The Quark Model. Introduction to Elementary Particle Physics. Diego Bettoni Anno Accademico

Size: px
Start display at page:

Download "The Quark Model. Introduction to Elementary Particle Physics. Diego Bettoni Anno Accademico"

Transcription

1 The Qark Moel trocto to Eleetary Partcle Phyc Dego Betto Ao Accaeco -

2 Otle yetre a grop overvew The flavor yetry q q tate: eo Peocalar a vector eo Zweg rle q q q tate: baryo Color Haro ae a agetc oet

3 yetre a Grop lltratve exaple: the rotato grop Two cceve rotato R followe by R are eqvalet to a gle rotato R=R R. The grop cloe er ltplcato. There a etty eleet o rotato. Every rotato R ha a vere R - rotate back aga. The proct ot ecearly cotatve R R R R bt the aocatve law alway hol: R R R = R R R. t a coto grop: each rotato ca be labele by a et of cotoly varyg paraeter whch ca be regare a the copoet of a vector recte alog the ax of rotato wth agte gve by the agle of rotato. The rotato grop a Le grop: every rotato ca be expree a the proct of a cceo of fteal rotato arbtrarly cloe to the etty. The grop the copletely efe by the eghborhoo of the etty.

4 Rotato are a bet of the Loretz traforato a they for a yetry grop of a phycal yte: Phyc varat er rotato. For exaple ppoe that er a rotato R the tate of a yte trafor a R Probablte t be chage by R: U + U = U t be a tary operator. U The operator UR for a grop wth exactly the ae trctre a the orgal grop R R : they are a to for a tary repreetato of the rotato grop. The Haltoa chage by a yetry operato R of the yte a the atrx eleet are preerve U U H U HU H U HU H [ U H ]

5 The traforato U ha o explct te epeace a the eqato of oto t t H t chage by the the yetry operato. A a coeqece the expectato vale of U a cotat of the oto t U UH HU

6 All grop properte follow fro coerg fteal rotato the eghborhoo of the etty. Exaple rotato throgh aro the -ax: calle the geerator of rotato aro the -ax. + = therefore herta a hece a obervable. Coer the effect of a rotato R o the wave fcto. varace er rotato reqre: For a fteal rotato aro the -ax: copoet alog the -ax of the aglar oet. varace er rotato correpo to the coervato of aglar oet. U O U U r U r R r xp y yp x y x x y z y x z x y y x r R z y x U U

7 For a rotato throgh a fte agle : The cotator algebra of the geerator : The are a to for a Le Algebra jk = trctre cotat of the grop Nolear fcto of the geerator whch cote wth all the geerator are calle varat or Car operator. For the rotato grop the oly Car operator : t follow that we ca cotrct ltaeo egetate of a oe of the geerator e.g. : ] [ e U U l l ] [ l l j j j j j j

8 The Grop U the lowet-eo otrval repreetato of the rotato grop j=½ the geerator ay be wrtte: k k k k atrc Pal The ba for th repreetato gve by the egevector of : ecrbg a p ½ partcle of p projecto p a ow. The traforato atrce: U are tary. The et of all tary atrce U Utary Grop. However U larger tha the grop U ce the all have zero trace. For ay herta tracele atrx t ca be how that: et e e Th property preerve atrx ltplcato. The et of tracele tary atrce for a bgrop of U calle U pecal Utary. The two-eoal repreetato the faetal repreetato. e Tr

9 For a copote yte j A j B A B > the operator = A + B atfe the Le algebra a the egevale + a M of a are coerve qat ber. The proct of the two rrecble repreetato j A + a j B + ay be ecopoe to the of rrecble repreetato of eo + wth ba j A j B M> where j A -j B j A +j B M= A + B j A j B M A C B A B M j A j B A B Clebch-Gora coeffcet Exaple: fro two two-eoal repreetato j=½ we obta oe -eoal = trplet a oe -eoal = golet repreetato. 4 qarplet p / oblet p /

10 U of op l l Geerator the faetal repreetato: k k k ba p

11 op for Atpartcle p p p e p C p Cp p p orer for the atoblet to trafor the ae way a the oblet we t: Reorer the oblet troce a g p p pp p pp p For NN

