Week 3 Classical Probability, Part I
|
|
- Janis Elliott
- 5 years ago
- Views:
Transcription
1 Week 3 Classical Probability, Part I
2 Week 3 Objectives Proper understanding of common statistical practices such as confidence intervals and hypothesis testing requires some familiarity with probability theory. We start with classical probability, which arose from games of chance such as rolling dice or dealing cards. In such experiments, where all outcomes are equally likely, the probability of an event is determined by enumerating the outcomes making up the event. After reviewing basic set operations, the necessary counting techniques are presented. The transition to experiments where the outcomes are not equally likely can be seamless. The axioms and properties of probability in general experiments are presented, and the important notion of a probability mass function is introduced.
3 1 2 3
4 Overview Outline The central role probability plays in statistics is the reason why statistics classes typically cover this subject. Classical probability, which arose from games of chance, includes combinatorics and the concepts of conditional probability and independence. Probability evolved to deal with modeling the randomness of phenomena such as the number of earthquakes, the amount of rainfall, the life time of a given electrical component, or the relation between education level and income, etc. Such probability models will be discussed in Chapters 3 and 4.
5 Sample Spaces The set of all possible outcomes of a random experiment is called the sample space of the experiment, and will be denoted by S. some examples follow. 1) The sample space of the experiment which selects two fuses and classifies each as non-defective or defective is S 1 = {(0, 0), (0, 1), (1, 0), (1, 1)}, where 0 and 1 stand for non-defective and defective, respectively. 2) The sample space of the experiment which selects two fuses and records how many are defective is S 2 = {0, 1, 2}. 3) The sample space of the experiment which records the number of fuses inspected until the second defective is found is S 3 = {2, 3, 4,...}.
6 4) Three undergraduate students are selected and their opinions about expanding the use of solar energy are recorded on a scale from 1 to 10. Give the sample space of this experiment. What is the size of this sample space? Solution: The set of all possible outcomes consist of the triplets (x 1, x 2, x 3 ), where x 1, x 2 and x 3 denote the response of the 1st, 2nd and 3rd student, respectively. Thus, S 4 = {(x 1, x 2, x 3 ) : x 1 = 1, 2,..., 10, x 2 = 1, 2,..., 10, x 3 = 1, 2,..., 10}. There are = 1000 possible outcomes.
7 5) Only the average rating, provided by the three undergraduate students of the previous example, is recorded. Give the sample space, S 5, of this experiment. What is the size of this sample space? Solution: S 5 is the collection of all distinct averages (x 1 + x 2 + x 3 )/3 formed from the 1000 triplets of S 4. The word distinct is emphasized because, for example, (5, 6, 7) and (4, 6, 8) both yield an average of 6. The size of S 5 is determined, most easily, in R: S4=expand.grid(x1=1:10,x2=1:10,x3=1:10) # all triplets in the sample space S 4 length(table(rowmeans(s4))) # returns 28 for the number of different averages
8 Events Outline In experiments with many possible outcomes, investigators often classify individual outcomes into distinct categories. Opinion ratings may be classified into low (L = {0, 1, 2, 3}), medium (M = {4, 5, 6}) and high (H = {7, 8, 9, 10}). Opinion ratings of three students can be classified according to the 28 distinct average values. Definition Collections of individual outcomes are called events. An event consisting of only one outcome is called a simple event. Events are denoted by letters such as A, B, C, etc.
9 We say that a particular event A has occurred if the outcome of the experiment is a member of (i.e., contained in) A. If the result of 5 coin tosses is two heads and three tails, the event A = {at most 3 heads in five tosses of a coin} has occurred. The sample space of an experiment is an event which always occurs when the experiment is performed.
10 Set Operations The union, A B, of events A and B, is the event consisting of all outcomes that are in A or in B or in both. The intersection, A B, of A and B, is the event consisting of all outcomes that are in both A and B. The complement, A or A c, of A is the event consisting of all outcomes that are not in A. The events A and B are said to be mutually exclusive or disjoint if they have no outcomes in common. That is, if A B =, where denotes the empty set. The difference A B is defined as A B c. A is a subset of B, A B, if e A implies e B. Two sets are equal, A = B, if A B and B A.
