Test One Mathematics Fall 2009

Size: px
Start display at page:

Download "Test One Mathematics Fall 2009"

Transcription

1 Test One Mathematics 35.2 Fall 29 TO GET FULL CREDIT YOU MUST SHOW ALL WORK! I have neither given nor received aid in the completion of this test. Signature: pts. 2 pts. 3 5 pts. 2 pts. 5 pts. 6(i) pts. 6(ii) 5 pts. 6(iii) 2 pts. 7 5 pts. 8 5 pts. Total pts. Your Score. pts. A, B, C, D are distinct mutually independent events. Compute P ((A D) (B C)) in terms of P (A), P (B), P (C), P (D). Solution. Let Ω be the sample space. Since A D and Ω (B C) are independent, since A and D are independent and since B and Ω C are independent we have () P ((A D) (B C) P ((A D))P (Ω (B C)) (P (A) P (D) P (A D))( P (B (Ω C))) (P (A) P (D) P (A)P (D))( (P (B)( P (C)))) 2. pts. A friend of yours tells you that of the 3 graduate students in history at Duke, 2 speak French, 5 speak German and speak Italian. Moreover, he says, speak French and German, 8 speak French and Italian and 3 speak all three languages. How would you explain to your friend that he cannot be right? Solution. Let F, G, I be the sets of the students that speak French, German, Italian, respectively. Let N be the set of the students that speak none of these.

2 2 (When I gave you this problem I intended that N be empty. So be it.) By the Inclusion-Exclusion Principle we have 3 F G I N F G I N ( F G F I G I ) F G I 2 5 ( 8 G I ) 3 3 G I N so G I N. Were N we would have G I which would be impossible since 3 F G I G I. So suppose N >. Using the fact that A B A A B for sets A and B we find that (2) (F G) I 3 7, (F I) G 8 3 5, (G I) F N 3. Using the fact that A (B C) A A B A C A B C for finite sets A, B, C we find that (3) F (G I) , G (I F ) 5 N 3 8 N, I (F G) 8 N 3 5 N. These three numbers in (2), the three numbers in (3), together with F G I and N should sum to 3 but they sum to pts. A certain set X is the disjoint union of subsets A, B, C containing, 8, 6 members, respectively. How many subsets D of X are there such that A D 9, B D 2 and C D? Solution. (( ) 9 ( )) (( ) 8 ( )) pts. Consider N urns U,..., U N such that U i contains i black balls and N i white balls. An urn is picked at random and a ball is drawn from it. What is the probability the j-th urn was chosen given that the ball drawn was white? (I want a squeaky clean answer for this. You will need the formula k j k(k )/2.) Solution. Let B i, i,..., N, be the event that Urn i was chosen and let W be the event that a white ball was drawn. By Bayes formula and the fact that the

3 3 P (B i ) /N we have P (B j W ) P (W B j )P (B j ) N i P (W B i)p (B i ) P (W B j ) N i P (W B i) N j N i N i N j N 2 N(N )/2 2(N j) N(N ) 5. pts. The continuous random variable X is such that x 3 if < x < 5, f X (x) C if x < or 5 < x. Determine C. Solution. We have C f X (x) dx C so C 2/3. 5 (x 3) dx C pts. X is a continuous random variable such that x f X (x) if 2 < x <, if x < 2 or < x. (i) ( pts.) Calculate P ( < X < ). (ii) (5 pts.) Calculate P ( X > ). (iii) (2 pts.) Explain why X 2 is continuous and calculate its probability density function (pdf). Solution. For (i), P ( < X < ) f X (x) dx x dx x dx 2. For (ii) note that x R : x > and 2 < x < } ( 2, ) (, ). Thus P ( X > ) ( 2 ) x dx x 2 dx x dx 9.