12 The Grop U t the grop of tary atrce wth etu=. The geerator ay be take to be ay -=8 learly epeet tracele herta atrce. There are therefore 8 geerator of whch are agoal. Th alo the ax ber of tally cotg geerator : Rak of the grop. t ca be how that the rak of the grop eqal to the ber of Car operator. The faetal repreetato of U a trplet e.g. the three color charge of a qark. The geerator are atrce: =..8 Gell-Ma atrce. 8 R G B ltaeo egevector of a 8. G- λ 6 +- λ 7 λ8 λ +- λ B- R λ 4 +- λ 5 λ

13 op a tragee: Flavor U The trocto of a eco atve qat ber ato to gget to elarge op yetry to a larger grop a grop of rak. 96 U wa propoe. The aget of partcle to U ltplet ot traghtforwar e to the hgh a fferece betwee the varo partcle trage a o trage. For exaple the baryoc octet grop partcle wth a fferece p to 4 MeV over a average octet a of MeV. U flavor yetry ch ore approxate tha U of op. We wll ee that th e to the fact that the trage qark ch heaver tha a. U yetry for the ba of the qark oel a t tr ot to be very efl to clafy haro a to erta oe of ther properte. Color U o the other ha a exact yetry of faetal org.

14 The Qark Moel Alreay 949 Fer a Yag otce that the eo ha the ae qat ber of a NN par a tate: B= P = - = Q=. They ggete that the col be coere a a NN bo tate wth a very hgh bg eergy. Wth the covery of trage partcle akata propoe to exte the oel to cle the. Th trplet allow to cotrct oe partcle all partcle kow the 96 bt there were everal reao to prefer a fferet et of ore faetal cottet: The reglarty wth whch partcle fale occre atre ggete a teral trctre alo for p. Thee reglarte col be well explae by a U yetry. 964 Gell-Ma a epeetly Zweg propoe a et of eleetary fero ter of whch all haro col be cotrcte. Gell-Ma calle the qark Zweg calle the ace.

15 They t be fero orer to cotrct both fero a boo. aalogy to the ea of Fer a Yag eo are q q par; baryo atbaryo are q q q q q q tate. orer to for otrage partcle wth charge at leat two qark are eee. Thee t for a op oblet orer to have both = a =. orer to for trage partcle a thr qark eee to whch by coveto age =-. The al ber of cottet th. To properly accot for baryo ber qark are age B=/. The plet p-party aget P =½ +. B Fro the Gell-Ma a Nhja forla Q t follow Q Q Q Qark have a fractoal electrc charge.

16 U lagage: the faetal ltplet fro whch all other ca be cotrcte a trplet. for the faetal ltplet. B coervato ple that t poble to create or etroy a gle qark. Qark-atqark par ca be create or ahlate. Flavor coervato trog teracto ple that flavor-chagg trato ca take place oly at the weak level for exaple: Q Y B etc lepto

17 q q tate: Meo Let tart wth two qark a - - Y = x Ag a thr qark there are 9 poble cobato: octet a glet er traforato U the 8 tate trafor aog theelve bt they ever x wth the glet. - - Y A B C 8

18 The glet tate C yetrc flavor: C A the etral eber of the op trplet: A A Qark have p ½ therefore the total p of the q q par ca be = or =. The p of the eo relt a the cobato of a of the relatve aglar oet L. The party P of the eo th: P L L Proct of the trc parte of fero a atfero The vale of C obtae lke potro : C L L Fero terchage

19 Qat Nber of Meo wth the Qark 5? 4 5? 8 5 * * * D Q A f f A H Q B L L MeV M qq qq L PC oet

20 Peocalar Meo PC = -+ π - - o - - η - π - + o + π η Meoe Dec M MeV

21 Vector Meo PC = -- ρ - * * ρ * - φ * o + ρ φ The q aq are a tate L= = =. The wave fcto for the glet a for the etral eber of the octet 8 are: 8 6 The phycal tate.e. the oe oberve atre are lear cobato of a 8 : 8 co co 8 the cae of eal xg 5

22 Meo Decay a the Zweg Rle 5.% 4.4% 49.% L.% 8.5% 88.8% For the phae pace wol favor the ecay wth repect to : MeV Q MeV M M Q MeV M M M Q Φ - + ω + π π π π + π Φ - π The agra for the ecay ppree becae t cota coecte qark le Zweg Rle. The keatcal ppreo of the ecay reflecte the all total wth of the : = MeV to be copare for exaple wth = 5..6 MeV.

