Counting and Probability CMSC 250

Size: px
Start display at page:

Download "Counting and Probability CMSC 250"

Transcription

1 Coutig ad Probabilit CMSC 50 1

2 Coutig Coutig elemets i a list: how ma itegers i the list from 1 to 10? how ma itegers i the list from m to? assumig m CMSC 50

3 How Ma i a List? How ma positive three-digit itegers are there? this meas ol the oes that require digits or fewer digit umbers 99 or fewer , 101,, 999 previous slide hudreds digits, 10 tes digits, 10 uit digits How ma three-digit itegers are divisible b 5? 0 5, 1 5,, cout the itegers betwee 0 ad CMSC 50

4 The breakfast problem Bill eats Rice Krispies, Corflakes, Raisi Bra, or Cheerios. Bill driks coffee, orage juice, or milk. How differet tpes of breakfast ca Bill have? CMSC 50

5 The multiplicatio rule If the 1 st step of a operatio ca be performed 1 was Ad the d step ca be performed was Ad the k th step ca be performed k was The the operatio ca be performed 1 k was CMSC 50 5

6 Usig the multiplicatio rule for selectig a PIN Number of digit PINs of 0,1,,. with repetitio allowed 56 with o repetitio allowed 1 Etra rules :. the period ca t be first or last 0 ca t be first with repetitio allowed without repetitio allowed 1 first colum, the last colum, the middle two CMSC 50 6

7 Permutatios Number of was to arrage differet objects Pick first object was Pick secod object -1 was Pick third object - was Etc. Pick th object 1 wa -1-1! CMSC 50 7

8 r-permutatios Number of was to arrage r differet objects out of Pick first object was Pick secod object -1 was Pick third object - was Etc. Pick rth object -r1 was -1- -r1! r! CMSC 50 8

9 Combiatios Problem: Choose r objects out of order does ot matter. Solutio: First choose r objects out of order does matter. The divide b umber of orderigs of r objects. r! r! r! CMSC 50 9

10 Permutatios with Idistiguishable Items I Eample: Assume ou have a set of 15 beads: 6 gree orage red black How ma permutatios? Select positios of the gree oes, the the orage oes, the the red oes, the the black oes ! 6!!!! CMSC 50 10

11 Permutatios with Idistiguishable Items II Eample: Assume ou have a set of 15 beads: 6 gree orage red black How ma permutatios? Take all permutatios. Divide b the umber of permutatios of the gree oes, the the orage oes, the the red oes, the the black oes. 15! 6!!!! CMSC 50 11

12 Permutatios with Idistiguishable Items Eample: Permutatios of revere 6!!! CMSC 50 1

13 Combiatios with repetitio How ma combiatios of 0 A's, B's, ad C's ca be made with ulimited repetitio allowed? Eamples: 10 A s, 7 B s, C s; 0 A s, 0 B s, 0 C s; 1 A s, 0 B s, 6 C s. Reformulate as how ma oegative solutios to CMSC

14 Geeralize The umber of oegative iteger solutios of the equatio r 1 The umber of selectios, with repetitio, of size r from a collectio of size. The umber of was r idetical objects ca be distributed amog distict cotaiers. Solve i class CMSC 50 1

15 Choosig r elemets out of elemets repetitio allowed repetitio ot allowed order matters r times! P, r r! r order does t matter r r 1 r! r! r! CMSC 50 15

16 Where the multiplicatio rule does t work People {Alice, Bob, Carol, Da} Need to be appoited as presidet, vice-presidet, ad treasurer, ad obod ca hold more tha oe office how ma was ca it be doe with o restrictios? how ma was ca it be doe if Alice does t wat to be presidet? how ma was ca it be doe if Alice does t wat to be presidet, ad ol Bob ad Da are willig to be vicepresidet? CMSC 50 16

17 Harder eamples of selectig represetatives Cadidates {Azar, Barack, Clito, Da, Eri, Fred} 1. Select two, with o restrictios. Select two, assumig that Azar ad Da must sta together. Select three, with o restrictios. Select three, assumig that Azar ad Da must sta together 5. Select three, assumig that Barack ad Clito refuse to serve together CMSC 50 17

18 Properties of combiatios ad their proofs r r CMSC 50 18

19 How ma subsets are there of {1,,, }? Solutio I: 1 i or out, i or out,, i or out: Hece Solutio II: Hece CMSC 50 Ca CHOOSE set with 0 elemets, or 1 elemet, or, or elemets: Hece i 0 A Combiatorial Idetit i0 i i 19

20 0 CMSC 50 The biomial theorem i i i i 0

21 Differet tpes of members {Alice, Bob, Carol, Da, Eri, Fred, George, Harr} Suppose Alice, Carol, ad Eri are MATH majors, ad the rest are CS majors. 8 people i the set: MATHs & 5 CSs make a 5-member team of MATHs ad CSs make a 5-member team that has ol oe MATH make a 5-member team that has o MATHs make a 5-member team that has at least oe MATH CMSC 50 1

22 Probabilit The likelihood of a specific evet. Sample space set of all possible outcomes Evet subset of sample space Equal probabilit formula: give a fiite sample space S where all outcomes are equall likel select a evet E from the sample space S the probabilit of evet E from sample space S: P E E S CMSC 50

23 Eamples of Sample Spaces Two cois sample space {H,H, H,T, T,H, T,T} Cards values:,,,5,6,7,8,9,10,j,q,k,a suits: D, H, S, C Dice sample space {1,1,1,,1,,1,,1,5,1,6,,1,,,,,,,,5,,6, 6,1,6,,6,,6,,6,5,6,6} CMSC 50

