Homework 3 Solution, due July 16
|
|
|
- Samuel Jack Wilson
- 9 years ago
- Views:
Transcription
1 Homework 3 Solution, due July 16 Problems from old actuarial exams are marked by a star. Problem 1*. Upon arrival at a hospital emergency room, patients are categorized according to their condition as critical, serious, or stable. In the past year: 10% of the emergency room patients were critical; 30% of the emergency room patients were serious; the rest of the emergency room patients were stable; 40% of the critical patients died; 10% of the serious patients died; 1% of the stable patients died. Given that a patient survived, what is the probability that the patient was categorized as serious upon arrival? Solution. Let F 1 = {patient is critical}, F 2 = {patient is serious}, F 3 = {patient is stable}, A = {patient survived}. Then Thus, by the second Bayes formula P(F 2 A) = P(F 1 ) = 0.1, P(F 2 ) = 0.3, P(F 3 ) = 0.6, P(A F 1 ) = 0.6, P(A F 2 ) = 0.9, P(A F 3 ) = P(F 2 )P(A F 2 ) P(F 1 )P(A F 1 ) + P(F 2 )P(A F 2 ) + P(F 3 )P(A F 3 ) = = 29% Problem 2*. Among a large group of patients recovering from shoulder injuries, it is found that 22% visit both a physical therapist and a chiropractor, whereas 12% visit neither of these. The probability that a patient visits a chiropractor exceeds by 0.14 the probability that a patient visits a physical therapist. Determine the probability that a randomly chosen member of this group visits a physical therapist. Solution. Suppose A = {patient visits physical therapist}, B = {patient visits chiropractor}. Then by conditions of the problem P(A B) = 0.22, P((A B) c ) = 0.12, and We know that P(B) = P(A) P(A B) = P(A) + P(B) P(A B), 1
2 and P(A B) = = P(A) + P(B) = 1.1, and 2P(A) = 1.1. So P(A) = 0.48 Problem 3*. An urn contains 10 balls: 4 red and 6 blue. A second urn contains 16 red balls and an unknown number of blue balls. A single ball is drawn from each urn. The probability that both balls are the same color is Calculate the number of blue balls in the second urn. Solution. Let the number of blue balls in the second urn be x. Then the probability that balls drawn from each urn are both red is The probability that both balls are blue is x. 6 x x. Their sum is the probability that the balls have the same color: Solving for x, we get: x + 6 x x = (16 + x) = x, 1.6x = 6.4, x = 4 Problem 4*. The probability that a visit to a primary care physicians (PCP) office results in neither lab work nor referral to a specialist is 35%. Of those coming to a PCPs office, 30% are referred to specialists and 40% require lab work. Determine the probability that a visit to a PCPs office results in both lab work and referral to a specialist. Solution. Let A = {lab work}, B = {specialist}. We know that P((A B) c ) = 0.35, P(A) = 0.4, P(B) = 0.3. Thus, P(A B) = = P(A B) = P(A) + P(B) P(A B) = 0.05 Problem 5*. An insurance company issues life insurance policies in three separate categories: standard, preferred, and ultra-preferred. Of the companys policyholders, 50% are standard, 40% are preferred, and 10% are ultra-preferred. Each standard policyholder has probability of dying in the next year, each preferred policyholder has probability of dying in the next year, and each ultra-preferred policyholder has probability of dying in the next year. A policyholder dies in the next year. What is the probability that the deceased policyholder was ultra-preferred? 2
3 Solution. Let F 1 = {standard}, F 2 = {preferred}, F 3 = {ultra-preferred}, A = {deceased next year}. Then P(F 1 ) = 0.5, P(F 2 ) = 0.4, P(F 3 ) = 0.1, By the second Bayes formula, P(F 3 A) = P(A F 1 ) = 0.01, P(A F 2 ) = 0.005, P(A F 3 ) = P(A F 3 )P(F 3 ) P(A F 1 )P(F 1 ) + P(A F 2 )P(F 2 ) + P(A F 3 )P(F 3 ) = 1 71 = 1.41% Problem 6*. For two events A and B, you are given Determine P(A). P(A B) = 0.7, P(A B c ) = 0.9. Solution. Let C = A B and D = A B c. Then C D = Ω ( everything ), and C D = A. Applying the inclusion-exclusion formula to C and D, we have: P(A) = P(C D) = P(C) + P(D) P(C D) = = 0.6 Problem 7*. An actuary is studying the prevalence of three health risk factors, denoted by A, B, and C, within a population of women. For each of the three factors, the probability is 0.1 that a woman in the population has only this risk factor (and no others). For any two of the three factors, the probability is 0.12 that she has exactly these two risk factors (but not the other). The probability that a woman has all three risk factors, given that she has A and B, is 1/3. What is the probability that a woman has none of the three risk factors, given that she does not have risk factor A? Solution. We know that P(A B c C c ) = P(A c B C c ) = P(A c B c C) = 0.1, and Also, P(A B C c ) = P(A B c C) = P(A c B C) = P(A B C A B) = 1 3. P(A B C) = 1 P(A B), 3 P(A B C c ) = P(A B) P(A B C) = 2 P(A B), 3 so P(A B) = (3/2)0.12 = 0.18, and P(A B C) = (1/3)0.18 = P(A) = P(A B C)+P(A B c C)+P(A B C c )+P(A B c C c ) = = 0.4. And P(A c ) = 0.6. Now, P(A B C) = =
