Section 1.6: Proof by Mathematical Induction

Size: px
Start display at page:

Download "Section 1.6: Proof by Mathematical Induction"

Transcription

1 Sectio.6 Proof by Iductio Sectio.6: Proof by Mathematical Iductio Purpose of Sectio: To itroduce the Priciple of Mathematical Iductio, both weak ad the strog versios, ad show how certai types of theorems ca be prove usig this techique. Itroductio The Priciple of Mathematical Iductio is a method of proof ormally used to prove that a propositio is true for all atural umbers,,3,, although there are may variatios of the basic method. The method is particularly importat i discrete mathematics, ad oe ofte sees theorems prove by iductio i areas like computer sciece. The techique is so ituitive ad familiar that it sometimes is used without referece to its use. For eample, suppose someoe tells you they are goig to color the atural umbers,,3, with some color ad that the umber will be colored blue, ad that if a give umber is colored blue, the the et umber will also be colored blue. Is there ay doubt i your mid that all the umbers will be colored blue? Of course ot. This is the iductio aiom. Ad the good thig is you do t have to proof it. It is a aiom. I 889 Italia mathematicia Guiseppe Peao (858-93) published a list of five aioms which defie the atural umbers. Peao s 5 th aiom is called the iductio aiom, which states that ay property which belogs to ad also to the successor of ay umber which has the property belogs to all umbers.

2 Sectio.6 Proof by Iductio Margi Note: Do t cofuse mathematical iductio with iductive reasoig associated with the atural scieces. Iductive reasoig i the scieces is a scietific method whereby oe iduces geeral priciples from observatios. Mathematical iductio is ot the same thig: it is a deductive form of reasoig used to establish the validity of a propositio for all atural umbers. Mathematical iductio provides a coveiet way to establish that a statemet is true for all atural umbers,,. The followig statemets are prime cadidates for proof by mathematical iductio. For all atural umbers, ( ) = If A is a set cotais elemets, the the collectio of all subsets of A cotai elemets. 3 5 for all atural umbers Here, the is how the method of mathematical iductio works. Mathematical Iductio The Priciple of Mathematical Iductio is a method of proof for verifyig that a propositio P( ) is true for all atural umbers. The methodology for provig theorems by iductio is as follows. Methodology of Mathematical Iductio To verify that a propositio P( ) holds for all atural umbers, the Priciple of Mathematical Iductio cosists of successfully carryig out the followig two steps. i Base Case: Prove that P () is true. i Iductio Step: Assume that P( ) is true for a arbitrary, the prove that P( + ) is true. Note: There are several; modificatios of the basic iductio proof stated here. For eample, there is o reaso the base case starts with P (). If the base case is replaced by the verificatio of P( a ), where " a " is ay iteger (positive or egative), oe would coclude P( ) true for all a. Also, if the

3 Sectio.6 3 Proof by Iductio iductio step is replaced by the implicatio P( ) P( + ), oe would coclude P( ) true for P(), P(3),..., P( + ),... Also, sometimes the base case cosists i verifyig more tha oe propositio, maybe P(), P () ad P (3) are required for the iitial step (look at Eample 7 i this sectio). Margi Note: Iductio works like domios. You tip over the first domio; the first domio tips over the secod oe; the secod oe tips over the third oe; ad so o. You get the idea. Evetually, all domioes are tipped over. Theorem (Famous Idetity by Iductio I ductio) ( + ) If is a positive iteger, the =. Proof: We deote P( ) as the statemet to be proved: ( + ) P( ) : = Base Case: Clearly P() is true sice P () says () = Iductio Step: We assume P( ) true for a arbitrary : ( + ) P( ) : = (assume true) Addig + to each side of this equatio, we get: ( + ) ( + ) = + ( + ) ( + ) + ( + ) = ( + )( + ) = which is the statemet P( + ). Hece, we have prove that P( + ) is true. By iductio the result is prove. The reader ca verify that P() ad P(3) are also true, but that is t relevat to proof by iductio.

4 Sectio.6 4 Proof by Iductio Note: Do we have to prove that the priciple of mathematical iductio holds? The aswer is o. We accept mathematical iductio as a logical aiom i much the same way as we accept the classical rules of Aristotelia logic. Famous Story i Mathematics The idetity ( + ) = ca also be prove usig the idea Gauss had whe he was 9 years old ad impressed his teacher by summig = Ideed: ( ) ( ) ( ) = = 50 0= Theorem (Iductio i Calculus) Prove that for every atural umbers, we have ( e ) d P( ) : = ( + ) e d Proof: We show P( ) is true for all atural umbers by iductio. Base Step: If = ad from the product rule for differetiatio, we ca write ( ) d e d Iductio Step: Assumig true for a arbitrary, we compute d e e ( ) e d = + = +. ( e ) d P( ) : = ( + ) e d

5 Sectio.6 5 Proof by Iductio ( ) d ( ) + d e d e P( + ) : = + d d d d = ( ) e d + = + e + e ( ) ( ) = + + e (iductio assumptio) (product rule) which proves P( + ). Hece the theorem is proved. Theorem 3 (Provig Closed Forms of Series by Iductio) iteger : For ay positive k = k = 0 + Proof: Lettig P( ) be the statemet + P( ) : = we verify Base Case: I this problem the iitial step starts at = 0 due to the way P( ) is defied. It is ot ecessary, but we evaluate both P (0) ad P (). 0 + P(0) : = = = P() : + = = + Iductio Step: Assumig P( ) is true for ay atural umber, we have = Addig + to each side of this equatio, gives

6 Sectio.6 6 Proof by Iductio = ( ) = + = + = which says the P( + ) is true. By iductio the theorem is prove. Margi Note: How do we kow if the proof of a theorem is correct? It would be ice if we could feed a theorem ito a computer ad let the machie verify if the proof, but ecept for very simple cases, this is ot feasible. May theorems are easy to follow, but may are etremely difficult. Whe Adrew Wiles proved Fermat s Last Theorem i 993, he preseted his results to a group of eperts. No oe could verify o the spot if the proof was correct due to its legth (00 pages) ad compleity. Oly after the proof was aalyzed by a committee of si specialists over several moths was the theorem validated. Theorem 4 (Iequality by Iductio) The iequality > holds for 5. Proof: Defiig P( ) : > we prove: Base Case: 5 P (5) : = 3 > 5 = 5. Iductio Step: P ( ) P( ) ( ) +, 5 + for 5. We must show > = +. We write + = > (iductio hypothesis) = + + = > + + = ( + ) 5 (we assume 5) Hece P( + ) is true ad so by iductio P( ) true for all 5.

