13. The Weak Law and the Strong Law of Large Numbers
|
|
- Jocelyn McCarthy
- 7 years ago
- Views:
Transcription
1 3. Th Wa Law ad th Strog Law of Larg Numbrs Jams Broull rovd th wa law of larg umbrs WLLN aroud 7 whch was ublshd osthumously 73 hs trats Ars Cojctad. osso gralzd Broull s thorm aroud, ad 66 Tchbychv dscovrd th mthod barg hs am. Latr o o of hs studts, Marov obsrvd that Tchbychv s rasog ca b usd to xtd Broull s thorm to ddt radom varabls as wll. I 99 th Frch mathmatca Eml Borl rovd a dr thorm ow as th strog law of larg umbrs that furthr gralzs Broull s thorm. I 96 Kolmogorov drvd codtos that wr cssary ad suffct for a st of mutually ddt radom varabls to oby th law of larg umbrs ṖILLAI
2 Lt X b ddt, dtcally dstrbutd Broull radom Varabls such that X, X, ad lt X X X rrst th umbr of succsss trals. Th th wa law du to Broull stats that [s Thorm 3-, ag, Txt]. 3-.., th rato total umbr of succsss to th total umbr of trals tds to robablty as crass. A strogr vrso of ths rsult du to Borl ad Catll stats that th abov rato / tds to ot oly robablty, but wth robablty. Ths s th strog law of larg umbrs SLLN.
3 What s th dffrc btw th wa law ad th strog law? Th strog law of larg umbrs stats that f s a suc of ostv umbrs covrgg to zro, th <. 3- From Borl-Catll lmma [s -69 Txt], wh 3- s satsfd th vts A ca occur oly for a ft umbr of dcs a ft suc, or uvaltly, th vts < occur ftly oft,.., th vt / covrgs to almost-surly. roof: To rov 3-, w rocd as follows. Sc 3
4 w hav ad hc whr By drct comutato < 3-3 X X E X E
5 E E Y 3 Y 3 j l j E Y 3 E YY Y Y 3 E Y 3 [ 3 ] E Y 3, 3- sc <, / < Substtutg 3- also 3-3 w obta j l j ca cocd wth j, or l, ad th scod varabl tas - valus E Y j j 3 Lt / so that th abov tgral rads ad hc 3/ 3 3 / x dx 3/ 3 9 <, 3-
6 thus rovg th strog law by xhbtg a suc of ostv / umbrs that covrgs to zro ad satsfs 3-. / W rtur bac to th sam usto: What s th dffrc btw th wa law ad th strog law?. Th wa law stats that for vry that s larg ough, th rato X / / s lly to b ar wth crta robablty that tds to as crass. Howvr, t dos ot say that / s boud to stay ar f th umbr of trals s crasd. Suos 3- s satsfd for a gv a crta umbr of trals. If addtoal trals ar coductd byod, th wa law dos ot guarat that th w / s boud to stay ar for such trals. I fact thr ca b vts for whch /, for som rgular mar. Th robablty for such a vt s th sum of a larg umbr of vry small robablts, ad th wa law s uabl to say aythg scfc about th covrgc of that sum. Howvr, th strog law stats through 3- that ot oly all such sums covrg, but th total umbr of all such vts 6
7 7 whr s fact ft! Ths mls that th robablty of th vts as crass bcoms ad rmas small, sc wth robablty oly ftly may volatos to th abov ualty tas lac as Itrstgly, f t ossbl to arrv at th sam cocluso usg a owrful boud ow as Brst s ualty that s basd o th WLLN. Brst s ualty : Not that ad for ay ths gvs Thus. /,.
8 Sc for ay ral x, Substtutg 3-7 to 3-6, w gt But s mmum for ad hc Smlarly. x x x.. / , / 3- / <
9 ad hc w obta Brst s ualty Brst s ualty s mor owrful tha Tchbyshv s ualty as t stats that th chacs for th rlatv frucy / xcdg ts robablty tds to zro xotally fast as. Chbyshv s ualty gvs th robablty of / to l btw ad for a scfc. W ca us Brst s ualty to stmat th robablty for / to l btw ad for all larg. Towards ths, lt so that / 3-9. y < c y To comut th robablty of th vt y, ot that ts m comlmt s gv by c c y y m m / 9
10 ad usg E. -6 Txt, y y Ths gvs m / c c /. m / m m m / y y as m m m / or,, for all m as m. Thus / s boud to stay ar for all larg ough, robablty, a cocluso alrady rachd by th SLLN. Dscusso: Lt.. Thus f w toss a far co, tms, from th wa law..