11 Union of A and B A B Intersection of A and B A B A B A B Figure: Venn diagrams for union and intersection
12 The complement of A A c The difference operation A B A A B Figure: Venn diagrams for complement and difference
13 A A B B Figure: Venn diagram illustrations of A, B disjoint, and A B
14 Commutative Laws: a) A B = B A, b) A B = B A Associative Laws: a) (A B) C = A (B C) b) (A B) C = A (B C) Distributive Laws: a) (A B) C = (A C) (B C), b) (A B) C = (A C) (B C) De Morgan s Laws: a) (A B) c = A c B c b) (A B) c = A c B c
15 Example The following table classifies 100 cell phone calls according to their duration and number of handovers a it undergoes. Number of Handovers Duration 0 1 > < Let A = {call undergoes 1 handover}, B = {call lasts < 3 min}. (a) How many of the 100 calls are in each of A B and A B? (b) Give word descriptions of (A B) c and A c B c. Are these sets equal? Which property is confirmed? a changes of the source cell, as when the phone is moving into the area covered by another source
16 Example (Continued) Solution: (a) There are 80 calls that undergo 1 handover or last < 3 min (or both). There are 15 calls that undergo 1 handover and last < 3 min. (b) (A B) c consists of the 20 calls that are not in A B, i.e., the calls that last 3 min and undergo either 0 or > 1 handovers. A c B c consists of the calls that do not undergo 1 handover and do not last < 3 min. Thus, (A B) c = A c B c confirming the fist of De Morgan s laws. Read also the examples in p. 57
17 Definition of Probability The probability of an event E, denoted by P(E), is used to quantify the likelihood of occurrence of E by assigning a number from the interval [0, 1]. Higher numbers indicate that the event is more likely to occur. A probability of 1 indicates that the event will occur with certainty, while a probability of 0 indicates that the event will not occur. The likelihood of occurrence of an event is also quantified as a percent or in terms of the odds; read Section , p. 60.
18 Assignment of Probabilities It is simplest to introduce probability in experiments with a finite number of equally likely outcomes, such as those used in games of chance, or simple random sampling. Probability for equally likely outcomes If the sample space consists of N outcomes which are equally likely to occur, then the probability of each outcome is 1/N.
19 Population Proportions as Probabilities Let a unit be selected by s.r. sampling from a finite statistical population of a categorical variable. If category i has N i units, then the probability the selected unit came from category i is p i = N i /N. where N is the total number of units (so N = N i ). Thus, (a) In rolling a die, the probability of a three is p = 1/6. (b) If 160 out of 500 tin plates have one scratch, and one tin plate is selected at random, the probability that the selected plate has one scratch is p = 160/500.
20 Efron s Dice Outline Die A: four 4s and two 0s Die B: six 3s Die C: four 2s and two 6s Die D: three 5 s and three 1 s Specify the events A > B, B > C, C > D, D > A. Find the probabilities that A > B, B > C, C > D, D > A. Hint: When two dice are rolled, the 36 possible outcomes are equally likely.
21 Outline 1 2 3
22 Even when the population units are equally likely, the values of the random variable recorded may not be equally likely. When die is rolled twice, each of the 36 possible outcomes are equally likely. But if we record the sum of the two rolls, these outcomes are not equally likely. Definition The probability mass function, or pmf, of a discrete random variable X, is a list of the probabilities p(x) for each value x of the sample space S X of X.
23 Example Roll a die twice. Find the pmf of X = the sum of the two die rolls. Solution: The sample space of X is S X = {2, 3,..., 12}. The pmf can be found with the R commands: options(digits = 2); S=expand.grid(X1=1:6,X2=1:6); table(s$x1+s$x2)/length(s$x1) The probabilities these commands return are show in the following table: x p(x) Try also which(s$x1+s$x2==7); S[which(S$X1+S$X2==7),].
24 Sample Space Population It is useful to think of a random experiment as random (but not simple random) sampling from the sample space population. 1 Sampling a tin plate and recording the number of scratches (recall there can be either 0, 1 or 2 scratches) can be thought of as random (but not simple random) sampling from S = {0, 1, 2}. 2 Sampling a US voter and recording his/her opinion, on a scale from 1 to 10, regarding expanding the use of solar energy can be thought of as random (but not simple random!) sampling from S = {0, 1,..., 10}.
25 Definition When a sample space is thought of as the set from which we sample, we refer to it as sample space population. The random sampling from the sample space population (which is need not be simple random sampling) is called probability sampling, or sampling from a pmf. This idea makes it possible to think of different experiments as sampling from the same population. For example Inspecting 50 products and recording the number of defectives, and Interviewing 50 people and recording if they read New York Times can both be thought as probability sampling from their common sample space S = {0, 1, 2,..., 50}.