7 Sectio.6 7 Proof by Iductio Theorem 5 (Tower of Beares Theorem) The Tower of Beares 3 puzzle (or tower of Haoi) cosists movig a collectio of disks from oe peg oto aother, where oe is oly allowed to move oe disk at a time ad o larger disk ca ever be above a smaller disk. The umber of steps required to move disks from oe peg to aother peg (either oe) is. Proof: Let ( ) = the umber of moves for disks is - P We prove the iductio steps: P ( ) is true ( ) P( + ) P i P ( ) is true sice for a sigle disk it takes = step to move oe disk from oe peg to aother. i Assume P ( ) is true. That is, it takes steps to trasfer disks. We ow show it requires + steps to move + disks. Let + disks be stacked o pet A. We move the top disks to oe of the other pegs, say peg C (which takes steps by assumptio), ad the move the largest disk, still sittig o peg A, to peg B (which takes step). 3 Accordig to leged, the Temple at Beares i aciet Idia marked the ceter of the world. Withi the temple priests moved golde disks from oe diamod eedle to aother. God placed 64 gold disks o oe eedle at the time of creatio. It was said that whe the temple priests completed their task the uiverse would come to a ed. Sice it takes 64 - moves to complete the task ad assumig the priest move oe disk per secod, it would take roughly 585 billio years to move the disks from oe eedle to aother..

8 Sectio.6 8 Proof by Iductio Fially, we move the disks sittig o peg C to the top of the largest disk restig o peg B (aother steps). Hece, we have the + disks sittig o pole B i the proper arragemet. We are doe. Addig up these steps, we fid ( ) ( ) umber of steps required to move + disks = + + = = + Hece, we have prove P ( + ) is true so by iductio the umber of steps required to move disks from oe pole to aother is for ay atural umber N. Is Mathematics Based o Logic? I the late 800s ad early 900s a few mathematicias ad logicias like Dedekid, Frege, Hilbert, Russell, ad Whitehead tried to costruct arithmetic from formal logical priciples ad aioms. The Italia logicia Giuseppe Peao (858-93) formulated five aioms from which oe could deduce the atural umbers,, 3,.. This brigs up the philosophical questio of what eactly should be the startig poit of mathematics ad arithmetic? To some mathematical ituitioists like Kroecker ad Poicare, felt the atural umbers are as ituitive ad basic as oe ca get ad should act as the startig poit, rather tha beig formulated o less ituitive logical aioms. Logicias would disagree. The type of iductio discussed thus far is called weak iductio. We ow itroduce the cocept of strog iductio, although techically the two methods are equivalet. Strog Iductio The Priciple of Mathematical Iductio stated thus far is sometimes called weak iductio i cotrast to a variatio of it called strog iductio. The two types of iductio are actually equivalet but sometimes weak iductio does t fit ito the proof i a atural way whereas strog iductio does.

9 Sectio.6 9 Proof by Iductio Methodology of o f (Strog) Mathematical Iductio To verify a propositio P( ) holds for all atural umbers, the Priciple of (Strog) Mathematical Iductio cosists of successfully carryig out the followig steps.. Base Case: Prove that P () is true.. Iductio Step: Show that for all N P() P() P( ) P( + ). A fudametal result i umber theory is the Fudametal al Theorem of Arithmetic, which ca be prove by strog iductio. Note: To prove that a propositio ( ) P is true for all atural umbers does ot mea you have to use iductio, but geerally iductio is the most effective route. Also, if you use some other method i lieu of iductio, you are might be usig some fact i your proof that does require iductio. Theorem 6 (Fudametal Theorem of Arithmetic) Every atural umber ca be writte as the product of prime umbers. For eample 350 = 5 7, 9 = Proof: Deotig we prove: P( ) = ca be writte as the product of prime umbers Base Case: P () holds sice is a prime umber. Iductio Step: For a arbitrary =,3,... we prove P() P(3) P( ) P( + ). Assume P(), P(3), P( ) is true. That is, each atural umber,3, ca be writte as the product of primes. We ow cosider two cases.

10 Sectio.6 0 Proof by Iductio Case : Suppose + is a prime umber. I this case P( + ) is true ad thus the iductio step is prove sice ay prime umber + ca be writte as a product of primes, amely + = +. Case : Suppose + is ot prime, which meas we ca factor + = qp, where clearly both of the factors p, q must be less tha + ad greater ad or equal to. (For eample if + = 5, we would could factor 5 = 5 3 where both factors 3,5 are less tha 5 ad greater tha or equal to ). But sice p, q are less tha P k are true for +, the iductio hypothesis (all ( ) k < + ) states they ca both be writte as the product of primes, say p = p p p q = q q q m But if both p ad q ca be writte as the product of primes so ca ( )( ) + = pq = p p p q q q. m Hece P( + ) is true ad so by the priciple of strog iductio P( ) is true for all. History of Mathematical Iductio Although some elemets of mathematical iductio have bee hited at sice the time of Euclid, oe of the oldest argumet usig iductio goes back to the Italia mathematicia Fracesco Maurolico, who used iductio i 575 to prove that the sum of the first odd atural umbers is. The method was later discovered idepedetly by the Swiss mathematicia Joh Beroulli, ad Frech mathematicias Blaise Pascal (63-66) ad Pierre de Fermat (60-665). Fially, i 889 the Italia logicia Guiseppe Peao (858-93) laid out five aioms for deducig the atural umbers, of which his fifth aiom was the Priciple of Mathematical Iductio. Hece, from that poit of view iductio is a formal aiom of arithmetic. Theorem 7 (Solutio of a Recurrece Relatio) Suppose a sequece u, u,..., u,... is defied by the recursio relatio with iitial coditios: 0 u+ = 3u u u0 =, u = 3 Fid the sequece u, =,,... that satisfies these equatios. Doig a few computatios we fid u = 3u u0 = 5, u 3 = 7, u 4 = 9, u 5 = 33 ad thus a reasoable guess would be u = +. To show P( ) : u = + satisfies the

11 Sectio.6 Proof by Iductio recurrece relatio for all 0, we use strog iductio startig with iitial step = 0.. Base Case: P u = + = (check) 0 (0) : 0 3 Iductio Step: Assumig P(0), P(),..., P( ) true, we ca write u = + = +, u ad from the recurrece relatio, we have u = 3u u + ( ) ( ) = = 3 + = + + which proves P( + ). Hece, by strog iductio we have that P( ) : u = + satisfies the recurrece relatio for all 0. This et eample shows a variatio of the base step from previous eamples. Each problem is differet ad you must adjust the iductio proof accordigly. Theorem 8 (Modifyig the Base Step) You are give two rulers: oe is 3 uits log, the other 5. Show that you ca measure ay uit distace greater tha or equal to 8 usig oly these two rulers. Proof: Let P( ) be the propositio ( ) = ay iteger legth 8 ca be measured with rulers of legth 3 ad 5 P It is useful to see the followig patter that develops.

