UNIVERSITY OF CALIFORNIA, BERKELEY

Size: px
Start display at page:

Download "UNIVERSITY OF CALIFORNIA, BERKELEY"

Transcription

1 UNIVERSITY OF CALIFORNIA, BERKELEY DEPARTMENT OF STATISTICS STAT 34: Concepts of Probability Spring 24 Instructor: Antar Bandyopadhyay Solution to the Midterm Examination. A point X, Y is randomly selected from the following finite set of points on the plane { } x, y y x n. a What are the marginal distributions of X and Y? Is the pair X, Y exchangeable? Explain your answer. [2 + ] First note that the total number of points in the given set is n n n +. 2 The values of the random variable X are {, 2,, n}. Fix x n, then P X x x 2x P X x, Y y n n +. The values of the random variable Y are {, 2,, n}. Fix y n, then P Y y y n P X x, Y y xy 2 n y +. n n + Note that always Y X and hence the pair X, Y is not exchangeable. b What is the distribution of the random variable X + Y? [5] Let Z : X + Y, then the values of Z are {2, 3,, 2n}. Fix 2 z 2n, then 2z minz,n z 2 nn+ if 2 z n + P Z z P X x, Y y x z 2 2n z + 2 nn+ if n + < z 2n, where u means the least integer greater or eual to the real number u.

2 c Find the conditional distribution of X given X + Y n. [4] Given [X + Y n] the values of the random variable X are { n 2, n 2 +,, n }. Fix n 2 x n, then P X x X + Y n P X x, Y n x P X + Y n This X X + Y n Uniform { n 2, n 2 +,, n }. n n Suppose N Negative-Binomial 2, p where < p <. Let X be a random variable such that the conditional distribution of X given N n is Uniform {, 2,, n }. Find the marginal distribution of X. What is the conditional distribution of N given X. [8 + 4] First we note that the values of X are {, 2, }. Now fix x, So we get X Geometric p. P X x nx+ nx+ P X x N n P N n n n p2 p n 2 p 2 p x p p p x. Now, give [X ], the values of N are {, 2, }. Fix n, P X N n P N n P N n X P X Thus N X + Geometric p. p p n. 3. In a Quidditch trail Harry ased Ginny to try to score a goal with Ron as the eeper. Ginny who is an excellent player has a chance of scoring a goal 9% of the times. The game was stopped as soon as Ginny scored a goal. It too exactly 9 minutes for her to score a goal. Find the expected time Ginny too for each of her tries. Explain the assumptions you are maing. [ + 2] We assume that each try of Ginny are independent Bernoulli trials with success probability.9. We also assume that that each try Ginny taes eual amount of time. Let X be the number of trials Ginny made, then X Geometric.9. So the expected time Ginny 2

3 too for each of her tries is [ ] 9 E X 9p 9p 9p 9p 9.9. [p.9 and.] t dt t dt dt t 9p log p 8 log Ron was given the tas of sending out the invitations for the wedding of Ginny and Harry. They wanted to invite of their friends. Unfortunately, Ron did not realized that his charm made the invitation letters to be randomly placed in the addressed envelop. The outcome was a disaster, it may have been that an invitation has gone inside an envelop addressed to a different person, or an envelop containing more than one invitations or none at all. In all such cases the recipients were either confused or upset and they did not show up. Only the invitees who received the invitations inside a correctly addressed envelop came for the wedding. Let X be the total number of people who came for the wedding. a Find P X and P X. [3 + 4] Let A i : i th invitation is the only one which has gone inside the i th envelop. Then by definition X Ai. Thus, Further, i P X P A i i. P X P X P A i i. The third euality is by the inclusion-exclusion formula. 3