4 For (iii) we let Y X 2 and we calculate F Y (y) P (Y y) P (X 2 y) if y <, P ( y X y ) if y if y <, F X ( y ) F X ( y ) if y if y <, F X ( y ) F X ( y ) if y < 5, F X ( y ) if 5 y. where we have used the fact that P (X x) for any x R since X is continuous. Now for any y we have and Thus 2 < y < y < 5 2 < y < y < 7. if y <, d dy f Y (y) (F X( y ) F X ( y )) if < y < 5, d dy (F X( y )) if 5 < y < 7, if 7 < y if y <, ( y 2 y ) y 2 if < y < 5, y y 2 if 5 < y < 7, y if 7 < y if y <, if < y < 5, 2 if 5 < y < 7, if 7 < y 7. 5 pts. A fair six sided die is thrown twice. Let X and X 2 be the outcomes of the first and second throw, respectively. Let X minx, X 2 }. Calculate the probability mass function (pmf) of X. (I suggest you picture the Cartesian product D D where D, 2, 3,, 5, 6}.) Solution. Let D, 2, 3,, 5, 6}. It should be clear that the range of X is D. Note that P (X x, X 2 x 2 ) 6 6 whenever x, x 2 D. 36 Suppose x R. Then the event X x} is the union of the (6 x) (6 x) 3 2x

5 5 disjoint events X x, X 2 x}; X x, X 2 y}, y R and x < y; X y, X 2 x}, y R and x < y each of which has probability /36. Thus p X (x) P (X x) 3 2x if x R, else. Since this last function has integral unity we infer that Y is continuous pts. X and Y are random variable on the same probability space such that (X, Y ) is uniform on the rectangle (2, 5) (3, 8). Calculate P (Y < X). Solution. Let R (2, 5) (3, 8) and let S (x, y) R 2 : y < x}. Then, as (X, Y ) is uniform on R, area(r S) P (Y < X) arear. Now R S is the open triangle with vertices at (3, 3), (3, 5), 5, 5) which has area 2 and R has area Thus P (Y < X) 2 5.

1 Preliminaries Sample Space and Events Interpretation of Probability... 13

1 Preliminaries Sample Space and Events Interpretation of Probability... 13 Summer 2017 UAkron Dept. of Stats [3470 : 461/561] Applied Statistics Ch 2: Probability Contents 1 Preliminaries 3 1.1 Sample Space and Events...........................................................

More information

MAT 271E Probability and Statistics

MAT 271E Probability and Statistics MAT 71E Probability and Statistics Spring 013 Instructor : Class Meets : Office Hours : Textbook : Supp. Text : İlker Bayram EEB 1103 ibayram@itu.edu.tr 13.30 1.30, Wednesday EEB 5303 10.00 1.00, Wednesday

More information

MAT2377. Ali Karimnezhad. Version September 9, Ali Karimnezhad

MAT2377. Ali Karimnezhad. Version September 9, Ali Karimnezhad MAT2377 Ali Karimnezhad Version September 9, 2015 Ali Karimnezhad Comments These slides cover material from Chapter 1. In class, I may use a blackboard. I recommend reading these slides before you come

More information

MATH2206 Prob Stat/20.Jan Weekly Review 1-2

MATH2206 Prob Stat/20.Jan Weekly Review 1-2 MATH2206 Prob Stat/20.Jan.2017 Weekly Review 1-2 This week I explained the idea behind the formula of the well-known statistic standard deviation so that it is clear now why it is a measure of dispersion

More information

Course: ESO-209 Home Work: 1 Instructor: Debasis Kundu

Course: ESO-209 Home Work: 1 Instructor: Debasis Kundu Home Work: 1 1. Describe the sample space when a coin is tossed (a) once, (b) three times, (c) n times, (d) an infinite number of times. 2. A coin is tossed until for the first time the same result appear

More information

MATH 3510: PROBABILITY AND STATS June 15, 2011 MIDTERM EXAM

MATH 3510: PROBABILITY AND STATS June 15, 2011 MIDTERM EXAM MATH 3510: PROBABILITY AND STATS June 15, 2011 MIDTERM EXAM YOUR NAME: KEY: Answers in Blue Show all your work. Answers out of the blue and without any supporting work may receive no credit even if they

More information

MATH 3510: PROBABILITY AND STATS July 1, 2011 FINAL EXAM

MATH 3510: PROBABILITY AND STATS July 1, 2011 FINAL EXAM MATH 3510: PROBABILITY AND STATS July 1, 2011 FINAL EXAM YOUR NAME: KEY: Answers in blue Show all your work. Answers out of the blue and without any supporting work may receive no credit even if they are

More information

Problem # Number of points 1 /20 2 /20 3 /20 4 /20 5 /20 6 /20 7 /20 8 /20 Total /150

Problem # Number of points 1 /20 2 /20 3 /20 4 /20 5 /20 6 /20 7 /20 8 /20 Total /150 Name Student ID # Instructor: SOLUTION Sergey Kirshner STAT 516 Fall 09 Practice Midterm #1 January 31, 2010 You are not allowed to use books or notes. Non-programmable non-graphic calculators are permitted.