11 Thus o th avrag 39 out of such vts ach wth or mor trals wll satsfy th ualty or, t s ut ossbl. that o out of such vts may ot satsfy t. As a rsult f w cotu th co tossg xrmt for a addtoal mor trals, wth rrstg th total umbr of succsss u to th currt tral, for, t s ut ossbl that for fw such th abov ualty may b volatd. Ths s stll cosstt wth th wa law, but ot so oft says th strog law. Accordg to th strog law such volatos ca occur oly a ft umbr of tms ach wth a ft robablty a ft suc of trals, ad hc almost always th abov ualty wll b satsfd,.., th saml sac of / cocds wth that of as. Nxt w loo at a xrmt to cofrm th strog law: Examl: rd cards ad blac cards all dstct ar shuffld togthr to form a sgl dc, ad th slt to half. What s th robablty that ach half wll cota rd ad blac cards?
12 Soluto: From a dc of cards, cards ca b chos dffrt ways. To dtrm th umbr of favorabl draws of rd ad blac cards ach half, cosdr th uu draw cosstg of rd cards ad blac cards ach half. Amog thos rd cards, of thm ca b chos dffrt ways; smlarly for ach such draw thr ar ways of choosg blac cards.thus th total umbr of favorabl draws cotag rd ad blac cards ach half ar amog a total of draws. Ths gvs th dsrd robablty to b!!! For larg, usg Stglg s formula w gt.
13 [ π π [ π ] ] π For a full dc of cards, w hav 3, whch gvs. ad for a artal dc of cards that cotas rd ad blac cards, w hav ad.36. O summr aftroo, cards cotag rd ad blac cards wr gv to a yar old chld. Th chld slt that artal dc to two ual halvs ad th outcom was dclard a succss f ach half cotad xactly rd ad blac cards. Wth adult survso trms of shufflg th xrmt was ratd tms that vry sam aftroo. Th rsults ar tabulatd blow Tabl 3., ad th rlatv frucy vs th umbr of trals lot Fg 3. shows th covrgc of / to. 3
14 Tabl Numbr of succsss Ext Numbr of succsss Ext Numbr of succsss Ext Numbr of succsss Ext Numbr of succsss Ext
15 Th fgur blow shows rsults of a xrmt of trals..337 Fg 3.
Finite Dimensional Vector Spaces.
Lctur 5. Ft Dmsoal Vctor Spacs. To b rad to th musc of th group Spac by D.Maruay DEFINITION OF A LINEAR SPACE Dfto: a vctor spac s a st R togthr wth a oprato calld vctor addto ad aothr oprato calld scalar
More informationCPS 220 Theory of Computation REGULAR LANGUAGES. Regular expressions
CPS 22 Thory of Computation REGULAR LANGUAGES Rgular xprssions Lik mathmatical xprssion (5+3) * 4. Rgular xprssion ar built using rgular oprations. (By th way, rgular xprssions show up in various languags:
More informationQuestion 3: How do you find the relative extrema of a function?
ustion 3: How do you find th rlativ trma of a function? Th stratgy for tracking th sign of th drivativ is usful for mor than dtrmining whr a function is incrasing or dcrasing. It is also usful for locating
More informationSTATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1
STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ
More informationQUANTITATIVE METHODS CLASSES WEEK SEVEN
QUANTITATIVE METHODS CLASSES WEEK SEVEN Th rgrssion modls studid in prvious classs assum that th rspons variabl is quantitativ. Oftn, howvr, w wish to study social procsss that lad to two diffrnt outcoms.
More informationOnline Insurance Consumer Targeting and Lifetime Value Evaluation - A Mathematics and Data Mining Approach
Ol Isurac Cosumr Targtg ad Lftm Valu Evaluato - A Mathmatcs ad Data Mg Approach Yuaya L,2, Gal Cook 3 ad Olvr Wrford 3 Rvr ad Harbor Dpartmt, Najg Hydraulc Rsarch Isttut, Najg, 224, 2 Ky Laboratory of
More informationProblem Set 6 Solutions
6.04/18.06J Mathmatics for Computr Scic March 15, 005 Srii Dvadas ad Eric Lhma Problm St 6 Solutios Du: Moday, March 8 at 9 PM Problm 1. Sammy th Shar is a fiacial srvic providr who offrs loas o th followig
More informationLong run: Law of one price Purchasing Power Parity. Short run: Market for foreign exchange Factors affecting the market for foreign exchange
Lctur 6: Th Forign xchang Markt xchang Rats in th long run CON 34 Mony and Banking Profssor Yamin Ahmad xchang Rats in th Short Run Intrst Parity Big Concpts Long run: Law of on pric Purchasing Powr Parity
More informationApproximation Algorithms for Scheduling with Rejection on Two Unrelated Parallel Machines
(ICS) Iteratoal oural of dvaced Comuter Scece ad lcatos Vol 6 No 05 romato lgorthms for Schedulg wth eecto o wo Urelated Parallel aches Feg Xahao Zhag Zega Ca College of Scece y Uversty y Shadog Cha 76005
More informationDEVELOPMENT OF MODEL FOR RUNNING DIESEL ENGINE ON RAPESEED OIL FUEL AND ITS BLENDS WITH FOSSIL DIESEL FUEL