4 b What is the expected number of people who came for the wedding? [5] This is given by 99 E [X] P A i i 5. There was a strange news reported in the seven o cloc news, apparently a newly built bridge in northern England collapsed during the pea commute hours without any problem reported about it earlier. The total number of casualties was yet to be determined but it was somewhere in between 6 and 8. Harry was clever to figure out that this must have been a wor of the Dar Lord and his followers. He uicly reported it to Professor Dumbledore, who told him that from his prior experiences, he new that the Dar Lord must had decided on the exact number of people he wanted to harm by either rolling a fair sided die and then taing the number which came on top, or tossing a coin times independently and then too the number of heads. He also did not thin that the Dar Lord had any preference on the choice of his method. When Harry told this to his friend Hermione, she was uic to mae a guess about the actual method the Dar Lord used. a What was Hermione s guess? Explain your answer. [6] Let Z be the exact number of casualties determined by the Dar Lord and let I is the indicator random variable taing value I if he rolled the sided die to determine Z, otherwise it taes the value I. Then the conditional distributions of Z I Uniform {, 2,, } and Z I Binomial, 2. Also P I P I 2. Now the event A : [6 Z 8] has been observed. So P A I P I P I A P A P A I P I P A I P I + P A I P I P 6 U 8 P 6 U 8 + P 6 V 8 where U Uniform {, 2,, } and V Binomial, 2. So P 6 U 8 2 and P 6 V 8 Φ6. Φ.9 Φ.9 Φ Note E [V ] 5 and Var V 25. Thus P I A So Hermione s must had guessed that the Dar Lord rolled the sided die. 4

5 b Will she mae a different guess if the news channel said the number of casualties were between 4 to 6? Explain your answer. [6] Let B : [4 Z 6], then similar argument will show P B I P I P I B P B P B I P I P B I P I + P B I P I P 4 U 6 P 4 U 6 + P 4 V 6 where U Uniform {, 2,, } and V Binomial, 2. Now P 4 U 6 2 and P 4 V 6 Φ2. Φ 2. 2Φ Once again note that E [V ] 5 and Var V 25. Finally, P I B So yes, in that case she must had guessed that the Dar Lord tossed the fair coin times. 5

UC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Solutions 5 Spring 2006

UC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Solutions 5 Spring 2006 Review problems UC Berkeley Department of Electrical Engineering and Computer Science EE 6: Probablity and Random Processes Solutions 5 Spring 006 Problem 5. On any given day your golf score is any integer

More information

Name: Firas Rassoul-Agha

Name: Firas Rassoul-Agha Midterm 1 - Math 5010 - Spring 016 Name: Firas Rassoul-Agha Solve the following 4 problems. You have to clearly explain your solution. The answer carries no points. Only the work does. CALCULATORS ARE

More information

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

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

More information

Conditional Probability

Conditional Probability Conditional Probability Idea have performed a chance experiment but don t know the outcome (ω), but have some partial information (event A) about ω. Question: given this partial information what s the

More information

Tutorial 2: Probability

Tutorial 2: Probability 1 Tutorial 2: Probability 1. Coin Tosses Recall that the probability of a specified outcome or outcome(s) is given by prob. of success = number of successful outcomes total number of possible outcomes.

More information

MAT 271E Probability and Statistics

MAT 271E Probability and Statistics MAT 271E Probability and Statistics Spring 2011 Instructor : Class Meets : Office Hours : Textbook : Supp. Text : İlker Bayram EEB 1103 ibayram@itu.edu.tr 13.30 16.30, Wednesday EEB? 10.00 12.00, Wednesday

More information

Senior Math Circles November 19, 2008 Probability II

Senior Math Circles November 19, 2008 Probability II University of Waterloo Faculty of Mathematics Centre for Education in Mathematics and Computing Senior Math Circles November 9, 2008 Probability II Probability Counting There are many situations where

More information

Random Variables Example:

Random Variables Example: Random Variables Example: We roll a fair die 6 times. Suppose we are interested in the number of 5 s in the 6 rolls. Let X = number of 5 s. Then X could be 0, 1, 2, 3, 4, 5, 6. X = 0 corresponds to the

More information

Section 2.4 Bernoulli Trials

Section 2.4 Bernoulli Trials Section 2.4 Bernoulli Trials A bernoulli trial is a repeated experiment with the following properties: 1. There are two outcomes of each trial: success and failure. 2. The probability of success in each

More information

Math & Literacy Centers

Math & Literacy Centers Math & Literacy Centers for kindergarten The Curriculum Corner 2 4 6 8 5 7 4 6 7 9 1 2 3 4 5 6 7 8 9 one two three four five six seven eight nine 10 ten 1 Complete the 100s Chart 1 11 21 31 41 51

More information

6.041/6.431 Spring 2009 Quiz 1 Wednesday, March 11, 7:30-9:30 PM. SOLUTIONS

6.041/6.431 Spring 2009 Quiz 1 Wednesday, March 11, 7:30-9:30 PM. SOLUTIONS 6.0/6.3 Spring 009 Quiz Wednesday, March, 7:30-9:30 PM. SOLUTIONS Name: Recitation Instructor: Question Part Score Out of 0 all 0 a 5 b c 5 d 5 e 5 f 5 3 a b c d 5 e 5 f 5 g 5 h 5 Total 00 Write your solutions

