n( black) n( joker) n( red joker) Solutions to Unit 1.3 1a) { 7 of diamonds} 1b) { ace of spades, ace of diamonds, ace of hearts, ace of clubs}

Size: px
Start display at page:

Download "n( black) n( joker) n( red joker) Solutions to Unit 1.3 1a) { 7 of diamonds} 1b) { ace of spades, ace of diamonds, ace of hearts, ace of clubs}"

Transcription

1 Solutios to Uit. a) { 7 of diamods} b) { ace of spades, ace of diamods, ace of hearts, ace of clubs} c) {,,, 5, 6, 7,, 9,0}all of clubs d) {,, 6,,0} of clubs, diamods, hearts, ad spades a) There are 5 possible outcomes b) P ( black) c) P ( red) 5 5 ( black) ( all possible) ( red) ( all possible) d) There are o possible outcomes therefore the probability is 0 a) P ( joker) ( joker) ( all possible cards) 5 7 b) P ( red joker) ( red joker) ( all possible cards) 5

2 c) P ( quee) d) P ( black) e) P ( <0) f) P ( red) 5a) P ( tail) 5b) P ( ) ( quee) ( all possible cards) 5 7 ( black) ( all possible cards) 7 5 ( <0) ( all possible cards) 6 5 ( red) ( all possible cards) 5 6 ( tail) ( all possible cards) ( ) ( all possible cards)

3 5c) P ( red) 5d) P ( B7) ( red) ( all possible cards) 6 5 5e) P ( <jack) ( B7) ( all possible cards) 5 7 ( <jack) ( all possible cards) 0 5 0

4 Uit. Solutios 6. a) P( A ) A S b) P( A ) c) P( A ) 7. a) P( A) ( odd umber) ( ay umber) A S ( divisible by ) ( ay umber) ( <) ( ay umber) A S A S ( red blocks) ( umber blocks)

5 b) P( A) ( A) ( red blocks) ( umber blocks). a) P( A) b) P( A ) c) P( A ) ( S's) ( letters) A S A S A S ( M's) ( letters) ( vowels) ( letters)

6 0 Possible values {HHH, HHT, HTH, HTT, THH, THT, TTH, TTT} ( A) 0. a) P( A) S 0 b) P( A) 0 c) P( A). ( all heads) ( possible values) 7 ( tail, tail, tail) ( possible values) A S ( HHT, HTH, THH) ( possible values) A S a) ( S ) 6 Die 5 6,,,, 5, 6,,,,, 5, 6,,,,, 5, 6,,,,, 5, 6, 5, 5, 5, 5, 5 5, 5 6, 5 6, 6, 6, 6, 6 5, 6 6, 6

7 b) P( A) A S ( totals 7) ( possible values) c) ( ) P( A) ( A) ( totals 7) ( possible values) % this will be equal to the probability ratio we eed 00 let the umber of caffeie-free diets be x, ( diet) 00 all driks 6 + x 00 + x 96 + x x 6 56x x 6

8 Uit. Solutios. a) {6, 9} b) {5,, 6, 9,, 0} c) {9,0} d) {,, 9, 0, 6, } e) f) {9} 6. a) S C T 0 7 b) P( C ) C S 0 0 c) P( C ) T 0

9 d) P( ) P( ) ( ) S HA C 6 The umber combied i both sets is 50- Let HA represet Head aches oly, B represet both, ad C represet colds Therefore HA+B0 C+B HA+B+C We have three equatios ad three variables, therefore we ca do this. Rewrite the first two equatios i terms of B HA0-B C-B Plug these ito the third equatio. (0-B)+B+(-B) 5-B B6 Therefore HA 0 6 C 6

10 b i) studets have just a headache P HA HA S b ii) ++6 have headache or cold ( or C) P HA ( or C) HA b iii) +6 have o cold symptom P ( oe) ( oe)

11 Uit.6 Solutios a) P( A B) P( A) + P( B) P( A B) P( A B) P( A B) P( A B) 0 Sice P( A B) 0, mutually exclusive. b) P( A B) P( A) + P( B) P( A B) Sice P( A B) 0.5, ot mutually exclusive c) P( A B) P( A) + P( B) P( A B) P( B) P( B) P( B) 0.6 Sice P( A B) 0, mutually exclusive ( a) remember ad B B Sice mutually exclusive P( A B) 0 ) b) P( A B) P( A) + P( B) P( A B) c Sice all probabilities must add up to, ad A ad B are mutually exclusive P(A)+P(B)+P(C) P P(C)0.5

12 5. Let A represet the evet that it is a female Let B represet the evet that it is a tetra ( female or tetra) ( ) P( A) + P( B) P( A B) ( A) ( B) ( A B) + P B Let A represet the evet of drawig a ace Let B represet the evet of drawig a club ( ) + ( ) ( A) ( B) ( A B) + B P B B a) Let A represet the evet of beig taller tha 5 cm Let B represet the evet of havig dark hair ( B) P( A) + P( B) P( A B) ( ) + P( A B) P B A B ( B) ( B)

13 b) Let A represet the evet of beig taller tha 5 cm Let B represet the evet of havig dark hair rom above P( A B) c) Let A represet the evet of beig taller tha 5 cm ( ) P( A) Uit.7 Solutios a) Let A represet the evet that the studet is male. Let B represet the evet that the studet likes school P B A ( B) P( A) A S 9 A ( B) ( B) P( B A ) 9 9

14 The probability of the evet that a studet likes school give that the studet is male is. 9 b) Let A represet the evet that the studet dislikes school. Let B represet the evet that the studet is female. P B A ( B) P( A) ( A) A ( B) P B A ( B) The probability of the evet that a studet is female give that the studet dislikes school is. 5a) Let A represet the evet that the patiet is a smoker. Let B represet the evet that the patiet is dyig from lug cacer. P B A ( B) P( A)

15 A S A ( B) ( B) P( B A ) The probability of the evet that a patiet will die from lug cacer give that the 6 patiet is a smoker is 50. 5b) Let A represet the evet that the patiet is a o-smoker. Let B represet the evet that the patiet is dyig from lug cacer. P B A ( B) P( A) A S A ( B) ( B)

16 0 P( B A ) The probability of the evet that a patiet will die from lug cacer give that the patiet is a o-smoker is 750.