More information

Statistical Theory 1

Statistical Theory 1 Statistical Theory 1 Set Theory and Probability Paolo Bautista September 12, 2017 Set Theory We start by defining terms in Set Theory which will be used in the following sections. Definition 1 A set is

More information

LECTURE 1. 1 Introduction. 1.1 Sample spaces and events

LECTURE 1. 1 Introduction. 1.1 Sample spaces and events LECTURE 1 1 Introduction The first part of our adventure is a highly selective review of probability theory, focusing especially on things that are most useful in statistics. 1.1 Sample spaces and events

More information

Sample Exam #1 Probability and Statistics I

Sample Exam #1 Probability and Statistics I Sample Exam # Probability and Statistics I Instructions. Show all work. No need to simplify answers.. 0 points Jack and Jill, along with four other students chosen from the same class of ten students,

More information

Statistics 100A Homework 1 Solutions

Statistics 100A Homework 1 Solutions Problem Statistics 00A Homework Solutions Ryan Rosario Suppose we flip a fair coin 4 times independently. () What is the sample space? By definition, the sample space, denoted as Ω, is the set of all possible

More information

Week 2. Section Texas A& M University. Department of Mathematics Texas A& M University, College Station 22 January-24 January 2019

Week 2. Section Texas A& M University. Department of Mathematics Texas A& M University, College Station 22 January-24 January 2019 Week 2 Section 1.2-1.4 Texas A& M University Department of Mathematics Texas A& M University, College Station 22 January-24 January 2019 Oğuz Gezmiş (TAMU) Topics in Contemporary Mathematics II Week2 1

More information

Probability Theory and Random Variables

Probability Theory and Random Variables Probability Theory and Random Variables One of the most noticeable aspects of many computer science related phenomena is the lack of certainty. When a job is submitted to a batch oriented computer system,

More information

P (A B) P ((B C) A) P (B A) = P (B A) + P (C A) P (A) = P (B A) + P (C A) = Q(A) + Q(B).

P (A B) P ((B C) A) P (B A) = P (B A) + P (C A) P (A) = P (B A) + P (C A) = Q(A) + Q(B). Lectures 7-8 jacques@ucsdedu 41 Conditional Probability Let (Ω, F, P ) be a probability space Suppose that we have prior information which leads us to conclude that an event A F occurs Based on this information,

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

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

Recitation 2: Probability

Recitation 2: Probability Recitation 2: Probability Colin White, Kenny Marino January 23, 2018 Outline Facts about sets Definitions and facts about probability Random Variables and Joint Distributions Characteristics of distributions

More information

CS626 Data Analysis and Simulation

CS626 Data Analysis and Simulation CS626 Data Analysis and Simulation Instructor: Peter Kemper R 104A, phone 221-3462, email:kemper@cs.wm.edu Today: Probability Primer Quick Reference: Sheldon Ross: Introduction to Probability Models 9th

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

Notes for Math 324, Part 12

Notes for Math 324, Part 12 72 Notes for Math 324, Part 12 Chapter 12 Definition and main properties of probability 12.1 Sample space, events We run an experiment which can have several outcomes. The set consisting by all possible

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

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

Conditional Probability

Conditional Probability Conditional Probability When we obtain additional information about a probability experiment, we want to use the additional information to reassess the probabilities of events given the new information.

More information

MATH 556: PROBABILITY PRIMER

MATH 556: PROBABILITY PRIMER MATH 6: PROBABILITY PRIMER 1 DEFINITIONS, TERMINOLOGY, NOTATION 1.1 EVENTS AND THE SAMPLE SPACE Definition 1.1 An experiment is a one-off or repeatable process or procedure for which (a there is a well-defined

More information

4. Probability of an event A for equally likely outcomes:

4. Probability of an event A for equally likely outcomes: University of California, Los Angeles Department of Statistics Statistics 110A Instructor: Nicolas Christou Probability Probability: A measure of the chance that something will occur. 1. Random experiment:

