Good Harbor Financial: A Multifactor Perspective

Size: px
Start display at page:

Download "Good Harbor Financial: A Multifactor Perspective"

Transcription

1 Good Harbor Fnancal, Inc. Chcago, IL Good Harbor Fnancal: A ultactor Prspctv Author: Nl. Pplnsk Good Harbor Fnancal, Inc. nl.pplnsk@goodharbornancal.com ABSTACT Th valu o a managr or an nvstmnt stratgy s otn basd upon th ablty to gnrat rturns abov and byond thos provdd by th gnral markt. A common valuaton tchnqu nvolvs a rgrsson o th rturns o ntrst on svral popular actor modls and a look at th rsultng rgrsson alphas. In ths papr I analyz th Good Harbor Asst Allocaton stratgy aganst th CAP, th Fama-Frnch 3-Factor modl and th Carhart 4-Factor modl usng smulatd data rom In ach o ths cass, th alphas ar shown to b statstcally and conomcally sgncant. INTODUCTION Asst prcs vary. As such, rturns on nvstmnts also vary. Consdr th smpl cas o buyng an ndvdual stock. Whl th hop s that th prc wll apprcat so that t can latr b sold at a prot, anyon who has nvstd knows that somtms prcs ar up and somtms thy ar down. Wth w xcptons, t s gnrally not possbl to avod ths luctuaton. Howvr, t s possbl and mportant to gt an da o th sourc and natur o ths volatlty. Knowng how prcs mov can b usul n portolo ormaton as w can otn dampn th volatlty o a portolo by choosng assts that ar uncorrlatd or ngatvly corrlatd to ach othr. It s also usul or assssng th ablty o a managr or nvstmnt stratgy to arn rturns abov and byond thos avalabl rom passv nvstng (.. buy and hold or ndx nvstng). Th most popular tchnqu n us today or valuatng rturns nvolvs rgrssng rturns on mult-actor modls and lookng at th slop and ntrcpt cocnts or statstcal and conomc sgncanc. Atr gvng an ovrvw o ach o th modls usd n ths papr, rsults ar prsntd or th varous rgrssons prormd, ncludng tsts o statstcal sgncanc. CAP OVEVIEW In th 1950 s Harry arkowtz, who latr won a Nobl Prz or hs orts, proposd a ground-brakng thory n nanc. athr than ocus on th undamntals o a company to dtrmn whthr to hold stock n th rm, h thorzd that a propr portolo could b stablshd smply by lookng at th avrag rturn and volatlty o vry stock along wth th corrlatons (covaranc) btwn stocks. Basd on ths noton, h argud that an cnt portolo could b ormd that would maxmz rturn or a gvn amount o rsk (standard dvaton). Thus was born th cnt rontr or man-varanc nvstmnt analyss. Undr th man-varanc ramwork, w plottd vry possbl combnaton o rsky assts n an conomy, w would arrv at a dagram lk th on n Fgur 1 blow (ths s just an xampl plot, gnor th actual valus). Portolo Expctd turn 30.00% 5.00% 0.00% 15.00% 10.00% 5.00% 0.00% Exampl an-varanc Opportunty St and Ecncy Frontr an Varanc Opportunty St Ecncy Frontr n. Varanc Portolo Entr ntror "dcoratd" wth non-cnt portolos. 0.00% 10.00% 0.00% 30.00% 40.00% 50.00% 60.00% 70.00% Portolo Standard Dvaton Fgur 1: Exampl an-varanc Spac From th gur, t s clar whr th trm cnt portolo coms rom. Namly, or a gvn rsk lvl (standard dvaton) th xpctd rturn s maxmzd th portolo s on th bold colord ln, or on th cncy rontr. Fgur 1 s or an conomy that only has rsky assts. I w now allow or a rsk-r nvstmnt, say th U.S. short-trm trasury blls, w now ntroduc a Captal Allocaton Ln (CAL) as sn n Fgur. Good Harbor Fnancal: Gnral Dstrbuton v.1 Pag 1 o 6

2 Good Harbor Fnancal, Inc. Chcago, IL Portolo Expctd turn 30.00% 5.00% 0.00% 15.00% 10.00% 5.00% Exampl an-varanc Opportunty St and Ecncy Frontr an Varanc Opportunty St Ecncy Frontr n. Varanc Portolo Captal Allocaton Ln Tangncy Portolo Entr ntror "dcoratd" wth non-cnt portolos. Followng th sam ramwork (and usng th sam assumptons) Sharp wnt on to show that th rsk prmum o th markt portolo s proportonat to th volatlty o th markt and th rsk-avrson o a rprsntatv nvstor. All o whch s ancy talk or th ollowng: Sharp drvd a smpl ormula dscrbng th xpctd xcss rturn (rturn abov th rsk-r rat) o th markt and showd that t s rlatd to how much nvstors dslk rsk. athmatcally: sk Fr at 0.00% 0.00% 10.00% 0.00% 30.00% 40.00% 50.00% 60.00% 70.00% Portolo Standard Dvaton Fgur : Ecncy Frontr wth a sk-fr asst. Th CAL touchs th rsky-asst cncy curv at only on locaton namd th tangncy portolo. Tak a scond to absorb ths pctur. What you ll ralz s qut proound. I th tangncy portolo rprsnts a tradabl scurty or a combnaton o tradabl scurts, w can maxmz our rturn or a gvn lvl o rsk (rght down to 0% standard dvaton) by holdng only th tangncy portolo and th rsk-r asst n propr proportons. That s t. No nd or xpnsv nvstmnt managrs amd at ntror dcoratng o portolos. All w nd to do s dnty th tangncy portolo, dn our rsk lvl, st up a passv nvstmnt account and w r don. arkowtz s thory was arthshattrng. Unortunatly dntyng th tangncy portolo was not so smpl. In act, arkowtz wrot hs papr n 195 and t wasn t untl 1 yars latr that Wllam Sharp proposd hs Captal Asst Prcng odl (CAP) whch ctvly drvd th tangncy portolo. Sharp s thory, whch was ormulatd undr many dalstc assumptons, ctvly stablshd that th tangncy portolo was n act th markt portolo. Undr hs assumptons, not th last o whch s that all nvstors bls about asst man and varancs ar th sam and that ths man and varancs ar constant ovr tm, Sharp showd that n an qulbrum condton all nvstors wll choos to hold a portolo o rsky assts n proportons that duplcat th rprsntaton o th assts n th ntr markt. For xampl, Company XYZ stock rprsnts 1% o th ntr markt, thn ach nvstor wll hold 1% o hs rsky-asst portolo n Company XYZ. Th sam holds tru or all othr assts. Thortcally th tangncy portolo s a wghtd avrag o ALL rsky assts n th conomy (.. stocks, bonds, ral-stat, collctbls, tc.). In practc w otn us a stock ndx, such as th S&P 500, as a proxy. ) ) A A Equaton 1: arkt rsk prmum. In words Equaton 1 s sayng that bcaus th markt s volatl ( ) and nvstors don t lk rsk (A), th markt can only ntc partcpaton by orng a hghr xpctd rturn than th rsk-r rat. In th sam mannr, Sharp showd that th rsk prmum or an ndvdual asst (.. how much rturn an asst must gv n ordr or an nvstor to buy t) s just a scald vrson o th markt prmum, wth th scalng actor bng dpndnt on how th asst movs wth markt. Agan, mathmatcally: E ( ) ) cov(, ) ) ) Equaton : Indvdual asst prmum. Or n trms o xcss rturns: ) Equaton 3: CAP n trms o xcss rturns. Aaaah, nally! Atr all that w rach th roots o th namous CAP bta. But what do w rally hav now? Th CAP ctvly provds an asst prcng modl that allows us to dtrmn th xpctd rturn on any asst smply by valuatng how ths asst movs wth rspct to th markt. And dspt th act that s was dvlopd undr som vry dalstc assumptons, th CAP actually works rmarkably wll n practc. al assts that hav rturns hghr than th S&P 500 ndx, or xampl, gnrally hav btas gratr than on. Lkws scurts that rturn lss than th markt ndx gnrally hav btas lss than on. And as you may hav gussd, scurts that hav th sam rturn as th ) Good Harbor Fnancal: Gnral Dstrbuton v.1 Pag o 6