More information

Midterm Exam 1 Solution

Midterm Exam 1 Solution EECS 126 Probability and Random Processes University of California, Berkeley: Fall 2015 Kannan Ramchandran September 22, 2015 Midterm Exam 1 Solution Last name First name SID Name of student on your left:

More information

Conditional Probability (cont'd)

Conditional Probability (cont'd) Conditional Probability (cont'd) April 26, 2006 Conditional Probability (cont'd) Midterm Problems In a ten-question true-false exam, nd the probability that a student get a grade of 70 percent or better

More information

University of California, Berkeley, Statistics 134: Concepts of Probability. Michael Lugo, Spring Exam 1

University of California, Berkeley, Statistics 134: Concepts of Probability. Michael Lugo, Spring Exam 1 University of California, Berkeley, Statistics 134: Concepts of Probability Michael Lugo, Spring 2011 Exam 1 February 16, 2011, 11:10 am - 12:00 noon Name: Solutions Student ID: This exam consists of seven

More information

ST 371 (V): Families of Discrete Distributions

ST 371 (V): Families of Discrete Distributions ST 371 (V): Families of Discrete Distributions Certain experiments and associated random variables can be grouped into families, where all random variables in the family share a certain structure and a

More information

1 The Basic Counting Principles

1 The Basic Counting Principles 1 The Basic Counting Principles The Multiplication Rule If an operation consists of k steps and the first step can be performed in n 1 ways, the second step can be performed in n ways [regardless of how

More information

STAT 345 Spring 2018 Homework 4 - Discrete Probability Distributions

STAT 345 Spring 2018 Homework 4 - Discrete Probability Distributions STAT 345 Spring 2018 Homework 4 - Discrete Probability Distributions Name: Please adhere to the homework rules as given in the Syllabus. 1. Coin Flipping. Timothy and Jimothy are playing a betting game.

More information

Introduction to Probability, Fall 2009

Introduction to Probability, Fall 2009 Introduction to Probability, Fall 2009 Math 30530 Review questions for exam 1 solutions 1. Let A, B and C be events. Some of the following statements are always true, and some are not. For those that are

More information

ELEG 3143 Probability & Stochastic Process Ch. 2 Discrete Random Variables

ELEG 3143 Probability & Stochastic Process Ch. 2 Discrete Random Variables Department of Electrical Engineering University of Arkansas ELEG 3143 Probability & Stochastic Process Ch. 2 Discrete Random Variables Dr. Jingxian Wu wuj@uark.edu OUTLINE 2 Random Variable Discrete Random

More information

Discrete Random Variable

Discrete Random Variable Discrete Random Variable Outcome of a random experiment need not to be a number. We are generally interested in some measurement or numerical attribute of the outcome, rather than the outcome itself. n

More information

Statistics 100A Homework 5 Solutions

Statistics 100A Homework 5 Solutions Chapter 5 Statistics 1A Homework 5 Solutions Ryan Rosario 1. Let X be a random variable with probability density function a What is the value of c? fx { c1 x 1 < x < 1 otherwise We know that for fx to

More information

Discrete random variables

Discrete random variables Discrete random variables The sample space associated with an experiment, together with a probability function defined on all its events, is a complete probabilistic description of that experiment Often

More information

Midterm Exam 1 (Solutions)

Midterm Exam 1 (Solutions) EECS 6 Probability and Random Processes University of California, Berkeley: Spring 07 Kannan Ramchandran February 3, 07 Midterm Exam (Solutions) Last name First name SID Name of student on your left: Name

More information

Probability Experiments, Trials, Outcomes, Sample Spaces Example 1 Example 2

Probability Experiments, Trials, Outcomes, Sample Spaces Example 1 Example 2 Probability 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. However, probability models underlie

More information

6.3 Bernoulli Trials Example Consider the following random experiments

6.3 Bernoulli Trials Example Consider the following random experiments 6.3 Bernoulli Trials Example 6.48. Consider the following random experiments (a) Flip a coin times. We are interested in the number of heads obtained. (b) Of all bits transmitted through a digital transmission

More information

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

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

More information

STT 315 Problem Set #3

STT 315 Problem Set #3 1. A student is asked to calculate the probability that x = 3.5 when x is chosen from a normal distribution with the following parameters: mean=3, sd=5. To calculate the answer, he uses this command: >

More information

Let us think of the situation as having a 50 sided fair die; any one number is equally likely to appear.