More information

Statistical Inference

Statistical Inference Statistical Inference Lecture 1: Probability Theory MING GAO DASE @ ECNU (for course related communications) mgao@dase.ecnu.edu.cn Sep. 11, 2018 Outline Introduction Set Theory Basics of Probability Theory

More information

CMPSCI 240: Reasoning about Uncertainty

CMPSCI 240: Reasoning about Uncertainty CMPSCI 240: Reasoning about Uncertainty Lecture 2: Sets and Events Andrew McGregor University of Massachusetts Last Compiled: January 27, 2017 Outline 1 Recap 2 Experiments and Events 3 Probabilistic Models

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

ELEG 3143 Probability & Stochastic Process Ch. 1 Probability

ELEG 3143 Probability & Stochastic Process Ch. 1 Probability Department of Electrical Engineering University of Arkansas ELEG 3143 Probability & Stochastic Process Ch. 1 Probability Dr. Jingxian Wu wuj@uark.edu OUTLINE 2 Applications Elementary Set Theory Random

More information

3 PROBABILITY TOPICS

3 PROBABILITY TOPICS Chapter 3 Probability Topics 135 3 PROBABILITY TOPICS Figure 3.1 Meteor showers are rare, but the probability of them occurring can be calculated. (credit: Navicore/flickr) Introduction It is often necessary

More information

Probability. Lecture Notes. Adolfo J. Rumbos

Probability. Lecture Notes. Adolfo J. Rumbos Probability Lecture Notes Adolfo J. Rumbos October 20, 204 2 Contents Introduction 5. An example from statistical inference................ 5 2 Probability Spaces 9 2. Sample Spaces and σ fields.....................

More information

Deep Learning for Computer Vision

Deep Learning for Computer Vision Deep Learning for Computer Vision Lecture 3: Probability, Bayes Theorem, and Bayes Classification Peter Belhumeur Computer Science Columbia University Probability Should you play this game? Game: A fair

More information

What is the probability of getting a heads when flipping a coin

What is the probability of getting a heads when flipping a coin Chapter 2 Probability Probability theory is a branch of mathematics dealing with chance phenomena. The origins of the subject date back to the Italian mathematician Cardano about 1550, and French mathematicians

More information

Math 416 Lecture 2 DEFINITION. Here are the multivariate versions: X, Y, Z iff P(X = x, Y = y, Z =z) = p(x, y, z) of X, Y, Z iff for all sets A, B, C,

Math 416 Lecture 2 DEFINITION. Here are the multivariate versions: X, Y, Z iff P(X = x, Y = y, Z =z) = p(x, y, z) of X, Y, Z iff for all sets A, B, C, Math 416 Lecture 2 DEFINITION. Here are the multivariate versions: PMF case: p(x, y, z) is the joint Probability Mass Function of X, Y, Z iff P(X = x, Y = y, Z =z) = p(x, y, z) PDF case: f(x, y, z) 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

Chapter 2 Class Notes

Chapter 2 Class Notes Chapter 2 Class Notes Probability can be thought of in many ways, for example as a relative frequency of a long series of trials (e.g. flips of a coin or die) Another approach is to let an expert (such

More information

Chapter 5 : Probability. Exercise Sheet. SHilal. 1 P a g e

Chapter 5 : Probability. Exercise Sheet. SHilal. 1 P a g e 1 P a g e experiment ( observing / measuring ) outcomes = results sample space = set of all outcomes events = subset of outcomes If we collect all outcomes we are forming a sample space If we collect some

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

Lecture Notes 1 Basic Probability. Elements of Probability. Conditional probability. Sequential Calculation of Probability

Lecture Notes 1 Basic Probability. Elements of Probability. Conditional probability. Sequential Calculation of Probability Lecture Notes 1 Basic Probability Set Theory Elements of Probability Conditional probability Sequential Calculation of Probability Total Probability and Bayes Rule Independence Counting EE 178/278A: Basic

More information

Lecture 3. January 7, () Lecture 3 January 7, / 35

Lecture 3. January 7, () Lecture 3 January 7, / 35 Lecture 3 January 7, 2013 () Lecture 3 January 7, 2013 1 / 35 Outline This week s lecture: Fast review of last week s lecture: Conditional probability. Partition, Partition theorem. Bayes theorem and its

More information

Conditional Probability and Independence

Conditional Probability and Independence Conditional Probability and Independence September 3, 2009 1 Restricting the Sample Space - Conditional Probability How do we modify the probability of an event in light of the fact that something is known?