3 Good Harbor Fnancal, Inc. Chcago, IL markt hav a bta qual to on. So ths s ntrstng, and arkowtz and Sharp wr obvously brllant acadmcs, but how s ths actually usul? In practc, th CAP can b usd or a multtud o purposs. Wthn th scop o ths papr, th CAP s partcularly usul or prormanc attrbuton. I w dscovr an asst or an nvstmnt stratgy that has a hghr xpctd rturn than that suggstd by ts bta (whch w can gt rom a tmsrs rgrsson), w hav a supror approach to nvstng. In othr words, w v batn th cncy rontr. W ll s how ths works whn w analyz rturns rom th Good Harbor Fnancal asst allocaton modl. FAA-FENCH 3-FACTO ODEL As mntond brly n th prvous scton, th CAP prorms rmarkably wll consdrng th assumptons undr whch t was drvd. But just how wll s rmarkably wll? In a rgrsson sns, th CAP otn lads to valus n th 80%-90%+ rang. Emprcally ths s prtty mprssv, but alas thr ar just a w too many nstancs whr th CAP alls short. A thory that dosn t rally xplan vrythng maks nanc prossors nrvous and as such th CAP alurs hav spawnd numrous avnus o rsarch amd at mprovng upon th CAP rsults. On o th outputs o ths rsarch was a thr actor modl dvlopd by Eugn Fama and Knnth Frnch. CAP posturs that th only stat varabl o concrn to nvstors s th volatlty o th ovrall markt ( stat varabl o concrn s just a ancy way o sayng what rsks nvstors car about). In thr analyss, Fama and Frnch dscovrd that thr was as sprad n th avrag rturns o compans sortd by book/markt rato and by sz that was not bng accuratly capturd by th CAP bta. In ssnc, Fama and Frnch proposd that n addton to ovrall markt volatlty, nvstors also car about whthr an asst s a valu stock (hgh book/markt rato) as wll as whthr t s a small rm compard to othrs. Ths ld to th Fama- Frnch 3-Factor odl (FF3F): ) ) h E HL s ESB Equaton 4: Fama-Frnch 3-Factor modl. Hr HL s a portolo ormd by buyng valu stocks and shortng growth stocks. SB s a portolo ormd by buyng small-szd rms and sllng larg cap rms. Th dntons o ths portolos hav bn clarly dntd by Fama-Frnch, and n act data on ths actors ar actvly mantand by Fama-Frnch or th purpos o supportng FF3F modl analyss. How wll dos th FF3F modl work? Compard to CAP, th FF3F modl tnds to do a bttr job n dscrbng rturn varatons ovrall, and by dsgn, ctvly capturs th sz and book/markt cts that wr mssd by th CAP. Howvr, snc th CAP s so wdly quotd, almost all nvstmnt analyss wll nclud a CAP basln chck. CAHAT 4-FACTO ODEL Unortunatly, whl bttr than th CAP, th FF3F modl also has ts struggls, partcularly n xplanng rturns rom portolos ormd by buyng past wnnrs and sllng past losrs. Ths so-calld momntum portolos produc rturns that ar not ully capturd by th, HL or SB actors. As such Carhart (1997) stablshs yt anothr actor, ladng to th Carhart 4- Factor (C4F) modl. )... s E ) h EHL... SB u EUD Equaton 5: Carhart's 4-actor modl. As you may hav gussd, UD rprsnts a portolo ormd by buyng prvous wnnrs and sllng prvous losrs. Unlk th othr actors whch hav bn argud to b proxs or rsks that nvstors car about (rcall th stat varabls o concrn?), UD has not rally bn wll stablshd as a orm o rsk. As such, Carhart s modl sn t gnrally accptd as an asst prcng modl. Howvr, or prormanc attrbuton t dos just n. call that what w r rally atr s whthr th rturns rom an nvstmnt can b xpland usng radly avalabl normaton. I ys, thn th und managr sn t rally provdng valu, snc th rturns could b duplcatd usng a passv or mchancal nvstmnt stratgy. In othr words, no nd to pay an nvstmnt managr or somthng you can do yoursl. In ths sns, Carhart s modl works n and n act s otn ctd n ltratur or just ths purpos. EGESSIONS AND ALPHA As suggstd arlr, th abov modls srv a vry usul purpos n dtrmnng a stratgy or managr s skll. By runnng tm srs rgrssons on actual rturns, w can xtract stmats or th varous modl paramtrs. Consdr th CAP. In ths cas w run th ollowng tm-srs rgrsson: Good Harbor Fnancal: Gnral Dstrbuton v.1 Pag 3 o 6

4 Good Harbor Fnancal, Inc. Chcago, IL, t Equaton 6: CAP tm-srs rgrsson. From ths rgrsson w gt stmats or alpha and bta. Pr Equaton 3, th CAP modl s corrct, thn all assts should l on th Scurty arkt Ln (SL) and all rgrsson alphas should b zro. I th alpha s postv, thn w hav a scurty that s arnng a hghr rturn than that prdctd by CAP (s Fgur 3 blow). ) 16.00% 14.00% 1.00% 10.00% 8.00% 6.00% 4.00%.00% 0.00% Scurty arkt Ln (Accordng to CAP, all assts ln on a straght ln.) Asst wth postv alpha Bta Fgur 3: Scurty arkt Ln (SL). Is arnng a postv alpha a good thng or a bad thng? Ths smngly smpl quston s actually not so straght orward. I th alpha s ral, thn t s obvously a good thng, snc w v ound an nvstmnt that s gvng a hghr rturn than what t should b accordng to ts rsk. Howvr, t s possbl that a postv alpha s just a sgn that our modl s wrong. In ths cas, th alpha could just b compnsaton or rsk that was not proprly capturd by our modl and as such dos not rlct th skll o th portolo managr or th valu o an nvstmnt stratgy. Th nvstmnt s arnng a hghr rturn smply bcaus t s rskr, whch may b undsrabl. Ths s on o th man motvatons or runnng rgrssons usng multpl modls. W want to try and tst our stratgy aganst th wll known rsk actors (HL, SB, UD, tc.) and not rly solly on CAP. In practc th alphas ar rarly xactly zro. Thror th standard t-tst s run and a corrspondng t-statstc s gvn. From ths w can asss whthr th valu w dd obsrv s statstcally drnt rom zro. A gnral rul o thumb s any t-statstc gratr than two s sgncant. t statstcally sgncant (t-statstc o two or gratr) and conomcally sgncant (a judgmnt call, how much alpha do you want?) atr runnng all th modls, thn w crtanly hav somthng o potntal valu. EGESSION ESULTS Th Good Harbor Fnancal, Inc. asst allocaton stratgy ams at algnng nvstmnt allocatons wth th busnss cycl. In ssnc whn th conomy s n a rcsson, th modl attmpts to wght a portolo mor towards dnsv scurts (such as bonds) and whn th conomy s xpandng (.. stock markt rally) th modl looks to b n a stock sctor that s posd to contnu hghr. Usng a proprtary algorthm and computr smulaton, a stram o rturns basd on ths prms was gnratd rom 1981 to 008. Svral rgrsson analyss wr thn conductd usng th modls outlnd abov to s a postv alpha was gnratd. Th rsults o ths rgrssons ar lstd n th ollowng tabls. For comparson purposs, rsults rom rgrssons usng S&P 500 rturns ar also lstd CAP grsson: t = + (,t) + t Tm Prod: Fb Dcmbr 008 Good Harbor Paramtr odl S&P 500 onthly an Excss turn E[ ] (%) Annual an Excss turn E[ ] (%) Sharp ato onthly (%) Annualzd (%) t( Tabl 1: CAP rgrsson rsults. FF3F grsson: t = + (,t) + h (HL t ) + s (SB t ) + t Tm Prod: Fb Dcmbr 008 Good Harbor Paramtr odl S&P 500 onthly an Excss turn E[ ] (%) Annual an Excss turn E[ ] (%) Sharp ato onthly (%) Annualzd (%) t( h s Tabl : Fama-Frnch 3-actor rgrsson rsults. Lk th CAP, smlar alpha chcks can b don wth th othr actor modls (FF3F, C4F). I our alphas ar Good Harbor Fnancal: Gnral Dstrbuton v.1 Pag 4 o 6