12 Sectio.6 Proof by Iductio P(8) = P(9) = P(0) = ( ) ( ) ( ) P() = P(8) + 3 = P() = P(9) + 3 = P(3) = P(0) + 3 = This patter will serve as a aid i decidig the base ad iductio steps which is ofte the most difficult part i a iductio proof. Base Step: For the base step, we verify the first three propositios: ( ) P(8) = 5 + 3, P(9) = , P 0 = Iductio Step: We ow prove the iductio step ( ) P(8), P(9),..., P( ) P +, 0 To prove this we make the simple observatio that if P ( ) is true (i.e. a legth of ca be measured with rulers of legth 3 ad 5), the P ( + ) is also true sice a legth of + is 3 uits loger tha. [ For eample P ( 9) = ad P( ) = ( ) + 3. Hece, we kow ( ) P ( 8) is true, ad P ( ) is true sice ( 9) P is true sice P is true, ad so o. ] Hece if P(8), P(9),..., P( ) 0 is true so is P ( + ) ad so by iductio P ( ) is true for all atural umbers. There are several variatios of the basic method of mathematical iductio. Oe such variatio is double iductio. Double Iductio Sometimes we would like to prove a propositio P ( m, ) ivolvig two atural umbers m by iteratig the iductive process. This is doe by performig a iductio o oe of the variables, say m, ad the a iductio

13 Sectio.6 3 Proof by Iductio o. This strategy is called double iductio ad is carried out by the followig steps. ( ) ( ) P ( m + ) ( ) ( + ). Prove P, is true. Prove P m,, 3. Prove P m, P m, for all atural umbers m You ca thik of double iductio as provig P ( m, ) at all poits i the first quadrat of the y -plae with iteger coordiates.

14 Sectio.6 4 Proof by Iductio Problems. (Proof by Iductio) propositios. Prove by weak or strog iductio the followig a) b) ( + )( + ) = 6 3 is divisible by 3 for. c) ( a + ib) = ( + )( a + b) i= 0 d) <! ( 4) u = u +, u = 5 has the solutio u = + 3 for. f) g) ( 4k 3) = ( ) k = h) 5 divides 7 for all positive itegers. i) > cosθ + i siθ = cos θ + isi θ (De Moivre s Theorem) j) ( ) k) si si l) a b a b < < ( a, b positive real umbers) d d m) = ( ) ( ) + l!. (Graphical Iductio) Draw some lies i the plae as show i Figure ad color adjoiig regios either red or blue. Show that it is possible to draw a arbitrary umber of lies i such a way that two adjoiig regios are ot

15 Sectio.6 5 Proof by Iductio colored the same color. Hit: Defie P( ) as the propositio it is possible to draw lies i the desired maer for arbitrary N. 3. (Clever Mary) To prove the idetity k = 0 Figure ( + ) k = Mary evaluated the left-had side of the equatio for = 0,, gettig. 0 F() 0 3 She the fit a polyomial to these three poits, gettig ( + ) f ( ) =. Mary tured this ito her professor. Is her proof 4 valid? 4 This problem is based o a problem i the book A = B by Marko Petkovsek, Doro Zeilberger ad Herbert Wilf. (This amazig book, icidetally, ca be dowloaded free o the iteret.)

16 Sectio.6 6 Proof by Iductio 4. (Proof Proofs without Words) They say a good picture is worth a thousad words, but i mathematics it might be closer to a millio. For the figures below, describe why the figure provides a visual proof of the statemet. a) 3 a + b = c b) ( + ) = c) ( ) = d) y = y + y ( )( )

17 Sectio.6 7 Proof by Iductio e) y y y = ( )( + ) f) a + b ab g) a a a + = + p / q q / p h) ( ) 0 t + t dt =

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008 I ite Sequeces Dr. Philippe B. Laval Keesaw State Uiversity October 9, 2008 Abstract This had out is a itroductio to i ite sequeces. mai de itios ad presets some elemetary results. It gives the I ite Sequeces

More information

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is 0_0605.qxd /5/05 0:45 AM Page 470 470 Chapter 6 Additioal Topics i Trigoometry 6.5 Trigoometric Form of a Complex Number What you should lear Plot complex umbers i the complex plae ad fid absolute values

More information

Infinite Sequences and Series

Infinite Sequences and Series CHAPTER 4 Ifiite Sequeces ad Series 4.1. Sequeces A sequece is a ifiite ordered list of umbers, for example the sequece of odd positive itegers: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29...