Let us think of the situation as having a 50 sided fair die; any one number is equally likely to appear. Probability_Homework Answers. Let the sample space consist of the integers through. {, 2, 3,, }. Consider the following events from that Sample Space. Event A: {a number is a multiple of 5 5, 0, 5,, }

More information

STAT:5100 (22S:193) Statistical Inference I

STAT:5100 (22S:193) Statistical Inference I STAT:5100 (22S:193) Statistical Inference I Week 3 Luke Tierney University of Iowa Fall 2015 Luke Tierney (U Iowa) STAT:5100 (22S:193) Statistical Inference I Fall 2015 1 Recap Matching problem Generalized

More information

Geometric Distribution The characteristics of a geometric experiment are: 1. There are one or more Bernoulli trials with all failures except the last

Geometric Distribution The characteristics of a geometric experiment are: 1. There are one or more Bernoulli trials with all failures except the last Geometric Distribution The characteristics of a geometric experiment are: 1. There are one or more Bernoulli trials with all failures except the last one, which is a success. In other words, you keep repeating

More information

Random variables (discrete)

Random variables (discrete) Random variables (discrete) Saad Mneimneh 1 Introducing random variables A random variable is a mapping from the sample space to the real line. We usually denote the random variable by X, and a value that

More information

Math & Literacy Centers

Math & Literacy Centers Math & Literacy Centers for kindergarten The Curriculum Corner 2 4 6 8 5 7 4 6 7 9 3 5 1 2 3 4 5 6 7 8 9 one two three four five six seven eight nine 10 ten 1 Complete the 100s Chart 1 11 21 31

More information

Discussion 01. b) What is the probability that the letter selected is a vowel?

Discussion 01. b) What is the probability that the letter selected is a vowel? STAT 400 Discussion 01 Spring 2018 1. Consider the following experiment: A letter is chosen at random from the word STATISTICS. a) List all possible outcomes and their probabilities. b) What is the probability

More information

Name: Exam 2 Solutions. March 13, 2017

Name: Exam 2 Solutions. March 13, 2017 Department of Mathematics University of Notre Dame Math 00 Finite Math Spring 07 Name: Instructors: Conant/Galvin Exam Solutions March, 07 This exam is in two parts on pages and contains problems worth

More information

Week 04 Discussion. a) What is the probability that of those selected for the in-depth interview 4 liked the new flavor and 1 did not?

Week 04 Discussion. a) What is the probability that of those selected for the in-depth interview 4 liked the new flavor and 1 did not? STAT Wee Discussion Fall 7. A new flavor of toothpaste has been developed. It was tested by a group of people. Nine of the group said they lied the new flavor, and the remaining 6 indicated they did not.

More information

Chapter 1 Logical Reasoning

Chapter 1 Logical Reasoning Chapter 1 Logical Reasoning It must seem rather redundant to have a chapter entirely devoted to a strategy referred to as logical reasoning. Regardless of the strategy being used to solve a problem, it

More information

Year 10 Mathematics Probability Practice Test 1

Year 10 Mathematics Probability Practice Test 1 Year 10 Mathematics Probability Practice Test 1 1 A letter is chosen randomly from the word TELEVISION. a How many letters are there in the word TELEVISION? b Find the probability that the letter is: i

More information

MA : Introductory Probability

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

More information

P (E) = P (A 1 )P (A 2 )... P (A n ).

P (E) = P (A 1 )P (A 2 )... P (A n ). Lecture 9: Conditional probability II: breaking complex events into smaller events, methods to solve probability problems, Bayes rule, law of total probability, Bayes theorem Discrete Structures II (Summer

More information

*Karle Laska s Sections: There is no class tomorrow and Friday! Have a good weekend! Scores will be posted in Compass early Friday morning

*Karle Laska s Sections: There is no class tomorrow and Friday! Have a good weekend! Scores will be posted in Compass early Friday morning STATISTICS 100 EXAM 3 Spring 2016 PRINT NAME (Last name) (First name) *NETID CIRCLE SECTION: Laska MWF L1 Laska Tues/Thurs L2 Robin Tu Write answers in appropriate blanks. When no blanks are provided CIRCLE

More information

Probabilistic models

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

More information

Introductory Probability

Introductory Probability Introductory Probability Bernoulli Trials and Binomial Probability Distributions Dr. Nguyen nicholas.nguyen@uky.edu Department of Mathematics UK February 04, 2019 Agenda Bernoulli Trials and Probability