4 Finally, P((A B C) c ) = = P((A B C) c A c ) = = 7 15 Problem 8*. A large pool of adults earning their first drivers license includes 50% lowrisk drivers, 30% moderate-risk drivers, and 20% high-risk drivers. Because these drivers have no prior driving record, an insurance company considers each driver to be randomly selected from the pool. This month, the insurance company writes 4 new policies for adults earning their first drivers license. What is the probability that these 4 will contain at least two more high-risk drivers than low-risk drivers? Solution. There are the following combinations which satisfy the condition that there are at least two more high-risk drivers than low-risk drivers: HHHH, HHHL, HHHM, HHMM (arrangements of letters can vary, we just stated the quantity of each letter). The HHHH case has probability The HHHL case has probability , because L can be in four different places. Similarly, the HHHM case has probability Finally, the HHMM case has probability , because the number of ways to choose places for two M letters in ( 4 2) = 6. The answer is the sum of all these four probabilities, which is Problem 9*. An insurer offers a health plan to the employees of a large company. As part of this plan, the individual employees may choose exactly two of the supplementary coverages A, B, and C, or they may choose no supplementary coverage. The proportions of the companys employees that choose coverages A, B, and C are 1/4, 1/3, and 5/12, respectively. Determine the probability that a randomly chosen employee will choose no supplementary coverage. Solution. Let C 0 be the event that people choose A and B: C 0 = A B. Similarly for A 0 = B C and B 0 = A C. Finally, let N = (A B C) c be the event that the individual chooses nothing. These four are non-intersecting events, by conditions of the problem. Then we know that A 0 B 0 C 0 N = Ω is everything. P(A) = P(B 0 ) + P(C 0 ), P(B) = P(A 0 ) + P(C 0 ), P(C) = P(A 0 ) + P(B 0 ). So Thus, P(A 0 ) + P(B 0 ) + P(C 0 ) = 1 2 (P(A) + P(B) + P(C)) = 1 2 ( ) = P(N) = 1 P(A 0 ) P(B 0 ) P(C 0 ) = 1 2 Problem 10*. A random person has a heart disease with probability 25%. A person with a heart disease is a smoker with probability twice as a person without a heart disease. What is the conditional probability that a smoker has a heart disease? 4
5 Solution. Let A = {smoker}, B = {heart disease}. Then P(B) = 0.25, and P(A B) = 2P(A B c ). P(A B) P(B) Since P(B) = 0.25 and P(B c ) = 0.75, we get: = 2 P(A Bc ). P(B c ) 4P(A B) = 8 3 P(A Bc ) P(A B c ) = 3 2 P(A Bc ). P(A) = P(A B) + P(A B c ) = 5 P(A B). 2 So P(B A) = P(B A)/P(A) = 2 5 = 40% Problem 11. Toss three fair coins. Are the following events A and B independent? (i) A = {first and second tosses resulted in exactly one H}, B = {second and third tosses resulted in exactly one H}; (ii) A = {first and second tosses resulted in exactly one H}, B = {second and third tosses resulted in at least one H}; (iii) A = {first and second tosses resulted in at least one H}, B = {second and third tosses resulted in at least one H}; (iv) A = {first toss in H}, B = {second and third tosses resulted in at least one H}. Solution. There are eight elementary outcomes: Ω = {HHH, HHT, HT H, T HH, T T H, T HT, HT T, T T T }, each with probability 1/8. (i) A = {HT T, T HT, HT H, T HH}, B = {HT H, T T H, HHT, T HT }, A B = {T HT, HT H}. So P(A) = 4/8 = 1/2, and P(B) = 4/8 = 1/2, while P(A B) = 2/8 = 1/4. Yes, they are independent. (ii) A = {HT T, T HT, HT H, T HH}, B = {HT H, T T H, HHT, T HT, HHH, T HH}, A B = {T HT, HT H, T HH}. So P(A) = 4/8 = 1/2, P(B) = 6/8 = 3/4, and P(A B) = 3/8. Yes, they are independent. (iii) A = {HHH, HHT, HT H, HT T, T HH, T HT }, B = {HT H, T T H, HHT, T HT, HHH, T HH}, A B = {T HT, HT