17 6. Let A represet the evet that first marble is red. Let B represet the evet that the secod marble is red. P( A B) P( A B) P( A) A S P B A B A 7 P( A B) 7 9a) Let A represet the evet that first draw is a joker. Let B represet the evet that the secod draw is a ace. P( A B) P( A B) P( A) P B A ( A) 5 B A 5 P( A B) 5 5

18 9b) Let A represet the evet that first draw is a umbered card. Let B represet the evet that the secod draw is a red joker. P( A B) P( A B) P( A) A S P B A 6 5 B A 5 6 P( A B) b) Let A represet the evet that first draw is a quee. Let B represet the evet that the secod draw is a quee. P( A B) P( A B) P( A) P B A ( A) 5 B A 5 P( A B)

19 9d) Let A represet the evet that first draw is a black card. Let B represet the evet that the secod draw is a black card. P( A B) P( A B) P( A) A S P B A 7 5 B A P( A B) e) Let A represet the evet that first draw is a umbered card below 0. Let B represet the evet that the secod draw is the same umbered card. P( A B) P( A B) P( A) A S P B A 5 B A 5 P( A B)

20 9f) Let A represet the evet that first draw is a red joker or red ace. Let B represet the evet that the secod draw is a red joker or ace. It is easier to fid complemetary because of the either draw aspect. ( B ) a) Let A represet the evet that first draw is a red marble. Let B represet the evet that the secod draw is gree marble. B ( B) P( A) A S P( A B) P( A B )

21 9b) Let A represet the evet that the first marble is red Let B represet the evet that the secod marble is red. P( A B) P( A B) P( A) P B A A S 6 0 B A P( A B) 9 0 9c) Let A represet the evet that the first marble is gree. Let B represet the evet that the secod marble is gree. P( A B) P( A B) P( A) P B A A S 0 B A 9 P( A B) 9 0 5

22 9b) Let A represet the evet that the first marble is red Let B represet the evet that the secod marble is red. We have two situatios ( red red) + ( gree red) ( red red) ( red) + ( red gree) ( gree) P P P P P P Uit. Solutios a) T T T T T T T T T T T T T T T

23 P b) ( perfect ) 6 ( perfect ) or

24 c) correct icorrect P correct or 6 we ca look at it this way, we multiply by because there are ways of havig correct. a) (7D7D) b) (AS7D) c) (CC, CC, CC 0C9C, 0C0C) d) (ASC, ASC, AS6C, AD0C) a) 6x66 b) The oly way to obtai a sum of is white ad red c) R W W R

25 Uit.9 Solutios 5a) ( cards) ( dice) There are 0 possible outcomes 5b) The elemets of the sample space (card umber ad suit, umber o dice) 5ci) Let A represet the evet of drawig a eve card Let B represet the evet of a eve umber o die P ( eve ad eve) P( A B) P( A) P( B) ( A) ( B) ( S ) ( S ) cii) Let A represet the evet of drawig a eve card Let B represet the evet of a odd umber o die P ( eve ad odd) P( A B ) P( A) P( B) ( A) ( B) ( S ) ( S ) 0 0 6

26 5ciii) Let A represet the evet of drawig a card Let B represet the evet of a umber less tha or equal to umber o die P ( ad ) P( A B ) P( A) P( B) ( A) ( B) ( S ) ( S ) civ) Let A represet the evet of drawig a suit of card Let B represet the evet of umber o die Arragemets are {(,6); (,6); (,); (,); (5,); (6,)} ( sum7) ( ) P( A) P( B) ( A) ( S ) ( B) ( S ) P B arragemets 5civ) Let A represet the evet of drawig a suit of card Let B represet the evet of umber o die Arragemets are {(5,6); (6,5); (7,); (,); (9,); (0,)} ( sum7) ( ) P( A) P( B) ( A) ( S ) ( B) ( S ) P B arragemets

27 6a) Let A represet the evet of drawig a joker first Let B represet the evet of drawig a ace o the secod ( ) B P B This is ot coditioal because we do ot require the fidig of the probability of drawig a ace give that the joker was draw. 6b) Let A represet the evet of drawig a umbered card first Let B represet the evet of drawig a red joker o the secod ( ) B P B c) Let A represet the evet of drawig a quee card first Let B represet the evet of drawig a quee o the secod ( ) B P B d) Let A represet the evet of drawig a black card first Let B represet the evet of drawig a black o the secod ( ) B P B

28 6e) Let A represet the evet of drawig a umbered card < 0 first Let B represet the evet of drawig the same o the secod ( ) B P B f) Let A represet the evet of drawig a red joker or red ace card first Let B represet the evet of drawig a red joker or red ace o the secod Easier to do complemet. ( ) ( ) ( ) B P B a) S 000 b) P ( edig with a 5) c) P ( first is or ) ( last is 5) ( or )

29 Uit.0 Solutios a) Let A represet the evet of beig a uio member A S 5 0 b) Let A represet the evet of beig a uio member Let B represet the evet of beig a uio member Notice the ame is ot replaced ( ) ( A) ( B) ( S ) ( S ) B P B c) Let A represet the evet of beig a uio member Let B represet the evet of beig a uio member Let C represet the evet of beig a uio member Let C represet the evet of beig a uio member Notice the ame is ot replaced ( ) ( A) ( B) ( C) ( D) ( S ) ( S ) ( S ) ( S ) B C D P B P C P D

30 d) We have possible situatios NUUU, UNUU, UUNU, UUUN We wat P ( NUUU UNUU UUNU UUUN ) P ( NUUU ) + P( UNUU ) + P ( UUNU ) + P ( UUUN ) a) Let A represet the evet of drawig a chocolate bar first Let B represet the evet of drawig a chocolate bar secod ( ) B P B b) Let A represet the evet of drawig a chocolate bar first Let B represet the evet of drawig a chocolate bar secod Let C represet the evet of drawig a fruit bar first Let D represet the evet of drawig a fruit bar secod Let E represet the evet of drawig a toffee bar first Let represet the evet of drawig a toffee bar secod ( ) + ( ) + ( ) P( A) P( B) P( C) P( D) P( E) P( ) B P C D P E keep ay cady c) P