More information

UC Berkeley Department of Electrical Engineering and Computer Sciences. EECS 126: Probability and Random Processes

UC Berkeley Department of Electrical Engineering and Computer Sciences. EECS 126: Probability and Random Processes UC Berkeley Department of Electrical Engineering and Computer Sciences EECS 26: Probability and Random Processes Problem Set Spring 209 Self-Graded Scores Due:.59 PM, Monday, February 4, 209 Submit your

More information

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

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

More information

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

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

More information

Edexcel past paper questions

Edexcel past paper questions Edexcel past paper questions Statistics 1 Discrete Random Variables Past examination questions Discrete Random variables Page 1 Discrete random variables Discrete Random variables Page 2 Discrete Random

More information

STAT 414: Introduction to Probability Theory

STAT 414: Introduction to Probability Theory STAT 414: Introduction to Probability Theory Spring 2016; Homework Assignments Latest updated on April 29, 2016 HW1 (Due on Jan. 21) Chapter 1 Problems 1, 8, 9, 10, 11, 18, 19, 26, 28, 30 Theoretical Exercises

More information

SS257a Midterm Exam Monday Oct 27 th 2008, 6:30-9:30 PM Talbot College 342 and 343. You may use simple, non-programmable scientific calculators.

SS257a Midterm Exam Monday Oct 27 th 2008, 6:30-9:30 PM Talbot College 342 and 343. You may use simple, non-programmable scientific calculators. SS657a Midterm Exam, October 7 th 008 pg. SS57a Midterm Exam Monday Oct 7 th 008, 6:30-9:30 PM Talbot College 34 and 343 You may use simple, non-programmable scientific calculators. This exam has 5 questions

More information

Distribusi Binomial, Poisson, dan Hipergeometrik

Distribusi Binomial, Poisson, dan Hipergeometrik Distribusi Binomial, Poisson, dan Hipergeometrik CHAPTER TOPICS The Probability of a Discrete Random Variable Covariance and Its Applications in Finance Binomial Distribution Poisson Distribution Hypergeometric

More information

Probability Rules. MATH 130, Elements of Statistics I. J. Robert Buchanan. Fall Department of Mathematics

Probability Rules. MATH 130, Elements of Statistics I. J. Robert Buchanan. Fall Department of Mathematics Probability Rules MATH 130, Elements of Statistics I J. Robert Buchanan Department of Mathematics Fall 2018 Introduction Probability is a measure of the likelihood of the occurrence of a certain behavior

More information

Mock Exam - 2 hours - use of basic (non-programmable) calculator is allowed - all exercises carry the same marks - exam is strictly individual

Mock Exam - 2 hours - use of basic (non-programmable) calculator is allowed - all exercises carry the same marks - exam is strictly individual Mock Exam - 2 hours - use of basic (non-programmable) calculator is allowed - all exercises carry the same marks - exam is strictly individual Question 1. Suppose you want to estimate the percentage of

More information

Discrete Random Variables

Discrete Random Variables Discrete Random Variables An Undergraduate Introduction to Financial Mathematics J. Robert Buchanan 2014 Introduction The markets can be thought of as a complex interaction of a large number of random

More information

Chapter 8: An Introduction to Probability and Statistics

Chapter 8: An Introduction to Probability and Statistics Course S3, 200 07 Chapter 8: An Introduction to Probability and Statistics This material is covered in the book: Erwin Kreyszig, Advanced Engineering Mathematics (9th edition) Chapter 24 (not including

More information

STAT 418: Probability and Stochastic Processes

STAT 418: Probability and Stochastic Processes STAT 418: Probability and Stochastic Processes Spring 2016; Homework Assignments Latest updated on April 29, 2016 HW1 (Due on Jan. 21) Chapter 1 Problems 1, 8, 9, 10, 11, 18, 19, 26, 28, 30 Theoretical

More information

MTH4451Test#2-Solutions Spring 2009

MTH4451Test#2-Solutions Spring 2009 Pat Rossi Instructions. MTH4451Test#2-Solutions Spring 2009 Name Show CLEARLY how you arrive at your answers. 1. A large jar contains US coins. In this jar, there are 350 pennies ($0.01), 300 nickels ($0.05),

More information

Semester 2 Final Exam Review Guide for AMS I

Semester 2 Final Exam Review Guide for AMS I Name: Semester 2 Final Exam Review Guide for AMS I Unit 4: Exponential Properties & Functions Lesson 1 Exponent Properties & Simplifying Radicals Products of Powers: when two powers with the same base