24 Probabilities with PINs Number of four letter PINs of {a,b,c,d} with repetitio allowed 56 with o repetitio allowed 1 What is the probabilit that our digit PIN has o repeated digits? What is the probabilit that our digit PIN does have repeated digits? Tree method: 1 CMSC 50

25 Straight Flush Four of a kid Full house Flush Straight Three of a kid Two pairs Pair Nothig Probabilit of Poker Hads Solve i class CMSC 50 5

26 Multi-level probabilit If a coi is tossed oce, the probabilit of head ½ If it s tossed 5 times the probabilit of all heads: the probabilit of eactl heads: This is because the coi tosses are all idepedet evets CMSC 50 6

27 Touramet pla Team A ad Team B compete i a best of touramet The each have a equal likelihood of wiig each game Do the leaves add up to 1? Do the alwas have to pla games? What's the probabilit the touramet fiishes i games? Do A ad B have a equal chace of wiig? CMSC 50 7

28 What if A wis each game with prob /? Each lie for A must have a / Each lie for B must have a 1/ CMSC 50 How likel is A to wi the touramet? How likel is B to wi the touramet? What is the probabilit the touramet fiishes i two games? 8

8. Combinatorial Structures

8. Combinatorial Structures Virtual Laboratories > 0. Foudatios > 1 2 3 4 5 6 7 8 9 8. Combiatorial Structures The purpose of this sectio is to study several combiatorial structures that are of basic importace i probability. Permutatios

More information

}, how many different strings of length n 1 exist? }, how many different strings of length n 2 exist that contain at least one a 1

}, how many different strings of length n 1 exist? }, how many different strings of length n 2 exist that contain at least one a 1 1. [5] Give sets A ad B, each of cardiality 1, how may fuctios map A i a oe-tooe fashio oto B? 2. [5] a. Give the set of r symbols { a 1, a 2,..., a r }, how may differet strigs of legth 1 exist? [5]b.

More information

We often find the probability of an event by counting the number of elements in a simple sample space.

We often find the probability of an event by counting the number of elements in a simple sample space. outig Methods We ofte fid the probability of a evet by coutig the umber of elemets i a simple sample space. Basic methods of coutig are: Permutatios ombiatios Permutatio A arragemet of objects i a defiite

More information

Combinatorics. Chapter Permutations. Reading questions. Counting Problems. Counting Technique: The Product Rule

Combinatorics. Chapter Permutations. Reading questions. Counting Problems. Counting Technique: The Product Rule Chapter 3 Combiatorics 3.1 Permutatios Readig questios 1. Defie what a permutatio is i your ow words. 2. What is a fixed poit i a permutatio? 3. What do we assume about mutual disjoitedess whe creatig

More information

PERMUTATION AND COMBINATION

PERMUTATION AND COMBINATION MPC 1 PERMUTATION AND COMBINATION Syllabus : Fudametal priciples of coutig; Permutatio as a arragemet ad combiatio as selectio, Meaig of P(, r) ad C(, r). Simple applicatios. Permutatios are arragemets

More information

PERMUTATIONS AND COMBINATIONS

PERMUTATIONS AND COMBINATIONS www.sakshieducatio.com PERMUTATIONS AND COMBINATIONS OBJECTIVE PROBLEMS. There are parcels ad 5 post-offices. I how may differet ways the registratio of parcel ca be made 5 (a) 0 (b) 5 (c) 5 (d) 5. I how

More information

CS3203 #5. 6/9/04 Janak J Parekh

CS3203 #5. 6/9/04 Janak J Parekh CS3203 #5 6/9/04 Jaak J Parekh Admiistrivia Exam o Moday All slides should be up We ll try ad have solutios for HWs #1 ad #2 out by Friday I kow the HW is due o the same day; ot much I ca do, uless you

More information

AMC AMS AMR ACS ACR ASR MSR MCR MCS CRS

AMC AMS AMR ACS ACR ASR MSR MCR MCS CRS Sectio 6.5: Combiatios Example Recall our five frieds, Ala, Cassie, Maggie, Seth ad Roger from the example at the begiig of the previous sectio. The have wo tickets for a cocert i Chicago ad everybody

More information

AS Exercise A: The multiplication principle. Probability using permutations and combinations. Multiplication principle. Example.

AS Exercise A: The multiplication principle. Probability using permutations and combinations. Multiplication principle. Example. Probability usig permutatios ad combiatios Multiplicatio priciple If A ca be doe i ways, ad B ca be doe i m ways, the A followed by B ca be doe i m ways. 1. A die ad a coi are throw together. How may results

More information

AMC AMS AMR ACS ACR ASR MSR MCR MCS CRS

AMC AMS AMR ACS ACR ASR MSR MCR MCS CRS Sectio 6.5: Combiatios Example Recall our five frieds, Ala, Cassie, Maggie, Seth ad Roger from the example at the begiig of the previous sectio. The have wo tickets for a cocert i Chicago ad everybody

More information

1. How many possible ways are there to form five-letter words using only the letters A H? How many such words consist of five distinct letters?

