BENFORD'S LAW AND PSYCHOLOGICAL BARRIERS IN. CERTAIN ebay AUCTIONS

Size: px
Start display at page:

Download "BENFORD'S LAW AND PSYCHOLOGICAL BARRIERS IN. CERTAIN ebay AUCTIONS"

Transcription

1 Eoomeris Workig Pper EWP0606 ISSN eprme of Eoomis BENFOR'S LAW AN PSYCHOLOGICAL BARRIERS IN CERTAIN eby AUCTIONS Oe F Lu & vid E. Giles* Asr eprme of Eoomis, Uiversiy of Viori Viori, B.C., Cd V8W Y Sepemer, 006 Usig geerlizios of Beford s Lw we es for he see of psyhologil rriers vrious prie levels i eby uios for professiol fooll ikes. Our empiril resuls idie h his hypohesis o e rejeed. Keywords: JEL Clssifiios: Psyhologil rriers, uio pries, Beford s lw C, C6, 44 * We re greful o Sophie Heslig for her ssise wih d olleio, d o o Ferguso for his helpful ommes.. Iroduio Auhor Co: vid Giles, ep. of Eoomis, Uiversiy of Viori, P.O. Box 700, STN CSC, Viori, B.C., Cd V8W Y; e-mil: dgiles@uvi.; FAX: (50) 7-64

2 . Iroduio Severl sudies hve ivesiged he possiiliy of psyhologil rriers or resise levels i fiil mrkes. os of hese sudies (e.g., oldso d Kim, 993; Ley d Vri, 994; Koedijk d Sork, 994; d Cyree e l., 999) ssume h he digis of he ssoied pries re uiformly disriued. However, e Ceuser e l. (997) use Beford s Lw o show h his ssumpio is ipproprie, d heir lysis quesios previous fidigs of suh rriers. I is url o sk wheher here re similr resise levels i oher mrkes, d uio prie d provides oe soure of iformio o ddress his quesio. Why wo eri idders go higher h some prie level? Wh re he hresholds for he ids? We lyze eby uio d for professiol fooll ikes d fid o sisil evidee of psyhologil rriers vriey of levels. The mehism ehid his reserh is o exr he sigifi digis of he uio pries o exmie wheher heir disriuios follow geerlized versios of Beford s Lw, whih re irodued i seio. Seio 3 desries our d d he use of -vlues defied y e Ceuser e l. (997) o pure iformio ou he relive proximiy o psyhologil rrier. Our empiril es resuls pper i seio 4, d some oludig remrks re give i seio 5.. Beford s Lw Beford (938) re-disovered Newom s (88) fidig h my urlly ourrig umeril d exhii speil feures wih regrd o he firs sigifi digi. He showed h he disriuio of firs sigifi digis is o-uiform. Beford s Lw idies h i umers from my rel-life d soures, he firs sigifi digi ours wih proiliy of lmos 3%, o.% (/9). The igger he digi, he less likely i is o our s he firs sigifi digi. Speifilly, le deoe he firs sigifi digi i umer N. If N = 95, he = 9; if N = , he = 4. Beford s Lw ses h Pr 0 k.[ = k] = log [ + (/ )] ; k =,,., 9. Wh is less well kow is h he joi disriuio for seod d higher sigifi digis is k i Pr.[ = d,..., k = dk ] = log0[ + ( di 0 ) ], k i=

3 for d {,,...,9} d d {0,,,...,9}, j >. So, he proiliy of he firs hree sigifi i j digis eig 5, d 8 is log 0 [ + ] = This disriuio is ivri o sle 58 (Pikhm, 96). Sok pries d x d (Geyer d Willimso, 004; Nigrii, 99; Nigrii d iermier 997) oey Beford s Lw, s do wiig ids for eri eby uios (Giles, 006). Beford s Lw provides foudio for esig for resise levels i mrkes. e Ceuser e l. (997) proposed es for psyhologil rriers usig ylil permuios of he oserved dily sok reurs d oluded h here ws o evidee of psyhologil rriers i vrious U.S., U.K. d Jpese sok mrke idies. Aggrwl d Luey (006) lysed gold pries i similr wy d foud evidee h psyhologil rriers he 00 s digis (prie levels suh s $00, $300, e.). 3. d -sisis Giles (006) hs show h he losig pries of suessful eby uios for pro-fooll gme ikes sisfy Beford s Lw, whih suggess he see of mrke ollusio mog idders or ierveio y sellers. Usig his d, our smple omprises ll,59 suessful uios for ikes for professiol U.S. fooll gmes i he eby eve ikes egory ewee 5 Novemer d eemer 004. As Beford s Lw is sisfied, we use hese d o osru sisis for esig for he exisee of vrious psyhologil rriers. We deoe he suessful uio pries s P, =,,, wih =,59. We osider poeil rriers he levels, 300, 400,, e. (oldso, 990; oldso d Kim, 993; Ley d Vri, 994; e Ceuser e l., 997), or: k 00, k =,,.., e. () I order o represe psyhologil rriers ll levels, we should osider rriers he levels, 0, 0,, 00, 00,,000, 0000,, e., for k 0, k =,,, 9; =, -, 0,, ; () or he more omprehesive se of levels, 0,,,, 00, 0,, 000, 00,, e., for k 0, k = 0,,, 99; =, -, 0,,. (3) We he eed o defie -vlues, whih re me o rry he iformio o he relive loseess o rrier. Correspodig o he ove levels defied i equio (), 3

4 = [ P]mod00, () where [P ] is he ieger pr of he pries, mod00 sds for modulo 00, d lerly, he vlues re mde up of he pir of rilig digis preedig he deiml poi. For rriers he levels defied y equio () d (3), he -vlues re (log p )mod [00 0 ]mod00 = (log P )mod [000 0 ]mod00 = (3) () seles he pir of digis i P preedig he deiml poi; seles he seod d hird sigifi digis; d piks he hird d fourh sigifi digis. y reserhers wrogly ssume h he -vlues re uiformly disriued i he see of psyhologil rrier, les i lrge smples. e Ceuser e l. (997) derived of he limi disriuios of he -vlues, whih we hve pplied i pr of he followig lysis: r + 0 limpr.( ) lim log i + + r = r k = k = L = (4) r i 00 = i= i = i + r = r k 9 i 0 + k + limpr.( = k) = log (5) i= i 0 + k i 0 + j 0 + k + limpr.( = k) = log. (6) 3 i= j = 0 i 0 + j 0 + k The limi proiliies i equios (4), (5) d (6) give us he relive frequeies over he smple =,,, of he -vlues whe here re o psyhologil rriers i he mrke, d he smple size eds o ifiiy. The frequeies for he -vlues i our d re expeed o e o-uiform for d, d uiform for. Alhough he uiformiy of seems o riolize he sdrd ssumpio of uiformiy esig for he presee of psyhologil rriers, oly pure psyhologil rriers he levels, 00, 300,, 3400, 3500,., e., d he series hose levels is o mulipliively regeerive (e Ceuser e l., 997). Therefore, resuls h re oied from ivesigig oly he he wrog olusio, d i is ruil h he d vlues ould led o vlues re lso osidered. 4