More information

Discrete Distributions

Discrete Distributions A simplest example of random experiment is a coin-tossing, formally called Bernoulli trial. It happens to be the case that many useful distributions are built upon this simplest form of experiment, whose

More information

Probability deals with modeling of random phenomena (phenomena or experiments whose outcomes may vary)

Probability deals with modeling of random phenomena (phenomena or experiments whose outcomes may vary) Chapter 14 From Randomness to Probability How to measure a likelihood of an event? How likely is it to answer correctly one out of two true-false questions on a quiz? Is it more, less, or equally likely

More information

Introduction to Probability 2017/18 Supplementary Problems

Introduction to Probability 2017/18 Supplementary Problems Introduction to Probability 2017/18 Supplementary Problems Problem 1: Let A and B denote two events with P(A B) 0. Show that P(A) 0 and P(B) 0. A A B implies P(A) P(A B) 0, hence P(A) 0. Similarly B A

More information

1 Basic continuous random variable problems

1 Basic continuous random variable problems Name M362K Final Here are problems concerning material from Chapters 5 and 6. To review the other chapters, look over previous practice sheets for the two exams, previous quizzes, previous homeworks and

More information

2014 SM4 Revision Questions Distributions

2014 SM4 Revision Questions Distributions 2014 SM4 Revision Questions Distributions Normal Q1. Professor Halen has 184 students in his college mathematics class. The scores on the semester exam are normally distributed with a mean of 72.3 and

More information

Notes for Math 324, Part 17

Notes for Math 324, Part 17 126 Notes for Math 324, Part 17 Chapter 17 Common discrete distributions 17.1 Binomial Consider an experiment consisting by a series of trials. The only possible outcomes of the trials are success and

More information

PRACTICE PROBLEMS FOR EXAM 1

PRACTICE PROBLEMS FOR EXAM 1 PRACTICE PROBLEMS FOR EXAM 1 Math 3160Q Spring 01 Professor Hohn Below is a list of practice questions for Exam 1. Any quiz, homework, or example problem has a chance of being on the exam. For more practice,

More information

Great Theoretical Ideas in Computer Science

Great Theoretical Ideas in Computer Science 15-251 Great Theoretical Ideas in Computer Science Probability Theory: Counting in Terms of Proportions Lecture 10 (September 27, 2007) Some Puzzles Teams A and B are equally good In any one game, each

More information

Chapter 3. Discrete Random Variables and Their Probability Distributions

Chapter 3. Discrete Random Variables and Their Probability Distributions Chapter 3. Discrete Random Variables and Their Probability Distributions 2.11 Definition of random variable 3.1 Definition of a discrete random variable 3.2 Probability distribution of a discrete random

More information

Probability and Probability Distributions. Dr. Mohammed Alahmed

Probability and Probability Distributions. Dr. Mohammed Alahmed Probability and Probability Distributions 1 Probability and Probability Distributions Usually we want to do more with data than just describing them! We might want to test certain specific inferences about

More information

Topic 3: The Expectation of a Random Variable

Topic 3: The Expectation of a Random Variable Topic 3: The Expectation of a Random Variable Course 003, 2017 Page 0 Expectation of a discrete random variable Definition (Expectation of a discrete r.v.): The expected value (also called the expectation

More information

A brief review of basics of probabilities

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

More information

Midterm 2. Your Exam Room: Name of Person Sitting on Your Left: Name of Person Sitting on Your Right: Name of Person Sitting in Front of You:

Midterm 2. Your Exam Room: Name of Person Sitting on Your Left: Name of Person Sitting on Your Right: Name of Person Sitting in Front of You: CS70 Discrete Mathematics and Probability Theory, Fall 2018 Midterm 2 8:00-10:00pm, 31 October Your First Name: SIGN Your Name: Your Last Name: Your SID Number: Your Exam Room: Name of Person Sitting on

More information

Example continued. Math 425 Intro to Probability Lecture 37. Example continued. Example

Example continued. Math 425 Intro to Probability Lecture 37. Example continued. Example continued : Coin tossing Math 425 Intro to Probability Lecture 37 Kenneth Harris kaharri@umich.edu Department of Mathematics University of Michigan April 8, 2009 Consider a Bernoulli trials process with

More information

Discrete Random Variables. Discrete Random Variables

Discrete Random Variables. Discrete Random Variables Random Variables In many situations, we are interested in numbers associated with the outcomes of a random experiment. For example: Testing cars from a production line, we are interested in variables such