ENGINEERING FOR RURAL DEVELOPMENT Jlgava, 3.-4.5.3. DEVELOPMENT OF MODEL FOR RUNNING DIESEL ENGINE ON RAPESEED OIL FUEL AND ITS BLENDS WITH FOSSIL DIESEL FUEL Ilmars Dukuls, Avars Brkavs Latva Uvrsty of
More informationMDM 4U PRACTICE EXAMINATION
MDM 4U RCTICE EXMINTION Ths s a ractce eam. It does ot cover all the materal ths course ad should ot be the oly revew that you do rearato for your fal eam. Your eam may cota questos that do ot aear o ths
More informationAverage Price Ratios
Average Prce Ratos Morgstar Methodology Paper August 3, 2005 2005 Morgstar, Ic. All rghts reserved. The formato ths documet s the property of Morgstar, Ic. Reproducto or trascrpto by ay meas, whole or
More informationAPPENDIX III THE ENVELOPE PROPERTY
Apped III APPENDIX III THE ENVELOPE PROPERTY Optmzato mposes a very strog structure o the problem cosdered Ths s the reaso why eoclasscal ecoomcs whch assumes optmzg behavour has bee the most successful
More informationEcon 371: Answer Key for Problem Set 1 (Chapter 12-13)
con 37: Answr Ky for Problm St (Chaptr 2-3) Instructor: Kanda Naknoi Sptmbr 4, 2005. (2 points) Is it possibl for a country to hav a currnt account dficit at th sam tim and has a surplus in its balanc
More informationInitial inventory levels for a book publishing firm
Mőhlytaulmáy Vállalatgazdaságta Itézt 93 Budapst, Fıvám tér 8. (+36 ) 482-5566, Fax: 482-5567 www.u-crvus.hu/vallgazd Ital vtry lvls fr a b publshg frm Imr Dbs Ágs Wmmr 23. sz. Mőhlytaulmáy HU ISSN 786-33
More informationNew Basis Functions. Section 8. Complex Fourier Series
Nw Basis Functions Sction 8 Complx Fourir Sris Th complx Fourir sris is prsntd first with priod 2, thn with gnral priod. Th connction with th ral-valud Fourir sris is xplaind and formula ar givn for convrting
More informationThe example is taken from Sect. 1.2 of Vol. 1 of the CPN book.
Rsourc Allocation Abstract This is a small toy xampl which is wll-suitd as a first introduction to Cnts. Th CN modl is dscribd in grat dtail, xplaining th basic concpts of C-nts. Hnc, it can b rad by popl
More informationSPECIAL VOWEL SOUNDS
SPECIAL VOWEL SOUNDS Plas consult th appropriat supplmnt for th corrsponding computr softwar lsson. Rfr to th 42 Sounds Postr for ach of th Spcial Vowl Sounds. TEACHER INFORMATION: Spcial Vowl Sounds (SVS)
More informationAP Calculus AB 2008 Scoring Guidelines
AP Calculus AB 8 Scoring Guidlins Th Collg Board: Conncting Studnts to Collg Succss Th Collg Board is a not-for-profit mmbrship association whos mission is to connct studnts to collg succss and opportunity.
More informationCategory 7: Employee Commuting
7 Catgory 7: Employ Commuting Catgory dscription This catgory includs missions from th transportation of mploys 4 btwn thir homs and thir worksits. Emissions from mploy commuting may aris from: Automobil
More informationNon-Linear and Unbalanced Three-Phase Load Static Compensation with Asymmetrical and Non Sinusoidal Supply
Non-Lnar and nbalancd Thr-Phas Load Statc Comnsaton wth Asymmtrcal and Non Snusodal Suly Rys S. Hrrra and P. Salmrón Elctrcal Engnrng Dartmnt Escula Poltécnca Suror, nvrsty of Hulva Ctra. Palos d la Frontra,
More informationIDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki
IDENIFICAION OF HE DYNAMICS OF HE GOOGLE S RANKING ALGORIHM A. Khak Sedgh, Mehd Roudak Cotrol Dvso, Departmet of Electrcal Egeerg, K.N.oos Uversty of echology P. O. Box: 16315-1355, ehra, Ira sedgh@eetd.ktu.ac.r,
More informationANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data
ANOVA Notes Page Aalss of Varace for a Oe-Wa Classfcato of Data Cosder a sgle factor or treatmet doe at levels (e, there are,, 3, dfferet varatos o the prescrbed treatmet) Wth a gve treatmet level there
More informationNEURAL DATA ENVELOPMENT ANALYSIS: A SIMULATION
Itratoal Joural f Idustral grg v... 4-4 4 NURAL ATA NVLPMNT ANALYSIS: A SIMULATIN Luz Bod Nto Marcos Prra stllta Ls la Goçalvs Goms João Carlos Corra Batsta Soars d Mllo 3 Fabao S. lvra. d g. ltrôca Tlcomucaçõs
More informationOnline Appendix: Measured Aggregate Gains from International Trade
Ole Appedx: Measured Aggregate Gas from Iteratoal Trade Arel Burste UCLA ad NBER Javer Cravo Uversty of Mchga March 3, 2014 I ths ole appedx we derve addtoal results dscussed the paper. I the frst secto,
More informationLecture 20: Emitter Follower and Differential Amplifiers
Whits, EE 3 Lctur 0 Pag of 8 Lctur 0: Emittr Followr and Diffrntial Amplifirs Th nxt two amplifir circuits w will discuss ar ry important to lctrical nginring in gnral, and to th NorCal 40A spcifically.