26 Simulations Outline The concepts of pmf and sample space population and probability sampling are useful for conducting simulations, i.e., for generating a large number of repetitions of an experiment. Such simulations are used to better understand aspects of complex populations. Example (Simulating an Experiment with R) Use the pmf of X = sum of two die rolls to simulate 1000 repetitions of the experiment which records the sum of two die rolls. Take their mean and use it to guess the population mean. The R commands are S=expand.grid(X1=1:6,X2=1:6) ; pmf=table(s$x1+s$x2)/36 mean(sample(2:12, size=1000, replace=t, prob=pmf))
27 Outline 1 2 3
28 Why Count? Outline In classical probability counting is used for calculating probabilities. if the sample space consists of a finite number of equally likely outcomes, then for the probability of an event A we need to know the number of outcomes in A, N(A), and the total number of outcomes, N(S), because P(A) = N(A) N(S) Some counting questions are difficult (e.g., how many different five-card hands are possible from a deck of 52 cards?). Thus we need specialized counting techniques.
29 Combinations The number of samples of size n that can be formed from N units is called the number of combinations of n objects selected from N, is denoted by ( N n), and equals ( ) N = n N!, where k! = 1 2 k. n!(n n)! ( ) 52 For example, there are = 52! = 2, 598, !47! different five-card hands from a deck of N = 52 cards. Knowing the above, we can calculate the probability of the hand with 4 aces and the king of hearts, as well as of A = {the hand has 4 aces}. What are these probabilities?
30 Other counting questions can also be thought of as the number of different samples, of a certain size, that can be taken from a population. How many different n-long sequences consisting of k 1s and n k 0s can be formed? The answer is the number of combinations ( n k). (Why?) When inspecting n items as they come off the assembly line, the probability of the event E = {k of the n inspected items are defective} is calculated using the concept of independence and from knowing the number of n-long sequences consisting of k 1s (for defective) and n k 0s (for non defective).
31 In what follows we will justify the formula for ( n k). In the process we will introduce the notion of permutations.
32 The Product Rules The Simple Product Rule: Suppose a task can be completed in two stages. If stage 1 has n 1 outcomes, and if stage 2 has n 2 outcomes regardless of the outcome in stage 1, then the task has n 1 n 2 outcomes. How many five-card hands with four aces are there? Solution: Think of the task of forming a hand with four aces from a deck of 52 cards. This task can be completed in two stages: First select the 4 aces, and then select one additional card. Here n 1 = 1 and n 2 = 48 (why?). Thus, there are 1 48 = 48 such hands.
33 Example (1) 1 In how many ways can we select the 1st and 2nd place winners from the four finalists Niki, George, Sophia and Martha? Answer: 4 3 = 12. (Why?) 2 In how many ways can we select two from Niki, George, Sophia and Martha? Answer: 12 2 = 6. (Why?) Note: 6 = # of combinations = ( ) 4. 2
34 The General Product Rule: If a task can be completed in k stages and stage i has n i outcomes, regardless of the outcomes the previous stages, then the task has n 1 n 2 n k outcomes How many binary sequences of length 10 (i.e., a 10-long sequence of 0s and 1s) are there? Answer: Think of the task of forming a binary sequence of length 10. This task consists of 10 stages and each stage has two outcomes (i.e., either 0 or 1) regardless of the outcomes the previous stages. Thus, there are 2 10 = 1024 different sequences. Read also Example 2.3-4, p. 63.
35 Example (2) 1 In how many ways can we select a 1st, 2nd and 3rd place winners from Niki, George, Sophia and Martha? Answer: = 24. (Why?) 2 In how many ways can we select three from Niki, George, Sophia and Martha? Answer: 24 6 = 4. (Why?) Note: 4 = # of combinations = ( ) 4. 3
36 Permutations Outline The answer to Example (1), part 1, i.e. 12, is the number of permutations of 2 items selected from 4. The answer to Example (2), part 1, i.e. 24, is the number of permutations of 3 items selected from 4. Definition The number of ordered selections (i.e. when we keep track of the order of selection) of k items from n is called the number of permutations of k items selected from n, it is denoted by P k,n, and equals P k,n = n (n 1)... (n k + 1) = n! (n k)!