1. How many possible ways are there to form five-letter words using only the letters A H? How many such words consist of five distinct letters? COMBINATORICS EXERCISES Stepha Wager 1. How may possible ways are there to form five-letter words usig oly the letters A H? How may such words cosist of five distict letters? 2. How may differet umber

More information

COMBINATORICS 2. Recall, in the previous lesson, we looked at Taxicabs machines, which always took the shortest path home

COMBINATORICS 2. Recall, in the previous lesson, we looked at Taxicabs machines, which always took the shortest path home COMBINATORICS BEGINNER CIRCLE 1/0/013 1. ADVANCE TAXICABS Recall, i the previous lesso, we looked at Taxicabs machies, which always took the shortest path home taxipath We couted the umber of ways that

More information

Permutation Enumeration

Permutation Enumeration RMT 2012 Power Roud Rubric February 18, 2012 Permutatio Eumeratio 1 (a List all permutatios of {1, 2, 3} (b Give a expressio for the umber of permutatios of {1, 2, 3,, } i terms of Compute the umber for

More information

PERMUTATIONS AND COMBINATIONS

PERMUTATIONS AND COMBINATIONS Chapter 7 PERMUTATIONS AND COMBINATIONS Every body of discovery is mathematical i form because there is o other guidace we ca have DARWIN 7.1 Itroductio Suppose you have a suitcase with a umber lock. The

More information

COLLEGE ALGEBRA LECTURES Copyrights and Author: Kevin Pinegar

COLLEGE ALGEBRA LECTURES Copyrights and Author: Kevin Pinegar OLLEGE ALGEBRA LETURES opyrights ad Author: Kevi iegar 8.3 Advaced outig Techiques: ermutatios Ad ombiatios Factorial Notatio Before we ca discuss permutatio ad combiatio formulas we must itroduce factorial

More information

Unit 5: Estimating with Confidence

Unit 5: Estimating with Confidence Uit 5: Estimatig with Cofidece Sectio 8.2 The Practice of Statistics, 4 th editio For AP* STARNES, YATES, MOORE Uit 5 Estimatig with Cofidece 8.1 8.2 8.3 Cofidece Itervals: The Basics Estimatig a Populatio

More information

7. Counting Measure. Definitions and Basic Properties

7. Counting Measure. Definitions and Basic Properties Virtual Laboratories > 0. Foudatios > 1 2 3 4 5 6 7 8 9 7. Coutig Measure Defiitios ad Basic Properties Suppose that S is a fiite set. If A S the the cardiality of A is the umber of elemets i A, ad is

More information

Counting III. Today we ll briefly review some facts you dervied in recitation on Friday and then turn to some applications of counting.

Counting III. Today we ll briefly review some facts you dervied in recitation on Friday and then turn to some applications of counting. 6.04/18.06J Mathematics for Computer Sciece April 5, 005 Srii Devadas ad Eric Lehma Lecture Notes Coutig III Today we ll briefly review some facts you dervied i recitatio o Friday ad the tur to some applicatios

More information

Grade 6 Math Review Unit 3(Chapter 1) Answer Key

Grade 6 Math Review Unit 3(Chapter 1) Answer Key Grade 6 Math Review Uit (Chapter 1) Aswer Key 1. A) A pottery makig class charges a registratio fee of $25.00. For each item of pottery you make you pay a additioal $5.00. Write a expressio to represet

More information

2. There are n letter and n addressed envelopes. The probability that all the letters are not kept in the right envelope, is. (c)

2. There are n letter and n addressed envelopes. The probability that all the letters are not kept in the right envelope, is. (c) PAGE # CHAPTER EXERCISE I. A sigle letter is selected at radom from the word PROBABILITY. The probability that the selected letter is a vowel is / / / 0. There are letter ad addressed evelopes. The probability

More information

Discrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 12

Discrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 12 EECS 70 Discrete Mathematics ad Probability Theory Sprig 204 Aat Sahai Note 2 Probability Examples Based o Coutig We will ow look at examples of radom experimets ad their correspodig sample spaces, alog

More information

Ch 9 Sequences, Series, and Probability

Ch 9 Sequences, Series, and Probability Ch 9 Sequeces, Series, ad Probability Have you ever bee to a casio ad played blackjack? It is the oly game i the casio that you ca wi based o the Law of large umbers. I the early 1990s a group of math

More information

Counting on r-fibonacci Numbers

Counting on r-fibonacci Numbers Claremot Colleges Scholarship @ Claremot All HMC Faculty Publicatios ad Research HMC Faculty Scholarship 5-1-2015 Coutig o r-fiboacci Numbers Arthur Bejami Harvey Mudd College Curtis Heberle Harvey Mudd

More information

Chapter (6) Discrete Probability Distributions Examples

Chapter (6) Discrete Probability Distributions Examples hapter () Discrete robability Distributios Eamples Eample () Two balaced dice are rolled. Let X be the sum of the two dice. Obtai the probability distributio of X. Solutio Whe the two balaced dice are

More information

lecture notes September 2, Sequential Choice

lecture notes September 2, Sequential Choice 18.310 lecture otes September 2, 2013 Sequetial Choice Lecturer: Michel Goemas 1 A game Cosider the followig game. I have 100 blak cards. I write dow 100 differet umbers o the cards; I ca choose ay umbers

More information

Logarithms APPENDIX IV. 265 Appendix

Logarithms APPENDIX IV. 265 Appendix APPENDIX IV Logarithms Sometimes, a umerical expressio may ivolve multiplicatio, divisio or ratioal powers of large umbers. For such calculatios, logarithms are very useful. They help us i makig difficult

More information

THE LUCAS TRIANGLE RECOUNTED. Arthur T. Benjamin Dept. of Mathematics, Harvey Mudd College, Claremont, CA Introduction

THE LUCAS TRIANGLE RECOUNTED. Arthur T. Benjamin Dept. of Mathematics, Harvey Mudd College, Claremont, CA Introduction THE LUCAS TRIANLE RECOUNTED Arthur T Bejami Dept of Mathematics, Harvey Mudd College, Claremot, CA 91711 bejami@hmcedu 1 Itroductio I 2], Neville Robbis explores may properties of the Lucas triagle, a

More information

CS 201: Adversary arguments. This handout presents two lower bounds for selection problems using adversary arguments ëknu73,

CS 201: Adversary arguments. This handout presents two lower bounds for selection problems using adversary arguments ëknu73, CS 01 Schlag Jauary 6, 1999 Witer `99 CS 01: Adversary argumets This hadout presets two lower bouds for selectio problems usig adversary argumets ëku73, HS78, FG76ë. I these proofs a imagiary adversary