5 Good Harbor Fnancal, Inc. Chcago, IL C4F grsson: t = + (,t) + h (HL t ) + s (SB t ) + u (UD t ) + t Tm Prod: Fb Dcmbr 008 Good Harbor Paramtr odl S&P 500 onthly an Excss turn E[ ] (%) Annual an Excss turn E[ ] (%) Sharp ato onthly (%) Annualzd (%) t( h s u Tabl 3: Carhart 4-actor rgrsson rsults. Th Good Harbor modl arns almost twc as much xcss rturn compard to th S&P 500 and t dos so wth lowr rsk as suggstd by th Sharp ato. Th alpha s postv and sgncant n all cass. Intrstngly th S&P 500 has a ngatv alpha. Ths s du to th act that th rgrssons us th valuwghtd rturn on all NYSE, AEX, and NASDAQ stocks as th markt proxy. Usng th S&P 500 as th markt proxy rsults n a Good Harbor alpha that s slghtly largr than thos lstd CAP Fama-Frnch 3-Factor Carhart 4-Factor 0 Excss turn Alpha -10 1/88 1/93 1/98 1/03 1/08 Fgur 4: turns and alphas ovr multpl prods. Fgur 4 compars alphas and rturns rom varous 5- yar sub-sampls (nd dat s lstd on th x-axs) or ach o th modls. As llustratd, th alphas and rturns vary ovr tm prods. In prods whr rturns and alphas ar smlar (.. 1/003) w can conclud that th Good Harbor modl s producng rturns vrtually ndpndnt o th modls (.. rturn = alpha and th actors ar zro). Cass whr th alpha s nar zro suggst that th modls ar ully capturng th Good Harbor rturns n thos prods. Ths mght b th cas, or xampl, whn th Good Harbor asst allocaton s havly nvstd n stocks and thror w mght xpct th Good Harbor rturns to b wll dscrbd by th ) actor n th cas o th CAP modl. ) -% t-stat ) -% t-stat CAP FF3F * * C4F * * Tabl 4: turn and alpha man statstcs. Tabl 4 contans th mans or ach o th curvs n Fgur 4. As bor, th avrag alpha s both statstcally and conomcally sgncant. SUAY & CONCLUSIONS Whn compard aganst th CAP, th Fama-Frnch 3- Factor modl and th Carhart 4-Factor modl usng smulatd data rom , th Good Harbor asst allocaton stratgy ylds conomcally and statstcally sgncant alphas. In th ull sampl CAP cas th alpha xcds 5% annually. For Fama-Frnch 3-Factor and Carhart 4-Factor th alpha s vn hghr. Usng alpha sgncanc as a mans o prormanc attrbuton, ths rsults suggst that th Good Harbor Fnancal modl ors valu to a dynamcally allocatd nvstmnt portolo. APPENDIX A: VAIABLE DEFINITIONS.) Expctaton (. avrag or man). ) Expctd rturn on th markt. sk-r rat (.. rturn on T-blls). A Invstor s rsk-avrson actor. Varanc o markt rturns. arkt xcss-rturn (.. markt rturn lss th rsk-r rat. 1-prod rturn on nvstmnt. E Expctd xcss-rturn rom nvstmnt. Loadng or th markt actor. HL Hypothtcal portolo mad by buyng hgh book/markt (valu) stocks and sllng low book/markt (growth) stocks. SB Hypothtcal portolo mad by buyng small company stocks and sllng larg company stocks. UD Hypothtcal portolo mad by buyng stocks wth th hghst rturns th prvous 1 months and sllng stocks wth th lowst rturns n th prvous 1 months. prsnts th uncondtonal man o nvstmnt. sults rom a tm-srs rgrsson. As a masur o prormanc, largr alphas ar dsrabl. t grsson rror trm at tm t., Excss rturn o th markt at tm t. t Good Harbor Fnancal: Gnral Dstrbuton v.1 Pag 5 o 6

6 Good Harbor Fnancal, Inc. Chcago, IL APPENDIX B: EFEENCES [1] Fama, E.F., and K.. Frnch (199). ultactor Explanatons o Asst Prcng Anomals, Journal o Fnanc Volum 51, Issu 1: [] Carhart,.. (1997). On prsstnc n mutual und prormanc, Journal o Fnanc Volum 5, Issu 1:57-8. [3] Bod, Z., A. Kan, and A.J. arcus (005). Invstmnts (cgraw-hll, Nw York). [4] Brnstn, P.L. (1993). Captal Idas Th Improbabl Orgns o odrn Wall Strt (Fr Prss, Nw York). [5] Cochran, J.H. (005). Asst Prcng (Prncton Unvrsty Prss, Nw Jrsy). [6] mba.tuck.dartmouth.du/pags/aculty/ kn.rnch/data_lbrary.html. [7] Lhabtant, F.S. (004). Hdg Funds Quanttatv Insghts (John Wly & Sons, England). Good Harbor Fnancal: Gnral Dstrbuton v.1 Pag 6 o 6

te Finance (4th Edition), July 2017.

te Finance (4th Edition), July 2017. Appndx Chaptr. Tchncal Background Gnral Mathmatcal and Statstcal Background Fndng a bas: 3 2 = 9 3 = 9 1 /2 x a = b x = b 1/a A powr of 1 / 2 s also quvalnt to th squar root opraton. Fndng an xponnt: 3

More information

First derivative analysis

First derivative analysis Robrto s Nots on Dirntial Calculus Chaptr 8: Graphical analysis Sction First drivativ analysis What you nd to know alrady: How to us drivativs to idntiy th critical valus o a unction and its trm points

More information

Fakultät III Wirtschaftswissenschaften Univ.-Prof. Dr. Jan Franke-Viebach

Fakultät III Wirtschaftswissenschaften Univ.-Prof. Dr. Jan Franke-Viebach Unvrstät Sgn Fakultät III Wrtschaftswssnschaftn Unv.-rof. Dr. Jan Frank-Vbach Exam Intrnatonal Fnancal Markts Summr Smstr 206 (2 nd Exam rod) Avalabl tm: 45 mnuts Soluton For your attnton:. las do not

More information

Fakultät III Univ.-Prof. Dr. Jan Franke-Viebach

Fakultät III Univ.-Prof. Dr. Jan Franke-Viebach Unv.Prof. r. J. FrankVbach WS 067: Intrnatonal Economcs ( st xam prod) Unvrstät Sgn Fakultät III Unv.Prof. r. Jan FrankVbach Exam Intrnatonal Economcs Wntr Smstr 067 ( st Exam Prod) Avalabl tm: 60 mnuts

More information

Naresuan University Journal: Science and Technology 2018; (26)1

Naresuan University Journal: Science and Technology 2018; (26)1 Narsuan Unvrsty Journal: Scnc and Tchnology 018; (6)1 Th Dvlopmnt o a Corrcton Mthod or Ensurng a Contnuty Valu o Th Ch-squar Tst wth a Small Expctd Cll Frquncy Kajta Matchma 1 *, Jumlong Vongprasrt and

More information

Economics 600: August, 2007 Dynamic Part: Problem Set 5. Problems on Differential Equations and Continuous Time Optimization

Economics 600: August, 2007 Dynamic Part: Problem Set 5. Problems on Differential Equations and Continuous Time Optimization THE UNIVERSITY OF MARYLAND COLLEGE PARK, MARYLAND Economcs 600: August, 007 Dynamc Part: Problm St 5 Problms on Dffrntal Equatons and Contnuous Tm Optmzaton Quston Solv th followng two dffrntal quatons.