37 Combinations In the answer to Example (1), part 2, i.e. 12 2, the 2 in the denominator is the number of permutations of 2 items selected from 2 (P 2,2 = 2 1). In the answer to Example (2), part 2, i.e. 24 6, the 6 in the denominator is the number of permutations of 3 items selected from 3 (P 3,3 = 3 2 1). Extending the reasoning used to obtain these answers, we have The number of combinations of k items selected from a group of n is (n ) k = P k,n k! = n! k!(n k)!
38 Binomial Coefficients The numbers ( n k) are called binomial coefficients because of the Binomial Theorem: (a + b) n = n k=0 ( ) n a k b n k. k
39 Example (a) How many paths going from the lower left corner of a 4 3 grid to its upper right corner are there? Assume one is allowed to move either to the right or upwards. (b) How many of the paths in (a) pass through the (2, 2) point of the grid? (c) What is the probability that a randomly selected path will pass through the (2, 2) point? Hints: (a) A path can be represented by a 7-long binary sequence with four 1s (1 denotes a step to the right) and three 0s (steps upwards). (b) The # of paths from the lower left corner to the (2, 2) point times the # of paths from (2, 2) to the upper right corner.
40 Example An order comes in for 5 palettes of low grade shingles. In the warehouse there are 10 palettes of high grade, 15 of medium grade, and 20 of low grade shingles. An inexperienced shipping clerk is unaware of the distinction in grades of asphalt shingles and he ships 5 randomly selected palettes. 1 How many different groups of 5 palettes are there? ( 45 5 ) = 1, 221, What is the probability that all of the shipped palettes are low grade? ( 20 5 ) / ( 45 5 ) = 15, 504/1, 221, 759 = What is the probability that 2 of the shipped palettes are of medium [ grade and 3 are from low grade? (15 )( 20 ) ] 2 3 / ( ) 45 5 = ( )/1, 221, 759 = =
41 Example A communication system consists of 15 indistinguishable antennas arranged in a line. The system functions as long as no two non-functioning antennas are next to each other. Suppose six antennas stop functioning. a) How many different arrangements of the six non-functioning antennas result in the system being functional? (Hint: The 9 functioning antennas, lined up among themselves, define 10 possible locations for the 6 non-functioning antennas so the system functions.) b) If the arrangement of the 15 antennas is random, what is the probability the system is functioning?
42 Example What is the probability that 5 randomly dealt cards form a full house? Solution: First, the number of all 5-card hands is ( 52 ) 52! 5 = = 2, 598, 960. Next, think of the task of forming a 5!47! full house as consisting of two stages. In Stage 1 choose two cards of the same kind, and in stage 2 choose three cards of the same kind. Since there are 13 kinds of cards, stage 1 can be completed in ( 13 1 )( 4 2) = (13)(6) = 78 ways (why?). For each outcome of stage 1, the task of stage 2 becomes that of selecting three of a kind from one of the remaining 12 kinds. This can be completed in ( 12 1 )( 4 3) = 48 ways. Thus there are (78)(48) = 3, 744 possible full houses, and the desired probability is
43 Multinomial Coefficients The number of ways n units can be divide in r groups of specified sizes n 1,..., n r is given by ( ) n n! = n 1, n 2,..., n r n 1!n 2! n r! These numbers are called multinomial coefficients because of the Multinomial Theorem. In how many ways can 8 engineers be assigned to work on projects A, B, and C, so that 3 work on project A, 2 work on B, and 3 work on C? Answer: 560
44 Read Examples 2.3-8, , pp. 66, 67
45 The axioms governing any assignment of probabilities are: Axiom 1: P(E) 0, for all events E Axiom 2: P(S) = 1 Axiom 3: If E 1, E 2,... are disjoint P(E 1 E 2...) = P(E i ) i=1
46 Properties of Probability The following properties follow from the three axioms: 1) P( ) = 0 2) If E 1,..., E m are disjoint, then P(E 1 E m ) = P(E 1 ) + + P(E m ) 3) If A B then P(A) P(B) 4) P(A) = 1 P(A c ), for any event A
47 Two additional properties deal with the probability of the union of events that are not disjoint: 5) P(A B) = P(A) + P(B) P(A B) 6) P(A B C) = P(A) + P(B) + P(C) P(A B) P(A C) P(B C) + P(A B C) The formula for property 6 follows the so-called inclusion - exclusion principle and extends to the union of more than three events.
