. 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 informationWeek 3 Classical Probability, Part I
Week 3 Classical Probability, Part I Week 3 Objectives Proper understanding of common statistical practices such as confidence intervals and hypothesis testing requires some familiarity with probability
More informationChapter 2. Permutations and Combinations
2. Permutations and Combinations Chapter 2. Permutations and Combinations In this chapter, we define sets and count the objects in them. Example Let S be the set of students in this classroom today. Find
More informationBlock 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 informationTheory 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 informationIf you roll a die, what is the probability you get a four OR a five? What is the General Education Statistics
If you roll a die, what is the probability you get a four OR a five? What is the General Education Statistics probability that you get neither? Class Notes The Addition Rule (for OR events) and Complements
More informationProbability. Engr. Jeffrey T. Dellosa.
Probability Engr. Jeffrey T. Dellosa Email: jtdellosa@gmail.com Outline Probability 2.1 Sample Space 2.2 Events 2.3 Counting Sample Points 2.4 Probability of an Event 2.5 Additive Rules 2.6 Conditional
More informationSTAT 3743: Probability and Statistics
STAT 3743: Probability and Statistics G. Jay Kerns, Youngstown State University Fall 2010 Probability Random experiment: outcome not known in advance Sample space: set of all possible outcomes (S) Probability
More informationNovember 11, Chapter 8: Probability: The Mathematics of Chance
Chapter 8: Probability: The Mathematics of Chance November 11, 2013 Last Time Probability Models and Rules Discrete Probability Models Equally Likely Outcomes Probability Rules Probability Rules Rule 1.
More informationMath 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 informationMath 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 informationChapter 3: Elements of Chance: Probability Methods
Chapter 3: Elements of Chance: Methods Department of Mathematics Izmir University of Economics Week 34 20142015 Introduction In this chapter we will focus on the definitions of random experiment, outcome,
More informationChapter 1: Sets and Probability
Chapter 1: Sets and Probability Section 1.31.5 Recap: Sample Spaces and Events An is an activity that has observable results. An is the result of an experiment. Example 1 Examples of experiments: Flipping
More informationLecture 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 two times? Figure below shows the results of tossing a coin 5000 times
More informationPROBABILITY. 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 informationMixed Counting Problems
We have studied a number of counting principles and techniques since the beginning of the course and when we tackle a counting problem, we may have to use one or a combination of these principles. The
More informationAxiomatic 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 informationProbability and Counting Techniques
Probability and Counting Techniques Diana Pell (Multiplication Principle) Suppose that a task consists of t choices performed consecutively. Suppose that choice 1 can be performed in m 1 ways; for each
More informationDef: The intersection of A and B is the set of all elements common to both set A and set B
Def: Sample Space the set of all possible outcomes Def: Element an item in the set Ex: The number "3" is an element of the "rolling a die" sample space Main concept write in Interactive Notebook Intersection:
More informationSection 7.1 Experiments, Sample Spaces, and Events
Section 7.1 Experiments, Sample Spaces, and Events Experiments An experiment is an activity with observable results. 1. Which of the follow are experiments? (a) Going into a room and turning on a light.
More informationMGF 1106: Exam 2 Solutions
MGF 1106: Exam 2 Solutions 1. (15 points) A coin and a die are tossed together onto a table. a. What is the sample space for this experiment? For example, one possible outcome is heads on the coin and
More information1. The chance of getting a flush in a 5card poker hand is about 2 in 1000.