More informationby John Donald, Lecturer, School of Accounting, Economics and Finance, Deakin University, Australia
Studnt Nots Cost Volum Profit Analysis by John Donald, Lcturr, School of Accounting, Economics and Financ, Dakin Univrsity, Australia As mntiond in th last st of Studnt Nots, th ability to catgoris costs
More informationCHAPTER 4c. ROOTS OF EQUATIONS
CHAPTER c. ROOTS OF EQUATIONS A. J. Clark School o Enginring Dpartmnt o Civil and Environmntal Enginring by Dr. Ibrahim A. Aakka Spring 00 ENCE 03 - Computation Mthod in Civil Enginring II Dpartmnt o Civil
More informationAuthenticated Encryption. Jeremy, Paul, Ken, and Mike
uthntcatd Encrypton Jrmy Paul Kn and M Objctvs Examn thr mthods of authntcatd ncrypton and dtrmn th bst soluton consdrng prformanc and scurty Basc Componnts Mssag uthntcaton Cod + Symmtrc Encrypton Both
More informationStatistical Pattern Recognition (CE-725) Department of Computer Engineering Sharif University of Technology
I The Name of God, The Compassoate, The ercful Name: Problems' eys Studet ID#:. Statstcal Patter Recogto (CE-725) Departmet of Computer Egeerg Sharf Uversty of Techology Fal Exam Soluto - Sprg 202 (50
More informationTIME VALUE OF MONEY: APPLICATION AND RATIONALITY- AN APPROACH USING DIFFERENTIAL EQUATIONS AND DEFINITE INTEGRALS
MPRA Muich Prsoal RPEc Archiv TIME VALUE OF MONEY: APPLICATION AND RATIONALITY- AN APPROACH USING DIFFERENTIAL EQUATIONS AND DEFINITE INTEGRALS Mahbub Parvz Daffodil Itratioal Uivrsy 6. Dcmbr 26 Oli at
More informationInfinite 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 informationREVISTA INVESTIGACIÓN OPERACIONAL VOL., 32, NO. 2, 93-106, 2011
REVISA IVESIGACIÓ OPERACIOAL VOL., 3, O., 93-6, A IEGRAED IVEORY POLICY WIH DEERIORAIO FOR A SIGLE VEDOR AD MULIPLE BUYERS I SUPPLY CHAI WHE DEMAD IS QUADRAIC ta H. Shah,Ajay S. Gor ad Chta Jhavr Dpartmt
More informationDYNAMIC PROGRAMMING APPROACH TO TESTING RESOURCE ALLOCATION PROBLEM FOR MODULAR SOFTWARE
DYAMIC PROGRAMMIG APPROACH TO TESTIG RESOURCE ALLOCATIO PROBLEM FOR MODULAR SOFTWARE P.K. Kpur P.C. Jh A.K. Brdh Astrct Tstg phs of softwr gs wth modul tstg. Durg ths prod moduls r tstd dpdtly to rmov
More informationCSSE463: Image Recognition Day 27
CSSE463: Image Recogto Da 27 Ths week Toda: Alcatos of PCA Suda ght: roject las ad relm work due Questos? Prcal Comoets Aalss weght grth c ( )( ) ( )( ( )( ) ) heght sze Gve a set of samles, fd the drecto(s)
More informationINFLUENCE OF DEBT FINANCING ON THE EFFECTIVENESS OF THE INVESTMENT PROJECT WITHIN THE MODIGLIANIMILLER THEORY
VOUME 2, 2 NFUENCE OF DEBT FNANCNG ON THE EFFECTVENE OF THE NVETMENT PROJECT WTHN THE MODGANMER THEORY Pr Brusov, Taaa Flaova, Naal Orhova, Pavl Brusov, Nasa Brusova Fac Uvrsy ur h Govrm of h Russa Frao,
More information6.7 Network analysis. 6.7.1 Introduction. References - Network analysis. Topological analysis
6.7 Network aalyss Le data that explctly store topologcal formato are called etwork data. Besdes spatal operatos, several methods of spatal aalyss are applcable to etwork data. Fgure: Network data Refereces
More information10.5 Future Value and Present Value of a General Annuity Due
Chapter 10 Autes 371 5. Thomas leases a car worth $4,000 at.99% compouded mothly. He agrees to make 36 lease paymets of $330 each at the begg of every moth. What s the buyout prce (resdual value of the
More informationOPTIMAL KNOWLEDGE FLOW ON THE INTERNET