More information

A Note on Estimability in Linear Models

A Note on Estimability in Linear Models Intrnatonal Journal of Statstcs and Applcatons 2014, 4(4): 212-216 DOI: 10.5923/j.statstcs.20140404.06 A Not on Estmablty n Lnar Modls S. O. Adymo 1,*, F. N. Nwob 2 1 Dpartmnt of Mathmatcs and Statstcs,

More information

Analyzing Frequencies

Analyzing Frequencies Frquncy (# ndvduals) Frquncy (# ndvduals) /3/16 H o : No dffrnc n obsrvd sz frquncs and that prdctd by growth modl How would you analyz ths data? 15 Obsrvd Numbr 15 Expctd Numbr from growth modl 1 1 5

More information

A Probabilistic Characterization of Simulation Model Uncertainties

A Probabilistic Characterization of Simulation Model Uncertainties A Proalstc Charactrzaton of Sulaton Modl Uncrtants Vctor Ontvros Mohaad Modarrs Cntr for Rsk and Rlalty Unvrsty of Maryland 1 Introducton Thr s uncrtanty n odl prdctons as wll as uncrtanty n xprnts Th

More information

Chapter 6 Student Lecture Notes 6-1

Chapter 6 Student Lecture Notes 6-1 Chaptr 6 Studnt Lctur Nots 6-1 Chaptr Goals QM353: Busnss Statstcs Chaptr 6 Goodnss-of-Ft Tsts and Contngncy Analyss Aftr compltng ths chaptr, you should b abl to: Us th ch-squar goodnss-of-ft tst to dtrmn

More information

ST 524 NCSU - Fall 2008 One way Analysis of variance Variances not homogeneous

ST 524 NCSU - Fall 2008 One way Analysis of variance Variances not homogeneous ST 54 NCSU - Fall 008 On way Analyss of varanc Varancs not homognous On way Analyss of varanc Exampl (Yandll, 997) A plant scntst masurd th concntraton of a partcular vrus n plant sap usng ELISA (nzym-lnkd

More information

Lucas Test is based on Euler s theorem which states that if n is any integer and a is coprime to n, then a φ(n) 1modn.

Lucas Test is based on Euler s theorem which states that if n is any integer and a is coprime to n, then a φ(n) 1modn. Modul 10 Addtonal Topcs 10.1 Lctur 1 Prambl: Dtrmnng whthr a gvn ntgr s prm or compost s known as prmalty tstng. Thr ar prmalty tsts whch mrly tll us whthr a gvn ntgr s prm or not, wthout gvng us th factors

More information

Chapter 13 GMM for Linear Factor Models in Discount Factor form. GMM on the pricing errors gives a crosssectional

Chapter 13 GMM for Linear Factor Models in Discount Factor form. GMM on the pricing errors gives a crosssectional Chaptr 13 GMM for Linar Factor Modls in Discount Factor form GMM on th pricing rrors givs a crosssctional rgrssion h cas of xcss rturns Hors rac sting for charactristic sting for pricd factors: lambdas

More information

The Hyperelastic material is examined in this section.

The Hyperelastic material is examined in this section. 4. Hyprlastcty h Hyprlastc matral s xad n ths scton. 4..1 Consttutv Equatons h rat of chang of ntrnal nrgy W pr unt rfrnc volum s gvn by th strss powr, whch can b xprssd n a numbr of dffrnt ways (s 3.7.6):

More information

Search sequence databases 3 10/25/2016

Search sequence databases 3 10/25/2016 Sarch squnc databass 3 10/25/2016 Etrm valu distribution Ø Suppos X is a random variabl with probability dnsity function p(, w sampl a larg numbr S of indpndnt valus of X from this distribution for an

More information

Review - Probabilistic Classification

Review - Probabilistic Classification Mmoral Unvrsty of wfoundland Pattrn Rcognton Lctur 8 May 5, 6 http://www.ngr.mun.ca/~charlsr Offc Hours: Tusdays Thursdays 8:3-9:3 PM E- (untl furthr notc) Gvn lablld sampls { ɛc,,,..., } {. Estmat Rvw

More information

+ f. e f. Ch. 8 Inflation, Interest Rates & FX Rates. Purchasing Power Parity. Purchasing Power Parity

+ f. e f. Ch. 8 Inflation, Interest Rates & FX Rates. Purchasing Power Parity. Purchasing Power Parity Ch. 8 Inlation, Intrst Rats & FX Rats Topics Purchasing Powr Parity Intrnational Fishr Ect Purchasing Powr Parity Purchasing Powr Parity (PPP: Th purchasing powr o a consumr will b similar whn purchasing

More information

Investing on the CAPM Pricing Error

Investing on the CAPM Pricing Error Tchnology and Invstmnt, 2017, 8, 67-82 http://www.scrp.org/journal/t ISSN Onln: 2150-4067 ISSN Prnt: 2150-4059 Invstng on th CAPM Prcng Error José Carlos d Souza Santos, Elas Cavalcant Flho Economcs Dpartmnt,

More information

CHAPTER 33: PARTICLE PHYSICS

CHAPTER 33: PARTICLE PHYSICS Collg Physcs Studnt s Manual Chaptr 33 CHAPTER 33: PARTICLE PHYSICS 33. THE FOUR BASIC FORCES 4. (a) Fnd th rato of th strngths of th wak and lctromagntc forcs undr ordnary crcumstancs. (b) What dos that

More information

The Variance-Covariance Matrix

The Variance-Covariance Matrix Th Varanc-Covaranc Marx Our bggs a so-ar has bn ng a lnar uncon o a s o daa by mnmzng h las squars drncs rom h o h daa wh mnsarch. Whn analyzng non-lnar daa you hav o us a program l Malab as many yps o

More information

Grand Canonical Ensemble

Grand Canonical Ensemble Th nsmbl of systms mmrsd n a partcl-hat rsrvor at constant tmpratur T, prssur P, and chmcal potntal. Consdr an nsmbl of M dntcal systms (M =,, 3,...M).. Thy ar mutually sharng th total numbr of partcls

More information

EXST Regression Techniques Page 1

EXST Regression Techniques Page 1 EXST704 - Rgrssion Tchniqus Pag 1 Masurmnt rrors in X W hav assumd that all variation is in Y. Masurmnt rror in this variabl will not ffct th rsults, as long as thy ar uncorrlatd and unbiasd, sinc thy

More information

Observer Bias and Reliability By Xunchi Pu

Observer Bias and Reliability By Xunchi Pu Obsrvr Bias and Rliability By Xunchi Pu Introduction Clarly all masurmnts or obsrvations nd to b mad as accuratly as possibl and invstigators nd to pay carful attntion to chcking th rliability of thir

More information

Answer Homework 5 PHA5127 Fall 1999 Jeff Stark

Answer Homework 5 PHA5127 Fall 1999 Jeff Stark Answr omwork 5 PA527 Fall 999 Jff Stark A patint is bing tratd with Drug X in a clinical stting. Upon admiion, an IV bolus dos of 000mg was givn which yildd an initial concntration of 5.56 µg/ml. A fw

More information

Journal of Theoretical and Applied Information Technology 10 th January Vol. 47 No JATIT & LLS. All rights reserved.