More information

Section 8.3 : De Moivre s Theorem and Applications

Section 8.3 : De Moivre s Theorem and Applications The Sectio 8 : De Moivre s Theorem ad Applicatios Let z 1 ad z be complex umbers, where z 1 = r 1, z = r, arg(z 1 ) = θ 1, arg(z ) = θ z 1 = r 1 (cos θ 1 + i si θ 1 ) z = r (cos θ + i si θ ) ad z 1 z =

More information

1. MATHEMATICAL INDUCTION

1. MATHEMATICAL INDUCTION 1. MATHEMATICAL INDUCTION EXAMPLE 1: Prove that for ay iteger 1. Proof: 1 + 2 + 3 +... + ( + 1 2 (1.1 STEP 1: For 1 (1.1 is true, sice 1 1(1 + 1. 2 STEP 2: Suppose (1.1 is true for some k 1, that is 1

More information

Incremental calculation of weighted mean and variance

Incremental calculation of weighted mean and variance Icremetal calculatio of weighted mea ad variace Toy Fich faf@cam.ac.uk dot@dotat.at Uiversity of Cambridge Computig Service February 009 Abstract I these otes I eplai how to derive formulae for umerically

More information

Lecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem

Lecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem Lecture 4: Cauchy sequeces, Bolzao-Weierstrass, ad the Squeeze theorem The purpose of this lecture is more modest tha the previous oes. It is to state certai coditios uder which we are guarateed that limits

More information

A Recursive Formula for Moments of a Binomial Distribution

A Recursive Formula for Moments of a Binomial Distribution A Recursive Formula for Momets of a Biomial Distributio Árpád Béyi beyi@mathumassedu, Uiversity of Massachusetts, Amherst, MA 01003 ad Saverio M Maago smmaago@psavymil Naval Postgraduate School, Moterey,

More information

S. Tanny MAT 344 Spring 1999. be the minimum number of moves required.

S. Tanny MAT 344 Spring 1999. be the minimum number of moves required. S. Tay MAT 344 Sprig 999 Recurrece Relatios Tower of Haoi Let T be the miimum umber of moves required. T 0 = 0, T = 7 Iitial Coditios * T = T + $ T is a sequece (f. o itegers). Solve for T? * is a recurrece,

More information

Chapter 5: Inner Product Spaces

Chapter 5: Inner Product Spaces Chapter 5: Ier Product Spaces Chapter 5: Ier Product Spaces SECION A Itroductio to Ier Product Spaces By the ed of this sectio you will be able to uderstad what is meat by a ier product space give examples

More information

CS103A Handout 23 Winter 2002 February 22, 2002 Solving Recurrence Relations

CS103A Handout 23 Winter 2002 February 22, 2002 Solving Recurrence Relations CS3A Hadout 3 Witer 00 February, 00 Solvig Recurrece Relatios Itroductio A wide variety of recurrece problems occur i models. Some of these recurrece relatios ca be solved usig iteratio or some other ad

More information

Soving Recurrence Relations

Soving Recurrence Relations Sovig Recurrece Relatios Part 1. Homogeeous liear 2d degree relatios with costat coefficiets. Cosider the recurrece relatio ( ) T () + at ( 1) + bt ( 2) = 0 This is called a homogeeous liear 2d degree

More information

Properties of MLE: consistency, asymptotic normality. Fisher information.

Properties of MLE: consistency, asymptotic normality. Fisher information. Lecture 3 Properties of MLE: cosistecy, asymptotic ormality. Fisher iformatio. I this sectio we will try to uderstad why MLEs are good. Let us recall two facts from probability that we be used ofte throughout

More information

WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER?

WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER? WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER? JÖRG JAHNEL 1. My Motivatio Some Sort of a Itroductio Last term I tought Topological Groups at the Göttige Georg August Uiversity. This

More information

Asymptotic Growth of Functions

Asymptotic Growth of Functions CMPS Itroductio to Aalysis of Algorithms Fall 3 Asymptotic Growth of Fuctios We itroduce several types of asymptotic otatio which are used to compare the performace ad efficiecy of algorithms As we ll

More information

Department of Computer Science, University of Otago

Department of Computer Science, University of Otago Departmet of Computer Sciece, Uiversity of Otago Techical Report OUCS-2006-09 Permutatios Cotaiig May Patters Authors: M.H. Albert Departmet of Computer Sciece, Uiversity of Otago Micah Colema, Rya Fly

More information

Convexity, Inequalities, and Norms

Convexity, Inequalities, and Norms Covexity, Iequalities, ad Norms Covex Fuctios You are probably familiar with the otio of cocavity of fuctios. Give a twicedifferetiable fuctio ϕ: R R, We say that ϕ is covex (or cocave up) if ϕ (x) 0 for

More information

Section 11.3: The Integral Test

Section 11.3: The Integral Test Sectio.3: The Itegral Test Most of the series we have looked at have either diverged or have coverged ad we have bee able to fid what they coverge to. I geeral however, the problem is much more difficult

More information

Sequences and Series

Sequences and Series CHAPTER 9 Sequeces ad Series 9.. Covergece: Defiitio ad Examples Sequeces The purpose of this chapter is to itroduce a particular way of geeratig algorithms for fidig the values of fuctios defied by their

More information

FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. 1. Powers of a matrix

FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. 1. Powers of a matrix FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. Powers of a matrix We begi with a propositio which illustrates the usefuless of the diagoalizatio. Recall that a square matrix A is diogaalizable if

More information

Lecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009)

Lecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009) 18.409 A Algorithmist s Toolkit October 27, 2009 Lecture 13 Lecturer: Joatha Keler Scribe: Joatha Pies (2009) 1 Outlie Last time, we proved the Bru-Mikowski iequality for boxes. Today we ll go over the

More information

Elementary Theory of Russian Roulette

Elementary Theory of Russian Roulette Elemetary Theory of Russia Roulette -iterestig patters of fractios- Satoshi Hashiba Daisuke Miematsu Ryohei Miyadera Itroductio. Today we are goig to study mathematical theory of Russia roulette. If some

More information

Building Blocks Problem Related to Harmonic Series

Building Blocks Problem Related to Harmonic Series TMME, vol3, o, p.76 Buildig Blocks Problem Related to Harmoic Series Yutaka Nishiyama Osaka Uiversity of Ecoomics, Japa Abstract: I this discussio I give a eplaatio of the divergece ad covergece of ifiite

More information

Discrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 13

Discrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 13 EECS 70 Discrete Mathematics ad Probability Theory Sprig 2014 Aat Sahai Note 13 Itroductio At this poit, we have see eough examples that it is worth just takig stock of our model of probability ad may

More information

CS103X: Discrete Structures Homework 4 Solutions

CS103X: Discrete Structures Homework 4 Solutions CS103X: Discrete Structures Homewor 4 Solutios Due February 22, 2008 Exercise 1 10 poits. Silico Valley questios: a How may possible six-figure salaries i whole dollar amouts are there that cotai at least

More information

Factors of sums of powers of binomial coefficients

Factors of sums of powers of binomial coefficients ACTA ARITHMETICA LXXXVI.1 (1998) Factors of sums of powers of biomial coefficiets by Neil J. Cali (Clemso, S.C.) Dedicated to the memory of Paul Erdős 1. Itroductio. It is well ow that if ( ) a f,a = the