H, T HH}. So P(A) = 6/8 = 3/4, and P(B) = 3/4, while P(A B) = 3/8. Dependent. (iv) Independent, because A depends on the first toss, B depends on the second and third tosses, but the coin does not have a memory. Formally: A = {HT H, HT T, HHH, HHT }, B = {HT H, T T H, HHT, T HT, HHH, T HH}, A B = {HT H, HT T, HHH}. So P(A) = 4 8 = 1 2, P(B) = 6 8 = 3 4, P(A B) = 3 8 = P(A)P(B). Problem 12. Genes related to albinism are denoted by A (non-albino) and a (albino). Each person has two genes. People with AA, Aa or aa are non-albino, while people with aa are albino. The non-albino gene A is called dominant. Any parent passes one of two genes (selected randomly with equal probability) to his offspring. If a person has either Aa 5
6 or aa, then he is called a carrier: he is a non-albino, but he can carry the albino gene to his offsprings. (i) If a person is non-albine, what is the conditional probability that he is a carrier? (ii) If a couple consists of a non-albino and an albino, what is the probability that their offsping will be albino? (iii) Suppose that a couple consists of two non-albinos. They have two offsprings. What is the probability that both offsprings are albinos? Solution. (i) Non-albino = {AA, Aa, aa}, carrier = {Aa, aa}. P(Non-albino) = 3/4, P(Carrier) = 2/4. So the conditional probability is (2/4)/(3/4) = 2/3 (ii) Let F 1 = {the non-albino parent is a carrier}, F 2 = F c 1, A = {offspring albino}. Then P(A F 2 ) = 0, because the non-albino parent who is not a carrier has AA, so he cannot pass the gene a to his offspring. Also, P(A F 1 ) = 1 2, because the albino parent will always pass the gene a to his offspring, while a non-albino carrier will do this with probability 1/2. Finally, P(F 1 ) = 2/3, P(F 2 ) = 1/3. by the first Bayes formula P(A) = P(A F 1 )P(F 1 ) + P(A F 2 )P(F 2 ) = 1 3 (iii) F 1 = {both parents carriers}, A = {both offsprings albinos}, F 2 = F c 1. Then P(F 1 ) = (2/3)(2/3) = 4/9. If both parents are carriers, then each will pass the a gene with probability 1/2 to his child. So the probability of one chiled being albino is 1/4, and P(A F 1 ) = (1/4) 2 = 1/16; also, P(A F 2 ) = 0, for the same reason as in (ii). by the first Bayes formula P(A) = P(A F 1 )P(F 1 ) + P(A F 2 )P(F 2 ) = = 1 36 Problem 13. Is it right that the following statement are true for all possible events A, B, C? (Venn diagrams might be helpful.) (i) A A B; (ii) A A B; (iii) A c A = ; (iv) A c A = Ω; (v) A c = Ω \ A; (vi) (A \ B) (B \ A) = A B; (vii) A (B C) = (A B) (A C); (viii) A \ (B C) = (A \ B) (A \ C). Solution. (i) Yes. (ii) No. (iii) Yes. (iv) Yes. (v) Yes. (vi) No. (vii) Yes. (viii) No. Problem 14. Consider two coins: a fair one and a magic one, which always falls H. Suppose you pick a coin at random (each coin with equal probability 1/2) and toss it n 6
7 times. All of the tosses are heads. You would like to convince me that the coin is magic, but I will believe you only if the probability that it is magic (given the result that all n tosses are H) exceeds 99%. How large the number n of tosses should be? Solution. F 1 = {magic}, F 2 = {fair}, A = {n heads}. Then P(F 1 ) = P(F 2 ) = 1/2, and P(A F 1 ) = 1, P(A F 2 ) = (1/2) n. By the second Bayes formula, P(F 2 A) = This is less than 0.01 when n 7 P(F 2 )P(A F 2 ) P(F 1 )P(A F 1 ) + P(F 2 )P(A F 2 ) = = n. (1/2)n 1 + (1/2) n Problem 15. Let Ω = {1, 2,..., 8}, A = {2, 4, 6, 8}, B = {1, 2, 3, 4}, C = {1, 3, 5, 7}. Find the following events: (i) A B; (ii) (A B C) c ; (iii) (A \ B) C; (iv) A \ C; (v) C \ A c. Solution. (i) A B = Ω; (ii) (A B C) c = c = Ω; (iii) (A \ B) C = ; (iv) A \ C = {2, 4, 6, 8}; (v) C \ A c =. 7
Math 370/408, Spring 2008 Prof. A.J. Hildebrand. Actuarial Exam Practice Problem Set 1
Math 370/408, Spring 2008 Prof. A.J. Hildebrand Actuarial Exam Practice Problem Set 1 About this problem set: These are problems from Course 1/P actuarial exams that I have collected over the years, grouped
Math 370, Actuarial Problemsolving Spring 2008 A.J. Hildebrand. Problem Set 1 (with solutions)
Math 370, Actuarial Problemsolving Spring 2008 A.J. Hildebrand Problem Set 1 (with solutions) About this problem set: These are problems from Course 1/P actuarial exams that I have collected over the years,
SOCIETY OF ACTUARIES/CASUALTY ACTUARIAL SOCIETY EXAM P PROBABILITY EXAM P SAMPLE QUESTIONS