31 0a) P( spedig $000) P( st moth) P( d moth) P( rd moth) 0b) The two situatios are spedig $5000 or $6000 P ( 6000) P ( 5000) we sped 000, 000, 000 we eed to multiply by because of three cases three cases (000, 000, 000); (000, 000, 000); (000, 000, 000) we ca look at it this way ( 000, 000, 000) ( 000,000, 000) ( 000, 000,000) P P P ( more) ( 5000) + ( 6000) P P P + spedig $ c) P

Mathematics. ( : Focus on free Education) (Chapter 16) (Probability) (Class XI) Exercise 16.2

Mathematics. (  : Focus on free Education) (Chapter 16) (Probability) (Class XI) Exercise 16.2 ( : Focus on free Education) Exercise 16.2 Question 1: A die is rolled. Let E be the event die shows 4 and F be the event die shows even number. Are E and F mutually exclusive? Answer 1: When a die is

More information

324 Stat Lecture Notes (1) Probability

324 Stat Lecture Notes (1) Probability 324 Stat Lecture Notes 1 robability Chapter 2 of the book pg 35-71 1 Definitions: Sample Space: Is the set of all possible outcomes of a statistical experiment, which is denoted by the symbol S Notes:

More information

CHAPTER - 16 PROBABILITY Random Experiment : If an experiment has more than one possible out come and it is not possible to predict the outcome in advance then experiment is called random experiment. Sample

More information

First Digit Tally Marks Final Count

First Digit Tally Marks Final Count Benford Test () Imagine that you are a forensic accountant, presented with the two data sets on this sheet of paper (front and back). Which of the two sets should be investigated further? Why? () () ()

More information

(i) Given that a student is female, what is the probability of having a GPA of at least 3.0?

(i) Given that a student is female, what is the probability of having a GPA of at least 3.0? MATH 382 Conditional Probability Dr. Neal, WKU We now shall consider probabilities of events that are restricted within a subset that is smaller than the entire sample space Ω. For example, let Ω be the

More information

3. One pencil costs 25 cents, and we have 5 pencils, so the cost is 25 5 = 125 cents. 60 =

3. One pencil costs 25 cents, and we have 5 pencils, so the cost is 25 5 = 125 cents. 60 = JHMMC 0 Grade Solutios October, 0. By coutig, there are 7 words i this questio.. + 4 + + 8 + 6 + 6.. Oe pecil costs cets, ad we have pecils, so the cost is cets. 4. A cube has edges.. + + 4 + 0 60 + 0

More information

Chapter 6 Conditional Probability

Chapter 6 Conditional Probability Lecture Notes o robability Coditioal robability 6. Suppose RE a radom experimet S sample space C subset of S φ (i.e. (C > 0 A ay evet Give that C must occur, the the probability that A happe is the coditioal

More information

4/17/2012. NE ( ) # of ways an event can happen NS ( ) # of events in the sample space

4/17/2012. NE ( ) # of ways an event can happen NS ( ) # of events in the sample space I. Vocabulary: A. Outcomes: the things that can happen in a probability experiment B. Sample Space (S): all possible outcomes C. Event (E): one outcome D. Probability of an Event (P(E)): the likelihood

More information

ARRANGEMENTS IN A CIRCLE

ARRANGEMENTS IN A CIRCLE ARRANGEMENTS IN A CIRCLE Whe objects are arraged i a circle, the total umber of arragemets is reduced. The arragemet of (say) four people i a lie is easy ad o problem (if they liste of course!!). With

More information

Generating Functions. 1 Operations on generating functions

Generating Functions. 1 Operations on generating functions Geeratig Fuctios The geeratig fuctio for a sequece a 0, a,..., a,... is defied to be the power series fx a x. 0 We say that a 0, a,... is the sequece geerated by fx ad a is the coefficiet of x. Example

More information

Topic 5: Basics of Probability

Topic 5: Basics of Probability Topic 5: Jue 1, 2011 1 Itroductio Mathematical structures lie Euclidea geometry or algebraic fields are defied by a set of axioms. Mathematical reality is the developed through the itroductio of cocepts

More information

What is Probability? Probability. Sample Spaces and Events. Simple Event

What is Probability? Probability. Sample Spaces and Events. Simple Event What is Probability? Probability Peter Lo Probability is the numerical measure of likelihood that the event will occur. Simple Event Joint Event Compound Event Lies between 0 & 1 Sum of events is 1 1.5

More information

UNIT 2 DIFFERENT APPROACHES TO PROBABILITY THEORY

UNIT 2 DIFFERENT APPROACHES TO PROBABILITY THEORY UNIT 2 DIFFERENT APPROACHES TO PROBABILITY THEORY Structure 2.1 Itroductio Objectives 2.2 Relative Frequecy Approach ad Statistical Probability 2. Problems Based o Relative Frequecy 2.4 Subjective Approach

More information

Outline. 1. Define likelihood 2. Interpretations of likelihoods 3. Likelihood plots 4. Maximum likelihood 5. Likelihood ratio benchmarks

Outline. 1. Define likelihood 2. Interpretations of likelihoods 3. Likelihood plots 4. Maximum likelihood 5. Likelihood ratio benchmarks Outline 1. Define likelihood 2. Interpretations of likelihoods 3. Likelihood plots 4. Maximum likelihood 5. Likelihood ratio benchmarks Likelihood A common and fruitful approach to statistics is to assume