More information

PROBABILITY VITTORIA SILVESTRI

PROBABILITY VITTORIA SILVESTRI PROBABILITY VITTORIA SILVESTRI Contents Preface. Introduction 2 2. Combinatorial analysis 5 3. Stirling s formula 8 4. Properties of Probability measures Preface These lecture notes are for the course

More information

Monty Hall Puzzle. Draw a tree diagram of possible choices (a possibility tree ) One for each strategy switch or no-switch

Monty Hall Puzzle. Draw a tree diagram of possible choices (a possibility tree ) One for each strategy switch or no-switch Monty Hall Puzzle Example: You are asked to select one of the three doors to open. There is a large prize behind one of the doors and if you select that door, you win the prize. After you select a door,

More information

MAT 271E Probability and Statistics

MAT 271E Probability and Statistics MAT 7E Probability and Statistics Spring 6 Instructor : Class Meets : Office Hours : Textbook : İlker Bayram EEB 3 ibayram@itu.edu.tr 3.3 6.3, Wednesday EEB 6.., Monday D. B. Bertsekas, J. N. Tsitsiklis,

More information

Fundamentals of Probability CE 311S

Fundamentals of Probability CE 311S Fundamentals of Probability CE 311S OUTLINE Review Elementary set theory Probability fundamentals: outcomes, sample spaces, events Outline ELEMENTARY SET THEORY Basic probability concepts can be cast in

More information

Brief Review of Probability

Brief Review of Probability Brief Review of Probability Nuno Vasconcelos (Ken Kreutz-Delgado) ECE Department, UCSD Probability Probability theory is a mathematical language to deal with processes or experiments that are non-deterministic

More information

Recap of Basic Probability Theory

Recap of Basic Probability Theory 02407 Stochastic Processes Recap of Basic Probability Theory Uffe Høgsbro Thygesen Informatics and Mathematical Modelling Technical University of Denmark 2800 Kgs. Lyngby Denmark Email: uht@imm.dtu.dk

More information

Notes Week 2 Chapter 3 Probability WEEK 2 page 1

Notes Week 2 Chapter 3 Probability WEEK 2 page 1 Notes Week 2 Chapter 3 Probability WEEK 2 page 1 The sample space of an experiment, sometimes denoted S or in probability theory, is the set that consists of all possible elementary outcomes of that experiment

More information

Module 1. Probability

Module 1. Probability Module 1 Probability 1. Introduction In our daily life we come across many processes whose nature cannot be predicted in advance. Such processes are referred to as random processes. The only way to derive

More information

Topic -2. Probability. Larson & Farber, Elementary Statistics: Picturing the World, 3e 1

Topic -2. Probability. Larson & Farber, Elementary Statistics: Picturing the World, 3e 1 Topic -2 Probability Larson & Farber, Elementary Statistics: Picturing the World, 3e 1 Probability Experiments Experiment : An experiment is an act that can be repeated under given condition. Rolling a

More information

Introduction to Probability. Ariel Yadin. Lecture 1. We begin with an example [this is known as Bertrand s paradox]. *** Nov.

Introduction to Probability. Ariel Yadin. Lecture 1. We begin with an example [this is known as Bertrand s paradox]. *** Nov. Introduction to Probability Ariel Yadin Lecture 1 1. Example: Bertrand s Paradox We begin with an example [this is known as Bertrand s paradox]. *** Nov. 1 *** Question 1.1. Consider a circle of radius

More information

Elementary Discrete Probability

Elementary Discrete Probability Elementary Discrete Probability MATH 472 Financial Mathematics J Robert Buchanan 2018 Objectives In this lesson we will learn: the terminology of elementary probability, elementary rules of probability,

More information

Recap of Basic Probability Theory

Recap of Basic Probability Theory 02407 Stochastic Processes? Recap of Basic Probability Theory Uffe Høgsbro Thygesen Informatics and Mathematical Modelling Technical University of Denmark 2800 Kgs. Lyngby Denmark Email: uht@imm.dtu.dk