More information

Math 140 Introductory Statistics

Math 140 Introductory Statistics 6. Probability Distributio from Data Math Itroductory Statistics Professor Silvia Ferádez Chapter 6 Based o the book Statistics i Actio by A. Watkis, R. Scheaffer, ad G. Cobb. We have three ways of specifyig

More information

Combinatorics. ChaPTer a The addition and multiplication principles introduction. The addition principle

Combinatorics. ChaPTer a The addition and multiplication principles introduction. The addition principle ChaPTer Combiatorics ChaPTer CoTeTS a The additio ad multiplicatio priciples b Permutatios C Factorials D Permutatios usig P r e Permutatios ivolvig restrictios F Arragemets i a circle G Combiatios usig

More information

x 1 + x x n n = x 1 x 2 + x x n n = x 2 x 3 + x x n n = x 3 x 5 + x x n = x n

x 1 + x x n n = x 1 x 2 + x x n n = x 2 x 3 + x x n n = x 3 x 5 + x x n = x n Sectio 6 7A Samplig Distributio of the Sample Meas To Create a Samplig Distributio of the Sample Meas take every possible sample of size from the distributio of x values ad the fid the mea of each sample

More information

Combinatorics and probability

Combinatorics and probability Departmet of Mathematics Ma 3/03 KC Border Itroductio to Probability ad Statistics Witer 208 Lecture 3: Combiatorics ad probability Relevat textboo passages: Pitma [2]: Sectios.5.6, pp. 7 77; Appedix,

More information

CCD Image Processing: Issues & Solutions

CCD Image Processing: Issues & Solutions CCD Image Processig: Issues & Solutios Correctio of Raw Image with Bias, Dark, Flat Images Raw File r x, y [ ] Dark Frame d[ x, y] Flat Field Image f [ xy, ] r[ x, y] d[ x, y] Raw Dark f [ xy, ] bxy [,

More information

Summary of Random Variable Concepts April 19, 2000

Summary of Random Variable Concepts April 19, 2000 Summary of Radom Variable Cocepts April 9, 2000 his is a list of importat cocepts we have covered, rather tha a review that derives or explais them. he first ad primary viewpoit: A radom process is a idexed

More information

Data Mining the Online Encyclopedia of Integer Sequences for New Identities Hieu Nguyen

Data Mining the Online Encyclopedia of Integer Sequences for New Identities Hieu Nguyen Slide 1 of 18 Data Miig the Olie Ecyclopedia of Iteger Sequeces for New Idetities Hieu Nguye Rowa Uiversity MAA-NJ Sectio Sprig Meetig March 31, 2012 2 MAA-NJ Sprig Meetig Data Miig OEIS.b ü Ackowledgemets

More information

Probability II. Overview. A Closer Look at Events The Probability of an Event. Dr Tom Ilvento Department of Food and Resource Economics

Probability II. Overview. A Closer Look at Events The Probability of an Event. Dr Tom Ilvento Department of Food and Resource Economics Oveview Pobability II D Tom Ilveto Depatmet of Food ad Resouce Ecoomics We will cotiue ou jouey though pobability This ivolves Moe tems!!! Defiig Evets Ways to lay out the sample space I will also give

More information

Extra Practice 1. Name Date. Lesson 1.1: Patterns in Division

Extra Practice 1. Name Date. Lesson 1.1: Patterns in Division Master 1.22 Extra Practice 1 Lesso 1.1: Patters i Divisio 1. Which umbers are divisible by 4? By 5? How do you kow? a) 90 b) 134 c) 395 d) 1724 e) 30 f) 560 g) 3015 h) 74 i) 748 2. Write a 5-digit umber

More information

Procedia - Social and Behavioral Sciences 128 ( 2014 ) EPC-TKS 2013

Procedia - Social and Behavioral Sciences 128 ( 2014 ) EPC-TKS 2013 Available olie at www.sciecedirect.com ScieceDirect Procedia - Social ad Behavioral Scieces 18 ( 014 ) 399 405 EPC-TKS 013 Iductive derivatio of formulae by a computer Sava Grozdev a *, Veseli Nekov b

More information

Application of Improved Genetic Algorithm to Two-side Assembly Line Balancing

Application of Improved Genetic Algorithm to Two-side Assembly Line Balancing 206 3 rd Iteratioal Coferece o Mechaical, Idustrial, ad Maufacturig Egieerig (MIME 206) ISBN: 978--60595-33-7 Applicatio of Improved Geetic Algorithm to Two-side Assembly Lie Balacig Ximi Zhag, Qia Wag,

More information

On the Number of Permutations on n Objects with. greatest cycle length

On the Number of Permutations on n Objects with. greatest cycle length Ž. ADVANCES IN APPLIED MATHEMATICS 0, 9807 998 ARTICLE NO. AM970567 O the Number of Permutatios o Obects with Greatest Cycle Legth k Solomo W. Golomb ad Peter Gaal Commuicatio Scieces Istitute, Uiersity

More information

Making sure metrics are meaningful

Making sure metrics are meaningful Makig sure metrics are meaigful Some thigs are quatifiable, but ot very useful CPU performace: MHz is ot the same as performace Cameras: Mega-Pixels is ot the same as quality Cosistet ad quatifiable metrics

More information

x y z HD(x, y) + HD(y, z) HD(x, z)

x y z HD(x, y) + HD(y, z) HD(x, z) Massachusetts Istitute of Techology Departmet of Electrical Egieerig ad Computer Sciece 6.02 Solutios to Chapter 5 Updated: February 16, 2012 Please sed iformatio about errors or omissios to hari; questios