More information

MODULE 2 RANDOM VARIABLE AND ITS DISTRIBUTION LECTURES DISTRIBUTION FUNCTION AND ITS PROPERTIES

MODULE 2 RANDOM VARIABLE AND ITS DISTRIBUTION LECTURES DISTRIBUTION FUNCTION AND ITS PROPERTIES MODULE 2 RANDOM VARIABLE AND ITS DISTRIBUTION LECTURES 7-11 Topics 2.1 RANDOM VARIABLE 2.2 INDUCED PROBABILITY MEASURE 2.3 DISTRIBUTION FUNCTION AND ITS PROPERTIES 2.4 TYPES OF RANDOM VARIABLES: DISCRETE,

More information

THE KENNESAW STATE UNIVERSITY HIGH SCHOOL MATHEMATICS COMPETITION PART II Calculators are NOT permitted Time allowed: 2 hours

THE KENNESAW STATE UNIVERSITY HIGH SCHOOL MATHEMATICS COMPETITION PART II Calculators are NOT permitted Time allowed: 2 hours THE 06-07 KENNESAW STATE UNIVERSITY HIGH SCHOOL MATHEMATICS COMPETITION PART II Calculators are NOT permitted Time allowed: hours Let x, y, ad A all be positive itegers with x y a) Prove that there are

More information

Probability theory and mathematical statistics:

Probability theory and mathematical statistics: N.I. Lobachevsky State Uiversity of Nizhi Novgorod Probability theory ad mathematical statistics: Law of Total Probability. Associate Professor A.V. Zorie Law of Total Probability. 1 / 14 Theorem Let H

More information

Assignment ( ) Class-XI. = iii. v. A B= A B '

Assignment ( ) Class-XI. = iii. v. A B= A B ' Assigmet (8-9) Class-XI. Proe that: ( A B)' = A' B ' i A ( BAC) = ( A B) ( A C) ii A ( B C) = ( A B) ( A C) iv. A B= A B= φ v. A B= A B ' v A B B ' A'. A relatio R is dified o the set z of itegers as:

More information

CS 330 Discussion - Probability

CS 330 Discussion - Probability CS 330 Discussio - Probability March 24 2017 1 Fudametals of Probability 11 Radom Variables ad Evets A radom variable X is oe whose value is o-determiistic For example, suppose we flip a coi ad set X =

More information

Math 475, Problem Set #12: Answers

Math 475, Problem Set #12: Answers Math 475, Problem Set #12: Aswers A. Chapter 8, problem 12, parts (b) ad (d). (b) S # (, 2) = 2 2, sice, from amog the 2 ways of puttig elemets ito 2 distiguishable boxes, exactly 2 of them result i oe

More information

Lecture 1 : The Mathematical Theory of Probability

Lecture 1 : The Mathematical Theory of Probability Lecture 1 : The Mathematical Theory of Probability 0/ 30 1. Introduction Today we will do 2.1 and 2.2. We will skip Chapter 1. We all have an intuitive notion of probability. Let s see. What is the probability

More information

Final Review for MATH 3510

Final Review for MATH 3510 Fial Review for MATH 50 Calculatio 5 Give a fairly simple probability mass fuctio or probability desity fuctio of a radom variable, you should be able to compute the expected value ad variace of the variable

More information

Sets. Sets. Operations on Sets Laws of Algebra of Sets Cardinal Number of a Finite and Infinite Set. Representation of Sets Power Set Venn Diagram

Sets. Sets. Operations on Sets Laws of Algebra of Sets Cardinal Number of a Finite and Infinite Set. Representation of Sets Power Set Venn Diagram Sets MILESTONE Sets Represetatio of Sets Power Set Ve Diagram Operatios o Sets Laws of lgebra of Sets ardial Number of a Fiite ad Ifiite Set I Mathematical laguage all livig ad o-livig thigs i uiverse

More information

ACCESS TO SCIENCE, ENGINEERING AND AGRICULTURE: MATHEMATICS 1 MATH00030 SEMESTER / Statistics

ACCESS TO SCIENCE, ENGINEERING AND AGRICULTURE: MATHEMATICS 1 MATH00030 SEMESTER / Statistics ACCESS TO SCIENCE, ENGINEERING AND AGRICULTURE: MATHEMATICS 1 MATH00030 SEMESTER 1 018/019 DR. ANTHONY BROWN 8. Statistics 8.1. Measures of Cetre: Mea, Media ad Mode. If we have a series of umbers the

More information

Axioms of Measure Theory

Axioms of Measure Theory MATH 532 Axioms of Measure Theory Dr. Neal, WKU I. The Space Throughout the course, we shall let X deote a geeric o-empty set. I geeral, we shall ot assume that ay algebraic structure exists o X so that

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

It is always the case that unions, intersections, complements, and set differences are preserved by the inverse image of a function.

It is always the case that unions, intersections, complements, and set differences are preserved by the inverse image of a function. MATH 532 Measurable Fuctios Dr. Neal, WKU Throughout, let ( X, F, µ) be a measure space ad let (!, F, P ) deote the special case of a probability space. We shall ow begi to study real-valued fuctios defied

More information

Probability. VCE Maths Methods - Unit 2 - Probability

Probability. VCE Maths Methods - Unit 2 - Probability Probability Probability Tree diagrams La ice diagrams Venn diagrams Karnough maps Probability tables Union & intersection rules Conditional probability Markov chains 1 Probability Probability is the mathematics

More information

Randomized Algorithms I, Spring 2018, Department of Computer Science, University of Helsinki Homework 1: Solutions (Discussed January 25, 2018)

Randomized Algorithms I, Spring 2018, Department of Computer Science, University of Helsinki Homework 1: Solutions (Discussed January 25, 2018) Radomized Algorithms I, Sprig 08, Departmet of Computer Sciece, Uiversity of Helsiki Homework : Solutios Discussed Jauary 5, 08). Exercise.: Cosider the followig balls-ad-bi game. We start with oe black

More information

Conditional Probability and Bayes Theorem (2.4) Independence (2.5)

Conditional Probability and Bayes Theorem (2.4) Independence (2.5) Conditional Probability and Bayes Theorem (2.4) Independence (2.5) Prof. Tesler Math 186 Winter 2019 Prof. Tesler Conditional Probability and Bayes Theorem Math 186 / Winter 2019 1 / 38 Scenario: Flip

More information

Putnam Training Exercise Counting, Probability, Pigeonhole Principle (Answers)

Putnam Training Exercise Counting, Probability, Pigeonhole Principle (Answers) Putam Traiig Exercise Coutig, Probability, Pigeohole Pricile (Aswers) November 24th, 2015 1. Fid the umber of iteger o-egative solutios to the followig Diohatie equatio: x 1 + x 2 + x 3 + x 4 + x 5 = 17.