More information

STAT Chapter 3: Probability

STAT Chapter 3: Probability Basic Definitions STAT 515 --- Chapter 3: Probability Experiment: A process which leads to a single outcome (called a sample point) that cannot be predicted with certainty. Sample Space (of an experiment):

More information

Mathematical Foundations of Computer Science Lecture Outline October 18, 2018

Mathematical Foundations of Computer Science Lecture Outline October 18, 2018 Mathematical Foundations of Computer Science Lecture Outline October 18, 2018 The Total Probability Theorem. Consider events E and F. Consider a sample point ω E. Observe that ω belongs to either F or

More information

Summary of basic probability theory Math 218, Mathematical Statistics D Joyce, Spring 2016

Summary of basic probability theory Math 218, Mathematical Statistics D Joyce, Spring 2016 8. For any two events E and F, P (E) = P (E F ) + P (E F c ). Summary of basic probability theory Math 218, Mathematical Statistics D Joyce, Spring 2016 Sample space. A sample space consists of a underlying

More information

If S = {O 1, O 2,, O n }, where O i is the i th elementary outcome, and p i is the probability of the i th elementary outcome, then

If S = {O 1, O 2,, O n }, where O i is the i th elementary outcome, and p i is the probability of the i th elementary outcome, then 1.1 Probabilities Def n: A random experiment is a process that, when performed, results in one and only one of many observations (or outcomes). The sample space S is the set of all elementary outcomes

More information

CS 246 Review of Proof Techniques and Probability 01/14/19

CS 246 Review of Proof Techniques and Probability 01/14/19 Note: This document has been adapted from a similar review session for CS224W (Autumn 2018). It was originally compiled by Jessica Su, with minor edits by Jayadev Bhaskaran. 1 Proof techniques Here we

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

MATH 243E Test #3 Solutions

MATH 243E Test #3 Solutions MATH 4E Test # Solutions () Find a recurrence relation for the number of bit strings of length n that contain a pair of consecutive 0s. You do not need to solve this recurrence relation. (Hint: Consider

More information

Why study probability? Set theory. ECE 6010 Lecture 1 Introduction; Review of Random Variables

Why study probability? Set theory. ECE 6010 Lecture 1 Introduction; Review of Random Variables ECE 6010 Lecture 1 Introduction; Review of Random Variables Readings from G&S: Chapter 1. Section 2.1, Section 2.3, Section 2.4, Section 3.1, Section 3.2, Section 3.5, Section 4.1, Section 4.2, Section

More information

Chapter 6: Probability The Study of Randomness

Chapter 6: Probability The Study of Randomness Chapter 6: Probability The Study of Randomness 6.1 The Idea of Probability 6.2 Probability Models 6.3 General Probability Rules 1 Simple Question: If tossing a coin, what is the probability of the coin

More information

Chapter 1: Introduction to Probability Theory

Chapter 1: Introduction to Probability Theory ECE5: Stochastic Signals and Systems Fall 8 Lecture - September 6, 8 Prof. Salim El Rouayheb Scribe: Peiwen Tian, Lu Liu, Ghadir Ayache Chapter : Introduction to Probability Theory Axioms of Probability

More information

2.6 Tools for Counting sample points

2.6 Tools for Counting sample points 2.6 Tools for Counting sample points When the number of simple events in S is too large, manual enumeration of every sample point in S is tedious or even impossible. (Example) If S contains N equiprobable

More information

CSC Discrete Math I, Spring Discrete Probability

CSC Discrete Math I, Spring Discrete Probability CSC 125 - Discrete Math I, Spring 2017 Discrete Probability Probability of an Event Pierre-Simon Laplace s classical theory of probability: Definition of terms: An experiment is a procedure that yields

More information

F71SM STATISTICAL METHODS

F71SM STATISTICAL METHODS F71SM STATISTICAL METHODS RJG SUMMARY NOTES 2 PROBABILITY 2.1 Introduction A random experiment is an experiment which is repeatable under identical conditions, and for which, at each repetition, the outcome

More information

Chapter 2: Probability Part 1

Chapter 2: Probability Part 1 Engineering Probability & Statistics (AGE 1150) Chapter 2: Probability Part 1 Dr. O. Phillips Agboola Sample Space (S) Experiment: is some procedure (or process) that we do and it results in an outcome.