More information

General Model :Algorithms in the Real World. Applications. Block Codes

General Model :Algorithms in the Real World. Applications. Block Codes Geeral Model 5-853:Algorithms i the Real World Error Correctig Codes I Overview Hammig Codes Liear Codes 5-853 Page message (m) coder codeword (c) oisy chael decoder codeword (c ) message or error Errors

More information

Optimal Arrangement of Buoys Observable by Means of Radar

Optimal Arrangement of Buoys Observable by Means of Radar Optimal Arragemet of Buoys Observable by Meas of Radar TOMASZ PRACZYK Istitute of Naval Weapo ad Computer Sciece Polish Naval Academy Śmidowicza 69, 8-03 Gdyia POLAND t.praczy@amw.gdyia.pl Abstract: -

More information

HIGHER SECONDARY FIRST YEAR MATHEMATICS. ALGEBRA Creative Questions Time : 1.15 Hrs Marks : 45 Part - I Choose the correct answer 10 1 = 10.

HIGHER SECONDARY FIRST YEAR MATHEMATICS. ALGEBRA Creative Questions Time : 1.15 Hrs Marks : 45 Part - I Choose the correct answer 10 1 = 10. www.tbtpsc.com HIGHER SEONDARY FIRST YEAR MATHEMATIS ALGEBRA eative Questios Time :. Hs Maks : Pat - I hoose the coect aswe =. The co-efficiet of middle tem i the epasio of is a) b)...( )! c).6,...( )

More information

Discrete Random Variables: Joint PMFs, Conditioning and Independence

Discrete Random Variables: Joint PMFs, Conditioning and Independence Discrete Radom Variables: Joit MFs Coditioig ad Ideedece Berli Che Deartmet of Comuter Sciece & Iformatio gieerig Natioal Taiwa Normal Uiversit Referece: - D.. Bertsekas J. N. Tsitsiklis Itroductio to

More information

APPLICATION NOTE UNDERSTANDING EFFECTIVE BITS

APPLICATION NOTE UNDERSTANDING EFFECTIVE BITS APPLICATION NOTE AN95091 INTRODUCTION UNDERSTANDING EFFECTIVE BITS Toy Girard, Sigatec, Desig ad Applicatios Egieer Oe criteria ofte used to evaluate a Aalog to Digital Coverter (ADC) or data acquisitio

More information

A generalization of Eulerian numbers via rook placements

A generalization of Eulerian numbers via rook placements A geeralizatio of Euleria umbers via rook placemets Esther Baaia Steve Butler Christopher Cox Jeffrey Davis Jacob Ladgraf Scarlitte Poce Abstract We cosider a geeralizatio of Euleria umbers which cout

More information

Lesson 5: Identifying Proportional and Non-Proportional Relationships in Graphs

Lesson 5: Identifying Proportional and Non-Proportional Relationships in Graphs NYS COMMON CORE MATHEMATICS CURRICULUM Lesson Lesson : Identifing Proportional and Non-Proportional Relationships in Graphs Student Outcomes Students decide whether two quantities are proportional to each

More information

BOUNDS FOR OUT DEGREE EQUITABLE DOMINATION NUMBERS IN GRAPHS

BOUNDS FOR OUT DEGREE EQUITABLE DOMINATION NUMBERS IN GRAPHS BULLETIN OF THE INTERNATIONAL MATHEMATICAL VIRTUAL INSTITUTE ISSN 2303-4874 (p), ISSN (o) 2303-4955 www.imvibl.org/bulletin Vol. 3(2013), 149-154 Former BULLETIN OF THE SOCIETY OF MATHEMATICIANS BANJA

More information

(2) The MOSFET. Review of. Learning Outcome. (Metal-Oxide-Semiconductor Field Effect Transistor) 2.0) Field Effect Transistor (FET)

(2) The MOSFET. Review of. Learning Outcome. (Metal-Oxide-Semiconductor Field Effect Transistor) 2.0) Field Effect Transistor (FET) EEEB73 Electroics Aalysis & esig II () Review of The MOSFET (Metal-Oxide-Semicoductor Field Effect Trasistor) Referece: Neame, Chapter 3 ad Chapter 4 Learig Outcome Able to describe ad use the followig:

More information

Shuffling Cards. D.J.W. Telkamp. Utrecht University Mathematics Bachelor s Thesis. Supervised by Dr. K. Dajani

Shuffling Cards. D.J.W. Telkamp. Utrecht University Mathematics Bachelor s Thesis. Supervised by Dr. K. Dajani Shufflig Cards Utrecht Uiversity Mathematics Bachelor s Thesis D.J.W. Telkamp Supervised by Dr. K. Dajai Jue 3, 207 Cotets Itroductio 2 2 Prerequisites 2 2. Problems with the variatio distace................

More information

X-Bar and S-Squared Charts

X-Bar and S-Squared Charts STATGRAPHICS Rev. 7/4/009 X-Bar ad S-Squared Charts Summary The X-Bar ad S-Squared Charts procedure creates cotrol charts for a sigle umeric variable where the data have bee collected i subgroups. It creates

More information

arxiv: v2 [math.co] 15 Oct 2018

arxiv: v2 [math.co] 15 Oct 2018 THE 21 CARD TRICK AND IT GENERALIZATION DIBYAJYOTI DEB arxiv:1809.04072v2 [math.co] 15 Oct 2018 Abstract. The 21 card trick is well kow. It was recetly show i a episode of the popular YouTube chael Numberphile.

More information

Final exam PS 30 December 2009

Final exam PS 30 December 2009 Fial exam PS 30 December 2009 Name: UID: TA ad sectio umber: This is a closed book exam. The oly thig you ca take ito this exam is yourself ad writig istrumets. Everythig you write should be your ow work.