5 4. Tes resuls Firs, we es wheher our d exhii properies osise wih he limi disriuios i equios (4) (6). The ull hypohesis is h here re o psyhologil rriers i pries for pro-fooll ikes i he eby uio mrke. The Kolmogorov-Smirov (K-S) es d oher o-prmeri ess suh s iegred deviios ess of he Crmér-vo ises ype re o pproprie here euse of he irulr ure of our d (Giles, 006). Kuiper s es is similr o he fmilir K-S es, whih uses he differee sisis omies + d + d. Kuiper's es io oe sisi, mkig he es s sesiive i he ils s he medi, d mkig i ivri uder ylil d rsformios. If he empiril disriuio fuio for he smple is F (x), d he ull populio disriuio fuio is F ( ) 0 x, Kuiper s es sisi is: where V, + = + + = sup. ( F ( x) F0 ( x)) < x< = sup. ( F0 ( x) F ( x)) < x<. Aoher impor feure of Kuiper s es is h he ull disriuio of he es sisi is ivri o he hypohesized disriuio, for ll. Sephes (970) ules riil vlues for he ull disriuio of he rsformed sisi V / = V ( * /. ) The 0%, 5% d % riil vlues re.60,.747 d.00 respeively, for ll. Our vlues for, d re 0.947,.663 d 4.0 respeively. So, our vlues for d re osise wih he hypohesis of o psyhologil rriers, he 5% level, u our vlue for implies rejeio of his hypohesis gis he mos geerl of he lerive hypoheses osidered. * V Our goodess-of-fi ess re desiged o exmie wheher he empiril d hve hrerisis osise wih he limi disriuios for he -vlues disussed ove. e Ceuser e l. (997) 5

6 show h exremely lrge smple sizes re eeded for hese limi disriuios o e pplile, d he fiie-smple disriuios re d-depede. We follow e Ceuser e l. (998) d lso es for psyhologil rriers usig ylil permuios of he d. Psyhologil rriers geere ormlly low umer of -vlues i he 00 regio. Le Ψ e se of -vlues i he eighourhood of psyhologil rrier, suh s Ψ = {00}, Ψ = {99, 00, 0}, e. For some Ψ, he uio prie P is i eighourhood of psyhologil rrier if Ψ. Le I Ψ ( ) = i his se, zero oherwise, d defie ψ = = I Ψ ( ). For y d Ψ, suffiiely smll ψ sigls psyhologil rrier. The disriuio of ψ is d-speifi, u p-vlues for he hypohesis of o psyhologil rrier e oosrpped s he proporio of oosrpped ψ vlues less h he empiril ψ. Our experimel resuls, for 0,000 repeiios, pper i Tle. The ull hypohesis of o psyhologil rriers i he uio pries for fooll ikes o e rejeed. 5. Colusios Usig some geerlizios of Beford s Lw we fid h psyhologil rriers re se i eby uios for pro-fooll ikes. This is osise wih e Ceuser e l. s (997) fidig vrious sok idies, u orss wih Aggrwl d Luey s (006) resuls for gold pries. Our resuls hve impliios for users of eby s proxy iddig servie. For exmple, offerig mximum id of $00.0 i iipio h oppoes hve psyhologil rrier or jus uder $00 my o e vile sregy. I would e ieresig o exed his lysis o oher eby egories. 6

7 Tle : Boosrp Resuls Ψ ψ-sisi Boosrp p-vlue e (%) {00} {99,.,0} {98,.,0} {97,.,03} {95,.,05} {90,.,0} {00} {99,.,0} {98,.,0} {97,.,03} {95,..05} {90,.,0} {00} {99,.,0} {98,.,0} {97,.,03} {95,.,05} {90,.,0} Noe: {97,, 03} implies {97, 98, 99, 00, 0, 0, 03}, e. 7

8 Referees Aggrwl, R. d B.. Luey, 006. Psyhologil rriers i gold pries?, Review of Fiil Eoomis, i press. Beford, F., 938. The lw of omlous umers, Proeedigs of he Ameri Philosophil Soiey 78, Cyree, K. B.,. L. omi,. A. Louo d E. J. Yoio (999). Evidee of psyhologil rriers i he odiiol momes of mjor world sok idies, Review of Fiil Eoomis 8, e Ceuser,. K. J., G. hee d T. Shem, 998. O he hypohesis of psyhologil rriers i sok mrkes d Beford s lw, Jourl of Empiril Fie 5, oldso, R. G. d H. Y. Kim, 993. Prie rriers i he ow Joes idusril verge, Jourl of Fiil d Quive Alysis 8, Geyer, C. L. d P. P. Willimso, 004. eeig frud i d ses usig Beford s lw, Commuiios i Sisis B 33, Giles,. E. A., 006. Beford s lw d urlly ourrig pries i eri eby uios, Applied Eoomis Leers, i press. Koedijk, K. G. d P. A. Sork (994). Should we re? Psyhologil rriers i sok mrkes, Eoomis Leers 44, Kuiper, N. H., 959. Alerive proof of heorem of Birhum d Pyke, Als of hemil Sisis 30, 5-5. Ley, E. d H. Vri, 994. Are here psyhologil rriers i he ow-joes idex?, Applied Fiil Eoomis 4, 7-4. Newom, S. (88), Noe o he frequey of use of differe digis i url umers, Ameri Jourl of hemis 4, Nigrii,. J., 99. The deeio of iome x evsio hrough lysis of digil frequeies, Ph.. isserio, Uiversiy of Ciii. Nigrii,. J. d L. I. iermier, 997. The use of Beford's lw s id i lyil proedures, Audiig: A Jourl of Prie d Theory 6, Pikhm, R. S., 96. O he disriuio of firs sigifi digis, Als of hemil Sisis

Review for the Midterm Exam.

Review for the Midterm Exam. Review for he iderm Exm Rememer! Gross re e re Vriles suh s,, /, p / p, r, d R re gross res 2 You should kow he disiio ewee he fesile se d he udge se, d kow how o derive hem The Fesile Se Wihou goverme

More information

Coefficient Inequalities for Certain Subclasses. of Analytic Functions

Coefficient Inequalities for Certain Subclasses. of Analytic Functions I. Jourl o Mh. Alysis, Vol., 00, o. 6, 77-78 Coeiie Iequliies or Ceri Sulsses o Alyi Fuios T. Rm Reddy d * R.. Shrm Deprme o Mhemis, Kkiy Uiversiy Wrgl 506009, Adhr Prdesh, Idi reddyr@yhoo.om, *rshrm_005@yhoo.o.i

More information

Week 8 Lecture 3: Problems 49, 50 Fourier analysis Courseware pp (don t look at French very confusing look in the Courseware instead)

Week 8 Lecture 3: Problems 49, 50 Fourier analysis Courseware pp (don t look at French very confusing look in the Courseware instead) Week 8 Lecure 3: Problems 49, 5 Fourier lysis Coursewre pp 6-7 (do look Frech very cofusig look i he Coursewre ised) Fourier lysis ivolves ddig wves d heir hrmoics, so i would hve urlly followed fer he