Journal of Theoretical and Applied Information Technology 10 th January Vol. 47 No JATIT & LLS. All rights reserved. Journal o Thortcal and Appld Inormaton Tchnology th January 3. Vol. 47 No. 5-3 JATIT & LLS. All rghts rsrvd. ISSN: 99-8645 www.att.org E-ISSN: 87-395 RESEARCH ON PROPERTIES OF E-PARTIAL DERIVATIVE OF LOGIC

More information

Econ107 Applied Econometrics Topic 10: Dummy Dependent Variable (Studenmund, Chapter 13)

Econ107 Applied Econometrics Topic 10: Dummy Dependent Variable (Studenmund, Chapter 13) Pag- Econ7 Appld Economtrcs Topc : Dummy Dpndnt Varabl (Studnmund, Chaptr 3) I. Th Lnar Probablty Modl Suppos w hav a cross scton of 8-24 yar-olds. W spcfy a smpl 2-varabl rgrsson modl. Th probablty of

More information

CHAPTER 7d. DIFFERENTIATION AND INTEGRATION

CHAPTER 7d. DIFFERENTIATION AND INTEGRATION CHAPTER 7d. DIFFERENTIATION AND INTEGRATION A. J. Clark School o Engnrng Dpartmnt o Cvl and Envronmntal Engnrng by Dr. Ibrahm A. Assakka Sprng ENCE - Computaton Mthods n Cvl Engnrng II Dpartmnt o Cvl and

More information

Soft k-means Clustering. Comp 135 Machine Learning Computer Science Tufts University. Mixture Models. Mixture of Normals in 1D

Soft k-means Clustering. Comp 135 Machine Learning Computer Science Tufts University. Mixture Models. Mixture of Normals in 1D Comp 35 Machn Larnng Computr Scnc Tufts Unvrsty Fall 207 Ron Khardon Th EM Algorthm Mxtur Modls Sm-Suprvsd Larnng Soft k-mans Clustrng ck k clustr cntrs : Assocat xampls wth cntrs p,j ~~ smlarty b/w cntr

More information

Brief Introduction to Statistical Mechanics

Brief Introduction to Statistical Mechanics Brif Introduction to Statistical Mchanics. Purpos: Ths nots ar intndd to provid a vry quick introduction to Statistical Mchanics. Th fild is of cours far mor vast than could b containd in ths fw pags.

More information

Logistic Regression I. HRP 261 2/10/ am

Logistic Regression I. HRP 261 2/10/ am Logstc Rgrsson I HRP 26 2/0/03 0- am Outln Introducton/rvw Th smplst logstc rgrsson from a 2x2 tabl llustrats how th math works Stp-by-stp xampls to b contnud nxt tm Dummy varabls Confoundng and ntracton

More information

10/7/14. Mixture Models. Comp 135 Introduction to Machine Learning and Data Mining. Maximum likelihood estimation. Mixture of Normals in 1D

10/7/14. Mixture Models. Comp 135 Introduction to Machine Learning and Data Mining. Maximum likelihood estimation. Mixture of Normals in 1D Comp 35 Introducton to Machn Larnng and Data Mnng Fall 204 rofssor: Ron Khardon Mxtur Modls Motvatd by soft k-mans w dvlopd a gnratv modl for clustrng. Assum thr ar k clustrs Clustrs ar not rqurd to hav

More information

Higher order derivatives

Higher order derivatives Robrto s Nots on Diffrntial Calculus Chaptr 4: Basic diffrntiation ruls Sction 7 Highr ordr drivativs What you nd to know alrady: Basic diffrntiation ruls. What you can larn hr: How to rpat th procss of

More information

Elements of Statistical Thermodynamics

Elements of Statistical Thermodynamics 24 Elmnts of Statistical Thrmodynamics Statistical thrmodynamics is a branch of knowldg that has its own postulats and tchniqus. W do not attmpt to giv hr vn an introduction to th fild. In this chaptr,

More information

Heisenberg Model. Sayed Mohammad Mahdi Sadrnezhaad. Supervisor: Prof. Abdollah Langari

Heisenberg Model. Sayed Mohammad Mahdi Sadrnezhaad. Supervisor: Prof. Abdollah Langari snbrg Modl Sad Mohammad Mahd Sadrnhaad Survsor: Prof. bdollah Langar bstract: n ths rsarch w tr to calculat analtcall gnvalus and gnvctors of fnt chan wth ½-sn artcls snbrg modl. W drov gnfuctons for closd

More information

The van der Waals interaction 1 D. E. Soper 2 University of Oregon 20 April 2012

The van der Waals interaction 1 D. E. Soper 2 University of Oregon 20 April 2012 Th van dr Waals intraction D. E. Sopr 2 Univrsity of Orgon 20 pril 202 Th van dr Waals intraction is discussd in Chaptr 5 of J. J. Sakurai, Modrn Quantum Mchanics. Hr I tak a look at it in a littl mor

More information

COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP

COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP ISAHP 00, Bal, Indonsa, August -9, 00 COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP Chkako MIYAKE, Kkch OHSAWA, Masahro KITO, and Masaak SHINOHARA Dpartmnt of Mathmatcal Informaton Engnrng

More information

Optimal Ordering Policy in a Two-Level Supply Chain with Budget Constraint

Optimal Ordering Policy in a Two-Level Supply Chain with Budget Constraint Optmal Ordrng Polcy n a Two-Lvl Supply Chan wth Budgt Constrant Rasoul aj Alrza aj Babak aj ABSTRACT Ths papr consdrs a two- lvl supply chan whch consst of a vndor and svral rtalrs. Unsatsfd dmands n rtalrs

More information

Advanced Macroeconomics

Advanced Macroeconomics Advancd Macroconomcs Chaptr 18 INFLATION, UNEMPLOYMENT AND AGGREGATE SUPPLY Thms of th chaptr Nomnal rgdts, xpctatonal rrors and mploymnt fluctuatons. Th short-run trad-off btwn nflaton and unmploymnt.

More information

SCITECH Volume 5, Issue 1 RESEARCH ORGANISATION November 17, 2015

SCITECH Volume 5, Issue 1 RESEARCH ORGANISATION November 17, 2015 Journal of Informaton Scncs and Computng Tchnologs(JISCT) ISSN: 394-966 SCITECH Volum 5, Issu RESEARCH ORGANISATION Novmbr 7, 5 Journal of Informaton Scncs and Computng Tchnologs www.sctcrsarch.com/journals

More information

Addition of angular momentum

Addition of angular momentum Addition of angular momntum April, 0 Oftn w nd to combin diffrnt sourcs of angular momntum to charactriz th total angular momntum of a systm, or to divid th total angular momntum into parts to valuat th

More information

??? Dynamic Causal Modelling for M/EEG. Electroencephalography (EEG) Dynamic Causal Modelling. M/EEG analysis at sensor level. time.

??? Dynamic Causal Modelling for M/EEG. Electroencephalography (EEG) Dynamic Causal Modelling. M/EEG analysis at sensor level. time. Elctroncphalography EEG Dynamc Causal Modllng for M/EEG ampltud μv tm ms tral typ 1 tm channls channls tral typ 2 C. Phllps, Cntr d Rchrchs du Cyclotron, ULg, Blgum Basd on slds from: S. Kbl M/EEG analyss

More information

Function Spaces. a x 3. (Letting x = 1 =)) a(0) + b + c (1) = 0. Row reducing the matrix. b 1. e 4 3. e 9. >: (x = 1 =)) a(0) + b + c (1) = 0

Function Spaces. a x 3. (Letting x = 1 =)) a(0) + b + c (1) = 0. Row reducing the matrix. b 1. e 4 3. e 9. >: (x = 1 =)) a(0) + b + c (1) = 0 unction Spacs Prrquisit: Sction 4.7, Coordinatization n this sction, w apply th tchniqus of Chaptr 4 to vctor spacs whos lmnts ar functions. Th vctor spacs P n and P ar familiar xampls of such spacs. Othr

More information

Electrochemical Equilibrium Electromotive Force. Relation between chemical and electric driving forces

Electrochemical Equilibrium Electromotive Force. Relation between chemical and electric driving forces C465/865, 26-3, Lctur 7, 2 th Sp., 26 lctrochmcal qulbrum lctromotv Forc Rlaton btwn chmcal and lctrc drvng forcs lctrochmcal systm at constant T and p: consdr G Consdr lctrochmcal racton (nvolvng transfr

More information

Advances in the study of intrinsic rotation with flux tube gyrokinetics

Advances in the study of intrinsic rotation with flux tube gyrokinetics Adans n th study o ntrns rotaton wth lux tub gyroknts F.I. Parra and M. arns Unrsty o Oxord Wolgang Paul Insttut, Vnna, Aprl 0 Introduton In th absn o obous momntum nput (apart rom th dg), tokamak plasmas

More information

Partial Derivatives: Suppose that z = f(x, y) is a function of two variables.