More information

20. CONFIDENCE INTERVALS FOR THE MEAN, UNKNOWN VARIANCE

20. CONFIDENCE INTERVALS FOR THE MEAN, UNKNOWN VARIANCE 20. CONFIDENCE INTERVALS FOR THE MEAN, UNKNOWN VARIANCE If the populatio tadard deviatio σ i ukow, a it uually will be i practice, we will have to etimate it by the ample tadard deviatio. Sice σ i ukow,

More information

Spread Spectrum Signal for Digital Communications

Spread Spectrum Signal for Digital Communications Wireless Iformatio Trasmissio System Lab. Spread Spectrum Sigal for Digital Commuicatios Istitute of Commuicatios Egieerig Natioal Su Yat-se Uiversity Spread Spectrum Commuicatios Defiitio: The trasmitted

More information

H2 Mathematics Pure Mathematics Section A Comprehensive Checklist of Concepts and Skills by Mr Wee Wen Shih. Visit: wenshih.wordpress.

H2 Mathematics Pure Mathematics Section A Comprehensive Checklist of Concepts and Skills by Mr Wee Wen Shih. Visit: wenshih.wordpress. H2 Mathematics Pure Mathematics Sectio A Comprehesive Checklist of Cocepts ad Skills by Mr Wee We Shih Visit: weshih.wordpress.com Updated: Ja 2010 Syllabus topic 1: Fuctios ad graphs 1.1 Checklist o Fuctios

More information

Chapter 2: Probability

Chapter 2: Probability hapter : roaility A {FF}, B {MM}, {MF, FM, MM} The, A B 0/, B {MM}, B {MF, FM}, A B {FF,MM}, A, B a A B A B c A B d A B A B 4 a 8 hapter : roaility 9 5 a A B A B A B B A A B A B B A B B B A A c A B A B

More information

High Speed Area Efficient Modulo 2 1

High Speed Area Efficient Modulo 2 1 High Speed Area Efficiet Modulo 2 1 1-Soali Sigh (PG Scholar VLSI, RKDF Ist Bhopal M.P) 2- Mr. Maish Trivedi (HOD EC Departmet, RKDF Ist Bhopal M.P) Adder Abstract Modular adder is oe of the key compoets

More information

Week 1: Probability models and counting

Week 1: Probability models and counting Week 1: Probability models and counting Part 1: Probability model Probability theory is the mathematical toolbox to describe phenomena or experiments where randomness occur. To have a probability model

More information

A Math Learning Center publication adapted and arranged by. EUGENE MAIER and LARRY LINNEN

A Math Learning Center publication adapted and arranged by. EUGENE MAIER and LARRY LINNEN A Math Learig Ceter publicatio adapted ad arraged by EUGENE MAIER ad LARRY LINNEN ALGEBRA THROUGH VISUAL PATTERNS, VOLUME 1 A Math Learig Ceter Resource Copyright 2005, 2004 by The Math Learig Ceter, PO

More information

ECONOMIC LOT SCHEDULING

ECONOMIC LOT SCHEDULING ECONOMIC LOT SCHEDULING JS, FFS ad ELS Job Shop (JS) - Each ob ca be differet from others - Make to order, low volume - Each ob has its ow sequece Fleible Flow Shop (FFS) - Limited umber of product types

More information

POWERS OF 3RD ORDER MAGIC SQUARES

POWERS OF 3RD ORDER MAGIC SQUARES Fuzzy Sets, Rough Sets ad Multivalued Operatios ad Applicatios, Vol. 4, No. 1, (Jauary-Jue 01): 37 43 Iteratioal Sciece Press POWERS OF 3RD ORDER MAGIC SQUARES Sreerajii K.S. 1 ad V. Madhukar Mallayya

More information

Using Tables of Equivalent Ratios

Using Tables of Equivalent Ratios LESSON Using Tables of Equivalent Ratios A table can be used to show the relationship between two quantities. You can use equivalent ratios to find a missing value in a table. EXAMPLE A The table shows

More information

Technical Explanation for Counters

Technical Explanation for Counters Techical Explaatio for ers CSM_er_TG_E Itroductio What Is a er? A er is a device that couts the umber of objects or the umber of operatios. It is called a er because it couts the umber of ON/OFF sigals

More information

On Parity based Divide and Conquer Recursive Functions

On Parity based Divide and Conquer Recursive Functions O Parity based Divide ad Coquer Recursive Fuctios Sug-Hyu Cha Abstract The parity based divide ad coquer recursio trees are itroduced where the sizes of the tree do ot grow mootoically as grows. These

More information

13 Legislative Bargaining

13 Legislative Bargaining 1 Legislative Bargaiig Oe of the most popular legislative models is a model due to Baro & Ferejoh (1989). The model has bee used i applicatios where the role of committees have bee studies, how the legislative

More information

ELEC 204 Digital Systems Design

ELEC 204 Digital Systems Design Fall 2013, Koç Uiversity ELEC 204 Digital Systems Desig Egi Erzi College of Egieerig Koç Uiversity,Istabul,Turkey eerzi@ku.edu.tr KU College of Egieerig Elec 204: Digital Systems Desig 1 Today: Datapaths

More information

Table Of Contents Blues Turnarounds

Table Of Contents Blues Turnarounds Table Of Cotets Blues Turarouds Turaroud #1 Turaroud # Turaroud # Turaroud # Turaroud # Turaroud # Turaroud # Turaroud # Turaroud # Blues Turarouds Blues Soloig Masterclass Week 1 Steve Stie A Blues Turaroud

More information

The Institute of Chartered Accountants of Sri Lanka

The Institute of Chartered Accountants of Sri Lanka The Istitute of Chartered Accoutats of Sri Laka Postgraduate Diploma i Busiess ad Fiace Quatitative Techiques for Busiess Hadout 02:Presetatio ad Aalysis of data Presetatio of Data The Stem ad Leaf Display