İstabul Tcaret Üverstes Fe Blmler Dergs Yıl: 5 Sayı:0 Güz 006/ s. - OPTIMAL KNOWLEDGE FLOW ON THE INTERNET Bura ORDİN *, Urfat NURİYEV ** ABSTRACT The flow roblem ad the mmum sag tree roblem are both fudametal
More informationCoordination, Cooperation, Contagion and Currency Crises 1
oorato, oorato, otago a urrc rss Olvr Losl * a Phl Mart ** Jauar 999, ths vrso Jauar 000 bstract W aalz th ffct of tra comtto, tratoal coorato a coorato o currc crss. To o ths, w rst a mcro-fou mol whr
More informationOnline Load Balancing and Correlated Randomness
Onln Load Balancng and Corrlatd Randomnss Sharayu Moharr, Sujay Sanghav Wrlss Ntworng and Communcatons Group (WNCG) Dpartmnt of Elctrcal & Computr Engnrng Th Unvrsty of Txas at Austn Austn, TX 787, USA
More informationTraffic Flow Analysis (2)
Traffic Flow Analysis () Statistical Proprtis. Flow rat distributions. Hadway distributions. Spd distributions by Dr. Gang-Ln Chang, Profssor Dirctor of Traffic safty and Oprations Lab. Univrsity of Maryland,
More information5.4 Exponential Functions: Differentiation and Integration TOOTLIFTST:
.4 Eponntial Functions: Diffrntiation an Intgration TOOTLIFTST: Eponntial functions ar of th form f ( ) Ab. W will, in this sction, look at a spcific typ of ponntial function whr th bas, b, is.78.... This
More informationApproximate Counters for Flash Memory
Approximat Coutrs for Flash Mmory Jack Cichoń ad Wojcich Macya Istitut of Mathmatics ad Computr Scic Wrocław Uivrsity of Tchology, Polad Abstract Flash mmory bcoms th a vry popular storag dvic Du to its
More informationGROUP MEDICAL INSURANCE PROPOSAL FORM GROUP MEDICAL INSURANCE PROPOSAL FORM
Call us: 920012331 www.acig.com.sa Allid Cooprativ Isurac Group (ACIG) شركة املجموعة املتحدة للتاأمني التعاوين ) أ سيج( GROUP MEDICAL INSURANCE GROUP MEDICAL INSURANCE Clit Dtails: - GROUP MEDICAL INSURANCE
More informationPhysics 106 Lecture 12. Oscillations II. Recap: SHM using phasors (uniform circular motion) music structural and mechanical engineering waves
Physics 6 Lctur Oscillations II SJ 7 th Ed.: Chap 5.4, Rad only 5.6 & 5.7 Rcap: SHM using phasors (unifor circular otion) Physical pndulu xapl apd haronic oscillations Forcd oscillations and rsonanc. Rsonanc
More informationTerm Structure of Interest Rates: The Theories
Handou 03 Econ 333 Abdul Munasb Trm Srucur of Inrs Ras: Th Thors Trm Srucur Facs Lookng a Fgur, w obsrv wo rm srucur facs Fac : Inrs ras for dffrn maurs nd o mov oghr ovr m Fac : Ylds on shor-rm bond mor
More informationBayesian Network Representation
Readgs: K&F 3., 3.2, 3.3, 3.4. Bayesa Network Represetato Lecture 2 Mar 30, 20 CSE 55, Statstcal Methods, Sprg 20 Istructor: Su-I Lee Uversty of Washgto, Seattle Last tme & today Last tme Probablty theory
More informationHow To Value An Annuity
Future Value of a Auty After payg all your blls, you have $200 left each payday (at the ed of each moth) that you wll put to savgs order to save up a dow paymet for a house. If you vest ths moey at 5%
More informationFacts About Chronc Fatgu Syndrom - sample thereof
Cardac Toxcty n Chronc Fatgu Syndrom: Rsults from a Randomzd 40-Wk Multcntr Doubl-blnd Placbo Control Tral of Rntatolmod Bruc C. Stouch, Ph.D 1 Davd Strayr, M.D 2. Wllam Cartr, M.D 2. 1 Phladlpha Collg
More informationPreprocess a planar map S. Given a query point p, report the face of S containing p. Goal: O(n)-size data structure that enables O(log n) query time.