More information

SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx

SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx SAMPLE QUESTIONS FOR FINAL EXAM REAL ANALYSIS I FALL 006 3 4 Fid the followig usig the defiitio of the Riema itegral: a 0 x + dx 3 Cosider the partitio P x 0 3, x 3 +, x 3 +,......, x 3 3 + 3 of the iterval

More information

3. Greatest Common Divisor - Least Common Multiple

3. Greatest Common Divisor - Least Common Multiple 3 Greatest Commo Divisor - Least Commo Multiple Defiitio 31: The greatest commo divisor of two atural umbers a ad b is the largest atural umber c which divides both a ad b We deote the greatest commo gcd

More information

Math C067 Sampling Distributions

Math C067 Sampling Distributions Math C067 Samplig Distributios Sample Mea ad Sample Proportio Richard Beigel Some time betwee April 16, 2007 ad April 16, 2007 Examples of Samplig A pollster may try to estimate the proportio of voters

More information

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth Questio 1: What is a ordiary auity? Let s look at a ordiary auity that is certai ad simple. By this, we mea a auity over a fixed term whose paymet period matches the iterest coversio period. Additioally,

More information

Example 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here).

Example 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here). BEGINNING ALGEBRA Roots ad Radicals (revised summer, 00 Olso) Packet to Supplemet the Curret Textbook - Part Review of Square Roots & Irratioals (This portio ca be ay time before Part ad should mostly

More information

THE ARITHMETIC OF INTEGERS. - multiplication, exponentiation, division, addition, and subtraction

THE ARITHMETIC OF INTEGERS. - multiplication, exponentiation, division, addition, and subtraction THE ARITHMETIC OF INTEGERS - multiplicatio, expoetiatio, divisio, additio, ad subtractio What to do ad what ot to do. THE INTEGERS Recall that a iteger is oe of the whole umbers, which may be either positive,

More information

Determining the sample size

Determining the sample size Determiig the sample size Oe of the most commo questios ay statisticia gets asked is How large a sample size do I eed? Researchers are ofte surprised to fid out that the aswer depeds o a umber of factors

More information

THE ABRACADABRA PROBLEM

THE ABRACADABRA PROBLEM THE ABRACADABRA PROBLEM FRANCESCO CARAVENNA Abstract. We preset a detailed solutio of Exercise E0.6 i [Wil9]: i a radom sequece of letters, draw idepedetly ad uiformly from the Eglish alphabet, the expected

More information

Sampling Distribution And Central Limit Theorem

Sampling Distribution And Central Limit Theorem () Samplig Distributio & Cetral Limit Samplig Distributio Ad Cetral Limit Samplig distributio of the sample mea If we sample a umber of samples (say k samples where k is very large umber) each of size,

More information

Overview of some probability distributions.

Overview of some probability distributions. Lecture Overview of some probability distributios. I this lecture we will review several commo distributios that will be used ofte throughtout the class. Each distributio is usually described by its probability

More information

Here are a couple of warnings to my students who may be here to get a copy of what happened on a day that you missed.

Here are a couple of warnings to my students who may be here to get a copy of what happened on a day that you missed. This documet was writte ad copyrighted by Paul Dawkis. Use of this documet ad its olie versio is govered by the Terms ad Coditios of Use located at http://tutorial.math.lamar.edu/terms.asp. The olie versio

More information

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES Read Sectio 1.5 (pages 5 9) Overview I Sectio 1.5 we lear to work with summatio otatio ad formulas. We will also itroduce a brief overview of sequeces,

More information

1 Computing the Standard Deviation of Sample Means

1 Computing the Standard Deviation of Sample Means Computig the Stadard Deviatio of Sample Meas Quality cotrol charts are based o sample meas ot o idividual values withi a sample. A sample is a group of items, which are cosidered all together for our aalysis.

More information

Hypothesis testing. Null and alternative hypotheses

Hypothesis testing. Null and alternative hypotheses Hypothesis testig Aother importat use of samplig distributios is to test hypotheses about populatio parameters, e.g. mea, proportio, regressio coefficiets, etc. For example, it is possible to stipulate

More information

5.3. Generalized Permutations and Combinations

5.3. Generalized Permutations and Combinations 53 GENERALIZED PERMUTATIONS AND COMBINATIONS 73 53 Geeralized Permutatios ad Combiatios 53 Permutatios with Repeated Elemets Assume that we have a alphabet with letters ad we wat to write all possible

More information

Integer Factorization Algorithms

Integer Factorization Algorithms Iteger Factorizatio Algorithms Coelly Bares Departmet of Physics, Orego State Uiversity December 7, 004 This documet has bee placed i the public domai. Cotets I. Itroductio 3 1. Termiology 3. Fudametal

More information

Solutions to Exercises Chapter 4: Recurrence relations and generating functions

Solutions to Exercises Chapter 4: Recurrence relations and generating functions Solutios to Exercises Chapter 4: Recurrece relatios ad geeratig fuctios 1 (a) There are seatig positios arraged i a lie. Prove that the umber of ways of choosig a subset of these positios, with o two chose

More information

GCE Further Mathematics (6360) Further Pure Unit 2 (MFP2) Textbook. Version: 1.4

GCE Further Mathematics (6360) Further Pure Unit 2 (MFP2) Textbook. Version: 1.4 GCE Further Mathematics (660) Further Pure Uit (MFP) Tetbook Versio: 4 MFP Tetbook A-level Further Mathematics 660 Further Pure : Cotets Chapter : Comple umbers 4 Itroductio 5 The geeral comple umber 5

More information

1. C. The formula for the confidence interval for a population mean is: x t, which was

1. C. The formula for the confidence interval for a population mean is: x t, which was s 1. C. The formula for the cofidece iterval for a populatio mea is: x t, which was based o the sample Mea. So, x is guarateed to be i the iterval you form.. D. Use the rule : p-value

More information

Our aim is to show that under reasonable assumptions a given 2π-periodic function f can be represented as convergent series

Our aim is to show that under reasonable assumptions a given 2π-periodic function f can be represented as convergent series 8 Fourier Series Our aim is to show that uder reasoable assumptios a give -periodic fuctio f ca be represeted as coverget series f(x) = a + (a cos x + b si x). (8.) By defiitio, the covergece of the series