SOCIETY OF ACTUARIES/CASUALTY ACTUARIAL SOCIETY EXAM P PROBABILITY EXAM P SAMPLE QUESTIONS Copyright 005 by the Society of Actuaries and the Casualty Actuarial Society Some of the questions in this study
SOCIETY OF ACTUARIES/CASUALTY ACTUARIAL SOCIETY EXAM P PROBABILITY EXAM P SAMPLE QUESTIONS
SOCIETY OF ACTUARIES/CASUALTY ACTUARIAL SOCIETY EXAM P PROBABILITY EXAM P SAMPLE QUESTIONS Copyright 007 by the Society of Actuaries and the Casualty Actuarial Society Some of the questions in this study
UNIVERSITY of TORONTO. Faculty of Arts and Science
UNIVERSITY of TORONTO Faculty of Arts and Science AUGUST 2005 EXAMINATION AT245HS uration - 3 hours Examination Aids: Non-programmable or SOA-approved calculator. Instruction:. There are 27 equally weighted
SOCIETY OF ACTUARIES EXAM P PROBABILITY EXAM P SAMPLE QUESTIONS
SOCIETY OF ACTUARIES EXAM P PROBABILITY EXAM P SAMPLE QUESTIONS Copyright 015 by the Society of Actuaries Some of the questions in this study note are taken from past examinations. Some of the questions
Statistics 100A Homework 2 Solutions
Statistics Homework Solutions Ryan Rosario Chapter 9. retail establishment accepts either the merican Express or the VIS credit card. total of percent of its customers carry an merican Express card, 6
STA 256: Statistics and Probability I
Al Nosedal. University of Toronto. Fall 2014 1 2 3 4 5 My momma always said: Life was like a box of chocolates. You never know what you re gonna get. Forrest Gump. Experiment, outcome, sample space, and
Lesson 1. Basics of Probability. Principles of Mathematics 12: Explained! www.math12.com 314
Lesson 1 Basics of Probability www.math12.com 314 Sample Spaces: Probability Lesson 1 Part I: Basic Elements of Probability Consider the following situation: A six sided die is rolled The sample space
SOCIETY OF ACTUARIES/CASUALTY ACTUARIAL SOCIETY EXAM P PROBABILITY EXAM P SAMPLE QUESTIONS
SOCIETY OF ACTUARIES/CASUALTY ACTUARIAL SOCIETY EXAM P PROBABILITY EXAM P SAMPLE QUESTIONS Copyright 5 by the Society of Actuaries and the Casualty Actuarial Society Some of the questions in this study
Homework 8 Solutions
CSE 21 - Winter 2014 Homework Homework 8 Solutions 1 Of 330 male and 270 female employees at the Flagstaff Mall, 210 of the men and 180 of the women are on flex-time (flexible working hours). Given that
STAT 35A HW2 Solutions
STAT 35A HW2 Solutions http://www.stat.ucla.edu/~dinov/courses_students.dir/09/spring/stat35.dir 1. A computer consulting firm presently has bids out on three projects. Let A i = { awarded project i },
36 Odds, Expected Value, and Conditional Probability
36 Odds, Expected Value, and Conditional Probability What s the difference between probabilities and odds? To answer this question, let s consider a game that involves rolling a die. If one gets the face
Definition and Calculus of Probability
In experiments with multivariate outcome variable, knowledge of the value of one variable may help predict another. For now, the word prediction will mean update the probabilities of events regarding the
Exam 3 Review/WIR 9 These problems will be started in class on April 7 and continued on April 8 at the WIR.
Exam 3 Review/WIR 9 These problems will be started in class on April 7 and continued on April 8 at the WIR. 1. Urn A contains 6 white marbles and 4 red marbles. Urn B contains 3 red marbles and two white
Math 3C Homework 3 Solutions
Math 3C Homework 3 s Ilhwan Jo and Akemi Kashiwada [email protected], [email protected] Assignment: Section 2.3 Problems 2, 7, 8, 9,, 3, 5, 8, 2, 22, 29, 3, 32 2. You draw three cards from a standard
EXAM. Exam #3. Math 1430, Spring 2002. April 21, 2001 ANSWERS
EXAM Exam #3 Math 1430, Spring 2002 April 21, 2001 ANSWERS i 60 pts. Problem 1. A city has two newspapers, the Gazette and the Journal. In a survey of 1, 200 residents, 500 read the Journal, 700 read the
Chapter 6. 1. What is the probability that a card chosen from an ordinary deck of 52 cards is an ace? Ans: 4/52.
Chapter 6 1. What is the probability that a card chosen from an ordinary deck of 52 cards is an ace? 4/52. 2. What is the probability that a randomly selected integer chosen from the first 100 positive
Basic Probability. Probability: The part of Mathematics devoted to quantify uncertainty
AMS 5 PROBABILITY Basic Probability Probability: The part of Mathematics devoted to quantify uncertainty Frequency Theory Bayesian Theory Game: Playing Backgammon. The chance of getting (6,6) is 1/36.