Partial Derivatives: Suppose that z = f(x, y) is a function of two variables. Chaptr Functions o Two Variabls Applid Calculus 61 Sction : Calculus o Functions o Two Variabls Now that ou hav som amiliarit with unctions o two variabls it s tim to start appling calculus to hlp us solv

More information

Chapter 13 Aggregate Supply

Chapter 13 Aggregate Supply Chaptr 13 Aggrgat Supply 0 1 Larning Objctivs thr modls of aggrgat supply in which output dpnds positivly on th pric lvl in th short run th short-run tradoff btwn inflation and unmploymnt known as th Phillips

More information

NEW APPLICATIONS OF THE ABEL-LIOUVILLE FORMULA

NEW APPLICATIONS OF THE ABEL-LIOUVILLE FORMULA NE APPLICATIONS OF THE ABEL-LIOUVILLE FORMULA Mirca I CÎRNU Ph Dp o Mathmatics III Faculty o Applid Scincs Univrsity Polithnica o Bucharst Cirnumirca @yahoocom Abstract In a rcnt papr [] 5 th indinit intgrals

More information

3.4 Properties of the Stress Tensor

3.4 Properties of the Stress Tensor cto.4.4 Proprts of th trss sor.4. trss rasformato Lt th compots of th Cauchy strss tsor a coordat systm wth bas vctors b. h compots a scod coordat systm wth bas vctors j,, ar gv by th tsor trasformato

More information

Pipe flow friction, small vs. big pipes

Pipe flow friction, small vs. big pipes Friction actor (t/0 t o pip) Friction small vs larg pips J. Chaurtt May 016 It is an intrsting act that riction is highr in small pips than largr pips or th sam vlocity o low and th sam lngth. Friction

More information

Consider a system of 2 simultaneous first order linear equations

Consider a system of 2 simultaneous first order linear equations Soluon of sysms of frs ordr lnar quaons onsdr a sysm of smulanous frs ordr lnar quaons a b c d I has h alrna mar-vcor rprsnaon a b c d Or, n shorhand A, f A s alrady known from con W know ha h abov sysm

More information

(Upside-Down o Direct Rotation) β - Numbers

(Upside-Down o Direct Rotation) β - Numbers Amrican Journal of Mathmatics and Statistics 014, 4(): 58-64 DOI: 10593/jajms0140400 (Upsid-Down o Dirct Rotation) β - Numbrs Ammar Sddiq Mahmood 1, Shukriyah Sabir Ali,* 1 Dpartmnt of Mathmatics, Collg

More information

A NEW GENERALISATION OF SAM-SOLAI S MULTIVARIATE ADDITIVE GAMMA DISTRIBUTION*

A NEW GENERALISATION OF SAM-SOLAI S MULTIVARIATE ADDITIVE GAMMA DISTRIBUTION* A NEW GENERALISATION OF SAM-SOLAI S MULTIVARIATE ADDITIVE GAMMA DISTRIBUTION* Dr. G.S. Davd Sam Jayakumar, Assstant Profssor, Jamal Insttut of Managmnt, Jamal Mohamd Collg, Truchraall 620 020, South Inda,

More information

u x v x dx u x v x v x u x dx d u x v x u x v x dx u x v x dx Integration by Parts Formula

u x v x dx u x v x v x u x dx d u x v x u x v x dx u x v x dx Integration by Parts Formula 7. Intgration by Parts Each drivativ formula givs ris to a corrsponding intgral formula, as w v sn many tims. Th drivativ product rul yilds a vry usful intgration tchniqu calld intgration by parts. Starting

More information

An Overview of Markov Random Field and Application to Texture Segmentation

An Overview of Markov Random Field and Application to Texture Segmentation An Ovrvw o Markov Random Fld and Applcaton to Txtur Sgmntaton Song-Wook Joo Octobr 003. What s MRF? MRF s an xtnson o Markov Procss MP (D squnc o r.v. s unlatral (causal: p(x t x,

More information

Outlier-tolerant parameter estimation

Outlier-tolerant parameter estimation Outlr-tolrant paramtr stmaton Baysan thods n physcs statstcs machn larnng and sgnal procssng (SS 003 Frdrch Fraundorfr fraunfr@cg.tu-graz.ac.at Computr Graphcs and Vson Graz Unvrsty of Tchnology Outln

More information

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D { 2 2... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data

More information

Addition of angular momentum

Addition of angular momentum Addition of angular momntum April, 07 Oftn w nd to combin diffrnt sourcs of angular momntum to charactriz th total angular momntum of a systm, or to divid th total angular momntum into parts to valuat

More information

VALUING SURRENDER OPTIONS IN KOREAN INTEREST INDEXED ANNUITIES

VALUING SURRENDER OPTIONS IN KOREAN INTEREST INDEXED ANNUITIES VALUING SURRENDER OPTIONS IN KOREAN INTEREST INDEXED ANNUITIES Changi Kim* * Dr. Changi Kim is Lcturr at Actuarial Studis Faculty of Commrc & Economics Th Univrsity of Nw South Wals Sydny NSW 2052 Australia.

More information

8-node quadrilateral element. Numerical integration

8-node quadrilateral element. Numerical integration Fnt Elmnt Mthod lctur nots _nod quadrlatral lmnt Pag of 0 -nod quadrlatral lmnt. Numrcal ntgraton h tchnqu usd for th formulaton of th lnar trangl can b formall tndd to construct quadrlatral lmnts as wll

More information

Unbalanced Panel Data Models

Unbalanced Panel Data Models Ubalacd Pal Data odls Chaptr 9 from Baltag: Ecoomtrc Aalyss of Pal Data 5 by Adrás alascs 4448 troducto balacd or complt pals: a pal data st whr data/obsrvatos ar avalabl for all crosssctoal uts th tr

More information

Ch. 24 Molecular Reaction Dynamics 1. Collision Theory

Ch. 24 Molecular Reaction Dynamics 1. Collision Theory Ch. 4 Molcular Raction Dynamics 1. Collision Thory Lctur 16. Diffusion-Controlld Raction 3. Th Matrial Balanc Equation 4. Transition Stat Thory: Th Eyring Equation 5. Transition Stat Thory: Thrmodynamic

More information

Physics of Very High Frequency (VHF) Capacitively Coupled Plasma Discharges

Physics of Very High Frequency (VHF) Capacitively Coupled Plasma Discharges Physcs of Vry Hgh Frquncy (VHF) Capactvly Coupld Plasma Dschargs Shahd Rauf, Kallol Bra, Stv Shannon, and Kn Collns Appld Matrals, Inc., Sunnyval, CA AVS 54 th Intrnatonal Symposum Sattl, WA Octobr 15-19,