More information

UC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Solutions 9 Spring 2006

UC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Solutions 9 Spring 2006 Exam format UC Bereley Departmet of Electrical Egieerig ad Computer Sciece EE 6: Probablity ad Radom Processes Solutios 9 Sprig 006 The secod midterm will be held o Wedesday May 7; CHECK the fial exam

More information

Week 3 Conditional probabilities, Bayes formula, WEEK 3 page 1 Expected value of a random variable

Week 3 Conditional probabilities, Bayes formula, WEEK 3 page 1 Expected value of a random variable Week 3 Coditioal probabilities, Bayes formula, WEEK 3 page 1 Expected value of a radom variable We recall our discussio of 5 card poker hads. Example 13 : a) What is the probability of evet A that a 5

More information

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n We will cosider the liear regressio model i matrix form. For simple liear regressio, meaig oe predictor, the model is i = + x i + ε i for i =,,,, This model icludes the assumptio that the ε i s are a sample

More information

NATIONAL SENIOR CERTIFICATE GRADE 12

NATIONAL SENIOR CERTIFICATE GRADE 12 NATIONAL SENIOR CERTIFICATE GRADE MATHEMATICS P EXEMPLAR 04 MARKS: 50 TIME: 3 hours This questio paper cosists of 8 pages ad iformatio sheet. Please tur over Mathematics/P DBE/04 NSC Grade Eemplar INSTRUCTIONS

More information

SEQUENCES AND SERIES

SEQUENCES AND SERIES Chapter 9 SEQUENCES AND SERIES Natural umbers are the product of huma spirit. DEDEKIND 9.1 Itroductio I mathematics, the word, sequece is used i much the same way as it is i ordiary Eglish. Whe we say

More information

How To Solve The Homewor Problem Beautifully

How To Solve The Homewor Problem Beautifully Egieerig 33 eautiful Homewor et 3 of 7 Kuszmar roblem.5.5 large departmet store sells sport shirts i three sizes small, medium, ad large, three patters plaid, prit, ad stripe, ad two sleeve legths log

More information

Project Deliverables. CS 361, Lecture 28. Outline. Project Deliverables. Administrative. Project Comments

Project Deliverables. CS 361, Lecture 28. Outline. Project Deliverables. Administrative. Project Comments Project Deliverables CS 361, Lecture 28 Jared Saia Uiversity of New Mexico Each Group should tur i oe group project cosistig of: About 6-12 pages of text (ca be loger with appedix) 6-12 figures (please

More information

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method Chapter 6: Variace, the law of large umbers ad the Mote-Carlo method Expected value, variace, ad Chebyshev iequality. If X is a radom variable recall that the expected value of X, E[X] is the average value

More information

Notes on exponential generating functions and structures.

Notes on exponential generating functions and structures. Notes o expoetial geeratig fuctios ad structures. 1. The cocept of a structure. Cosider the followig coutig problems: (1) to fid for each the umber of partitios of a -elemet set, (2) to fid for each the

More information

Chapter 5 O A Cojecture Of Erdíos Proceedigs NCUR VIII è1994è, Vol II, pp 794í798 Jeærey F Gold Departmet of Mathematics, Departmet of Physics Uiversity of Utah Do H Tucker Departmet of Mathematics Uiversity

More information

I. Chi-squared Distributions

I. Chi-squared Distributions 1 M 358K Supplemet to Chapter 23: CHI-SQUARED DISTRIBUTIONS, T-DISTRIBUTIONS, AND DEGREES OF FREEDOM To uderstad t-distributios, we first eed to look at aother family of distributios, the chi-squared distributios.

More information

Irreducible polynomials with consecutive zero coefficients

Irreducible polynomials with consecutive zero coefficients Irreducible polyomials with cosecutive zero coefficiets Theodoulos Garefalakis Departmet of Mathematics, Uiversity of Crete, 71409 Heraklio, Greece Abstract Let q be a prime power. We cosider the problem

More information

INFINITE SERIES KEITH CONRAD

INFINITE SERIES KEITH CONRAD INFINITE SERIES KEITH CONRAD. Itroductio The two basic cocepts of calculus, differetiatio ad itegratio, are defied i terms of limits (Newto quotiets ad Riema sums). I additio to these is a third fudametal

More information

BENEFIT-COST ANALYSIS Financial and Economic Appraisal using Spreadsheets

BENEFIT-COST ANALYSIS Financial and Economic Appraisal using Spreadsheets BENEIT-CST ANALYSIS iacial ad Ecoomic Appraisal usig Spreadsheets Ch. 2: Ivestmet Appraisal - Priciples Harry Campbell & Richard Brow School of Ecoomics The Uiversity of Queeslad Review of basic cocepts

More information

The analysis of the Cournot oligopoly model considering the subjective motive in the strategy selection

The analysis of the Cournot oligopoly model considering the subjective motive in the strategy selection The aalysis of the Courot oligopoly model cosiderig the subjective motive i the strategy selectio Shigehito Furuyama Teruhisa Nakai Departmet of Systems Maagemet Egieerig Faculty of Egieerig Kasai Uiversity

More information

Ekkehart Schlicht: Economic Surplus and Derived Demand

Ekkehart Schlicht: Economic Surplus and Derived Demand Ekkehart Schlicht: Ecoomic Surplus ad Derived Demad Muich Discussio Paper No. 2006-17 Departmet of Ecoomics Uiversity of Muich Volkswirtschaftliche Fakultät Ludwig-Maximilias-Uiversität Müche Olie at http://epub.ub.ui-mueche.de/940/

More information

Cooley-Tukey. Tukey FFT Algorithms. FFT Algorithms. Cooley

Cooley-Tukey. Tukey FFT Algorithms. FFT Algorithms. Cooley Cooley Cooley-Tuey Tuey FFT Algorithms FFT Algorithms Cosider a legth- sequece x[ with a -poit DFT X[ where Represet the idices ad as +, +, Cooley Cooley-Tuey Tuey FFT Algorithms FFT Algorithms Usig these

More information

3 Basic Definitions of Probability Theory

3 Basic Definitions of Probability Theory 3 Basic Defiitios of Probability Theory 3defprob.tex: Feb 10, 2003 Classical probability Frequecy probability axiomatic probability Historical developemet: Classical Frequecy Axiomatic The Axiomatic defiitio