More information

Math 113 Exam 3 Practice

Math 113 Exam 3 Practice Math Exam Practice Exam will cover.-.9. This sheet has three sectios. The first sectio will remid you about techiques ad formulas that you should kow. The secod gives a umber of practice questios for you

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

Zeros of Polynomials

Zeros of Polynomials Math 160 www.timetodare.com 4.5 4.6 Zeros of Polyomials I these sectios we will study polyomials algebraically. Most of our work will be cocered with fidig the solutios of polyomial equatios of ay degree

More information

COS 341 Discrete Mathematics. Exponential Generating Functions and Recurrence Relations

COS 341 Discrete Mathematics. Exponential Generating Functions and Recurrence Relations COS 341 Discrete Mathematics Epoetial Geeratig Fuctios ad Recurrece Relatios 1 Tetbook? 1 studets said they eeded the tetbook, but oly oe studet bought the tet from Triagle. If you do t have the book,

More information

Chapter 13, Probability from Applied Finite Mathematics by Rupinder Sekhon was developed by OpenStax College, licensed by Rice University, and is

Chapter 13, Probability from Applied Finite Mathematics by Rupinder Sekhon was developed by OpenStax College, licensed by Rice University, and is Chapter 13, Probability from Applied Finite Mathematics by Rupinder Sekhon was developed by OpenStax College, licensed by Rice University, and is available on the Connexions website. It is used under a

More information

STA 2023 EXAM-2 Practice Problems. Ven Mudunuru. From Chapters 4, 5, & Partly 6. With SOLUTIONS

STA 2023 EXAM-2 Practice Problems. Ven Mudunuru. From Chapters 4, 5, & Partly 6. With SOLUTIONS STA 2023 EXAM-2 Practice Problems From Chapters 4, 5, & Partly 6 With SOLUTIONS Mudunuru, Venkateswara Rao STA 2023 Spring 2016 1 1. A committee of 5 persons is to be formed from 6 men and 4 women. What

More information

Different kinds of Mathematical Induction

Different kinds of Mathematical Induction Differet ids of Mathematical Iductio () Mathematical Iductio Give A N, [ A (a A a A)] A N () (First) Priciple of Mathematical Iductio Let P() be a propositio (ope setece), if we put A { : N p() is true}

More information

Why should you care?? Intellectual curiosity. Gambling. Mathematically the same as the ESP decision problem we discussed in Week 4.

Why should you care?? Intellectual curiosity. Gambling. Mathematically the same as the ESP decision problem we discussed in Week 4. I. Probability basics (Sections 4.1 and 4.2) Flip a fair (probability of HEADS is 1/2) coin ten times. What is the probability of getting exactly 5 HEADS? What is the probability of getting exactly 10

More information

PUTNAM TRAINING PROBABILITY

PUTNAM TRAINING PROBABILITY PUTNAM TRAINING PROBABILITY (Last udated: December, 207) Remark. This is a list of exercises o robability. Miguel A. Lerma Exercises. Prove that the umber of subsets of {, 2,..., } with odd cardiality

More information

Homework 3. = k 1. Let S be a set of n elements, and let a, b, c be distinct elements of S. The number of k-subsets of S is

Homework 3. = k 1. Let S be a set of n elements, and let a, b, c be distinct elements of S. The number of k-subsets of S is Homewor 3 Chapter 5 pp53: 3 40 45 Chapter 6 p85: 4 6 4 30 Use combiatorial reasoig to prove the idetity 3 3 Proof Let S be a set of elemets ad let a b c be distict elemets of S The umber of -subsets of

More information

Chapter 1 (Definitions)

Chapter 1 (Definitions) FINAL EXAM REVIEW Chapter 1 (Defiitios) Qualitative: Nomial: Ordial: Quatitative: Ordial: Iterval: Ratio: Observatioal Study: Desiged Experimet: Samplig: Cluster: Stratified: Systematic: Coveiece: Simple

More information

Probability Pearson Education, Inc. Slide

Probability Pearson Education, Inc. Slide Probability The study of probability is concerned with random phenomena. Even though we cannot be certain whether a given result will occur, we often can obtain a good measure of its likelihood, or probability.

More information

n How to represent uncertainty in knowledge? n Which action to choose under uncertainty? q Assume the car does not have a flat tire

n How to represent uncertainty in knowledge? n Which action to choose under uncertainty? q Assume the car does not have a flat tire Uncertainty Uncertainty Russell & Norvig Chapter 13 Let A t be the action of leaving for the airport t minutes before your flight Will A t get you there on time? A purely logical approach either 1. risks

More information

RADICAL EXPRESSION. If a and x are real numbers and n is a positive integer, then x is an. n th root theorems: Example 1 Simplify

RADICAL EXPRESSION. If a and x are real numbers and n is a positive integer, then x is an. n th root theorems: Example 1 Simplify Example 1 Simplify 1.2A Radical Operatios a) 4 2 b) 16 1 2 c) 16 d) 2 e) 8 1 f) 8 What is the relatioship betwee a, b, c? What is the relatioship betwee d, e, f? If x = a, the x = = th root theorems: RADICAL

More information

STA 2023 EXAM-2 Practice Problems From Chapters 4, 5, & Partly 6. With SOLUTIONS

STA 2023 EXAM-2 Practice Problems From Chapters 4, 5, & Partly 6. With SOLUTIONS STA 2023 EXAM-2 Practice Problems From Chapters 4, 5, & Partly 6 With SOLUTIONS Mudunuru Venkateswara Rao, Ph.D. STA 2023 Fall 2016 Venkat Mu ALL THE CONTENT IN THESE SOLUTIONS PRESENTED IN BLUE AND BLACK