48 Example The probability that a firm will open a branch office in Toronto is 0.7, that it will open one in Mexico City is 0.4, and that it will open an office in at least one of the cities is 0.8. Find the probabilities that the firm will open an office in: 1 neither of the cities (Answer: = 0.2) 2 both cities (Answer: = 0.3) 3 exactly one of the cities (Answer: ( ) + ( ) = 0.5, or = 0.5)
49 Example The R commands attach(expand.grid(x1=0:1,x2=0:1, X3=0:1,X4=0:1)); table(x1+x2+x3+x4)/length(x1) yields the following pmf for the random variable X = number of heads in four flips of a coin: x p(x) (a) What do Axiom 2 and property 2 say about the sum of all probabilities? (Answer: They sum to 1) (b) What is P(X 2)? (Answer: = ) Read also Examples 2.4-2, 2.4-3, pp. 75, 76
November 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 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 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 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 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 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 informationContents 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 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 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 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 informationSection 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 informationChapter 6: Probability and Simulation. The study of randomness
Chapter 6: Probability and Simulation The study of randomness Introduction Probability is the study of chance. 6.1 focuses on simulation since actual observations are often not feasible. When we produce
More informationChapter 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 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 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 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 informationProbability 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 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 informationThe 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 information8.2 Union, Intersection, and Complement of Events; Odds
8.2 Union, Intersection, and Complement of Events; Odds Since we defined an event as a subset of a sample space it is natural to consider set operations like union, intersection or complement in the context
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 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 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 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 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 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 informationChapter 3: Elements of Chance: Probability Methods
Chapter 3: Elements of Chance: Methods Department of Mathematics Izmir University of Economics Week 3-4 2014-2015 Introduction In this chapter we will focus on the definitions of random experiment, outcome,
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 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 informationBusiness Statistics. Chapter 4 Using Probability and Probability Distributions QMIS 120. Dr. Mohammad Zainal
Department of Quantitative Methods & Information Systems Business Statistics Chapter 4 Using Probability and Probability Distributions QMIS 120 Dr. Mohammad Zainal Chapter Goals After completing this chapter,
More informationLesson 4: Chapter 4 Sections 1-2
Lesson 4: Chapter 4 Sections 1-2 Caleb Moxley BSC Mathematics 14 September 15 4.1 Randomness What s randomness? 4.1 Randomness What s randomness? Definition (random) A phenomenon is random if individual
More informationThe 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 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 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 informationA Probability Work Sheet
A Probability Work Sheet October 19, 2006 Introduction: Rolling a Die Suppose Geoff is given a fair six-sided die, which he rolls. What are the chances he rolls a six? In order to solve this problem, we
More informationChapter 6: Probability and Simulation. The study of randomness
Chapter 6: Probability and Simulation The study of randomness 6.1 Randomness Probability describes the pattern of chance outcomes. Probability is the basis of inference Meaning, the pattern of chance outcomes
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 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 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 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 (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 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 informationProbability. 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 information2. Combinatorics: the systematic study of counting. The Basic Principle of Counting (BPC)
2. Combinatorics: the systematic study of counting The Basic Principle of Counting (BPC) Suppose r experiments will be performed. The 1st has n 1 possible outcomes, for each of these outcomes there are
More informationGrade 6 Math Circles Fall Oct 14/15 Probability
1 Faculty of Mathematics Waterloo, Ontario Centre for Education in Mathematics and Computing Grade 6 Math Circles Fall 2014 - Oct 14/15 Probability Probability is the likelihood of an event occurring.
More informationIntroduction 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 informationProbability - 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 informationTextbook: 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 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 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 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 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 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. March 06, J. Boulton MDM 4U1. P(A) = n(a) n(s) Introductory Probability
Most people think they understand odds and probability. Do you? Decision 1: Pick a card Decision 2: Switch or don't Outcomes: Make a tree diagram Do you think you understand probability? Probability Write
More information, -the of all of a probability experiment. consists of outcomes. (b) List the elements of the event consisting of a number that is greater than 4.