Probability: The Study of Randomness Randomness and Probability Models. IPS Chapters 4 Sections 4.1 4.2
Probability: The Study of Randomness Randomness and Probability Models IPS Chapters 4 Sections 4.1 4.2 Chapter 4 Overview Key Concepts Random Experiment/Process Sample Space Events Probability Models Probability
Chapter 4 - Practice Problems 1
Chapter 4 - Practice Problems SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question. Provide an appropriate response. ) Compare the relative frequency formula
Math 370, Spring 2008 Prof. A.J. Hildebrand. Practice Test 2 Solutions
Math 370, Spring 008 Prof. A.J. Hildebrand Practice Test Solutions About this test. This is a practice test made up of a random collection of 5 problems from past Course /P actuarial exams. Most of the
Bayesian Tutorial (Sheet Updated 20 March)
Bayesian Tutorial (Sheet Updated 20 March) Practice Questions (for discussing in Class) Week starting 21 March 2016 1. What is the probability that the total of two dice will be greater than 8, given that
Statistics 100A Homework 3 Solutions
Chapter Statistics 00A Homework Solutions Ryan Rosario. Two balls are chosen randomly from an urn containing 8 white, black, and orange balls. Suppose that we win $ for each black ball selected and we
Notes on Probability. Peter J. Cameron
Notes on Probability Peter J. Cameron ii Preface Here are the course lecture notes for the course MAS108, Probability I, at Queen Mary, University of London, taken by most Mathematics students and some
V. RANDOM VARIABLES, PROBABILITY DISTRIBUTIONS, EXPECTED VALUE
V. RANDOM VARIABLES, PROBABILITY DISTRIBUTIONS, EXPETED VALUE A game of chance featured at an amusement park is played as follows: You pay $ to play. A penny and a nickel are flipped. You win $ if either
PROBABILITY. Chapter. 0009T_c04_133-192.qxd 06/03/03 19:53 Page 133
0009T_c04_133-192.qxd 06/03/03 19:53 Page 133 Chapter 4 PROBABILITY Please stand up in front of the class and give your oral report on describing data using statistical methods. Does this request to speak
Question of the Day. Key Concepts. Vocabulary. Mathematical Ideas. QuestionofDay
QuestionofDay Question of the Day What is the probability that in a family with two children, both are boys? What is the probability that in a family with two children, both are boys, if we already know
Probability --QUESTIONS-- Principles of Math 12 - Probability Practice Exam 1 www.math12.com
Probability --QUESTIONS-- Principles of Math - Probability Practice Exam www.math.com Principles of Math : Probability Practice Exam Use this sheet to record your answers:... 4... 4... 4.. 6. 4.. 6. 7..
Statistics and Data Analysis B01.1305
Statistics and Data Analysis B01.1305 Professor William Greene Phone: 212.998.0876 Office: KMC 7-78 Home page: www.stern.nyu.edu/~wgreene Email: [email protected] Course web page: www.stern.nyu.edu/~wgreene/statistics/outline.htm
Conditional Probability, Independence and Bayes Theorem Class 3, 18.05, Spring 2014 Jeremy Orloff and Jonathan Bloom
Conditional Probability, Independence and Bayes Theorem Class 3, 18.05, Spring 2014 Jeremy Orloff and Jonathan Bloom 1 Learning Goals 1. Know the definitions of conditional probability and independence
Chapter 4 & 5 practice set. The actual exam is not multiple choice nor does it contain like questions.
Chapter 4 & 5 practice set. The actual exam is not multiple choice nor does it contain like questions. MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
3.2 Conditional Probability and Independent Events
Ismor Fischer, 5/29/2012 3.2-1 3.2 Conditional Probability and Independent Events Using population-based health studies to estimate probabilities relating potential risk factors to a particular disease,
AP Stats - Probability Review
AP Stats - Probability Review Multiple Choice Identify the choice that best completes the statement or answers the question. 1. I toss a penny and observe whether it lands heads up or tails up. Suppose
PROBABILITY. The theory of probabilities is simply the Science of logic quantitatively treated. C.S. PEIRCE
PROBABILITY 53 Chapter 3 PROBABILITY The theory of probabilities is simply the Science of logic quantitatively treated. C.S. PEIRCE 3. Introduction In earlier Classes, we have studied the probability as
Probability: Terminology and Examples Class 2, 18.05, Spring 2014 Jeremy Orloff and Jonathan Bloom
Probability: Terminology and Examples Class 2, 18.05, Spring 2014 Jeremy Orloff and Jonathan Bloom 1 Learning Goals 1. Know the definitions of sample space, event and probability function. 2. Be able to
STATISTICS 8: CHAPTERS 7 TO 10, SAMPLE MULTIPLE CHOICE QUESTIONS
STATISTICS 8: CHAPTERS 7 TO 10, SAMPLE MULTIPLE CHOICE QUESTIONS 1. If two events (both with probability greater than 0) are mutually exclusive, then: A. They also must be independent. B. They also could
Chapter 5 A Survey of Probability Concepts
Chapter 5 A Survey of Probability Concepts True/False 1. Based on a classical approach, the probability of an event is defined as the number of favorable outcomes divided by the total number of possible
Probability Review Solutions
Probability Review Solutions. A family has three children. Using b to stand for and g to stand for, and using ordered triples such as bbg, find the following. a. draw a tree diagram to determine the sample
ACMS 10140 Section 02 Elements of Statistics October 28, 2010. Midterm Examination II
ACMS 10140 Section 02 Elements of Statistics October 28, 2010 Midterm Examination II Name DO NOT remove this answer page. DO turn in the entire exam. Make sure that you have all ten (10) pages of the examination
Probabilities. Probability of a event. From Random Variables to Events. From Random Variables to Events. Probability Theory I
Victor Adamchi Danny Sleator Great Theoretical Ideas In Computer Science Probability Theory I CS 5-25 Spring 200 Lecture Feb. 6, 200 Carnegie Mellon University We will consider chance experiments with
Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 13. Random Variables: Distribution and Expectation
CS 70 Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 3 Random Variables: Distribution and Expectation Random Variables Question: The homeworks of 20 students are collected
For two disjoint subsets A and B of Ω, say that A and B are disjoint events. For disjoint events A and B we take an axiom P(A B) = P(A) + P(B)
Basic probability A probability space or event space is a set Ω together with a probability measure P on it. This means that to each subset A Ω we associate the probability P(A) = probability of A with
Binomial random variables
Binomial and Poisson Random Variables Solutions STAT-UB.0103 Statistics for Business Control and Regression Models Binomial random variables 1. A certain coin has a 5% of landing heads, and a 75% chance
Introduction to Probability
3 Introduction to Probability Given a fair coin, what can we expect to be the frequency of tails in a sequence of 10 coin tosses? Tossing a coin is an example of a chance experiment, namely a process which
Session 8 Probability
Key Terms for This Session Session 8 Probability Previously Introduced frequency New in This Session binomial experiment binomial probability model experimental probability mathematical probability outcome
1. A survey of a group s viewing habits over the last year revealed the following
1. A survey of a group s viewing habits over the last year revealed the following information: (i) 8% watched gymnastics (ii) 9% watched baseball (iii) 19% watched soccer (iv) 14% watched gymnastics and
Lecture Note 1 Set and Probability Theory. MIT 14.30 Spring 2006 Herman Bennett
Lecture Note 1 Set and Probability Theory MIT 14.30 Spring 2006 Herman Bennett 1 Set Theory 1.1 Definitions and Theorems 1. Experiment: any action or process whose outcome is subject to uncertainty. 2.