More information

1 Review of Probability & Statistics

1 Review of Probability & Statistics 1 Review of Probability & Statistics a. I a group of 000 people, it has bee reported that there are: 61 smokers 670 over 5 960 people who imbibe (drik alcohol) 86 smokers who imbibe 90 imbibers over 5

More information

STAT 430/510 Probability

STAT 430/510 Probability STAT 430/510 Probability Hui Nie Lecture 3 May 28th, 2009 Review We have discussed counting techniques in Chapter 1. Introduce the concept of the probability of an event. Compute probabilities in certain

More information

Elementary Statistics

Elementary Statistics Elemetary Statistics M. Ghamsary, Ph.D. Sprig 004 Chap 0 Descriptive Statistics Raw Data: Whe data are collected i origial form, they are called raw data. The followig are the scores o the first test of

More information

Conditional Probability

Conditional Probability Conditional Probability Conditional Probability The Law of Total Probability Let A 1, A 2,..., A k be mutually exclusive and exhaustive events. Then for any other event B, P(B) = P(B A 1 ) P(A 1 ) + P(B

More information

n m CHAPTER 3 RATIONAL EXPONENTS AND RADICAL FUNCTIONS 3-1 Evaluate n th Roots and Use Rational Exponents Real nth Roots of a n th Root of a

n m CHAPTER 3 RATIONAL EXPONENTS AND RADICAL FUNCTIONS 3-1 Evaluate n th Roots and Use Rational Exponents Real nth Roots of a n th Root of a CHAPTER RATIONAL EXPONENTS AND RADICAL FUNCTIONS Big IDEAS: 1) Usig ratioal expoets ) Performig fuctio operatios ad fidig iverse fuctios ) Graphig radical fuctios ad solvig radical equatios Sectio: Essetial

More information

Events A and B are said to be independent if the occurrence of A does not affect the probability of B.

Events A and B are said to be independent if the occurrence of A does not affect the probability of B. Independent Events Events A and B are said to be independent if the occurrence of A does not affect the probability of B. Probability experiment of flipping a coin and rolling a dice. Sample Space: {(H,

More information

LECTURE NOTES by DR. J.S.V.R. KRISHNA PRASAD

LECTURE NOTES by DR. J.S.V.R. KRISHNA PRASAD .0 Introduction: The theory of probability has its origin in the games of chance related to gambling such as tossing of a coin, throwing of a die, drawing cards from a pack of cards etc. Jerame Cardon,

More information

05 - PERMUTATIONS AND COMBINATIONS Page 1 ( Answers at the end of all questions )

05 - PERMUTATIONS AND COMBINATIONS Page 1 ( Answers at the end of all questions ) 05 - PERMUTATIONS AND COMBINATIONS Page 1 ( Aswers at the ed of all questios ) ( 1 ) If the letters of the word SACHIN are arraged i all possible ways ad these words are writte out as i dictioary, the

More information

Chapter Vectors

Chapter Vectors Chapter 4. Vectors fter readig this chapter you should be able to:. defie a vector. add ad subtract vectors. fid liear combiatios of vectors ad their relatioship to a set of equatios 4. explai what it

More information

CSE 21 Mathematics for

CSE 21 Mathematics for CSE 2 Mathematics for Algorithm ad System Aalysis Summer, 2005 Outlie What a geeratig fuctio is How to create a geeratig fuctio to model a problem Fidig the desired coefficiet Partitios Expoetial geeratig

More information

Statistics Statistical Process Control & Control Charting

Statistics Statistical Process Control & Control Charting Statistics Statistical Process Control & Control Charting Cayman Systems International 1/22/98 1 Recommended Statistical Course Attendance Basic Business Office, Staff, & Management Advanced Business Selected

More information

Conditional Probability. Given an event M with non zero probability and the condition P( M ) > 0, Independent Events P A P (AB) B P (B)

Conditional Probability. Given an event M with non zero probability and the condition P( M ) > 0, Independent Events P A P (AB) B P (B) Coditioal robability Give a evet M with o zero probability ad the coditio ( M ) > 0, ( / M ) ( M ) ( M ) Idepedet Evets () ( ) ( ) () ( ) ( ) ( ) Examples ) Let items be chose at radom from a lot cotaiig

More information

PROBABILITY. Note : Probability of occurrence of an event A is denoted by P(A).

PROBABILITY. Note : Probability of occurrence of an event A is denoted by P(A). J-Mathematics PROBABILITY INTRODUCTION : The theory of probability has bee origiated from the game of chace ad gamblig. I old days, gamblers used to gamble i a gamblig house with a die to wi the amout

More information

Properties and Tests of Zeros of Polynomial Functions

Properties and Tests of Zeros of Polynomial Functions Properties ad Tests of Zeros of Polyomial Fuctios The Remaider ad Factor Theorems: Sythetic divisio ca be used to fid the values of polyomials i a sometimes easier way tha substitutio. This is show by

More information

LESSON 2: SIMPLIFYING RADICALS

LESSON 2: SIMPLIFYING RADICALS High School: Workig with Epressios LESSON : SIMPLIFYING RADICALS N.RN.. C N.RN.. B 5 5 C t t t t t E a b a a b N.RN.. 4 6 N.RN. 4. N.RN. 5. N.RN. 6. 7 8 N.RN. 7. A 7 N.RN. 8. 6 80 448 4 5 6 48 00 6 6 6

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

2. Conditional Probability

2. Conditional Probability ENGG 2430 / ESTR 2004: Probability and Statistics Spring 2019 2. Conditional Probability Andrej Bogdanov Coins game Toss 3 coins. You win if at least two come out heads. S = { HHH, HHT, HTH, HTT, THH,

More information

Uncertainty. Russell & Norvig Chapter 13.