Computatoal Geometry Chapter 6 Pot Locato 1 Problem Defto Preprocess a plaar map S. Gve a query pot p, report the face of S cotag p. S Goal: O()-sze data structure that eables O(log ) query tme. C p E
More informationThe simple linear Regression Model
The smple lear Regresso Model Correlato coeffcet s o-parametrc ad just dcates that two varables are assocated wth oe aother, but t does ot gve a deas of the kd of relatoshp. Regresso models help vestgatg
More informationMathematics. Mathematics 3. hsn.uk.net. Higher HSN23000
hsn uknt Highr Mathmatics UNIT Mathmatics HSN000 This documnt was producd spcially for th HSNuknt wbsit, and w rquir that any copis or drivativ works attribut th work to Highr Still Nots For mor dtails
More informationhttp://www.wwnorton.com/chemistry/tutorials/ch14.htm Repulsive Force
ctivation nrgis http://www.wwnorton.com/chmistry/tutorials/ch14.htm (back to collision thory...) Potntial and Kintic nrgy during a collision + + ngativly chargd lctron cloud Rpulsiv Forc ngativly chargd
More informationSimple Linear Regression
Smple Lear Regresso Regresso equato a equato that descrbes the average relatoshp betwee a respose (depedet) ad a eplaator (depedet) varable. 6 8 Slope-tercept equato for a le m b (,6) slope. (,) 6 6 8
More informationAbraham Zaks. Technion I.I.T. Haifa ISRAEL. and. University of Haifa, Haifa ISRAEL. Abstract
Preset Value of Autes Uder Radom Rates of Iterest By Abraham Zas Techo I.I.T. Hafa ISRAEL ad Uversty of Hafa, Hafa ISRAEL Abstract Some attempts were made to evaluate the future value (FV) of the expected
More informationEvaluating Direct Marketing Practices On the Internet via the Fuzzy Cognitive Mapping Method
Itratoal Joural of Busss ad Maagmt Dcmbr, 28 Evaluatg Drct Marktg Practcs O th Itrt va th Fuzzy Cogtv Mappg Mthod Slcuk Burak Hasloglu (Corrspodg author) Dpartmt of Marktg, Faculty of Ecoomc ad Admstratv
More informationChapter 10 Function of a Matrix
EE448/58 Vrsion. John Stnsby Chatr Function of a atrix t f(z) b a comlx-valud function of a comlx variabl z. t A b an n n comlxvalud matrix. In this chatr, w giv a dfinition for th n n matrix f(a). Also,
More informationLecture 3: Diffusion: Fick s first law
Lctur 3: Diffusion: Fick s first law Today s topics What is diffusion? What drivs diffusion to occur? Undrstand why diffusion can surprisingly occur against th concntration gradint? Larn how to dduc th
More informationEntity-Relationship Model
Entity-Rlationship Modl Kuang-hua Chn Dpartmnt of Library and Information Scinc National Taiwan Univrsity A Company Databas Kps track of a company s mploys, dpartmnts and projcts Aftr th rquirmnts collction
More informationSharp bounds for Sándor mean in terms of arithmetic, geometric and harmonic means
Qian t al. Journal of Inqualitis and Applications (015) 015:1 DOI 10.1186/s1660-015-0741-1 R E S E A R C H Opn Accss Sharp bounds for Sándor man in trms of arithmtic, gomtric and harmonic mans Wi-Mao Qian
More informationVersion 1.0. General Certificate of Education (A-level) January 2012. Mathematics MPC3. (Specification 6360) Pure Core 3. Final.
Vrsion.0 Gnral Crtificat of Education (A-lvl) January 0 Mathmatics MPC (Spcification 660) Pur Cor Final Mark Schm Mark schms ar prpard by th Principal Eaminr and considrd, togthr with th rlvant qustions,
More informationProperties 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 informationLogo Design/Development 1-on-1
Logo Dsign/Dvlopmnt 1-on-1 If your company is looking to mak an imprssion and grow in th marktplac, you ll nd a logo. Fortunatly, a good graphic dsignr can crat on for you. Whil th pric tags for thos famous
More informationModern Portfolio Theory (MPT) Statistics
Modrn Portfolo Thory (MPT) Statstcs Mornngstar Mthodology Papr Novmr 30, 007 007 Mornngstar, Inc. All rghts rsrvd. Th nformaton n ths documnt s th proprty of Mornngstar, Inc. Rproducton or transcrpton
More informationLecture 7. Norms and Condition Numbers
Lecture 7 Norms ad Codto Numbers To dscuss the errors umerca probems vovg vectors, t s usefu to empo orms. Vector Norm O a vector space V, a orm s a fucto from V to the set of o-egatve reas that obes three
More informationERLANG C FORMULA AND ITS USE IN THE CALL CENTERS
IFORTIO D OUITIO TEHOLOGIES D SERVIES, VOL. 9, O., RH 2 7 ERLG FORUL D ITS USE I THE LL ETERS Er HROY., Tbor ISUTH., atj KVKY. Dpartmnt of Tlcommuncatons, Faculty of Elctrcal Engnrng and Informaton Tchnology,