More information

FINAL EXAM: Monday 8-10am

FINAL EXAM: Monday 8-10am ECE 30: Probabilistic Methods in Electrical and Computer Engineering Fall 016 Instructor: Prof. A. R. Reibman FINAL EXAM: Monday 8-10am Fall 016, TTh 3-4:15pm (December 1, 016) This is a closed book exam.

More information

the time it takes until a radioactive substance undergoes a decay

the time it takes until a radioactive substance undergoes a decay 1 Probabilities 1.1 Experiments with randomness Wewillusethetermexperimentinaverygeneralwaytorefertosomeprocess that produces a random outcome. Examples: (Ask class for some first) Here are some discrete

More information

Stat 225 Week 1, 8/20/12-8/24/12, Notes: Set Theory

Stat 225 Week 1, 8/20/12-8/24/12, Notes: Set Theory Stat 225 Week 1, 8/20/12-8/24/12, Notes: Set Theory The Fall 2012 Stat 225 T.A.s September 7, 2012 The material in this handout is intended to cover general set theory topics. Information includes (but

More information

Thus, X is connected by Problem 4. Case 3: X = (a, b]. This case is analogous to Case 2. Case 4: X = (a, b). Choose ε < b a

Thus, X is connected by Problem 4. Case 3: X = (a, b]. This case is analogous to Case 2. Case 4: X = (a, b). Choose ε < b a Solutions to Homework #6 1. Complete the proof of the backwards direction of Theorem 12.2 from class (which asserts the any interval in R is connected). Solution: Let X R be a closed interval. Case 1:

More information

Statistical Methods for the Social Sciences, Autumn 2012

Statistical Methods for the Social Sciences, Autumn 2012 Statistical Methods for the Social Sciences, Autumn 2012 Review Session 3: Probability. Exercises Ch.4. More on Stata TA: Anastasia Aladysheva anastasia.aladysheva@graduateinstitute.ch Office hours: Mon

More information

Probability (Devore Chapter Two)

Probability (Devore Chapter Two) Probability (Devore Chapter Two) 1016-345-01: Probability and Statistics for Engineers Fall 2012 Contents 0 Administrata 2 0.1 Outline....................................... 3 1 Axiomatic Probability 3

More information

DO NOT OPEN THIS TEST BOOKLET UNTIL YOU ARE ASKED TO DO SO

DO NOT OPEN THIS TEST BOOKLET UNTIL YOU ARE ASKED TO DO SO DO NOT OPEN THIS TEST BOOKLET UNTIL YOU ARE ASKED TO DO SO T.B.C. : P-AQNA-L-ZNGU Serial No.- TEST BOOKLET MATHEMATICS Test Booklet Series Time Allowed : Two Hours and Thirty Minutes Maximum Marks : 00

More information

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

Introduction to Probability Theory

Introduction to Probability Theory Introduction to Probability Theory Overview The concept of probability is commonly used in everyday life, and can be expressed in many ways. For example, there is a 50:50 chance of a head when a fair coin

More information

Discrete Mathematics and Probability Theory Fall 2014 Anant Sahai Note 15. Random Variables: Distributions, Independence, and Expectations

Discrete Mathematics and Probability Theory Fall 2014 Anant Sahai Note 15. Random Variables: Distributions, Independence, and Expectations EECS 70 Discrete Mathematics and Probability Theory Fall 204 Anant Sahai Note 5 Random Variables: Distributions, Independence, and Expectations In the last note, we saw how useful it is to have a way of

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

Volume: The Disk Method. Using the integral to find volume.

Volume: The Disk Method. Using the integral to find volume. Volume: The Disk Method Using the integral to find volume. If a region in a plane is revolved about a line, the resulting solid is a solid of revolution and the line is called the axis of revolution. y

More information

ECE353: Probability and Random Processes. Lecture 3 - Independence and Sequential Experiments

ECE353: Probability and Random Processes. Lecture 3 - Independence and Sequential Experiments ECE353: Probability and Random Processes Lecture 3 - Independence and Sequential Experiments Xiao Fu School of Electrical Engineering and Computer Science Oregon State University E-mail: xiao.fu@oregonstate.edu

More information

Conditional Probability

Conditional Probability Chapter 3 Conditional Probability 3.1 Definition of conditional probability In spite of our misgivings, let us persist with the frequency definition of probability. Consider an experiment conducted N times

More information

Properties of Probability

Properties of Probability Econ 325 Notes on Probability 1 By Hiro Kasahara Properties of Probability In statistics, we consider random experiments, experiments for which the outcome is random, i.e., cannot be predicted with certainty.