More information

The Structures of Fuzzifying Measure

The Structures of Fuzzifying Measure Sesors & Trasduers Vol 7 Issue 5 May 04 pp 56-6 Sesors & Trasduers 04 by IFSA Publishig S L hp://wwwsesorsporalom The Sruures of Fuzzifyig Measure Shi Hua Luo Peg Che Qia Sheg Zhag Shool of Saisis Jiagxi

More information

DIFFERENCE EQUATIONS

DIFFERENCE EQUATIONS DIFFERECE EQUATIOS Lier Cos-Coeffiie Differee Eqios Differee Eqios I disree-ime ssems, esseil feres of ip d op sigls pper ol speifi iss of ime, d he m o e defied ewee disree ime seps or he m e os. These

More information

ERROR ESTIMATES FOR APPROXIMATING THE FOURIER TRANSFORM OF FUNCTIONS OF BOUNDED VARIATION

ERROR ESTIMATES FOR APPROXIMATING THE FOURIER TRANSFORM OF FUNCTIONS OF BOUNDED VARIATION ERROR ESTIMATES FOR APPROXIMATING THE FOURIER TRANSFORM OF FUNCTIONS OF BOUNDED VARIATION N.S. BARNETT, S.S. DRAGOMIR, AND G. HANNA Absrc. I his pper we poi ou pproximio for he Fourier rsform for fucios

More information

SPH3UW Unit 7.5 Snell s Law Page 1 of Total Internal Reflection occurs when the incoming refraction angle is

SPH3UW Unit 7.5 Snell s Law Page 1 of Total Internal Reflection occurs when the incoming refraction angle is SPH3UW Uit 7.5 Sell s Lw Pge 1 of 7 Notes Physis Tool ox Refrtio is the hge i diretio of wve due to hge i its speed. This is most ommoly see whe wve psses from oe medium to other. Idex of refrtio lso lled

More information

LOCUS 1. Definite Integration CONCEPT NOTES. 01. Basic Properties. 02. More Properties. 03. Integration as Limit of a Sum

LOCUS 1. Definite Integration CONCEPT NOTES. 01. Basic Properties. 02. More Properties. 03. Integration as Limit of a Sum LOCUS Defiie egrio CONCEPT NOTES. Bsic Properies. More Properies. egrio s Limi of Sum LOCUS Defiie egrio As eplied i he chper iled egrio Bsics, he fudmel heorem of clculus ells us h o evlue he re uder

More information

SLOW INCREASING FUNCTIONS AND THEIR APPLICATIONS TO SOME PROBLEMS IN NUMBER THEORY

SLOW INCREASING FUNCTIONS AND THEIR APPLICATIONS TO SOME PROBLEMS IN NUMBER THEORY VOL. 8, NO. 7, JULY 03 ISSN 89-6608 ARPN Jourl of Egieerig d Applied Sciece 006-03 Ai Reerch Publihig Nework (ARPN). All righ reerved. www.rpjourl.com SLOW INCREASING FUNCTIONS AND THEIR APPLICATIONS TO

More information

Supplement: Gauss-Jordan Reduction

Supplement: Gauss-Jordan Reduction Suppleme: Guss-Jord Reducio. Coefficie mri d ugmeed mri: The coefficie mri derived from sysem of lier equios m m m m is m m m A O d he ugmeed mri derived from he ove sysem of lier equios is [ ] m m m m

More information

Reinforcement Learning

Reinforcement Learning Reiforceme Corol lerig Corol polices h choose opiml cios Q lerig Covergece Chper 13 Reiforceme 1 Corol Cosider lerig o choose cios, e.g., Robo lerig o dock o bery chrger o choose cios o opimize fcory oupu

More information

NEIGHBOURHOODS OF A CERTAIN SUBCLASS OF STARLIKE FUNCTIONS. P. Thirupathi Reddy. E. mail:

NEIGHBOURHOODS OF A CERTAIN SUBCLASS OF STARLIKE FUNCTIONS. P. Thirupathi Reddy. E. mail: NEIGHOURHOOD OF CERTIN UCL OF TRLIKE FUNCTION P Tirupi Reddy E mil: reddyp@yooom sr: Te im o is pper is o rodue e lss ( sulss o ( sisyig e odio wi is ( ) p < 0< E We sudy eigouroods o is lss d lso prove

More information

Existence Of Solutions For Nonlinear Fractional Differential Equation With Integral Boundary Conditions

Existence Of Solutions For Nonlinear Fractional Differential Equation With Integral Boundary Conditions Reserch Ivey: Ieriol Jourl Of Egieerig Ad Sciece Vol., Issue (April 3), Pp 8- Iss(e): 78-47, Iss(p):39-6483, Www.Reserchivey.Com Exisece Of Soluios For Nolier Frciol Differeil Equio Wih Iegrl Boudry Codiios,

More information

ONE RANDOM VARIABLE F ( ) [ ] x P X x x x 3

ONE RANDOM VARIABLE F ( ) [ ] x P X x x x 3 The Cumulive Disribuio Fucio (cd) ONE RANDOM VARIABLE cd is deied s he probbiliy o he eve { x}: F ( ) [ ] x P x x - Applies o discree s well s coiuous RV. Exmple: hree osses o coi x 8 3 x 8 8 F 3 3 7 x

More information

MATH 104: INTRODUCTORY ANALYSIS SPRING 2008/09 PROBLEM SET 10 SOLUTIONS. f m. and. f m = 0. and x i = a + i. a + i. a + n 2. n(n + 1) = a(b a) +

MATH 104: INTRODUCTORY ANALYSIS SPRING 2008/09 PROBLEM SET 10 SOLUTIONS. f m. and. f m = 0. and x i = a + i. a + i. a + n 2. n(n + 1) = a(b a) + MATH 04: INTRODUCTORY ANALYSIS SPRING 008/09 PROBLEM SET 0 SOLUTIONS Throughout this problem set, B[, b] will deote the set of ll rel-vlued futios bouded o [, b], C[, b] the set of ll rel-vlued futios

More information

An EOQ Model for Deteriorating Items Quadratic Demand and Shortages

An EOQ Model for Deteriorating Items Quadratic Demand and Shortages Ieriol Jourl of Iveory Corol d Mgeme Speil Issue o Ieriol Coferee o Applied Mhemis & Sisis De ISSN- 975-79, AACS. (www.sjourls.om) All righ reserved. A EOQ Model for Deeriorig Iems Qudri Demd d Shorges

More information

F.Y. Diploma : Sem. II [CE/CR/CS] Applied Mathematics

F.Y. Diploma : Sem. II [CE/CR/CS] Applied Mathematics F.Y. Diplom : Sem. II [CE/CR/CS] Applied Mhemics Prelim Quesio Pper Soluio Q. Aemp y FIVE of he followig : [0] Q. () Defie Eve d odd fucios. [] As.: A fucio f() is sid o e eve fucio if f() f() A fucio