Decision Making Under Uncertainty. Professor Peter Cramton Economics 300
Decision Making Under Uncertainty Professor Peter Cramton Economics 300 Uncertainty Consumers and firms are usually uncertain about the payoffs from their choices Example 1: A farmer chooses to cultivate
Exam. Name. How many distinguishable permutations of letters are possible in the word? 1) CRITICS
Exam Name How many distinguishable permutations of letters are possible in the word? 1) CRITICS 2) GIGGLE An order of award presentations has been devised for seven people: Jeff, Karen, Lyle, Maria, Norm,
CONTINGENCY (CROSS- TABULATION) TABLES
CONTINGENCY (CROSS- TABULATION) TABLES Presents counts of two or more variables A 1 A 2 Total B 1 a b a+b B 2 c d c+d Total a+c b+d n = a+b+c+d 1 Joint, Marginal, and Conditional Probability We study methods
Lecture 1 Introduction Properties of Probability Methods of Enumeration Asrat Temesgen Stockholm University
Lecture 1 Introduction Properties of Probability Methods of Enumeration Asrat Temesgen Stockholm University 1 Chapter 1 Probability 1.1 Basic Concepts In the study of statistics, we consider experiments
ACMS 10140 Section 02 Elements of Statistics October 28, 2010 Midterm Examination II Answers
ACMS 10140 Section 02 Elements of Statistics October 28, 2010 Midterm Examination II Answers Name DO NOT remove this answer page. DO turn in the entire exam. Make sure that you have all ten (10) pages
ECE302 Spring 2006 HW1 Solutions January 16, 2006 1
ECE302 Spring 2006 HW1 Solutions January 16, 2006 1 Solutions to HW1 Note: These solutions were generated by R. D. Yates and D. J. Goodman, the authors of our textbook. I have added comments in italics
Chapter 13 & 14 - Probability PART
Chapter 13 & 14 - Probability PART IV : PROBABILITY Dr. Joseph Brennan Math 148, BU Dr. Joseph Brennan (Math 148, BU) Chapter 13 & 14 - Probability 1 / 91 Why Should We Learn Probability Theory? Dr. Joseph
( ) = 1 x. ! 2x = 2. The region where that joint density is positive is indicated with dotted lines in the graph below. y = x
Errata for the ASM Study Manual for Exam P, Eleventh Edition By Dr. Krzysztof M. Ostaszewski, FSA, CERA, FSAS, CFA, MAAA Web site: http://www.krzysio.net E-mail: [email protected] Posted September 21,
Chapter 4 Lecture Notes
Chapter 4 Lecture Notes Random Variables October 27, 2015 1 Section 4.1 Random Variables A random variable is typically a real-valued function defined on the sample space of some experiment. For instance,
Math/Stats 425 Introduction to Probability. 1. Uncertainty and the axioms of probability
Math/Stats 425 Introduction to Probability 1. Uncertainty and the axioms of probability Processes in the real world are random if outcomes cannot be predicted with certainty. Example: coin tossing, stock
Math 141. Lecture 2: More Probability! Albyn Jones 1. [email protected] www.people.reed.edu/ jones/courses/141. 1 Library 304. Albyn Jones Math 141
Math 141 Lecture 2: More Probability! Albyn Jones 1 1 Library 304 [email protected] www.people.reed.edu/ jones/courses/141 Outline Law of total probability Bayes Theorem the Multiplication Rule, again Recall
Math 408, Actuarial Statistics I, Spring 2008. Solutions to combinatorial problems
, Spring 2008 Word counting problems 1. Find the number of possible character passwords under the following restrictions: Note there are 26 letters in the alphabet. a All characters must be lower case
MA 1125 Lecture 14 - Expected Values. Friday, February 28, 2014. Objectives: Introduce expected values.