More information

Sampling Distribution Theory

Sampling Distribution Theory Poulatio ad amle: amlig Distributio Theory. A oulatio is a well-defied grou of idividuals whose characteristics are to be studied. Poulatios may be fiite or ifiite. (a) Fiite Poulatio: A oulatio is said

More information

MDM 4U MATH OF DATA MANAGEMENT FINAL EXAMINATION

MDM 4U MATH OF DATA MANAGEMENT FINAL EXAMINATION Caadia Iteratioal Matriculatio rogramme Suway Uiversity College MDM 4U MTH OF DT MNGEMENT FINL EXMINTION Date: November 28 th, 2006 Time: 11.30a.m 1.30p.m Legth: 2 HOURS Lecturers: lease circle your teacher

More information

PHY-MAC dialogue with Multi-Packet Reception

PHY-MAC dialogue with Multi-Packet Reception PHY-AC dialogue with ulti-packet Receptio arc Realp 1 ad Aa I. Pérez-Neira 1 CTTC-Cetre Tecològic de Telecomuicacios de Cataluya Edifici Nexus C/Gra Capità, - 0803-Barceloa (Cataluya-Spai) marc.realp@cttc.es

More information

[MT93a, ALS94, Nie95, MNR95]. All these algorithms exploit kow characterizatios of extesios of default theories i terms of sets of geeratig defaults,

[MT93a, ALS94, Nie95, MNR95]. All these algorithms exploit kow characterizatios of extesios of default theories i terms of sets of geeratig defaults, Miimal umber of permutatios suciet to compute all extesios a ite default theory Pawe l Cholewiski ad Miros law Truszczyski Departmet of Computer Sciece Uiversity of Ketucky Lexigto, KY 40506-0046 pawel

More information

Finite Math - Fall 2016

Finite Math - Fall 2016 Finite Math - Fall 206 Lecture Notes - /28/206 Section 7.4 - Permutations and Combinations There are often situations in which we have to multiply many consecutive numbers together, for example, in examples

More information

Math 7 Flipped Mastery Self Tester Worksheet Name: Class:. Chapter 1 (Unit 1) Patterns and Relationships - Accommodated 1.1 Patterns In Division /36

Math 7 Flipped Mastery Self Tester Worksheet Name: Class:. Chapter 1 (Unit 1) Patterns and Relationships - Accommodated 1.1 Patterns In Division /36 Chapter 1 (Uit 1) Patters ad Relatioships - Accommodated 1.1 Patters I Divisio /36 Divisibility Rule Cheats; A whole umber is divisible by 2 if it is a eve umber A whole umber is divisible by 4 if the

More information

T able 5.1 E xample of a S olid B eef Manur e T es t R epor t. Lab Units 1 % and ppm. Mois tur e Content Total N

T able 5.1 E xample of a S olid B eef Manur e T es t R epor t. Lab Units 1 % and ppm. Mois tur e Content Total N 24 5 Uderstadig the Soil Test & Maure Test Reports 5.1 The Soil Test Report ad the Fertilizer Recommedatio The purpose of soil testig is to measure the amout of available utriets i the soil i order to

More information

THE CONTEST CORNER No. 65 John McLoughlin

THE CONTEST CORNER No. 65 John McLoughlin THE CONTEST CORNER /185 THE CONTEST CORNER No. 65 Joh McLoughli The problems featured i this sectio have appeared i, or have bee ispired by, a mathematics cotest questio at either the high school or the

More information

The topic for the third and final major portion of the course is Probability. We will aim to make sense of statements such as the following:

The topic for the third and final major portion of the course is Probability. We will aim to make sense of statements such as the following: CS 70 Discrete Mathematics for CS Spring 2006 Vazirani Lecture 17 Introduction to Probability The topic for the third and final major portion of the course is Probability. We will aim to make sense of

More information

A New Design of Log-Periodic Dipole Array (LPDA) Antenna

A New Design of Log-Periodic Dipole Array (LPDA) Antenna Joural of Commuicatio Egieerig, Vol., No., Ja.-Jue 0 67 A New Desig of Log-Periodic Dipole Array (LPDA) Atea Javad Ghalibafa, Seyed Mohammad Hashemi, ad Seyed Hassa Sedighy Departmet of Electrical Egieerig,

More information

I. WHAT IS PROBABILITY?

I. WHAT IS PROBABILITY? C HAPTER 3 PROAILITY Random Experiments I. WHAT IS PROAILITY? The weatherman on 10 o clock news program states that there is a 20% chance that it will snow tomorrow, a 65% chance that it will rain and

More information

Methods to Reduce Arc-Flash Hazards

Methods to Reduce Arc-Flash Hazards Methods to Reduce Arc-Flash Hazards Exercise: Implemetig Istataeous Settigs for a Maiteace Mode Scheme Below is a oe-lie diagram of a substatio with a mai ad two feeders. Because there is virtually o differece

More information

COS 126 Atomic Theory of Matter

COS 126 Atomic Theory of Matter COS 126 Atomic Theory of Matter 1 Goal of the Assigmet Video Calculate Avogadro s umber Usig Eistei s equatios Usig fluorescet imagig Iput data Output Frames Blobs/Beads Estimate of Avogadro s umber 7.1833

More information

ECON 214 Elements of Statistics for Economists

ECON 214 Elements of Statistics for Economists ECON 214 Elements of Statistics for Economists Session 4 Probability Lecturer: Dr. Bernardin Senadza, Dept. of Economics Contact Information: bsenadza@ug.edu.gh College of Education School of Continuing

More information

The study of probability is concerned with the likelihood of events occurring. Many situations can be analyzed using a simplified model of probability