More information

Quasi-Classical States of the Simple Harmonic Oscillator

Quasi-Classical States of the Simple Harmonic Oscillator Quasi-Classical Stats of th Simpl Harmonic Oscillator (Draft Vrsion) Introduction: Why Look for Eignstats of th Annihilation Oprator? Excpt for th ground stat, th corrspondnc btwn th quantum nrgy ignstats

More information

EEC 686/785 Modeling & Performance Evaluation of Computer Systems. Lecture 12

EEC 686/785 Modeling & Performance Evaluation of Computer Systems. Lecture 12 EEC 686/785 Modlng & Prformanc Evaluaton of Computr Systms Lctur Dpartmnt of Elctrcal and Computr Engnrng Clvland Stat Unvrsty wnbng@.org (basd on Dr. Ra Jan s lctur nots) Outln Rvw of lctur k r Factoral

More information

Diploma Macro Paper 2

Diploma Macro Paper 2 Diploma Macro Papr 2 Montary Macroconomics Lctur 6 Aggrgat supply and putting AD and AS togthr Mark Hays 1 Exognous: M, G, T, i*, π Goods markt KX and IS (Y, C, I) Mony markt (LM) (i, Y) Labour markt (P,

More information

4037 ADDITIONAL MATHEMATICS

4037 ADDITIONAL MATHEMATICS CAMBRIDGE INTERNATIONAL EXAMINATIONS GCE Ordinary Lvl MARK SCHEME for th Octobr/Novmbr 0 sris 40 ADDITIONAL MATHEMATICS 40/ Papr, maimum raw mark 80 This mark schm is publishd as an aid to tachrs and candidats,

More information

On Properties of the difference between two modified C p statistics in the nested multivariate linear regression models

On Properties of the difference between two modified C p statistics in the nested multivariate linear regression models Global Journal o Pur Ald Mathatcs. ISSN 0973-1768 Volu 1, Nubr 1 (016),. 481-491 Rsarch Inda Publcatons htt://www.rublcaton.co On Prorts o th drnc btwn two odd C statstcs n th nstd ultvarat lnar rgrsson

More information

Background: We have discussed the PIB, HO, and the energy of the RR model. In this chapter, the H-atom, and atomic orbitals.

Background: We have discussed the PIB, HO, and the energy of the RR model. In this chapter, the H-atom, and atomic orbitals. Chaptr 7 Th Hydrogn Atom Background: W hav discussd th PIB HO and th nrgy of th RR modl. In this chaptr th H-atom and atomic orbitals. * A singl particl moving undr a cntral forc adoptd from Scott Kirby

More information

Guo, James C.Y. (1998). "Overland Flow on a Pervious Surface," IWRA International J. of Water, Vol 23, No 2, June.

Guo, James C.Y. (1998). Overland Flow on a Pervious Surface, IWRA International J. of Water, Vol 23, No 2, June. Guo, Jams C.Y. (006). Knmatc Wav Unt Hyrograph for Storm Watr Prctons, Vol 3, No. 4, ASCE J. of Irrgaton an Dranag Engnrng, July/August. Guo, Jams C.Y. (998). "Ovrlan Flow on a Prvous Surfac," IWRA Intrnatonal

More information

A Propagating Wave Packet Group Velocity Dispersion

A Propagating Wave Packet Group Velocity Dispersion Lctur 8 Phys 375 A Propagating Wav Packt Group Vlocity Disprsion Ovrviw and Motivation: In th last lctur w lookd at a localizd solution t) to th 1D fr-particl Schrödingr quation (SE) that corrsponds to

More information

6.1 Integration by Parts and Present Value. Copyright Cengage Learning. All rights reserved.

6.1 Integration by Parts and Present Value. Copyright Cengage Learning. All rights reserved. 6.1 Intgration by Parts and Prsnt Valu Copyright Cngag Larning. All rights rsrvd. Warm-Up: Find f () 1. F() = ln(+1). F() = 3 3. F() =. F() = ln ( 1) 5. F() = 6. F() = - Objctivs, Day #1 Studnts will b

More information

Reliability of time dependent stress-strength system for various distributions

Reliability of time dependent stress-strength system for various distributions IOS Joural of Mathmatcs (IOS-JM ISSN: 78-578. Volum 3, Issu 6 (Sp-Oct., PP -7 www.osrjourals.org lablty of tm dpdt strss-strgth systm for varous dstrbutos N.Swath, T.S.Uma Mahswar,, Dpartmt of Mathmatcs,

More information

4. Money cannot be neutral in the short-run the neutrality of money is exclusively a medium run phenomenon.

4. Money cannot be neutral in the short-run the neutrality of money is exclusively a medium run phenomenon. PART I TRUE/FALSE/UNCERTAIN (5 points ach) 1. Lik xpansionary montary policy, xpansionary fiscal policy rturns output in th mdium run to its natural lvl, and incrass prics. Thrfor, fiscal policy is also

More information

Problem Set 6 Solutions

Problem Set 6 Solutions 6.04/18.06J Mathmatics for Computr Scinc March 15, 005 Srini Dvadas and Eric Lhman Problm St 6 Solutions Du: Monday, March 8 at 9 PM in Room 3-044 Problm 1. Sammy th Shark is a financial srvic providr

More information

Lecture 1: Empirical economic relations

Lecture 1: Empirical economic relations Ecoomcs 53 Lctur : Emprcal coomc rlatos What s coomtrcs? Ecoomtrcs s masurmt of coomc rlatos. W d to kow What s a coomc rlato? How do w masur such a rlato? Dfto: A coomc rlato s a rlato btw coomc varabls.

More information

Chapter 14 Aggregate Supply and the Short-run Tradeoff Between Inflation and Unemployment

Chapter 14 Aggregate Supply and the Short-run Tradeoff Between Inflation and Unemployment Chaptr 14 Aggrgat Supply and th Short-run Tradoff Btwn Inflation and Unmploymnt Modifid by Yun Wang Eco 3203 Intrmdiat Macroconomics Florida Intrnational Univrsity Summr 2017 2016 Worth Publishrs, all

More information

22/ Breakdown of the Born-Oppenheimer approximation. Selection rules for rotational-vibrational transitions. P, R branches.

22/ Breakdown of the Born-Oppenheimer approximation. Selection rules for rotational-vibrational transitions. P, R branches. Subjct Chmistry Papr No and Titl Modul No and Titl Modul Tag 8/ Physical Spctroscopy / Brakdown of th Born-Oppnhimr approximation. Slction ruls for rotational-vibrational transitions. P, R branchs. CHE_P8_M

More information

Physics 256: Lecture 2. Physics

Physics 256: Lecture 2. Physics Physcs 56: Lctur Intro to Quantum Physcs Agnda for Today Complx Numbrs Intrfrnc of lght Intrfrnc Two slt ntrfrnc Dffracton Sngl slt dffracton Physcs 01: Lctur 1, Pg 1 Constructv Intrfrnc Ths wll occur

More information

EEO 401 Digital Signal Processing Prof. Mark Fowler

EEO 401 Digital Signal Processing Prof. Mark Fowler EEO 401 Digital Signal Procssing Prof. Mark Fowlr Dtails of th ot St #19 Rading Assignmnt: Sct. 7.1.2, 7.1.3, & 7.2 of Proakis & Manolakis Dfinition of th So Givn signal data points x[n] for n = 0,, -1

More information

Inflation and Unemployment

Inflation and Unemployment C H A P T E R 13 Aggrgat Supply and th Short-run Tradoff Btwn Inflation and Unmploymnt MACROECONOMICS SIXTH EDITION N. GREGORY MANKIW PowrPoint Slids by Ron Cronovich 2008 Worth Publishrs, all rights rsrvd