Uncertainty. Russell & Norvig Chapter 13. Uncertainty Russell & Norvig Chapter 13 http://toonut.com/wp-content/uploads/2011/12/69wp.jpg Uncertainty Let A t be the action of leaving for the airport t minutes before your flight Will A t get you

More information

( 1) n (4x + 1) n. n=0

( 1) n (4x + 1) n. n=0 Problem 1 (10.6, #). Fid the radius of covergece for the series: ( 1) (4x + 1). For what values of x does the series coverge absolutely, ad for what values of x does the series coverge coditioally? Solutio.

More information

Random Variables, Sampling and Estimation

Random Variables, Sampling and Estimation Chapter 1 Radom Variables, Samplig ad Estimatio 1.1 Itroductio This chapter will cover the most importat basic statistical theory you eed i order to uderstad the ecoometric material that will be comig

More information

Mini Lecture 10.1 Radical Expressions and Functions. 81x d. x 4x 4

Mini Lecture 10.1 Radical Expressions and Functions. 81x d. x 4x 4 Mii Lecture 0. Radical Expressios ad Fuctios Learig Objectives:. Evaluate square roots.. Evaluate square root fuctios.. Fid the domai of square root fuctios.. Use models that are square root fuctios. 5.

More information

Outline. 1. Define likelihood 2. Interpretations of likelihoods 3. Likelihood plots 4. Maximum likelihood 5. Likelihood ratio benchmarks

Outline. 1. Define likelihood 2. Interpretations of likelihoods 3. Likelihood plots 4. Maximum likelihood 5. Likelihood ratio benchmarks This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this

More information

Introduction to Probability. Ariel Yadin. Lecture 2

Introduction to Probability. Ariel Yadin. Lecture 2 Itroductio to Probability Ariel Yadi Lecture 2 1. Discrete Probability Spaces Discrete probability spaces are those for which the sample space is coutable. We have already see that i this case we ca take

More information

27 Binary Arithmetic: An Application to Programming

27 Binary Arithmetic: An Application to Programming 27 Binary Arithmetic: An Application to Programming In the previous section we looked at the binomial distribution. The binomial distribution is essentially the mathematics of repeatedly flipping a coin

More information

Probability (10A) Young Won Lim 6/12/17

Probability (10A) Young Won Lim 6/12/17 Probability (10A) Copyright (c) 2017 Young W. Lim. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later

More information

9/6/2016. Section 5.1 Probability. Equally Likely Model. The Division Rule: P(A)=#(A)/#(S) Some Popular Randomizers.

9/6/2016. Section 5.1 Probability. Equally Likely Model. The Division Rule: P(A)=#(A)/#(S) Some Popular Randomizers. Chapter 5: Probability and Discrete Probability Distribution Learn. Probability Binomial Distribution Poisson Distribution Some Popular Randomizers Rolling dice Spinning a wheel Flipping a coin Drawing

More information

Probability Distributions for Discrete RV

Probability Distributions for Discrete RV An example: Assume we toss a coin 3 times and record the outcomes. Let X i be a random variable defined by { 1, if the i th outcome is Head; X i = 0, if the i th outcome is Tail; Let X be the random variable

More information

PROBLEM SET 5 SOLUTIONS 126 = , 37 = , 15 = , 7 = 7 1.

PROBLEM SET 5 SOLUTIONS 126 = , 37 = , 15 = , 7 = 7 1. Math 7 Sprig 06 PROBLEM SET 5 SOLUTIONS Notatios. Give a real umber x, we will defie sequeces (a k ), (x k ), (p k ), (q k ) as i lecture.. (a) (5 pts) Fid the simple cotiued fractio represetatios of 6

More information

PRACTICE PROBLEMS FOR THE FINAL

PRACTICE PROBLEMS FOR THE FINAL PRACTICE PROBLEMS FOR THE FINAL Math 36Q Fall 25 Professor Hoh Below is a list of practice questios for the Fial Exam. I would suggest also goig over the practice problems ad exams for Exam ad Exam 2 to

More information

Discrete probability distributions

Discrete probability distributions Discrete probability distributios I the chapter o probability we used the classical method to calculate the probability of various values of a radom variable. I some cases, however, we may be able to develop

More information

1 Generating functions for balls in boxes

1 Generating functions for balls in boxes Math 566 Fall 05 Some otes o geeratig fuctios Give a sequece a 0, a, a,..., a,..., a geeratig fuctio some way of represetig the sequece as a fuctio. There are may ways to do this, with the most commo ways

More information

Section 5.1 The Basics of Counting

Section 5.1 The Basics of Counting 1 Sectio 5.1 The Basics of Coutig Combiatorics, the study of arragemets of objects, is a importat part of discrete mathematics. I this chapter, we will lear basic techiques of coutig which has a lot of

More information

Apply change-of-basis formula to rewrite x as a linear combination of eigenvectors v j.

Apply change-of-basis formula to rewrite x as a linear combination of eigenvectors v j. Eigevalue-Eigevector Istructor: Nam Su Wag eigemcd Ay vector i real Euclidea space of dimesio ca be uiquely epressed as a liear combiatio of liearly idepedet vectors (ie, basis) g j, j,,, α g α g α g α

More information

SDS 321: Introduction to Probability and Statistics

SDS 321: Introduction to Probability and Statistics SDS 321: Introduction to Probability and Statistics Lecture 2: Conditional probability Purnamrita Sarkar Department of Statistics and Data Science The University of Texas at Austin www.cs.cmu.edu/ psarkar/teaching

More information

The Random Walk For Dummies

The Random Walk For Dummies The Radom Walk For Dummies Richard A Mote Abstract We look at the priciples goverig the oe-dimesioal discrete radom walk First we review five basic cocepts of probability theory The we cosider the Beroulli

More information

Topic 1 2: Sequences and Series. A sequence is an ordered list of numbers, e.g. 1, 2, 4, 8, 16, or

Topic 1 2: Sequences and Series. A sequence is an ordered list of numbers, e.g. 1, 2, 4, 8, 16, or Topic : Sequeces ad Series A sequece is a ordered list of umbers, e.g.,,, 8, 6, or,,,.... A series is a sum of the terms of a sequece, e.g. + + + 8 + 6 + or... Sigma Notatio b The otatio f ( k) is shorthad

More information

3.2 Probability Rules

3.2 Probability Rules 3.2 Probability Rules The idea of probability rests on the fact that chance behavior is predictable in the long run. In the last section, we used simulation to imitate chance behavior. Do we always need

More information

The picture in figure 1.1 helps us to see that the area represents the distance traveled. Figure 1: Area represents distance travelled

The picture in figure 1.1 helps us to see that the area represents the distance traveled. Figure 1: Area represents distance travelled 1 Lecture : Area Area ad distace traveled Approximatig area by rectagles Summatio The area uder a parabola 1.1 Area ad distace Suppose we have the followig iformatio about the velocity of a particle, how

More information

What is Probability?