4-1 Sample Spaces and Probability as a general concept can be defined as the chance of an event occurring. In addition to being used in games of chance, probability is used in the fields of,, and forecasting,
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 informationMath 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 informationBasic 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 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 informationCSCI 2200 Foundations of Computer Science (FoCS) Solutions for Homework 7
CSCI 00 Foundations of Computer Science (FoCS) Solutions for Homework 7 Homework Problems. [0 POINTS] Problem.4(e)-(f) [or F7 Problem.7(e)-(f)]: In each case, count. (e) The number of orders in which a
More informationAlgebra 2 Notes Section 10.1: Apply the Counting Principle and Permutations
Algebra 2 Notes Section 10.1: Apply the Counting Principle and Permutations Objective(s): Vocabulary: I. Fundamental Counting Principle: Two Events: Three or more Events: II. Permutation: (top of p. 684)
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 informationThe Teachers Circle Mar. 20, 2012 HOW TO GAMBLE IF YOU MUST (I ll bet you $5 that if you give me $10, I ll give you $20.)
The Teachers Circle Mar. 2, 22 HOW TO GAMBLE IF YOU MUST (I ll bet you $ that if you give me $, I ll give you $2.) Instructor: Paul Zeitz (zeitzp@usfca.edu) Basic Laws and Definitions of Probability If
More informationCHAPTERS 14 & 15 PROBABILITY STAT 203
CHAPTERS 14 & 15 PROBABILITY STAT 203 Where this fits in 2 Up to now, we ve mostly discussed how to handle data (descriptive statistics) and how to collect data. Regression has been the only form of statistical
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 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 informationApplications 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 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 informationBasic Probability Concepts
6.1 Basic Probability Concepts How likely is rain tomorrow? What are the chances that you will pass your driving test on the first attempt? What are the odds that the flight will be on time when you go
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 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 informationChapter 3: PROBABILITY
Chapter 3 Math 3201 1 3.1 Exploring Probability: P(event) = Chapter 3: PROBABILITY number of outcomes favourable to the event total number of outcomes in the sample space An event is any collection of
More informationProbability Theory. Mohamed I. Riffi. Islamic University of Gaza
Probability Theory Mohamed I. Riffi Islamic University of Gaza Table of contents 1. Chapter 1 Probability Properties of probability Counting techniques 1 Chapter 1 Probability Probability Theorem P(φ)
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 informationSection : 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 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 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 informationExam 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 informationSection 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 informationCSE 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 informationIntroduction to Probability and Statistics I Lecture 7 and 8
Introduction to Probability and Statistics I Lecture 7 and 8 Basic Probability and Counting Methods Computing theoretical probabilities:counting methods Great for gambling! Fun to compute! If outcomes
More informationWeek 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 informationCHAPTER 7 Probability
CHAPTER 7 Probability 7.1. Sets A set is a well-defined 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 informationChapter 1: Sets and Probability
Chapter 1: Sets and Probability Section 1.3-1.5 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 information4.3 Finding Probability Using Sets
4.3 Finding Probability Using ets When rolling a die with sides numbered from 1 to 20, if event A is the event that a number divisible by 5 is rolled: a) What is the sample space,? b) What is the event
More informationCS 237: Probability in Computing
CS 237: Probability in Computing Wayne Snyder Computer Science Department Boston University Lecture 5: o Independence reviewed; Bayes' Rule o Counting principles and combinatorics; o Counting considered
More informationRaise 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 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 informationMULTIPLE 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 informationHonors Precalculus Chapter 9 Summary Basic Combinatorics
Honors Precalculus Chapter 9 Summary Basic Combinatorics A. Factorial: n! means 0! = Why? B. Counting principle: 1. How many different ways can a license plate be formed a) if 7 letters are used and each
More informationRANDOM 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 informationCSC/MATA67 Tutorial, Week 12
CSC/MATA67 Tutorial, Week 12 November 23, 2017 1 More counting problems A class consists of 15 students of whom 5 are prefects. Q: How many committees of 8 can be formed if each consists of a) exactly
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 informationReview Questions on Ch4 and Ch5
Review Questions on Ch4 and Ch5 1. Find the mean of the distribution shown. x 1 2 P(x) 0.40 0.60 A) 1.60 B) 0.87 C) 1.33 D) 1.09 2. A married couple has three children, find the probability they are all
More informationCIS 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 informationModule 4 Project Maths Development Team Draft (Version 2)
5 Week Modular Course in Statistics & Probability Strand 1 Module 4 Set Theory and Probability It is often said that the three basic rules of probability are: 1. Draw a picture 2. Draw a picture 3. Draw
More informationCHAPTER 9 - COUNTING PRINCIPLES AND PROBABILITY
CHAPTER 9 - COUNTING PRINCIPLES AND PROBABILITY Probability is the Probability is used in many real-world fields, such as insurance, medical research, law enforcement, and political science. Objectives:
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 one-pair
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 information