More information

University of California, San Diego Department of Computer Science and Engineering CSE 20. Solutions to Midterm Exam Winter 2018

University of California, San Diego Department of Computer Science and Engineering CSE 20. Solutions to Midterm Exam Winter 2018 University of California, San Diego Department of Computer Science and Engineering CSE 20 Solutions to Midterm Exam Winter 2018 Problem 1 (30 points) a. The boolean function f(p, q) = (q p) ( q p) is specified

More information

Lecture 10. Variance and standard deviation

Lecture 10. Variance and standard deviation 18.440: Lecture 10 Variance and standard deviation Scott Sheffield MIT 1 Outline Defining variance Examples Properties Decomposition trick 2 Outline Defining variance Examples Properties Decomposition

More information

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

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

More information

Discrete Random Variables

Discrete Random Variables Discrete Random Variables An Undergraduate Introduction to Financial Mathematics J. Robert Buchanan Introduction The markets can be thought of as a complex interaction of a large number of random processes,

More information

Probabilistic models

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

More information

ECEn 370 Introduction to Probability

ECEn 370 Introduction to Probability RED- You can write on this exam. ECEn 370 Introduction to Probability Section 00 Final Winter, 2009 Instructor Professor Brian Mazzeo Closed Book Non-graphing Calculator Allowed No Time Limit IMPORTANT!

More information

14 - PROBABILITY Page 1 ( Answers at the end of all questions )

14 - PROBABILITY Page 1 ( Answers at the end of all questions ) - PROBABILITY Page ( ) Three houses are available in a locality. Three persons apply for the houses. Each applies for one house without consulting others. The probability that all the three apply for the

More information

Final Exam. Math Su10. by Prof. Michael Cap Khoury

Final Exam. Math Su10. by Prof. Michael Cap Khoury Final Exam Math 45-0 Su0 by Prof. Michael Cap Khoury Name: Directions: Please print your name legibly in the box above. You have 0 minutes to complete this exam. You may use any type of conventional calculator,

More information

Exam 1 Solutions. Problem Points Score Total 145

Exam 1 Solutions. Problem Points Score Total 145 Exam Solutions Read each question carefully and answer all to the best of your ability. Show work to receive as much credit as possible. At the end of the exam, please sign the box below. Problem Points

More information

MATH 3C: MIDTERM 1 REVIEW. 1. Counting

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

More information

PRACTICE PROBLEMS FOR EXAM 2

PRACTICE PROBLEMS FOR EXAM 2 PRACTICE PROBLEMS FOR EXAM 2 Math 3160Q Fall 2015 Professor Hohn Below is a list of practice questions for Exam 2. Any quiz, homework, or example problem has a chance of being on the exam. For more practice,

More information

STAT 311 Practice Exam 2 Key Spring 2016 INSTRUCTIONS

STAT 311 Practice Exam 2 Key Spring 2016 INSTRUCTIONS STAT 311 Practice Exam 2 Key Spring 2016 Name: Key INSTRUCTIONS 1. Nonprogrammable calculators (or a programmable calculator cleared in front of the professor before class) are allowed. Exam is closed

More information

Discrete Random Variables

Discrete Random Variables CPSC 53 Systems Modeling and Simulation Discrete Random Variables Dr. Anirban Mahanti Department of Computer Science University of Calgary mahanti@cpsc.ucalgary.ca Random Variables A random variable is

More information

M378K In-Class Assignment #1

M378K In-Class Assignment #1 The following problems are a review of M6K. M7K In-Class Assignment # Problem.. Complete the definition of mutual exclusivity of events below: Events A, B Ω are said to be mutually exclusive if A B =.

More information

Example. If 4 tickets are drawn with replacement from ,

Example. If 4 tickets are drawn with replacement from , Example. If 4 tickets are drawn with replacement from 1 2 2 4 6, what are the chances that we observe exactly two 2 s? Exactly two 2 s in a sequence of four draws can occur in many ways. For example, (

More information

Chapter 2 Random Variables

Chapter 2 Random Variables Stochastic Processes Chapter 2 Random Variables Prof. Jernan Juang Dept. of Engineering Science National Cheng Kung University Prof. Chun-Hung Liu Dept. of Electrical and Computer Eng. National Chiao Tung