More information

e t dt e t dt = lim e t dt T (1 e T ) = 1

e t dt e t dt = lim e t dt T (1 e T ) = 1 Improper Inegrls There re wo ypes of improper inegrls - hose wih infinie limis of inegrion, nd hose wih inegrnds h pproch some poin wihin he limis of inegrion. Firs we will consider inegrls wih infinie

More information

Big O Notation for Time Complexity of Algorithms

Big O Notation for Time Complexity of Algorithms BRONX COMMUNITY COLLEGE of he Ciy Uiversiy of New York DEPARTMENT OF MATHEMATICS AND COMPUTER SCIENCE CSI 33 Secio E01 Hadou 1 Fall 2014 Sepember 3, 2014 Big O Noaio for Time Complexiy of Algorihms Time

More information

On the Existence and Uniqueness of Solutions for. Q-Fractional Boundary Value Problem

On the Existence and Uniqueness of Solutions for. Q-Fractional Boundary Value Problem I Joural of ah Aalysis, Vol 5, 2, o 33, 69-63 O he Eisee ad Uiueess of Soluios for Q-Fraioal Boudary Value Prolem ousafa El-Shahed Deparme of ahemais, College of Eduaio Qassim Uiversiy PO Bo 377 Uizah,

More information

Figure 1. Optical paths for forward (green) and reverse (blue) double reflections returning to a traveling source.

Figure 1. Optical paths for forward (green) and reverse (blue) double reflections returning to a traveling source. ISSN: 456-648 jprmpedior@sisholrs.om Olie Puliio e: Ooer, 7 Volume, No. SCHOARS SCITECH RESEARCH ORGANIZATION Jourl of Progressie Reserh i Moder Physis d Chemisry www.sisholrs.om iffrio of de Broglie Wes

More information

Degree of Approximation of Conjugate of Signals (Functions) by Lower Triangular Matrix Operator

Degree of Approximation of Conjugate of Signals (Functions) by Lower Triangular Matrix Operator Alied Mhemics 2 2 448-452 doi:.4236/m.2.2226 Pulished Olie Decemer 2 (h://www.scirp.org/jourl/m) Degree of Aroimio of Cojuge of Sigls (Fucios) y Lower Trigulr Mri Oeror Asrc Vishu Nry Mishr Huzoor H. Kh

More information

CS 331 Design and Analysis of Algorithms. -- Divide and Conquer. Dr. Daisy Tang

CS 331 Design and Analysis of Algorithms. -- Divide and Conquer. Dr. Daisy Tang CS 33 Desig d Alysis of Algorithms -- Divide d Coquer Dr. Disy Tg Divide-Ad-Coquer Geerl ide: Divide problem ito subproblems of the sme id; solve subproblems usig the sme pproh, d ombie prtil solutios,

More information

ON BILATERAL GENERATING FUNCTIONS INVOLVING MODIFIED JACOBI POLYNOMIALS

ON BILATERAL GENERATING FUNCTIONS INVOLVING MODIFIED JACOBI POLYNOMIALS Jourl of Sciece d Ars Yer 4 No 227-6 24 ORIINAL AER ON BILATERAL ENERATIN FUNCTIONS INVOLVIN MODIFIED JACOBI OLYNOMIALS CHANDRA SEKHAR BERA Muscri received: 424; Acceed er: 3524; ublished olie: 3624 Absrc

More information

N! AND THE GAMMA FUNCTION

N! AND THE GAMMA FUNCTION N! AND THE GAMMA FUNCTION Cosider he produc of he firs posiive iegers- 3 4 5 6 (-) =! Oe calls his produc he facorial ad has ha produc of he firs five iegers equals 5!=0. Direcly relaed o he discree! fucio

More information

BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS

BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS Opimal ear Forecasig Alhough we have o meioed hem explicily so far i he course, here are geeral saisical priciples for derivig he bes liear forecas, ad

More information

Inference on One Population Mean Hypothesis Testing

Inference on One Population Mean Hypothesis Testing Iferece o Oe Popultio Me ypothesis Testig Scerio 1. Whe the popultio is orml, d the popultio vrice is kow i. i. d. Dt : X 1, X,, X ~ N(, ypothesis test, for istce: Exmple: : : : : : 5'7" (ull hypothesis:

More information

NOTES ON BERNOULLI NUMBERS AND EULER S SUMMATION FORMULA. B r = [m = 0] r

NOTES ON BERNOULLI NUMBERS AND EULER S SUMMATION FORMULA. B r = [m = 0] r NOTES ON BERNOULLI NUMBERS AND EULER S SUMMATION FORMULA MARK WILDON. Beroulli umbers.. Defiiio. We defie he Beroulli umbers B m for m by m ( m + ( B r [m ] r r Beroulli umbers re med fer Joh Beroulli

More information

Introduction of Fourier Series to First Year Undergraduate Engineering Students

Introduction of Fourier Series to First Year Undergraduate Engineering Students Itertiol Jourl of Adved Reserh i Computer Egieerig & Tehology (IJARCET) Volume 3 Issue 4, April 4 Itrodutio of Fourier Series to First Yer Udergrdute Egieerig Studets Pwr Tejkumr Dtttry, Hiremth Suresh

More information

Limit of a function:

Limit of a function: - Limit of fuctio: We sy tht f ( ) eists d is equl with (rel) umer L if f( ) gets s close s we wt to L if is close eough to (This defiitio c e geerlized for L y syig tht f( ) ecomes s lrge (or s lrge egtive

More information

MAHALAKSHMI ENGINEERING COLLEGE TIRUCHIRAPALLI

MAHALAKSHMI ENGINEERING COLLEGE TIRUCHIRAPALLI MAHALAKSHMI EGIEERIG COLLEGE TIRUCHIRAALLI 6 QUESTIO BAK - ASWERS -SEMESTER: V MA 6 - ROBABILITY AD QUEUEIG THEORY UIT IV:QUEUEIG THEORY ART-A Quesio : AUC M / J Wha are he haraerisis of a queueig heory?

More information

Interval Estimation. Consider a random variable X with a mean of X. Let X be distributed as X X

Interval Estimation. Consider a random variable X with a mean of X. Let X be distributed as X X ECON 37: Ecoomercs Hypohess Tesg Iervl Esmo Wh we hve doe so fr s o udersd how we c ob esmors of ecoomcs reloshp we wsh o sudy. The queso s how comforble re we wh our esmors? We frs exme how o produce

More information

Riemann Integral Oct 31, such that

Riemann Integral Oct 31, such that Riem Itegrl Ot 31, 2007 Itegrtio of Step Futios A prtitio P of [, ] is olletio {x k } k=0 suh tht = x 0 < x 1 < < x 1 < x =. More suitly, prtitio is fiite suset of [, ] otiig d. It is helpful to thik of

More information

ACCURACY ASSESSEMENT OF SHORT RUN MACROECONOMIC FORECASTS IN ROMANIA

ACCURACY ASSESSEMENT OF SHORT RUN MACROECONOMIC FORECASTS IN ROMANIA Mihel Bru 6 ISSN 07-789 Mihel Bru, Aury Assesseme of Shor u Mroeoomi Foress i omi, Eoomis & Soiology, Vol. 5, No, 0, pp. 6-38. Mihel Bru Fuly of Cyereis, Sisis d Eoomi Iformis Buhres Uiversiy of Eoomis