The study of probability is concerned with the likelihood of events occurring. Many situations can be analyzed using a simplified model of probability The study of probability is concerned with the likelihood of events occurring Like combinatorics, the origins of probability theory can be traced back to the study of gambling games Still a popular branch

More information

A Novel Three Value Logic for Computing Purposes

A Novel Three Value Logic for Computing Purposes Iteratioal Joural o Iormatio ad Electroics Egieerig, Vol. 3, No. 4, July 23 A Novel Three Value Logic or Computig Purposes Ali Soltai ad Saeed Mohammadi Abstract The aim o this article is to suggest a

More information

Chapter 3 Digital Logic Structures

Chapter 3 Digital Logic Structures Copyright The McGraw-HillCompaies, Ic. Permissio required for reproductio or display. Computig Layers Chapter 3 Digital Logic Structures Problems Algorithms Laguage Istructio Set Architecture Microarchitecture

More information

Mathematical Explorations of Card Tricks

Mathematical Explorations of Card Tricks Joh Carroll Uiversity Carroll Collected Seior Hoors Projects Theses, Essays, ad Seior Hoors Projects Sprig 2015 Mathematical Exploratios of Card Tricks Timothy R. Weeks Joh Carroll Uiversity, tweeks15@jcu.edu

More information

Wavelet Transform. CSEP 590 Data Compression Autumn Wavelet Transformed Barbara (Enhanced) Wavelet Transformed Barbara (Actual)

Wavelet Transform. CSEP 590 Data Compression Autumn Wavelet Transformed Barbara (Enhanced) Wavelet Transformed Barbara (Actual) Wavelet Trasform CSEP 59 Data Compressio Autum 7 Wavelet Trasform Codig PACW Wavelet Trasform A family of atios that filters the data ito low resolutio data plus detail data high pass filter low pass filter

More information

Maximizing the Capacity of Large Wireless Networks: Optimal and Distributed Solutions

Maximizing the Capacity of Large Wireless Networks: Optimal and Distributed Solutions Maximizig the Capacity of Large Wireless Networks Optimal ad Distributed Solutios Saad G. Kiai ad David Gesbert Mobile Commuicatios Departmet, Eurecom Istitute, 06560 Sophia Atipolis, Frace Email {kiai,

More information

信號與系統 Signals and Systems

信號與系統 Signals and Systems Sprig 24 信號與系統 Sigals ad Systems Chapter SS- Sigals ad Systems Feg-Li Lia NTU-EE Feb4 Ju4 Figures ad images used i these lecture otes are adopted from Sigals & Systems by Ala V. Oppeheim ad Ala S. Willsky,

More information

Cross-Entropy-Based Sign-Selection Algorithms for Peak-to-Average Power Ratio Reduction of OFDM Systems

Cross-Entropy-Based Sign-Selection Algorithms for Peak-to-Average Power Ratio Reduction of OFDM Systems 4990 IEEE TRASACTIOS O SIGAL PROCESSIG, VOL. 56, O. 10, OCTOBER 2008 Cross-Etropy-Based Sig-Selectio Algorithms for Peak-to-Average Power Ratio Reductio of OFDM Systems Luqig Wag ad Chitha Tellambura Abstract

More information

Single Bit DACs in a Nutshell. Part I DAC Basics

Single Bit DACs in a Nutshell. Part I DAC Basics Sigle Bit DACs i a Nutshell Part I DAC Basics By Dave Va Ess, Pricipal Applicatio Egieer, Cypress Semicoductor May embedded applicatios require geeratig aalog outputs uder digital cotrol. It may be a DC

More information

Name. Is the game fair or not? Prove your answer with math. If the game is fair, play it 36 times and record the results.

Name. Is the game fair or not? Prove your answer with math. If the game is fair, play it 36 times and record the results. Homework 5.1C You must complete table. Use math to decide if the game is fair or not. If Period the game is not fair, change the point system to make it fair. Game 1 Circle one: Fair or Not 2 six sided

More information

Lossless image compression Using Hashing (using collision resolution) Amritpal Singh 1 and Rachna rajpoot 2

Lossless image compression Using Hashing (using collision resolution) Amritpal Singh 1 and Rachna rajpoot 2 Lossless image compressio Usig Hashig (usig collisio resolutio) Amritpal Sigh 1 ad Racha rajpoot 2 1 M.Tech.* CSE Departmet, 2 Departmet of iformatio techology Guru Kashi UiversityTalwadi Sabo, Bathida

More information

Lecture 4: Frequency Reuse Concepts

Lecture 4: Frequency Reuse Concepts EE 499: Wireless & Mobile Commuicatios (8) Lecture 4: Frequecy euse Cocepts Distace betwee Co-Chael Cell Ceters Kowig the relatio betwee,, ad, we ca easily fid distace betwee the ceter poits of two co

More information

Alignment in linear space

Alignment in linear space Sequece Aligmet: Liear Space Aligmet i liear space Chapter 7 of Joes ad Pevzer Q. Ca we avoid usig quadratic space? Easy. Optimal value i O(m + ) space ad O(m) time. Compute OPT(i, ) from OPT(i-1, ). No

More information

4. INTERSYMBOL INTERFERENCE

4. INTERSYMBOL INTERFERENCE DATA COMMUNICATIONS 59 4. INTERSYMBOL INTERFERENCE 4.1 OBJECT The effects of restricted badwidth i basebad data trasmissio will be studied. Measuremets relative to itersymbol iterferece, usig the eye patter

More information

3. Error Correcting Codes

3. Error Correcting Codes 3. Error Correctig Codes Refereces V. Bhargava, Forward Error Correctio Schemes for Digital Commuicatios, IEEE Commuicatios Magazie, Vol 21 No1 11 19, Jauary 1983 Mischa Schwartz, Iformatio Trasmissio

More information