More information

5. Conditional Distributions

5. Conditional Distributions 1 of 12 7/16/2009 5:36 AM Virtual Laboratories > 3. Distributions > 1 2 3 4 5 6 7 8 5. Conditional Distributions Basic Theory As usual, we start with a random experiment with probability measure P on an

More information

Random Variables. Definition: A random variable (r.v.) X on the probability space (Ω, F, P) is a mapping

Random Variables. Definition: A random variable (r.v.) X on the probability space (Ω, F, P) is a mapping Random Variables Example: We roll a fair die 6 times. Suppose we are interested in the number of 5 s in the 6 rolls. Let X = number of 5 s. Then X could be 0, 1, 2, 3, 4, 5, 6. X = 0 corresponds to the

More information

Probability Notes (A) , Fall 2010

Probability Notes (A) , Fall 2010 Probability Notes (A) 18.310, Fall 2010 We are going to be spending around four lectures on probability theory this year. These notes cover approximately the first three lectures on it. Probability theory

More information

Class 26: review for final exam 18.05, Spring 2014

Class 26: review for final exam 18.05, Spring 2014 Probability Class 26: review for final eam 8.05, Spring 204 Counting Sets Inclusion-eclusion principle Rule of product (multiplication rule) Permutation and combinations Basics Outcome, sample space, event

More information

DISCRETE VARIABLE PROBLEMS ONLY

DISCRETE VARIABLE PROBLEMS ONLY DISCRETE VARIABLE PROBLEMS ONLY. A biased die with four faces is used in a game. A player pays 0 counters to roll the die. The table below shows the possible scores on the die, the probability of each

More information

MATH1231 Algebra, 2017 Chapter 9: Probability and Statistics

MATH1231 Algebra, 2017 Chapter 9: Probability and Statistics MATH1231 Algebra, 2017 Chapter 9: Probability and Statistics A/Prof. Daniel Chan School of Mathematics and Statistics University of New South Wales danielc@unsw.edu.au Daniel Chan (UNSW) MATH1231 Algebra

More information

STA 247 Solutions to Assignment #1

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

More information

TOPIC 12 PROBABILITY SCHEMATIC DIAGRAM

TOPIC 12 PROBABILITY SCHEMATIC DIAGRAM TOPIC 12 PROBABILITY SCHEMATIC DIAGRAM Topic Concepts Degree of Importance References NCERT Book Vol. II Probability (i) Conditional Probability *** Article 1.2 and 1.2.1 Solved Examples 1 to 6 Q. Nos

More information

Math 183 Statistical Methods

Math 183 Statistical Methods Math 183 Statistical Methods Eddie Aamari S.E.W. Assistant Professor eaamari@ucsd.edu math.ucsd.edu/~eaamari/ AP&M 5880A Today: Chapter 3 (continued) Negative Binomial Model Poisson Model Practice these

More information

EXAMINATIONS OF THE ROYAL STATISTICAL SOCIETY (formerly the Examinations of the Institute of Statisticians) HIGHER CERTIFICATE IN STATISTICS, 2000

EXAMINATIONS OF THE ROYAL STATISTICAL SOCIETY (formerly the Examinations of the Institute of Statisticians) HIGHER CERTIFICATE IN STATISTICS, 2000 EXAMINATIONS OF THE ROYAL STATISTICAL SOCIETY (formerly the Examinations of the Institute of Statisticians) HIGHER CERTIFICATE IN STATISTICS, 2000 CERTIFICATE IN OFFICIAL STATISTICS, 2000 Paper I : Statistical

More information

Math st Homework. First part of Chapter 2. Due Friday, September 17, 1999.

Math st Homework. First part of Chapter 2. Due Friday, September 17, 1999. Math 447. 1st Homework. First part of Chapter 2. Due Friday, September 17, 1999. 1. How many different seven place license plates are possible if the first 3 places are to be occupied by letters and the

More information

Conditional Probability (cont...) 10/06/2005

Conditional Probability (cont...) 10/06/2005 Conditional Probability (cont...) 10/06/2005 Independent Events Two events E and F are independent if both E and F have positive probability and if P (E F ) = P (E), and P (F E) = P (F ). 1 Theorem. If

More information

Example 1. The sample space of an experiment where we flip a pair of coins is denoted by:

Example 1. The sample space of an experiment where we flip a pair of coins is denoted by: Chapter 8 Probability 8. Preliminaries Definition (Sample Space). A Sample Space, Ω, is the set of all possible outcomes of an experiment. Such a sample space is considered discrete if Ω has finite cardinality.

More information