More information

B. Examples 1. Finite Sums finite sums are an example of Riemann Sums in which each subinterval has the same length and the same x i

B. Examples 1. Finite Sums finite sums are an example of Riemann Sums in which each subinterval has the same length and the same x i Mth 06 Clculus Sec. 5.: The Defiite Itegrl I. Riem Sums A. Def : Give y=f(x):. Let f e defied o closed itervl[,].. Prtitio [,] ito suitervls[x (i-),x i ] of legth Δx i = x i -x (i-). Let P deote the prtitio

More information

Introduction to Matrix Algebra

Introduction to Matrix Algebra Itrodutio to Mtri Alger George H Olso, Ph D Dotorl Progrm i Edutiol Ledership Applhi Stte Uiversit Septemer Wht is mtri? Dimesios d order of mtri A p q dimesioed mtri is p (rows) q (olums) rr of umers,

More information

Economics 8723 Macroeconomic Theory Problem Set 2 Professor Sanjay Chugh Spring 2017

Economics 8723 Macroeconomic Theory Problem Set 2 Professor Sanjay Chugh Spring 2017 Deparme of Ecoomics The Ohio Sae Uiversiy Ecoomics 8723 Macroecoomic Theory Problem Se 2 Professor Sajay Chugh Sprig 207 Labor Icome Taxes, Nash-Bargaied Wages, ad Proporioally-Bargaied Wages. I a ecoomy

More information

Week 13 Notes: 1) Riemann Sum. Aim: Compute Area Under a Graph. Suppose we want to find out the area of a graph, like the one on the right:

Week 13 Notes: 1) Riemann Sum. Aim: Compute Area Under a Graph. Suppose we want to find out the area of a graph, like the one on the right: Week 1 Notes: 1) Riem Sum Aim: Compute Are Uder Grph Suppose we wt to fid out the re of grph, like the oe o the right: We wt to kow the re of the red re. Here re some wys to pproximte the re: We cut the

More information

Lecture 15 First Properties of the Brownian Motion

Lecture 15 First Properties of the Brownian Motion Lecure 15: Firs Properies 1 of 8 Course: Theory of Probabiliy II Term: Sprig 2015 Isrucor: Gorda Zikovic Lecure 15 Firs Properies of he Browia Moio This lecure deals wih some of he more immediae properies

More information

STK4080/9080 Survival and event history analysis

STK4080/9080 Survival and event history analysis STK48/98 Survival ad eve hisory aalysis Marigales i discree ime Cosider a sochasic process The process M is a marigale if Lecure 3: Marigales ad oher sochasic processes i discree ime (recap) where (formally

More information

Another Approach to Solution. of Fuzzy Differential Equations

Another Approach to Solution. of Fuzzy Differential Equations Applied Memil Siees, Vol. 4, 00, o. 6, 777-790 Aoer Appro o Soluio o Fuzz Diereil Equios C. Durism Deprme o Memis ogu Egieerig College Peruduri, Erode-68 05 Tmildu, Idi d@kogu..i B. Us Deprme o Memis ogu

More information

Lecture 8 April 18, 2018

Lecture 8 April 18, 2018 Sas 300C: Theory of Saisics Sprig 2018 Lecure 8 April 18, 2018 Prof Emmauel Cades Scribe: Emmauel Cades Oulie Ageda: Muliple Tesig Problems 1 Empirical Process Viewpoi of BHq 2 Empirical Process Viewpoi

More information

Transient Solution of the M/M/C 1 Queue with Additional C 2 Servers for Longer Queues and Balking

Transient Solution of the M/M/C 1 Queue with Additional C 2 Servers for Longer Queues and Balking Jourl of Mhemics d Sisics 4 (): 2-25, 28 ISSN 549-3644 28 Sciece ublicios Trsie Soluio of he M/M/C Queue wih Addiiol C 2 Servers for Loger Queues d Blkig R. O. Al-Seedy, A. A. El-Sherbiy,,2 S. A. EL-Shehwy

More information

HOMEWORK 6 - INTEGRATION. READING: Read the following parts from the Calculus Biographies that I have given (online supplement of our textbook):

HOMEWORK 6 - INTEGRATION. READING: Read the following parts from the Calculus Biographies that I have given (online supplement of our textbook): MAT 3 CALCULUS I 5.. Dokuz Eylül Uiversiy Fculy of Sciece Deprme of Mhemics Isrucors: Egi Mermu d Cell Cem Srıoğlu HOMEWORK 6 - INTEGRATION web: hp://kisi.deu.edu.r/egi.mermu/ Tebook: Uiversiy Clculus,

More information

Fermat Numbers in Multinomial Coefficients

Fermat Numbers in Multinomial Coefficients 1 3 47 6 3 11 Joural of Ieger Sequeces, Vol. 17 (014, Aricle 14.3. Ferma Numbers i Muliomial Coefficies Shae Cher Deparme of Mahemaics Zhejiag Uiversiy Hagzhou, 31007 Chia chexiaohag9@gmail.com Absrac

More information

4.8 Improper Integrals

4.8 Improper Integrals 4.8 Improper Inegrls Well you ve mde i hrough ll he inegrion echniques. Congrs! Unforunely for us, we sill need o cover one more inegrl. They re clled Improper Inegrls. A his poin, we ve only del wih inegrls

More information

Extension of Hardy Inequality on Weighted Sequence Spaces

Extension of Hardy Inequality on Weighted Sequence Spaces Jourl of Scieces Islic Reublic of Ir 20(2): 59-66 (2009) Uiversiy of ehr ISS 06-04 h://sciecesucir Eesio of Hrdy Iequliy o Weighed Sequece Sces R Lshriour d D Foroui 2 Dere of Mheics Fculy of Mheics Uiversiy

More information

ENGR 3861 Digital Logic Boolean Algebra. Fall 2007

ENGR 3861 Digital Logic Boolean Algebra. Fall 2007 ENGR 386 Digitl Logi Boole Alger Fll 007 Boole Alger A two vlued lgeri system Iveted y George Boole i 854 Very similr to the lger tht you lredy kow Sme opertios ivolved dditio sutrtio multiplitio Repled

More information

Extremal graph theory II: K t and K t,t

Extremal graph theory II: K t and K t,t Exremal graph heory II: K ad K, Lecure Graph Theory 06 EPFL Frak de Zeeuw I his lecure, we geeralize he wo mai heorems from he las lecure, from riagles K 3 o complee graphs K, ad from squares K, o complee

More information

LIPSCHITZ ESTIMATES FOR MULTILINEAR COMMUTATOR OF MARCINKIEWICZ OPERATOR

LIPSCHITZ ESTIMATES FOR MULTILINEAR COMMUTATOR OF MARCINKIEWICZ OPERATOR Reseh d ouiios i heis d hei Siees Vo. Issue Pges -46 ISSN 9-699 Puished Oie o Deee 7 Joi Adei Pess h://oideiess.e IPSHITZ ESTIATES FOR UTIINEAR OUTATOR OF ARINKIEWIZ OPERATOR DAZHAO HEN Dee o Siee d Ioio