What is Probability? Quatificatio of ucertaity. What is Probability? Mathematical model for thigs that occur radomly. Radom ot haphazard, do t kow what will happe o ay oe experimet, but has a log ru order. The cocept of probability

More information

Permutations & Combinations. Dr Patrick Chan. Multiplication / Addition Principle Inclusion-Exclusion Principle Permutation / Combination

Permutations & Combinations. Dr Patrick Chan. Multiplication / Addition Principle Inclusion-Exclusion Principle Permutation / Combination Discrete Mathematic Chapter 3: C outig 3. The Basics of Coutig 3.3 Permutatios & Combiatios 3.5 Geeralized Permutatios & Combiatios 3.6 Geeratig Permutatios & Combiatios Dr Patrick Cha School of Computer

More information

Name of the Student:

Name of the Student: SUBJECT NAME : Discrete Mathematics SUBJECT CODE : MA 65 MATERIAL NAME : Part A questios MATERIAL CODE : JM08AM1013 REGULATION : R008 UPDATED ON : May-Jue 016 (Sca the above Q.R code for the direct dowload

More information

Estimation for Complete Data

Estimation for Complete Data Estimatio for Complete Data complete data: there is o loss of iformatio durig study. complete idividual complete data= grouped data A complete idividual data is the oe i which the complete iformatio of

More information

Homework 5 Solutions

Homework 5 Solutions Homework 5 Solutios p329 # 12 No. To estimate the chace you eed the expected value ad stadard error. To do get the expected value you eed the average of the box ad to get the stadard error you eed the

More information

Week 5-6: The Binomial Coefficients

Week 5-6: The Binomial Coefficients Wee 5-6: The Biomial Coefficiets March 6, 2018 1 Pascal Formula Theorem 11 (Pascal s Formula For itegers ad such that 1, ( ( ( 1 1 + 1 The umbers ( 2 ( 1 2 ( 2 are triagle umbers, that is, The petago umbers

More information

Permutations, Combinations, and the Binomial Theorem

Permutations, Combinations, and the Binomial Theorem Permutatios, ombiatios, ad the Biomial Theorem Sectio Permutatios outig methods are used to determie the umber of members of a specific set as well as outcomes of a evet. There are may differet ways to

More information

Sampling of Households Sampling Unit Different from Elementary Unit. Sampling of Households

Sampling of Households Sampling Unit Different from Elementary Unit. Sampling of Households Samplig of Households Samplig Uit Differet from Elemetary Uit Ratio Estimator (which acts like a mea) Samplig of Households Samplig Uit Differet from Elemetary Uit Elemetary Uits r = Variable Variable

More information

Probability and Statistics

Probability and Statistics robability ad Statistics rof. Zheg Zheg Radom Variable A fiite sigle valued fuctio.) that maps the set of all eperimetal outcomes ito the set of real umbers R is a r.v., if the set ) is a evet F ) for

More information

4. Basic probability theory

4. Basic probability theory Cotets Basic cocepts Discrete radom variables Discrete distributios (br distributios) Cotiuous radom variables Cotiuous distributios (time distributios) Other radom variables Lect04.ppt S-38.45 - Itroductio

More information

STAT Homework 1 - Solutions

STAT Homework 1 - Solutions STAT-36700 Homework 1 - Solutios Fall 018 September 11, 018 This cotais solutios for Homework 1. Please ote that we have icluded several additioal commets ad approaches to the problems to give you better

More information

Probability: Terminology and Examples Class 2, Jeremy Orloff and Jonathan Bloom

Probability: Terminology and Examples Class 2, Jeremy Orloff and Jonathan Bloom 1 Learning Goals Probability: Terminology and Examples Class 2, 18.05 Jeremy Orloff and Jonathan Bloom 1. Know the definitions of sample space, event and probability function. 2. Be able to organize a

More information

Lecture 3 - Axioms of Probability

Lecture 3 - Axioms of Probability Lecture 3 - Axioms of Probability Sta102 / BME102 January 25, 2016 Colin Rundel Axioms of Probability What does it mean to say that: The probability of flipping a coin and getting heads is 1/2? 3 What

More information

Chapter Unary Matrix Operations

Chapter Unary Matrix Operations Chapter 04.04 Uary atrix Operatios After readig this chapter, you should be able to:. kow what uary operatios meas, 2. fid the traspose of a square matrix ad it s relatioship to symmetric matrices,. fid

More information

Economics 250 Assignment 1 Suggested Answers. 1. We have the following data set on the lengths (in minutes) of a sample of long-distance phone calls

Economics 250 Assignment 1 Suggested Answers. 1. We have the following data set on the lengths (in minutes) of a sample of long-distance phone calls Ecoomics 250 Assigmet 1 Suggested Aswers 1. We have the followig data set o the legths (i miutes) of a sample of log-distace phoe calls 1 20 10 20 13 23 3 7 18 7 4 5 15 7 29 10 18 10 10 23 4 12 8 6 (1)

More information

Intermediate Math Circles November 8, 2017 Probability II

Intermediate Math Circles November 8, 2017 Probability II Intersection of Events and Independence Consider two groups of pairs of events Intermediate Math Circles November 8, 017 Probability II Group 1 (Dependent Events) A = {a sales associate has training} B

More information