More information

2-3 The Remainder and Factor Theorems

2-3 The Remainder and Factor Theorems - The Remaider ad Factor Theorems Factor each polyomial completely usig the give factor ad log divisio 1 x + x x 60; x + So, x + x x 60 = (x + )(x x 15) Factorig the quadratic expressio yields x + x x

More information

Confidence Intervals for One Mean

Confidence Intervals for One Mean Chapter 420 Cofidece Itervals for Oe Mea Itroductio This routie calculates the sample size ecessary to achieve a specified distace from the mea to the cofidece limit(s) at a stated cofidece level for a

More information

Lecture 4: Cheeger s Inequality

Lecture 4: Cheeger s Inequality Spectral Graph Theory ad Applicatios WS 0/0 Lecture 4: Cheeger s Iequality Lecturer: Thomas Sauerwald & He Su Statemet of Cheeger s Iequality I this lecture we assume for simplicity that G is a d-regular

More information

The following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles

The following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles The followig eample will help us uderstad The Samplig Distributio of the Mea Review: The populatio is the etire collectio of all idividuals or objects of iterest The sample is the portio of the populatio

More information

Repeating Decimals are decimal numbers that have number(s) after the decimal point that repeat in a pattern.

Repeating Decimals are decimal numbers that have number(s) after the decimal point that repeat in a pattern. 5.5 Fractios ad Decimals Steps for Chagig a Fractio to a Decimal. Simplify the fractio, if possible. 2. Divide the umerator by the deomiator. d d Repeatig Decimals Repeatig Decimals are decimal umbers

More information

*The most important feature of MRP as compared with ordinary inventory control analysis is its time phasing feature.

*The most important feature of MRP as compared with ordinary inventory control analysis is its time phasing feature. Itegrated Productio ad Ivetory Cotrol System MRP ad MRP II Framework of Maufacturig System Ivetory cotrol, productio schedulig, capacity plaig ad fiacial ad busiess decisios i a productio system are iterrelated.

More information

A probabilistic proof of a binomial identity

A probabilistic proof of a binomial identity A probabilistic proof of a biomial idetity Joatho Peterso Abstract We give a elemetary probabilistic proof of a biomial idetity. The proof is obtaied by computig the probability of a certai evet i two

More information

AP Calculus AB 2006 Scoring Guidelines Form B

AP Calculus AB 2006 Scoring Guidelines Form B AP Calculus AB 6 Scorig Guidelies Form B The College Board: Coectig Studets to College Success The College Board is a ot-for-profit membership associatio whose missio is to coect studets to college success

More information

Betting on Football Pools

Betting on Football Pools Bettig o Football Pools by Edward A. Beder I a pool, oe tries to guess the wiers i a set of games. For example, oe may have te matches this weeked ad oe bets o who the wiers will be. We ve put wiers i

More information

Approximating Area under a curve with rectangles. To find the area under a curve we approximate the area using rectangles and then use limits to find

Approximating Area under a curve with rectangles. To find the area under a curve we approximate the area using rectangles and then use limits to find 1.8 Approximatig Area uder a curve with rectagles 1.6 To fid the area uder a curve we approximate the area usig rectagles ad the use limits to fid 1.4 the area. Example 1 Suppose we wat to estimate 1.

More information

Lesson 17 Pearson s Correlation Coefficient

Lesson 17 Pearson s Correlation Coefficient Outlie Measures of Relatioships Pearso s Correlatio Coefficiet (r) -types of data -scatter plots -measure of directio -measure of stregth Computatio -covariatio of X ad Y -uique variatio i X ad Y -measurig

More information

Class Meeting # 16: The Fourier Transform on R n

Class Meeting # 16: The Fourier Transform on R n MATH 18.152 COUSE NOTES - CLASS MEETING # 16 18.152 Itroductio to PDEs, Fall 2011 Professor: Jared Speck Class Meetig # 16: The Fourier Trasform o 1. Itroductio to the Fourier Trasform Earlier i the course,

More information

Chapter 7 - Sampling Distributions. 1 Introduction. What is statistics? It consist of three major areas:

Chapter 7 - Sampling Distributions. 1 Introduction. What is statistics? It consist of three major areas: Chapter 7 - Samplig Distributios 1 Itroductio What is statistics? It cosist of three major areas: Data Collectio: samplig plas ad experimetal desigs Descriptive Statistics: umerical ad graphical summaries

More information

Analysis Notes (only a draft, and the first one!)

Analysis Notes (only a draft, and the first one!) Aalysis Notes (oly a draft, ad the first oe!) Ali Nesi Mathematics Departmet Istabul Bilgi Uiversity Kuştepe Şişli Istabul Turkey aesi@bilgi.edu.tr Jue 22, 2004 2 Cotets 1 Prelimiaries 9 1.1 Biary Operatio...........................

More information

Definition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean

Definition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean 1 Social Studies 201 October 13, 2004 Note: The examples i these otes may be differet tha used i class. However, the examples are similar ad the methods used are idetical to what was preseted i class.

More information

Theorems About Power Series

Theorems About Power Series Physics 6A Witer 20 Theorems About Power Series Cosider a power series, f(x) = a x, () where the a are real coefficiets ad x is a real variable. There exists a real o-egative umber R, called the radius

More information

Basic Elements of Arithmetic Sequences and Series

Basic Elements of Arithmetic Sequences and Series MA40S PRE-CALCULUS UNIT G GEOMETRIC SEQUENCES CLASS NOTES (COMPLETED NO NEED TO COPY NOTES FROM OVERHEAD) Basic Elemets of Arithmetic Sequeces ad Series Objective: To establish basic elemets of arithmetic

More information

5: Introduction to Estimation

5: Introduction to Estimation 5: Itroductio to Estimatio Cotets Acroyms ad symbols... 1 Statistical iferece... Estimatig µ with cofidece... 3 Samplig distributio of the mea... 3 Cofidece Iterval for μ whe σ is kow before had... 4 Sample

More information

0.7 0.6 0.2 0 0 96 96.5 97 97.5 98 98.5 99 99.5 100 100.5 96.5 97 97.5 98 98.5 99 99.5 100 100.5

0.7 0.6 0.2 0 0 96 96.5 97 97.5 98 98.5 99 99.5 100 100.5 96.5 97 97.5 98 98.5 99 99.5 100 100.5 Sectio 13 Kolmogorov-Smirov test. Suppose that we have a i.i.d. sample X 1,..., X with some ukow distributio P ad we would like to test the hypothesis that P is equal to a particular distributio P 0, i.e.