More information

1 Notes on Little s Law (l = λw)

1 Notes on Little s Law (l = λw) Copyrigh c 26 by Karl Sigma Noes o Lile s Law (l λw) We cosider here a famous ad very useful law i queueig heory called Lile s Law, also kow as l λw, which assers ha he ime average umber of cusomers i

More information

MATH 104: INTRODUCTORY ANALYSIS SPRING 2009/10 PROBLEM SET 8 SOLUTIONS. and x i = a + i. i + n(n + 1)(2n + 1) + 2a. (b a)3 6n 2

MATH 104: INTRODUCTORY ANALYSIS SPRING 2009/10 PROBLEM SET 8 SOLUTIONS. and x i = a + i. i + n(n + 1)(2n + 1) + 2a. (b a)3 6n 2 MATH 104: INTRODUCTORY ANALYSIS SPRING 2009/10 PROBLEM SET 8 SOLUTIONS 6.9: Let f(x) { x 2 if x Q [, b], 0 if x (R \ Q) [, b], where > 0. Prove tht b. Solutio. Let P { x 0 < x 1 < < x b} be regulr prtitio

More information

ECE-314 Fall 2012 Review Questions

ECE-314 Fall 2012 Review Questions ECE-34 Fall 0 Review Quesios. A liear ime-ivaria sysem has he ipu-oupu characerisics show i he firs row of he diagram below. Deermie he oupu for he ipu show o he secod row of he diagram. Jusify your aswer.

More information

Project 3: Using Identities to Rewrite Expressions

Project 3: Using Identities to Rewrite Expressions MAT 5 Projet 3: Usig Idetities to Rewrite Expressios Wldis I lger, equtios tht desrie properties or ptters re ofte lled idetities. Idetities desrie expressio e repled with equl or equivlet expressio tht

More information

EXPONENTS AND LOGARITHMS

EXPONENTS AND LOGARITHMS 978--07-6- Mthemtis Stdrd Level for IB Diplom Eerpt EXPONENTS AND LOGARITHMS WHAT YOU NEED TO KNOW The rules of epoets: m = m+ m = m ( m ) = m m m = = () = The reltioship etwee epoets d rithms: = g where

More information

Graphing Review Part 3: Polynomials

Graphing Review Part 3: Polynomials Grphig Review Prt : Polomils Prbols Recll, tht the grph of f ( ) is prbol. It is eve fuctio, hece it is smmetric bout the bout the -is. This mes tht f ( ) f ( ). Its grph is show below. The poit ( 0,0)

More information

Chapter Simpson s 1/3 Rule of Integration. ( x)

Chapter Simpson s 1/3 Rule of Integration. ( x) Cper 7. Smpso s / Rule o Iegro Aer redg s per, you sould e le o. derve e ormul or Smpso s / rule o egro,. use Smpso s / rule o solve egrls,. develop e ormul or mulple-segme Smpso s / rule o egro,. use

More information

12.2 The Definite Integrals (5.2)

12.2 The Definite Integrals (5.2) Course: Aelerted Egieerig Clulus I Istrutor: Mihel Medvisky. The Defiite Itegrls 5. Def: Let fx e defied o itervl [,]. Divide [,] ito suitervls of equl width Δx, so x, x + Δx, x + jδx, x. Let x j j e ritrry

More information

Contraction Mapping Principle Approach to Differential Equations

Contraction Mapping Principle Approach to Differential Equations epl Journl of Science echnology 0 (009) 49-53 Conrcion pping Principle pproch o Differenil Equions Bishnu P. Dhungn Deprmen of hemics, hendr Rn Cmpus ribhuvn Universiy, Khmu epl bsrc Using n eension of

More information

Math 2414 Homework Set 7 Solutions 10 Points

Math 2414 Homework Set 7 Solutions 10 Points Mah Homework Se 7 Soluios 0 Pois #. ( ps) Firs verify ha we ca use he iegral es. The erms are clearly posiive (he epoeial is always posiive ad + is posiive if >, which i is i his case). For decreasig we

More information

L-functions and Class Numbers

L-functions and Class Numbers L-fucios ad Class Numbers Sude Number Theory Semiar S. M.-C. 4 Sepember 05 We follow Romyar Sharifi s Noes o Iwasawa Theory, wih some help from Neukirch s Algebraic Number Theory. L-fucios of Dirichle

More information

n 2 + 3n + 1 4n = n2 + 3n + 1 n n 2 = n + 1

n 2 + 3n + 1 4n = n2 + 3n + 1 n n 2 = n + 1 Ifiite Series Some Tests for Divergece d Covergece Divergece Test: If lim u or if the limit does ot exist, the series diverget. + 3 + 4 + 3 EXAMPLE: Show tht the series diverges. = u = + 3 + 4 + 3 + 3

More information

Let. Then. k n. And. Φ npq. npq. ε 2. Φ npq npq. npq. = ε. k will be very close to p. If n is large enough, the ratio n

Let. Then. k n. And. Φ npq. npq. ε 2. Φ npq npq. npq. = ε. k will be very close to p. If n is large enough, the ratio n Let The m ( ) ( + ) where > very smll { } { ( ) ( + ) } Ad + + { } Φ Φ Φ Φ Φ Let, the Φ( ) lim This is lled thelw of lrge umbers If is lrge eough, the rtio will be very lose to. Exmle -Tossig oi times.

More information

Approximate Integration

Approximate Integration Study Sheet (7.7) Approimte Itegrtio I this sectio, we will ler: How to fid pproimte vlues of defiite itegrls. There re two situtios i which it is impossile to fid the ect vlue of defiite itegrl. Situtio:

More information

REAL ANALYSIS I HOMEWORK 3. Chapter 1

REAL ANALYSIS I HOMEWORK 3. Chapter 1 REAL ANALYSIS I HOMEWORK 3 CİHAN BAHRAN The quesions re from Sein nd Shkrchi s e. Chper 1 18. Prove he following sserion: Every mesurble funcion is he limi.e. of sequence of coninuous funcions. We firs

More information

Horizontal product differentiation: Consumers have different preferences along one dimension of a good.

Horizontal product differentiation: Consumers have different preferences along one dimension of a good. Produc Differeiio Firms see o e uique log some dimesio h is vlued y cosumers. If he firm/roduc is uique i some resec, he firm c commd rice greer h cos. Horizol roduc differeiio: Cosumers hve differe refereces

More information

Comparison between Fourier and Corrected Fourier Series Methods

Comparison between Fourier and Corrected Fourier Series Methods Malaysia Joural of Mahemaical Scieces 7(): 73-8 (13) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES Joural homepage: hp://eispem.upm.edu.my/oural Compariso bewee Fourier ad Correced Fourier Series Mehods 1

More information

Special Functions. Leon M. Hall. Professor of Mathematics University of Missouri-Rolla. Copyright c 1995 by Leon M. Hall. All rights reserved.