More information(Analytic Formula for the European Normal Black Scholes Formula)
(Analytic Formula for th Europan Normal Black Schols Formula) by Kazuhiro Iwasawa Dcmbr 2, 2001 In this short summary papr, a brif summary of Black Schols typ formula for Normal modl will b givn. Usually
More informationCloud and Big Data Summer School, Stockholm, Aug., 2015 Jeffrey D. Ullman
Cloud and Big Data Summr Scool, Stockolm, Aug., 2015 Jffry D. Ullman Givn a st of points, wit a notion of distanc btwn points, group t points into som numbr of clustrs, so tat mmbrs of a clustr ar clos
More informationESCI 241 Meteorology Lesson 6 Humidity
ESCI 41 Mtorology Lsson 6 Humiity Raing: MT Chatr 5 PARTIAL PRESSURE In a mixtur of gass, ach gas scis contributs to th total rssur. ο Th rssur xrt by a singl gas scis is known as th artial rssur for that
More informationAn Optimal Algorithm for On-line Bipartite Matching. University of California at Berkeley & International Computer Science Institute
A Optimal Algorithm for O-li Bipartit Matchig Richard M. Karp Uivrsity of Califoria at Brkly & Itratioal Computr Scic Istitut Umsh V. Vazirai Uivrsity of Califoria at Brkly Vijay V. Vazirai Corll Uivrsity
More informationBasis risk. When speaking about forward or futures contracts, basis risk is the market
Basis risk Whn spaking about forward or futurs contracts, basis risk is th markt risk mismatch btwn a position in th spot asst and th corrsponding futurs contract. Mor broadly spaking, basis risk (also
More informationE X C H A N G E R U L E S A N D C L E A R I N G R U L E S O F N A S D A Q O M X D E R I V A T I V E S M A R K E T S
E X C H A N G E R U L E S A N D C L E A R I N G R U L E S O F N A S D A Q O M X D E R I V A T I V E S M A R K E T S Fair Valu 1 Valuation Variabls Tabl 1 blow shows th variabls us in th rspctiv valuation
More information11 Multiple Linear Regression
11 Multpl Lar Rgrsso Multpl lar rgrsso (MLR) s a mthod usd to modl th lar rlatoshp btw a dpdt varabl ad o or mor dpdt varabls. Th dpdt varabl s somtms also calld th prdctad, ad th dpdt varabls th prdctors.
More informationRural and Remote Broadband Access: Issues and Solutions in Australia
Rural and Rmot Broadband Accss: Issus and Solutions in Australia Dr Tony Warrn Group Managr Rgulatory Stratgy Tlstra Corp Pag 1 Tlstra in confidnc Ovrviw Australia s gographical siz and population dnsity
More informationIn 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 informationOutside Cut 1 of fabric Cut 1 of interfacing
a a Outsi Cut o abric Cut o intracing a a b b Outsi Cut o abric Cut o intracing Placmnt lin or Mony Pockts Dix Not: F. Cut Fol b. Pin t /8 in 5. Nx bottom pics sw th 6. For t Prss, 7. Lay togth on th 8.
More informationADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN
Colloquum Bometrcum 4 ADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN Zofa Hausz, Joaa Tarasńska Departmet of Appled Mathematcs ad Computer Scece Uversty of Lfe Sceces Lubl Akademcka 3, -95 Lubl
More informationSun Synchronous Orbits for the Earth Solar Power Satellite System
Sun Synchrnus Orbts fr th Earth Sar Pwr Satt Systm Sm f th mst prmsng rbts fr th Earth Sar Pwr Systm ar crcuar Sun synchrnus rbts whch nvr ntr Earth's shaw. In ths rbts, gravty grant stabz "pwr twrs" w
More informationAdverse Selection and Moral Hazard in a Model With 2 States of the World
Advrs Slction and Moral Hazard in a Modl With 2 Stats of th World A modl of a risky situation with two discrt stats of th world has th advantag that it can b natly rprsntd using indiffrnc curv diagrams,
More information16. Mean Square Estimation
6 Me Sque stmto Gve some fomto tht s elted to uow qutty of teest the poblem s to obt good estmte fo the uow tems of the obseved dt Suppose epeset sequece of dom vbles bout whom oe set of obsevtos e vlble
More informationThe Gompertz-Makeham distribution. Fredrik Norström. Supervisor: Yuri Belyaev
The Gompertz-Makeham dstrbuto by Fredrk Norström Master s thess Mathematcal Statstcs, Umeå Uversty, 997 Supervsor: Yur Belyaev Abstract Ths work s about the Gompertz-Makeham dstrbuto. The dstrbuto has
More informationUse a high-level conceptual data model (ER Model). Identify objects of interest (entities) and relationships between these objects