More information

Permutations, the Parity Theorem, and Determinants

Permutations, the Parity Theorem, and Determinants 1 Permutatios, the Parity Theorem, ad Determiats Joh A. Guber Departmet of Electrical ad Computer Egieerig Uiversity of Wiscosi Madiso Cotets 1 What is a Permutatio 1 2 Cycles 2 2.1 Traspositios 4 3 Orbits

More information

Measures of Spread and Boxplots Discrete Math, Section 9.4

Measures of Spread and Boxplots Discrete Math, Section 9.4 Measures of Spread ad Boxplots Discrete Math, Sectio 9.4 We start with a example: Example 1: Comparig Mea ad Media Compute the mea ad media of each data set: S 1 = {4, 6, 8, 10, 1, 14, 16} S = {4, 7, 9,

More information

Factoring x n 1: cyclotomic and Aurifeuillian polynomials Paul Garrett <garrett@math.umn.edu>

Factoring x n 1: cyclotomic and Aurifeuillian polynomials Paul Garrett <garrett@math.umn.edu> (March 16, 004) Factorig x 1: cyclotomic ad Aurifeuillia polyomials Paul Garrett Polyomials of the form x 1, x 3 1, x 4 1 have at least oe systematic factorizatio x 1 = (x 1)(x 1

More information

EGYPTIAN FRACTION EXPANSIONS FOR RATIONAL NUMBERS BETWEEN 0 AND 1 OBTAINED WITH ENGEL SERIES

EGYPTIAN FRACTION EXPANSIONS FOR RATIONAL NUMBERS BETWEEN 0 AND 1 OBTAINED WITH ENGEL SERIES EGYPTIAN FRACTION EXPANSIONS FOR RATIONAL NUMBERS BETWEEN 0 AND OBTAINED WITH ENGEL SERIES ELVIA NIDIA GONZÁLEZ AND JULIA BERGNER, PHD DEPARTMENT OF MATHEMATICS Abstract. The aciet Egyptias epressed ratioal

More information

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling Taig DCOP to the Real World: Efficiet Complete Solutios for Distributed Multi-Evet Schedulig Rajiv T. Maheswara, Milid Tambe, Emma Bowrig, Joatha P. Pearce, ad Pradeep araatham Uiversity of Souther Califoria

More information

The Stable Marriage Problem

The Stable Marriage Problem The Stable Marriage Problem William Hut Lae Departmet of Computer Sciece ad Electrical Egieerig, West Virgiia Uiversity, Morgatow, WV William.Hut@mail.wvu.edu 1 Itroductio Imagie you are a matchmaker,

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

Normal Distribution.

Normal Distribution. Normal Distributio www.icrf.l Normal distributio I probability theory, the ormal or Gaussia distributio, is a cotiuous probability distributio that is ofte used as a first approimatio to describe realvalued

More information

On Formula to Compute Primes. and the n th Prime

On Formula to Compute Primes. and the n th Prime Applied Mathematical cieces, Vol., 0, o., 35-35 O Formula to Compute Primes ad the th Prime Issam Kaddoura Lebaese Iteratioal Uiversity Faculty of Arts ad cieces, Lebao issam.kaddoura@liu.edu.lb amih Abdul-Nabi

More information

Chapter 7 Methods of Finding Estimators

Chapter 7 Methods of Finding Estimators Chapter 7 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 011 Chapter 7 Methods of Fidig Estimators Sectio 7.1 Itroductio Defiitio 7.1.1 A poit estimator is ay fuctio W( X) W( X1, X,, X ) of

More information

Confidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the.

Confidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the. Cofidece Itervals A cofidece iterval is a iterval whose purpose is to estimate a parameter (a umber that could, i theory, be calculated from the populatio, if measuremets were available for the whole populatio).

More information

How Euler Did It. In a more modern treatment, Hardy and Wright [H+W] state this same theorem as. n n+ is perfect.

How Euler Did It. In a more modern treatment, Hardy and Wright [H+W] state this same theorem as. n n+ is perfect. Amicable umbers November 005 How Euler Did It by Ed Sadifer Six is a special umber. It is divisible by, ad 3, ad, i what at first looks like a strage coicidece, 6 = + + 3. The umber 8 shares this remarkable

More information

Modified Line Search Method for Global Optimization

Modified Line Search Method for Global Optimization Modified Lie Search Method for Global Optimizatio Cria Grosa ad Ajith Abraham Ceter of Excellece for Quatifiable Quality of Service Norwegia Uiversity of Sciece ad Techology Trodheim, Norway {cria, ajith}@q2s.tu.o

More information

On the L p -conjecture for locally compact groups

On the L p -conjecture for locally compact groups Arch. Math. 89 (2007), 237 242 c 2007 Birkhäuser Verlag Basel/Switzerlad 0003/889X/030237-6, ublished olie 2007-08-0 DOI 0.007/s0003-007-993-x Archiv der Mathematik O the L -cojecture for locally comact

More information

Maximum Likelihood Estimators.

Maximum Likelihood Estimators. Lecture 2 Maximum Likelihood Estimators. Matlab example. As a motivatio, let us look at oe Matlab example. Let us geerate a radom sample of size 00 from beta distributio Beta(5, 2). We will lear the defiitio

More information

Descriptive Statistics

Descriptive Statistics Descriptive Statistics We leared to describe data sets graphically. We ca also describe a data set umerically. Measures of Locatio Defiitio The sample mea is the arithmetic average of values. We deote

More information

Annuities Under Random Rates of Interest II By Abraham Zaks. Technion I.I.T. Haifa ISRAEL and Haifa University Haifa ISRAEL.

Annuities Under Random Rates of Interest II By Abraham Zaks. Technion I.I.T. Haifa ISRAEL and Haifa University Haifa ISRAEL. Auities Uder Radom Rates of Iterest II By Abraham Zas Techio I.I.T. Haifa ISRAEL ad Haifa Uiversity Haifa ISRAEL Departmet of Mathematics, Techio - Israel Istitute of Techology, 3000, Haifa, Israel I memory

More information