MA 5 Lecture 4 - Expected Values Friday, February 2, 24. Objectives: Introduce expected values.. Means, Variances, and Standard Deviations of Probability Distributions Two classes ago, we computed the
Math 370, Spring 2008 Prof. A.J. Hildebrand. Practice Test 2
Math 370, Spring 2008 Prof. A.J. Hildebrand Practice Test 2 About this test. This is a practice test made up of a random collection of 15 problems from past Course 1/P actuarial exams. Most of the problems
Find the indicated probability. 1) If a single fair die is rolled, find the probability of a 4 given that the number rolled is odd.
Math 0 Practice Test 3 Fall 2009 Covers 7.5, 8.-8.3 MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Find the indicated probability. ) If a single
13.0 Central Limit Theorem
13.0 Central Limit Theorem Discuss Midterm/Answer Questions Box Models Expected Value and Standard Error Central Limit Theorem 1 13.1 Box Models A Box Model describes a process in terms of making repeated
Chapter 7 Probability. Example of a random circumstance. Random Circumstance. What does probability mean?? Goals in this chapter
Homework (due Wed, Oct 27) Chapter 7: #17, 27, 28 Announcements: Midterm exams keys on web. (For a few hours the answer to MC#1 was incorrect on Version A.) No grade disputes now. Will have a chance to
Probability. a number between 0 and 1 that indicates how likely it is that a specific event or set of events will occur.
Probability Probability Simple experiment Sample space Sample point, or elementary event Event, or event class Mutually exclusive outcomes Independent events a number between 0 and 1 that indicates how
Probability definitions
Probability definitions 1. Probability of an event = chance that the event will occur. 2. Experiment = any action or process that generates observations. In some contexts, we speak of a data-generating
STA 371G: Statistics and Modeling
STA 371G: Statistics and Modeling Decision Making Under Uncertainty: Probability, Betting Odds and Bayes Theorem Mingyuan Zhou McCombs School of Business The University of Texas at Austin http://mingyuanzhou.github.io/sta371g
Probability & Probability Distributions
Probability & Probability Distributions Carolyn J. Anderson EdPsych 580 Fall 2005 Probability & Probability Distributions p. 1/61 Probability & Probability Distributions Elementary Probability Theory Definitions
Department of Industrial Engineering IE 202: Engineering Statistics Example Questions Spring 2012
Department of Industrial Engineering IE 202: Engineering Statistics Example Questions Spring 202. Twenty workers are to be assigned to 20 different jobs, one to each job. How many different assignments
Question: What is the probability that a five-card poker hand contains a flush, that is, five cards of the same suit?
ECS20 Discrete Mathematics Quarter: Spring 2007 Instructor: John Steinberger Assistant: Sophie Engle (prepared by Sophie Engle) Homework 8 Hints Due Wednesday June 6 th 2007 Section 6.1 #16 What is the
Section 6.2 Definition of Probability
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 that it will
Lecture 13. Understanding Probability and Long-Term Expectations
Lecture 13 Understanding Probability and Long-Term Expectations Thinking Challenge What s the probability of getting a head on the toss of a single fair coin? Use a scale from 0 (no way) to 1 (sure thing).
Basic Probability Theory II
RECAP Basic Probability heory II Dr. om Ilvento FREC 408 We said the approach to establishing probabilities for events is to Define the experiment List the sample points Assign probabilities to the sample
2. How many ways can the letters in PHOENIX be rearranged? 7! = 5,040 ways.
Math 142 September 27, 2011 1. How many ways can 9 people be arranged in order? 9! = 362,880 ways 2. How many ways can the letters in PHOENIX be rearranged? 7! = 5,040 ways. 3. The letters in MATH are
STAT 319 Probability and Statistics For Engineers PROBABILITY. Engineering College, Hail University, Saudi Arabia
STAT 319 robability and Statistics For Engineers LECTURE 03 ROAILITY Engineering College, Hail University, Saudi Arabia Overview robability is the study of random events. The probability, or chance, that
Determine the empirical probability that a person selected at random from the 1000 surveyed uses Mastercard.
Math 120 Practice Exam II Name You must show work for credit. 1) A pair of fair dice is rolled 50 times and the sum of the dots on the faces is noted. Outcome 2 4 5 6 7 8 9 10 11 12 Frequency 6 8 8 1 5
Chapter 5 Section 2 day 1 2014f.notebook. November 17, 2014. Honors Statistics
Chapter 5 Section 2 day 1 2014f.notebook November 17, 2014 Honors Statistics Monday November 17, 2014 1 1. Welcome to class Daily Agenda 2. Please find folder and take your seat. 3. Review Homework C5#3
AP Statistics 7!3! 6!
Lesson 6-4 Introduction to Binomial Distributions Factorials 3!= Definition: n! = n( n 1)( n 2)...(3)(2)(1), n 0 Note: 0! = 1 (by definition) Ex. #1 Evaluate: a) 5! b) 3!(4!) c) 7!3! 6! d) 22! 21! 20!