CS 70 Discrete Mathematics for CS Spring 2008 David Wagner Note 15 Introduction to Discrete Probability Probability theory has its origins in gambling analyzing card games, dice, roulette wheels. Today
More informationSection Summary. Finite Probability Probabilities of Complements and Unions of Events Probabilistic Reasoning
Section 7.1 Section Summary Finite Probability Probabilities of Complements and Unions of Events Probabilistic Reasoning Probability of an Event PierreSimon Laplace (17491827) We first study PierreSimon
More informationCSE 312: Foundations of Computing II Quiz Section #2: InclusionExclusion, Pigeonhole, Introduction to Probability (solutions)
CSE 31: Foundations of Computing II Quiz Section #: InclusionExclusion, Pigeonhole, Introduction to Probability (solutions) Review: Main Theorems and Concepts Binomial Theorem: x, y R, n N: (x + y) n
More informationDiscrete 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 informationThe study of probability is concerned with the likelihood of events occurring. Many situations can be analyzed using a simplified model of probability
The study of probability is concerned with the likelihood of events occurring Like combinatorics, the origins of probability theory can be traced back to the study of gambling games Still a popular branch
More informationSimple 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 informationSTAT 155 Introductory Statistics. Lecture 11: Randomness and Probability Model
The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL STAT 155 Introductory Statistics Lecture 11: Randomness and Probability Model 10/5/06 Lecture 11 1 The Monty Hall Problem Let s Make A Deal: a game show
More information3 The multiplication rule/miscellaneous counting problems
Practice for Exam 1 1 Axioms of probability, disjoint and independent events 1 Suppose P (A 0, P (B 05 (a If A and B are independent, what is P (A B? What is P (A B? (b If A and B are disjoint, what is
More informationProbability Models. Section 6.2
Probability Models Section 6.2 The Language of Probability What is random? Empirical means that it is based on observation rather than theorizing. Probability describes what happens in MANY trials. Example
More informationName: Exam 1. September 14, 2017
Department of Mathematics University of Notre Dame Math 10120 Finite Math Fall 2017 Name: Instructors: Basit & Migliore Exam 1 September 14, 2017 This exam is in two parts on 9 pages and contains 14 problems
More informationMath 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 informationImportant 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 informationCHAPTER 2 PROBABILITY. 2.1 Sample Space. 2.2 Events
CHAPTER 2 PROBABILITY 2.1 Sample Space A probability model consists of the sample space and the way to assign probabilities. Sample space & sample point The sample space S, is the set of all possible outcomes
More informationSTOR 155 Introductory Statistics. Lecture 10: Randomness and Probability Model
The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL STOR 155 Introductory Statistics Lecture 10: Randomness and Probability Model 10/6/09 Lecture 10 1 The Monty Hall Problem Let s Make A Deal: a game show
More informationPermutations: The number of arrangements of n objects taken r at a time is. P (n, r) = n (n 1) (n r + 1) =
Section 6.6: Mixed Counting Problems We have studied a number of counting principles and techniques since the beginning of the course and when we tackle a counting problem, we may have to use one or a
More informationProbability 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 informationProbability. 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 informationCounting and Probability
Counting and Probability Lecture 42 Section 9.1 Robb T. Koether HampdenSydney College Wed, Apr 9, 2014 Robb T. Koether (HampdenSydney College) Counting and Probability Wed, Apr 9, 2014 1 / 17 1 Probability
More informationDefine and Diagram Outcomes (Subsets) of the Sample Space (Universal Set)
12.3 and 12.4 Notes Geometry 1 Diagramming the Sample Space using Venn Diagrams A sample space represents all things that could occur for a given event. In set theory language this would be known as the
More informationMath 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 informationDiscrete Structures for Computer Science
Discrete Structures for Computer Science William Garrison bill@cs.pitt.edu 6311 Sennott Square Lecture #23: Discrete Probability Based on materials developed by Dr. Adam Lee The study of probability is
More informationCSE 312: Foundations of Computing II Quiz Section #2: InclusionExclusion, Pigeonhole, Introduction to Probability
CSE 312: Foundations of Computing II Quiz Section #2: InclusionExclusion, Pigeonhole, Introduction to Probability Review: Main Theorems and Concepts Binomial Theorem: Principle of InclusionExclusion
More informationProbability 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 informationChapter 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 informationECON 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 informationCombinatorics: The Fine Art of Counting
Combinatorics: The Fine Art of Counting Week 6 Lecture Notes Discrete Probability Note Binomial coefficients are written horizontally. The symbol ~ is used to mean approximately equal. Introduction and
More informationMATHEMATICS 152, FALL 2004 METHODS OF DISCRETE MATHEMATICS Outline #10 (Sets and Probability)
MATHEMATICS 152, FALL 2004 METHODS OF DISCRETE MATHEMATICS Outline #10 (Sets and Probability) Last modified: November 10, 2004 This follows very closely Apostol, Chapter 13, the course pack. Attachments
More informationThe point value of each problem is in the lefthand margin. You must show your work to receive any credit, except on problems 1 & 2. Work neatly.
Introduction to Statistics Math 1040 Sample Exam II Chapters 57 4 Problem Pages 4 Formula/Table Pages Time Limit: 90 Minutes 1 No Scratch Paper Calculator Allowed: Scientific Name: The point value of
More informationINDIAN STATISTICAL INSTITUTE
INDIAN STATISTICAL INSTITUTE B1/BVR Probability Home Assignment 1 200707 1. A poker hand means a set of five cards selected at random from usual deck of playing cards. (a) Find the probability that it
More informationApplied 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 informationProbability (Devore Chapter Two)
Probability (Devore Chapter Two) 101635101 Probability Winter 20112012 Contents 1 Axiomatic Probability 2 1.1 Outcomes and Events............................... 2 1.2 Rules of Probability................................