Special Functions. Leon M. Hall. Professor of Mathematics University of Missouri-Rolla. Copyright c 1995 by Leon M. Hall. All rights reserved. Specil Fucios Leo M. Hll Professor of Mhemics Uiversiy of Missouri-Roll Copyrigh c 995 y Leo M. Hll. All righs reserved. Chper 5. Orhogol Fucios 5.. Geerig Fucios Cosider fucio f of wo vriles, ( x,), d

More information

a f(x)dx is divergent.

a f(x)dx is divergent. Mth 250 Exm 2 review. Thursdy Mrh 5. Brig TI 30 lultor but NO NOTES. Emphsis o setios 5.5, 6., 6.2, 6.3, 3.7, 6.6, 8., 8.2, 8.3, prt of 8.4; HW- 2; Q-. Kow for trig futios tht 0.707 2/2 d 0.866 3/2. From

More information

8.1 Arc Length. What is the length of a curve? How can we approximate it? We could do it following the pattern we ve used before

8.1 Arc Length. What is the length of a curve? How can we approximate it? We could do it following the pattern we ve used before 8.1 Arc Legth Wht is the legth of curve? How c we pproximte it? We could do it followig the ptter we ve used efore Use sequece of icresigly short segmets to pproximte the curve: As the segmets get smller

More information

Math 6710, Fall 2016 Final Exam Solutions

Math 6710, Fall 2016 Final Exam Solutions Mah 67, Fall 6 Fial Exam Soluios. Firs, a sude poied ou a suble hig: if P (X i p >, he X + + X (X + + X / ( evaluaes o / wih probabiliy p >. This is roublesome because a radom variable is supposed o be

More information

BINOMIAL THEOREM OBJECTIVE PROBLEMS in the expansion of ( 3 +kx ) are equal. Then k =

BINOMIAL THEOREM OBJECTIVE PROBLEMS in the expansion of ( 3 +kx ) are equal. Then k = wwwskshieduciocom BINOMIAL HEOREM OBJEIVE PROBLEMS he coefficies of, i e esio of k e equl he k /7 If e coefficie of, d ems i e i AP, e e vlue of is he coefficies i e,, 7 ems i e esio of e i AP he 7 7 em

More information

DERIVING THE DEMAND CURVE ASSUMING THAT THE MARGINAL UTILITY FUNCTIONS ARE LINEAR

DERIVING THE DEMAND CURVE ASSUMING THAT THE MARGINAL UTILITY FUNCTIONS ARE LINEAR Bllei UASVM, Horilre 65(/008 pissn 1843-554; eissn 1843-5394 DERIVING THE DEMAND CURVE ASSUMING THAT THE MARGINAL UTILITY FUNCTIONS ARE LINEAR Crii C. MERCE Uiveriy of Agrilrl iee d Veeriry Mediie Clj-Npo,

More information

Exercise 3 Stochastic Models of Manufacturing Systems 4T400, 6 May

Exercise 3 Stochastic Models of Manufacturing Systems 4T400, 6 May Exercise 3 Sochasic Models of Maufacurig Sysems 4T4, 6 May. Each week a very popular loery i Adorra pris 4 ickes. Each ickes has wo 4-digi umbers o i, oe visible ad he oher covered. The umbers are radomly

More information

Laws of Integral Indices

Laws of Integral Indices A Lws of Itegrl Idices A. Positive Itegrl Idices I, is clled the se, is clled the idex lso clled the expoet. mes times.... Exmple Simplify 5 6 c Solutio 8 5 6 c 6 Exmple Simplify Solutio The results i

More information

Thomas J. Osler Mathematics Department Rowan University Glassboro NJ Introduction

Thomas J. Osler Mathematics Department Rowan University Glassboro NJ Introduction Ot 0 006 Euler s little summtio formul d speil vlues of te zet futio Toms J Osler temtis Deprtmet Row Uiversity Glssboro J 0608 Osler@rowedu Itrodutio I tis ote we preset elemetry metod of determiig vlues

More information

two values, false and true used in mathematical logic, and to two voltage levels, LOW and HIGH used in switching circuits.

two values, false and true used in mathematical logic, and to two voltage levels, LOW and HIGH used in switching circuits. Digil Logi/Design. L. 3 Mrh 2, 26 3 Logi Ges nd Boolen Alger 3. CMOS Tehnology Digil devises re predominnly mnufured in he Complemenry-Mel-Oide-Semionduor (CMOS) ehnology. Two ypes of swihes, s disussed

More information

A new approach to Kudryashov s method for solving some nonlinear physical models

A new approach to Kudryashov s method for solving some nonlinear physical models Ieriol Jourl of Physicl Scieces Vol. 7() pp. 860-866 0 My 0 Avilble olie hp://www.cdeicourls.org/ijps DOI: 0.897/IJPS.07 ISS 99-90 0 Acdeic Jourls Full Legh Reserch Pper A ew pproch o Kudryshov s ehod

More information

Right Angle Trigonometry

Right Angle Trigonometry Righ gl Trigoomry I. si Fs d Dfiiios. Righ gl gl msurig 90. Srigh gl gl msurig 80. u gl gl msurig w 0 d 90 4. omplmry gls wo gls whos sum is 90 5. Supplmry gls wo gls whos sum is 80 6. Righ rigl rigl wih

More information

Convergence rates of approximate sums of Riemann integrals

Convergence rates of approximate sums of Riemann integrals Covergece rtes of pproximte sums of Riem itegrls Hiroyuki Tski Grdute School of Pure d Applied Sciece, Uiversity of Tsuku Tsuku Irki 5-857 Jp tski@mth.tsuku.c.jp Keywords : covergece rte; Riem sum; Riem

More information

λiv Av = 0 or ( λi Av ) = 0. In order for a vector v to be an eigenvector, it must be in the kernel of λi

λiv Av = 0 or ( λi Av ) = 0. In order for a vector v to be an eigenvector, it must be in the kernel of λi Liear lgebra Lecure #9 Noes This week s lecure focuses o wha migh be called he srucural aalysis of liear rasformaios Wha are he irisic properies of a liear rasformaio? re here ay fixed direcios? The discussio

More information

Review of the Riemann Integral

Review of the Riemann Integral Chpter 1 Review of the Riem Itegrl This chpter provides quick review of the bsic properties of the Riem itegrl. 1.0 Itegrls d Riem Sums Defiitio 1.0.1. Let [, b] be fiite, closed itervl. A prtitio P of

More information

Ideal Amplifier/Attenuator. Memoryless. where k is some real constant. Integrator. System with memory

Ideal Amplifier/Attenuator. Memoryless. where k is some real constant. Integrator. System with memory Liear Time-Ivaria Sysems (LTI Sysems) Oulie Basic Sysem Properies Memoryless ad sysems wih memory (saic or dyamic) Causal ad o-causal sysems (Causaliy) Liear ad o-liear sysems (Lieariy) Sable ad o-sable