The Calculus of Probability
The Calculus of Probability Let A and B be events in a sample space S. Partition rule: P(A) = P(A B) + P(A B ) Example: Roll a pair of fair dice P(Total of 10) = P(Total of 10 and double) + P(Total of
Name: Date: Use the following to answer questions 2-4:
Name: Date: 1. A phenomenon is observed many, many times under identical conditions. The proportion of times a particular event A occurs is recorded. What does this proportion represent? A) The probability
Chapter 4 - Practice Problems 2
Chapter - Practice Problems 2 MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Find the indicated probability. 1) If you flip a coin three times, the
Statistics in Geophysics: Introduction and Probability Theory
Statistics in Geophysics: Introduction and Steffen Unkel Department of Statistics Ludwig-Maximilians-University Munich, Germany Winter Term 2013/14 1/32 What is Statistics? Introduction Statistics is the
Contemporary Mathematics- MAT 130. Probability. a) What is the probability of obtaining a number less than 4?
Contemporary Mathematics- MAT 30 Solve the following problems:. A fair die is tossed. What is the probability of obtaining a number less than 4? What is the probability of obtaining a number less than
Genetics 1. Defective enzyme that does not make melanin. Very pale skin and hair color (albino)
Genetics 1 We all know that children tend to resemble their parents. Parents and their children tend to have similar appearance because children inherit genes from their parents and these genes influence
Answer: (a) Since we cannot repeat men on the committee, and the order we select them in does not matter, ( )
1. (Chapter 1 supplementary, problem 7): There are 12 men at a dance. (a) In how many ways can eight of them be selected to form a cleanup crew? (b) How many ways are there to pair off eight women at the
Introduction to the Practice of Statistics Sixth Edition Moore, McCabe Section 4.5 Homework Answers
Introduction to the Practice of Statistics Sixth Edition Moore, McCabe Section 4.5 Homework Answers 4.103 Attendance at 2 year and 4 year colleges. In a large national population of college students, 61%
TEST 2 STUDY GUIDE. 1. Consider the data shown below.
2006 by The Arizona Board of Regents for The University of Arizona All rights reserved Business Mathematics I TEST 2 STUDY GUIDE 1 Consider the data shown below (a) Fill in the Frequency and Relative Frequency
1) The table lists the smoking habits of a group of college students. Answer: 0.218
FINAL EXAM REVIEW Name ) The table lists the smoking habits of a group of college students. Sex Non-smoker Regular Smoker Heavy Smoker Total Man 5 52 5 92 Woman 8 2 2 220 Total 22 2 If a student is chosen
A probability experiment is a chance process that leads to well-defined outcomes. 3) What is the difference between an outcome and an event?
Ch 4.2 pg.191~(1-10 all), 12 (a, c, e, g), 13, 14, (a, b, c, d, e, h, i, j), 17, 21, 25, 31, 32. 1) What is a probability experiment? A probability experiment is a chance process that leads to well-defined
Final Mathematics 5010, Section 1, Fall 2004 Instructor: D.A. Levin
Final Mathematics 51, Section 1, Fall 24 Instructor: D.A. Levin Name YOU MUST SHOW YOUR WORK TO RECEIVE CREDIT. A CORRECT ANSWER WITHOUT SHOWING YOUR REASONING WILL NOT RECEIVE CREDIT. Problem Points Possible
MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
Exam Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Provide an appropriate response. 1) A coin is tossed. Find the probability that the result
X X X X X X X X X X X
AP Statistics Solutions to Packet 6 Probability The Study of Randomness The Idea of Probability Probability Models General Probability Rules HW #37 3, 4, 8, 11, 14, 15, 17, 18 6.3 SHAQ The basketball player
Section 6-5 Sample Spaces and Probability
492 6 SEQUENCES, SERIES, AND PROBABILITY 52. How many committees of 4 people are possible from a group of 9 people if (A) There are no restrictions? (B) Both Juan and Mary must be on the committee? (C)
Probability and statistical hypothesis testing. Holger Diessel [email protected]
Probability and statistical hypothesis testing Holger Diessel [email protected] Probability Two reasons why probability is important for the analysis of linguistic data: Joint and conditional
4.1 4.2 Probability Distribution for Discrete Random Variables
4.1 4.2 Probability Distribution for Discrete Random Variables Key concepts: discrete random variable, probability distribution, expected value, variance, and standard deviation of a discrete random variable.
Remarks on the Concept of Probability
5. Probability A. Introduction B. Basic Concepts C. Permutations and Combinations D. Poisson Distribution E. Multinomial Distribution F. Hypergeometric Distribution G. Base Rates H. Exercises Probability
Variations on a Human Face Lab
Variations on a Human Face Lab Introduction: Have you ever wondered why everybody has a different appearance even if they are closely related? It is because of the large variety or characteristics that
Probability. Sample space: all the possible outcomes of a probability experiment, i.e., the population of outcomes
Probability Basic Concepts: Probability experiment: process that leads to welldefined results, called outcomes Outcome: result of a single trial of a probability experiment (a datum) Sample space: all
Mathematical goals. Starting points. Materials required. Time needed
Level S2 of challenge: B/C S2 Mathematical goals Starting points Materials required Time needed Evaluating probability statements To help learners to: discuss and clarify some common misconceptions about