Chaptr 3: Entity Rlationship Modl Databas Dsign Procss Us a high-lvl concptual data modl (ER Modl). Idntify objcts of intrst (ntitis) and rlationships btwn ths objcts Idntify constraints (conditions) End
More information5.3. 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 informationCurve Fitting and Solution of Equation
UNIT V Curve Fttg ad Soluto of Equato 5. CURVE FITTING I ma braches of appled mathematcs ad egeerg sceces we come across epermets ad problems, whch volve two varables. For eample, t s kow that the speed
More informationAC Circuits Three-Phase Circuits
AC Circuits Thr-Phs Circuits Contnts Wht is Thr-Phs Circuit? Blnc Thr-Phs oltgs Blnc Thr-Phs Connction Powr in Blncd Systm Unblncd Thr-Phs Systms Aliction Rsidntil Wiring Sinusoidl voltg sourcs A siml
More informationAn Evaluation of Naïve Bayesian Anti-Spam Filtering Techniques
Proceedgs of the 2007 IEEE Workshop o Iformato Assurace Uted tates Mltary Academy, West Pot, Y 20-22 Jue 2007 A Evaluato of aïve Bayesa At-pam Flterg Techques Vkas P. Deshpade, Robert F. Erbacher, ad Chrs
More informationMath 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[ ] These are the motor parameters that are needed: Motor voltage constant. J total (lb-in-sec^2)
MEASURING MOOR PARAMEERS Fil: Motor paramtrs hs ar th motor paramtrs that ar ndd: Motor voltag constant (volts-sc/rad Motor torqu constant (lb-in/amp Motor rsistanc R a (ohms Motor inductanc L a (Hnris
More informationLoad and Resistance Factor Design (LRFD)
53:134 Structural Desg II Load ad Resstace Factor Desg (LRFD) Specfcatos ad Buldg Codes: Structural steel desg of buldgs the US s prcpally based o the specfcatos of the Amerca Isttute of Steel Costructo
More informationSecurity Analysis of RAPP: An RFID Authentication Protocol based on Permutation
Securty Aalyss of RAPP: A RFID Authetcato Protocol based o Permutato Wag Shao-hu,,, Ha Zhje,, Lu Sujua,, Che Da-we, {College of Computer, Najg Uversty of Posts ad Telecommucatos, Najg 004, Cha Jagsu Hgh
More informationHow do bookmakers (or FdJ 1 ) ALWAYS manage to win?
How do bookakers (or FdJ ALWAYS aage to w? Itroducto otatos & varables Bookaker's beeft eected value 4 4 Bookaker's strateges5 4 The hoest bookaker 6 4 "real lfe" bookaker 6 4 La FdJ 8 5 How ca we estate
More informationChapter Eight. f : R R
Chapter Eght f : R R 8. Itroducto We shall ow tur our atteto to the very mportat specal case of fuctos that are real, or scalar, valued. These are sometmes called scalar felds. I the very, but mportat,
More informationMathematics of Finance
CATE Mathematcs of ace.. TODUCTO ths chapter we wll dscuss mathematcal methods ad formulae whch are helpful busess ad persoal face. Oe of the fudametal cocepts the mathematcs of face s the tme value of
More informationPARTICULAR RELIABILITY CHARACTERISTICS OF TWO ELEMENT PARALLEL TECHNICAL (MECHATRONIC) SYSTEMS
Maagm Sysms Produco Egrg No 3 7 pp 3 8 PARICULAR RELIABILIY CHARACERISICS O WO ELEMEN PARALLEL ECHNICAL MECHARONIC SYSEMS Zbgw MAUSZAK Marm Uvrsy o Szczc Absrac: h papr characrzs h basc dsrbuos o alur
More informationREFINED CALCULATION AND SIMULATION SYSTEM OF LOCAL LARGE DEFORMATION FOR ACCIDENT VEHICLE
2 3 4 5 6 7 8 9 0 2 3 4 5 6 7 8 9 20 2 22 23 24 25 26 27 28 29 30 3 32 33 34 35 36 37 38 39 40 4 42 43 44 REFINED CALCULATION AND SIMULATION SYSTEM OF LOCAL LARGE DEFORMATION FOR ACCIDENT VEHICLE WagFag
More informationExponential Generating Functions
Epotl Grtg Fuctos COS 3 Dscrt Mthmtcs Epotl Grtg Fuctos (,,, ) : squc of rl umbrs Epotl Grtg fucto of ths squc s th powr srs ( )! 3 Ordry Grtg Fuctos (,,, ) : squc of rl umbrs Ordry Grtg Fucto of ths squc
More information1. Online Event Registration 2. Event Marketing 3. Automated Event Progress Reports 4. Web based Point of Sale Terminal 5. Email Marketing System
2 t v E S d Ivit 3 M o it o r ro la 1 r g 1 Oli Evt Rgitratio 2 Evt Marktig 3 Automatd Evt rogr Rport 4 Wb bad oit of Sal Trmial 5 Email Marktig Sytm ag 1 of 6 Copyright 2004-2011 myvillag oli Evt Maagmt
More informationAnalysis of one-dimensional consolidation of soft soils with non-darcian flow caused by non-newtonian liquid
Joural of Rock Mechacs ad Geotechcal Egeerg., 4 (3): 5 57 Aalyss of oe-dmesoal cosoldato of soft sols wth o-darca flow caused by o-newtoa lqud Kaghe Xe, Chuaxu L, *, Xgwag Lu 3, Yul Wag Isttute of Geotechcal
More information