More information3 The multiplication rule/miscellaneous counting problems
Practice for Exam 1 1 Axioms of probability, disjoint and independent events 1. Suppose P (A) = 0.4, P (B) = 0.5. (a) If A and B are independent, what is P (A B)? What is P (A B)? (b) If A and B are disjoint,
More informationBefore giving a formal definition of probability, we explain some terms related to probability.
probability 22 INTRODUCTION In our daytoday 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 informationName: Partners: Math Academy I. Review 6 Version A. 5. There are over a billion different possible orders for a line of 14 people.
Name: Partners: Math Academy I Date: Review 6 Version A [A] Circle whether each statement is true or false. 1. Odd and less than 4 are mutually exclusive. 2. The probability of a card being red given it
More informationProbability 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, x {1, 2, k}, where k > 0. (a) Write down P(X = 2). (1) (b) Show that k = 3. (4) Find E(X). (2) (Total 7 marks)
1. The probability distribution of a discrete random variable X is given by 2 x P(X = x) = 14, x {1, 2, k}, where k > 0. Write down P(X = 2). (1) Show that k = 3. Find E(X). (Total 7 marks) 2. In a game
More informationMathematical Foundations HW 5 By 11:59pm, 12 Dec, 2015
1 Probability Axioms Let A,B,C be three arbitrary events. Find the probability of exactly one of these events occuring. Sample space S: {ABC, AB, AC, BC, A, B, C, }, and S = 8. P(A or B or C) = 3 8. note:
More informationMAT104: Fundamentals of Mathematics II Summary of Counting Techniques and Probability. Preliminary Concepts, Formulas, and Terminology
MAT104: Fundamentals of Mathematics II Summary of Counting Techniques and Probability Preliminary Concepts, Formulas, and Terminology Meanings of Basic Arithmetic Operations in Mathematics Addition: Generally
More informationSuch 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 informationRANDOM EXPERIMENTS AND EVENTS
Random Experiments and Events 18 RANDOM EXPERIMENTS AND EVENTS In daytoday 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 informationLC OL Probability. ARNMaths.weebly.com. As part of Leaving Certificate Ordinary Level Math you should be able to complete the following.
A Ryan LC OL Probability ARNMaths.weebly.com Learning Outcomes As part of Leaving Certificate Ordinary Level Math you should be able to complete the following. Counting List outcomes of an experiment Apply
More informationChapter 4: Probability and Counting Rules
Chapter 4: Probability and Counting Rules Before we can move from descriptive statistics to inferential statistics, we need to have some understanding of probability: Ch4: Probability and Counting Rules
More informationMAT104: Fundamentals of Mathematics II Counting Techniques Class Exercises Solutions
MAT104: Fundamentals of Mathematics II Counting Techniques Class Exercises Solutions 1. Appetizers: Salads: Entrées: Desserts: 2. Letters: (A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U,
More informationTotal. 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 informationProbability 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 informationSlide 1 Math 1520, Lecture 13
Slide 1 Math 1520, Lecture 13 In chapter 7, we discuss background leading up to probability. Probability is one of the most commonly used pieces of mathematics in the world. Understanding the basic concepts
More informationSection 6.1 #16. Question: What is the probability that a fivecard poker hand contains a flush, that is, five cards of the same suit?
Section 6.1 #16 What is the probability that a fivecard 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 informationChapter 4: Introduction to Probability
MTH 243 Chapter 4: Introduction to Probability Suppose that we found that one of our pieces of data was unusual. For example suppose our pack of M&M s only had 30 and that was 3.1 standard deviations below
More informationSample Spaces, Events, Probability
Sample Spaces, Events, Probability CS 3130/ECE 3530: Probability and Statistics for Engineers August 28, 2014 Sets A set is a collection of unique objects. Sets A set is a collection of unique objects.
More informationCHAPTER 7 Probability
CHAPTER 7 Probability 7.1. Sets A set is a welldefined collection of distinct objects. Welldefined means that we can determine whether an object is an element of a set or not. Distinct means that we can
More informationMATH 215 DISCRETE MATHEMATICS INSTRUCTOR: P. WENG
MATH DISCRETE MATHEMATICS INSTRUCTOR: P. WENG Counting and Probability Suggested Problems Basic Counting Skills, InclusionExclusion, and Complement. (a An office building contains 7 floors and has 7 offices
More informationSection 5.4 Permutations and Combinations
Section 5.4 Permutations and Combinations Definition: nfactorial For any natural number n, n! n( n 1)( n 2) 3 2 1. 0! = 1 A combination of a set is arranging the elements of the set without regard to
More informationProbability 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 informationStatistics Intermediate Probability
Session 6 oscardavid.barrerarodriguez@sciencespo.fr April 3, 2018 and Sampling from a Population Outline 1 The Monty Hall Paradox Some Concepts: Event Algebra Axioms and Things About that are True Counting
More informationGrade 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