More information

ON THE COMPLEXITY OF K-STEP AND K-HOP DOMINATING SETS IN GRAPHS

ON THE COMPLEXITY OF K-STEP AND K-HOP DOMINATING SETS IN GRAPHS MATEMATICA MONTISNIRI Vol XL (2017) MATEMATICS ON TE COMPLEXITY OF K-STEP AN K-OP OMINATIN SETS IN RAPS M FARAI JALALVAN AN N JAFARI RA partmnt of Mathmatcs Shahrood Unrsty of Tchnology Shahrood Iran Emals:

More information

Computation of Greeks Using Binomial Tree

Computation of Greeks Using Binomial Tree Journal of Mathmatcal Fnanc, 07, 7, 597-63 http://www.scrp.org/journal/jmf ISSN Onln: 6-44 ISSN Prnt: 6-434 Computaton of Grks Usng Bnomal Tr Yoshfum Muro, Shntaro Suda Graduat School of conomcs and Managmnt,

More information

Discrete Shells Simulation

Discrete Shells Simulation Dscrt Shlls Smulaton Xaofng M hs proct s an mplmntaton of Grnspun s dscrt shlls, th modl of whch s govrnd by nonlnar mmbran and flxural nrgs. hs nrgs masur dffrncs btwns th undformd confguraton and th

More information

UNTYPED LAMBDA CALCULUS (II)

UNTYPED LAMBDA CALCULUS (II) 1 UNTYPED LAMBDA CALCULUS (II) RECALL: CALL-BY-VALUE O.S. Basic rul Sarch ruls: (\x.) v [v/x] 1 1 1 1 v v CALL-BY-VALUE EVALUATION EXAMPLE (\x. x x) (\y. y) x x [\y. y / x] = (\y. y) (\y. y) y [\y. y /

More information

167 T componnt oftforc on atom B can b drvd as: F B =, E =,K (, ) (.2) wr w av usd 2 = ( ) =2 (.3) T scond drvatv: 2 E = K (, ) = K (1, ) + 3 (.4).2.2

167 T componnt oftforc on atom B can b drvd as: F B =, E =,K (, ) (.2) wr w av usd 2 = ( ) =2 (.3) T scond drvatv: 2 E = K (, ) = K (1, ) + 3 (.4).2.2 166 ppnd Valnc Forc Flds.1 Introducton Valnc forc lds ar usd to dscrb ntra-molcular ntractons n trms of 2-body, 3-body, and 4-body (and gr) ntractons. W mplmntd many popular functonal forms n our program..2

More information

Lecture 14. Relic neutrinos Temperature at neutrino decoupling and today Effective degeneracy factor Neutrino mass limits Saha equation

Lecture 14. Relic neutrinos Temperature at neutrino decoupling and today Effective degeneracy factor Neutrino mass limits Saha equation Lctur Rlc nutrnos mpratur at nutrno dcoupln and today Effctv dnracy factor Nutrno mass lmts Saha quaton Physcal Cosmoloy Lnt 005 Rlc Nutrnos Nutrnos ar wakly ntractn partcls (lptons),,,,,,, typcal ractons

More information

1 Minimum Cut Problem

1 Minimum Cut Problem CS 6 Lctur 6 Min Cut and argr s Algorithm Scribs: Png Hui How (05), Virginia Dat: May 4, 06 Minimum Cut Problm Today, w introduc th minimum cut problm. This problm has many motivations, on of which coms

More information

0 +1e Radionuclides - can spontaneously emit particles and radiation which can be expressed by a nuclear equation.

0 +1e Radionuclides - can spontaneously emit particles and radiation which can be expressed by a nuclear equation. Radioactivity Radionuclids - can spontanously mit particls and radiation which can b xprssd by a nuclar quation. Spontanous Emission: Mass and charg ar consrvd. 4 2α -β Alpha mission Bta mission 238 92U

More information

Hydrogen Atom and One Electron Ions

Hydrogen Atom and One Electron Ions Hydrogn Atom and On Elctron Ions Th Schrödingr quation for this two-body problm starts out th sam as th gnral two-body Schrödingr quation. First w sparat out th motion of th cntr of mass. Th intrnal potntial

More information

Alpha and beta decay equation practice

Alpha and beta decay equation practice Alpha and bta dcay quation practic Introduction Alpha and bta particls may b rprsntd in quations in svral diffrnt ways. Diffrnt xam boards hav thir own prfrnc. For xampl: Alpha Bta α β alpha bta Dspit

More information

Davisson Germer experiment

Davisson Germer experiment Announcmnts: Davisson Grmr xprimnt Homwork st 5 is today. Homwork st 6 will b postd latr today. Mad a good guss about th Nobl Priz for 2013 Clinton Davisson and Lstr Grmr. Davisson won Nobl Priz in 1937.

More information

Relate p and T at equilibrium between two phases. An open system where a new phase may form or a new component can be added

Relate p and T at equilibrium between two phases. An open system where a new phase may form or a new component can be added 4.3, 4.4 Phas Equlbrum Dtrmn th slops of th f lns Rlat p and at qulbrum btwn two phass ts consdr th Gbbs functon dg η + V Appls to a homognous systm An opn systm whr a nw phas may form or a nw componnt

More information

Integration by Parts

Integration by Parts Intgration by Parts Intgration by parts is a tchniqu primarily for valuating intgrals whos intgrand is th product of two functions whr substitution dosn t work. For ampl, sin d or d. Th rul is: u ( ) v'(

More information

Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers Roy D. Yates and David J.

Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers Roy D. Yates and David J. Probability and Stochastic Procsss: A Frindly Introduction for Elctrical and Computr Enginrs Roy D. Yats and David J. Goodman Problm Solutions : Yats and Goodman,4.3. 4.3.4 4.3. 4.4. 4.4.4 4.4.6 4.. 4..7

More information

Polytropic Process. A polytropic process is a quasiequilibrium process described by

Polytropic Process. A polytropic process is a quasiequilibrium process described by Polytropc Procss A polytropc procss s a quasqulbrum procss dscrbd by pv n = constant (Eq. 3.5 Th xponnt, n, may tak on any valu from to dpndng on th partcular procss. For any gas (or lqud, whn n = 0, th

More information

Forces. Quantum ElectroDynamics. α = = We have now:

Forces. Quantum ElectroDynamics. α = = We have now: W hav now: Forcs Considrd th gnral proprtis of forcs mdiatd by xchang (Yukawa potntial); Examind consrvation laws which ar obyd by (som) forcs. W will nxt look at thr forcs in mor dtail: Elctromagntic

More information

ph People Grade Level: basic Duration: minutes Setting: classroom or field site

ph People Grade Level: basic Duration: minutes Setting: classroom or field site ph Popl Adaptd from: Whr Ar th Frogs? in Projct WET: Curriculum & Activity Guid. Bozman: Th Watrcours and th Council for Environmntal Education, 1995. ph Grad Lvl: basic Duration: 10 15 minuts Stting:

More information

Differentiation of Exponential Functions

Differentiation of Exponential Functions Calculus Modul C Diffrntiation of Eponntial Functions Copyright This publication Th Northrn Albrta Institut of Tchnology 007. All Rights Rsrvd. LAST REVISED March, 009 Introduction to Diffrntiation of

More information

Y 0. Standing Wave Interference between the incident & reflected waves Standing wave. A string with one end fixed on a wall

Y 0. Standing Wave Interference between the incident & reflected waves Standing wave. A string with one end fixed on a wall Staning Wav Intrfrnc btwn th incint & rflct wavs Staning wav A string with on n fix on a wall Incint: y, t) Y cos( t ) 1( Y 1 ( ) Y (St th incint wav s phas to b, i.., Y + ral & positiv.) Rflct: y, t)

More information

Atomic energy levels. Announcements:

Atomic energy levels. Announcements: Atomic nrgy lvls Announcmnts: Exam solutions ar postd. Problm solving sssions ar M3-5 and Tusday 1-3 in G-140. Will nd arly and hand back your Midtrm Exam at nd of class. http://www.colorado.du/physics/phys2170/

More information