More information

An Extension of Hermite Polynomials

An Extension of Hermite Polynomials I J Coemp Mh Scieces, Vol 9, 014, o 10, 455-459 HIKARI Ld, wwwm-hikricom hp://dxdoiorg/101988/ijcms0144663 A Exesio of Hermie Polyomils Ghulm Frid Globl Isiue Lhore New Grde Tow, Lhore, Pkis G M Hbibullh

More information

Frequency-domain Characteristics of Discrete-time LTI Systems

Frequency-domain Characteristics of Discrete-time LTI Systems requecy-domi Chrcteristics of Discrete-time LTI Systems Prof. Siripog Potisuk LTI System descriptio Previous bsis fuctio: uit smple or DT impulse The iput sequece is represeted s lier combitio of shifted

More information

Numbers (Part I) -- Solutions

Numbers (Part I) -- Solutions Ley College -- For AMATYC SML Mth Competitio Cochig Sessios v.., [/7/00] sme s /6/009 versio, with presettio improvemets Numbers Prt I) -- Solutios. The equtio b c 008 hs solutio i which, b, c re distict

More information

POWER SERIES R. E. SHOWALTER

POWER SERIES R. E. SHOWALTER POWER SERIES R. E. SHOWALTER. sequeces We deote by lim = tht the limit of the sequece { } is the umber. By this we me tht for y ε > 0 there is iteger N such tht < ε for ll itegers N. This mkes precise

More information

Riemann Integral and Bounded function. Ng Tze Beng

Riemann Integral and Bounded function. Ng Tze Beng Riem Itegrl d Bouded fuctio. Ng Tze Beg I geerlistio of re uder grph of fuctio, it is ormlly ssumed tht the fuctio uder cosidertio e ouded. For ouded fuctio, the rge of the fuctio is ouded d hece y suset

More information

f(bx) dx = f dx = dx l dx f(0) log b x a + l log b a 2ɛ log b a.

f(bx) dx = f dx = dx l dx f(0) log b x a + l log b a 2ɛ log b a. Eercise 5 For y < A < B, we hve B A f fb B d = = A B A f d f d For y ɛ >, there re N > δ >, such tht d The for y < A < δ d B > N, we hve ba f d f A bb f d l By ba A A B A bb ba fb d f d = ba < m{, b}δ

More information

Convergence rates of approximate sums of Riemann integrals

Convergence rates of approximate sums of Riemann integrals Jourl of Approximtio Theory 6 (9 477 49 www.elsevier.com/locte/jt Covergece rtes of pproximte sums of Riem itegrls Hiroyuki Tski Grdute School of Pure d Applied Sciece, Uiversity of Tsukub, Tsukub Ibrki

More information

ASYMPTOTIC BEHAVIOR OF SOLUTIONS OF DISCRETE EQUATIONS ON DISCRETE REAL TIME SCALES

ASYMPTOTIC BEHAVIOR OF SOLUTIONS OF DISCRETE EQUATIONS ON DISCRETE REAL TIME SCALES ASYPTOTI BEHAVIOR OF SOLUTIONS OF DISRETE EQUATIONS ON DISRETE REAL TIE SALES J. Dlí B. Válvíová 2 Bro Uversy of Tehology Bro zeh Repul 2 Deprme of heml Alyss d Appled hems Fuly of See Uversy of Zl Žl

More information

The Eigen Function of Linear Systems

The Eigen Function of Linear Systems 1/25/211 The Eige Fucio of Liear Sysems.doc 1/7 The Eige Fucio of Liear Sysems Recall ha ha we ca express (expad) a ime-limied sigal wih a weighed summaio of basis fucios: v ( ) a ψ ( ) = where v ( ) =

More information

2 f(x) dx = 1, 0. 2f(x 1) dx d) 1 4t t6 t. t 2 dt i)

2 f(x) dx = 1, 0. 2f(x 1) dx d) 1 4t t6 t. t 2 dt i) Mah PracTes Be sure o review Lab (ad all labs) There are los of good quesios o i a) Sae he Mea Value Theorem ad draw a graph ha illusraes b) Name a impora heorem where he Mea Value Theorem was used i he

More information

ROUTH-HURWITZ CRITERION

ROUTH-HURWITZ CRITERION Automti Cotrol Sytem, Deprtmet of Mehtroi Egieerig, Germ Jordi Uiverity Routh-Hurwitz Criterio ite.google.om/ite/ziydmoud 7 ROUTH-HURWITZ CRITERION The Routh-Hurwitz riterio i lytil proedure for determiig

More information

OLS bias for econometric models with errors-in-variables. The Lucas-critique Supplementary note to Lecture 17

OLS bias for econometric models with errors-in-variables. The Lucas-critique Supplementary note to Lecture 17 OLS bias for ecoomeric models wih errors-i-variables. The Lucas-criique Supplemeary oe o Lecure 7 RNy May 6, 03 Properies of OLS i RE models I Lecure 7 we discussed he followig example of a raioal expecaios

More information

LINEAR 2 nd ORDER DIFFERENTIAL EQUATIONS WITH CONSTANT COEFFICIENTS

LINEAR 2 nd ORDER DIFFERENTIAL EQUATIONS WITH CONSTANT COEFFICIENTS Diol Bgyoko (0) I.INTRODUCTION LINEAR d ORDER DIFFERENTIAL EQUATIONS WITH CONSTANT COEFFICIENTS I. Dfiiio All suh diffril quios s i h sdrd or oil form: y + y + y Q( x) dy d y wih y d y d dx dx whr,, d

More information

Addendum. Addendum. Vector Review. Department of Computer Science and Engineering 1-1

Addendum. Addendum. Vector Review. Department of Computer Science and Engineering 1-1 Addedum Addedum Vetor Review Deprtmet of Computer Siee d Egieerig - Coordite Systems Right hded oordite system Addedum y z Deprtmet of Computer Siee d Egieerig - -3 Deprtmet of Computer Siee d Egieerig

More information

3. Renewal Limit Theorems

3. Renewal Limit Theorems Virul Lborories > 14. Renewl Processes > 1 2 3 3. Renewl Limi Theorems In he inroducion o renewl processes, we noed h he rrivl ime process nd he couning process re inverses, in sens The rrivl ime process

More information

Suggested Solution for Pure Mathematics 2011 By Y.K. Ng (last update: 8/4/2011) Paper I. (b) (c)

Suggested Solution for Pure Mathematics 2011 By Y.K. Ng (last update: 8/4/2011) Paper I. (b) (c) per I. Le α 7 d β 7. The α d β re he roos o he equio, such h α α, β β, --- α β d αβ. For, α β For, α β α β αβ 66 The seme is rue or,. ssume Cosider, α β d α β y deiiio α α α α β or some posiive ieer.

More information

Chapter 2. LOGARITHMS

Chapter 2. LOGARITHMS Chpter. LOGARITHMS Dte: - 009 A. INTRODUCTION At the lst hpter, you hve studied bout Idies d Surds. Now you re omig to Logrithms. Logrithm is ivers of idies form. So Logrithms, Idies, d Surds hve strog

More information