More information

Tutorial 3: Random Processes

Tutorial 3: Random Processes August 18 Semester 2018-II Tutorial 3: Random Processes Lecturer: Utpal Mukherji/ Parimal Parag Prepared by: Prathamesh Mayekar Note: LaTeX template courtesy of UC Berkeley EECS dept. 3.1 Conditional Probability

More information

UC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Problem Set 3: Solutions Fall 2007

UC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Problem Set 3: Solutions Fall 2007 UC Berkeley Department of Electrical Engineering and Computer Science EE 126: Probablity and Random Processes Problem Set 3: Solutions Fall 2007 Issued: Thursday, September 13, 2007 Due: Friday, September

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

With Question/Answer Animations. Chapter 7

With Question/Answer Animations. Chapter 7 With Question/Answer Animations Chapter 7 Chapter Summary Introduction to Discrete Probability Probability Theory Bayes Theorem Section 7.1 Section Summary Finite Probability Probabilities of Complements

More information

Hour Exam #2 Math 3 Oct. 31, 2012

Hour Exam #2 Math 3 Oct. 31, 2012 Hour Exam #2 Math 3 Oct. 31, 2012 Name (Print): Last First On this, the second of the two Math 3 hour-long exams in Fall 2012, and on the final examination I will work individually, neither giving nor

More information

Discrete Probability. Mark Huiskes, LIACS Probability and Statistics, Mark Huiskes, LIACS, Lecture 2

Discrete Probability. Mark Huiskes, LIACS Probability and Statistics, Mark Huiskes, LIACS, Lecture 2 Discrete Probability Mark Huiskes, LIACS mark.huiskes@liacs.nl Probability: Basic Definitions In probability theory we consider experiments whose outcome depends on chance or are uncertain. How do we model

More information

Introduction to Set Operations

Introduction to Set Operations Introduction to Set Operations CIS008-2 Logic and Foundations of Mathematics David Goodwin david.goodwin@perisic.com 12:00, Friday 21 st October 2011 Outline 1 Recap 2 Introduction to sets 3 Class Exercises

More information

Probability (continued)

Probability (continued) DS-GA 1002 Lecture notes 2 September 21, 15 Probability (continued) 1 Random variables (continued) 1.1 Conditioning on an event Given a random variable X with a certain distribution, imagine that it is

More information

Chapter 5 Random vectors, Joint distributions. Lectures 18-23

Chapter 5 Random vectors, Joint distributions. Lectures 18-23 Chapter 5 Random vectors, Joint distributions Lectures 18-23 In many real life problems, one often encounter multiple random objects. For example, if one is interested in the future price of two different

More information

Multiple Choice Practice Set 1

Multiple Choice Practice Set 1 Multiple Choice Practice Set 1 This set of questions covers material from Chapter 1. Multiple choice is the same format as for the midterm. Q1. Two events each have probability 0.2 of occurring and are

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

Probability theory basics

Probability theory basics Probability theory basics Michael Franke Basics of probability theory: axiomatic definition, interpretation, joint distributions, marginalization, conditional probability & Bayes rule. Random variables:

More information

Fall Math 140 Week-in-Review #5 courtesy: Kendra Kilmer (covering Sections 3.4 and 4.1) Section 3.4

Fall Math 140 Week-in-Review #5 courtesy: Kendra Kilmer (covering Sections 3.4 and 4.1) Section 3.4 Section 3.4 A Standard Maximization Problem has the following properties: The objective function is to be maximized. All variables are non-negative. Fall 2017 Math 140 Week-in-Review #5 courtesy: Kendra

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

Chapter 1 (Basic Probability)

Chapter 1 (Basic Probability) Chapter 1 (Basic Probability) What is probability? Consider the following experiments: 1. Count the number of arrival requests to a web server in a day. 2. Determine the execution time of a program. 3.

More information

Review of Basic Probability

Review of Basic Probability Review of Basic Probability Erik G. Learned-Miller Department of Computer Science University of Massachusetts, Amherst Amherst, MA 01003 September 16, 2009 Abstract This document reviews basic discrete

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