23 Leptoc Decay of Vector Meo Coer the ecay e l V l l V The partal wth gve by: 6 M V Q l l V ; ; V l V M a V M a Q Q have lar ae cotate M V 9 8 : : : Q l l V 9 :: : :.. e e e e e e e e Experetally Va Roye - Wekopf

24 Qark tate: Baryo Y - - = x Let ow a the thr qark Y - - Y

25 The Baryo Decplet + ++ Δ Δ Δ Δ + Σ - Σ Σ Ξ - They have p-party - Ω Ξ Δ Σ 84 Ξ 5 Ω67 The wave fcto yetrc wth repect to the exchage of ay qark par. They are wave they have parallel p therefore alo the pace a p wave fcto are yetrc. P Pal prcple? The olto le the fact that qark have a frther teral egree of freeocolor whch ca take o three vale RGB. Qark for the faetal trplet of a U color yetry. Haro are color etral.e. they belog to a glet repreetato of color U. th way the overall wave fcto atyetrc er terchage of ay qark par. qqq c.. 6 RGB RBG BRG BGR GBR GRB

26 The Baryo Octet p N99 Σ Σ Σ - Λ + Σ 9 Λ 6 P Ξ - Ξ Ξ 8 The octet tate are copletely yetrc er the ltaeo exchage of flavor a p of ay qark par.

27 + ++ Δ Δ Δ Δ + - Σ Σ Σ Ξ - Ξ Δ Σ Ξ 5 5 p N99 N99 Σ Σ Σ - Λ + Σ 9 9 Λ Ω Ω67 67 Ξ - Ξ Ξ 8 8 f a fferece were olely e to the fact that the qark heaver tha the a qark we hol have: P MeV 49 MeV 9 MeV P N MeV 49 MeV The orer of agte correct bt crepace are tll gfcat. A qattatve ertag of haro ae t take to accot the effect of the hyperfe plttg qark teracto.

28 Haro Mae f flavor U yetry were exact all eber of a gve ltplet wol have exactly the ae a. Yet t ot o..78 GeV. GeV *. 89 GeV f we coer haro ae a the of the ae of the cottet qark we obta:.9 GeV Effectve ae of qark bo haro. Cottet ae. There are frther proble:.5gev N a N cota the ae qark a o a. P

29 ce haro ae caot be explae olely ter of the ae of the cottet qark t eceary to coer the effect of qark teracto. the hyroge ato the p-p teracto lea to the hyperfe trctre of level. For two potlke fero of agetc oet a j the teracto eergy j r Drac theory gve: j e The hyperfe eparato gve by: E hf t a cotact teracto: t cota the qare of the wave fcto at zero eparato a therefore t oly apple to L= tate.

30 For qark the agetc teracto aocate to charge a p of the orer of the MeV. Bt qark teract throgh ther color charge wth a potetal of the for: V r r At all tace the ter /r oate a all eoght to ake the trog hyperfe plttg portat: 8 th chee haro ae are gve by: 4 E QQ E QQ 9 9 kr 4 q q q q q a a j j j

31 For two qark or for qark-atqark: Hece the egevale of are: ] [ 4 larly for -qark yte: ] [ 4 j j * a a a E a E N

32 Ug the experetally eare a vale t poble to ft the paraeter a a a. The relt are: 6 58 MeV MeV a a 6 MeV MeV th way the agreeet wth experetal ata of the orer of % or better.

33 Electroagetc Ma Dfferece A frther cotrbto to haro a coe fro the electroagetc teracto. Let take a a exaple the baryo the octet a let ae that the charge trbto are lar. We expect lar electroagetc cotrbto : p Let a the bare haro ae a thee eqato: p p p. MeV 8 MeV 6.4 MeV.6 MeV Colea-Glahow. Ma fferece are aocate wth op yetry breakg.

34 Electroagetc a fferece are e to three effect: Dfferece a of the a qark; ce > p we expect >. Colob eergy fferece aocate wth the electrcal eergy betwee par of qark of the orer of: e MeV R Magetc eergy fferece aocate wth the agetc oet hyperfe teracto betwee qark par: e c R MeV Fttg the exact for of thee ter to the ata t fo that: MeV The approxate op varace ca be aocate wth the ear eqalty of the a qark ae.

35 Baryo Magetc Moet Baryo agetc oet ca be calclate a the vector of the oet of the cottet qark. For a Drac potlke partcle of a a charge e: A a exaple let calclate the agetc oet of the proto. The two qark are a trplet tate. Cobg wth a frther we get: e p p 4 p p e.79

36 Coparo betwee precte a eare agetc oet for oe baryo: exp p th

Collapsing to Sample and Remainder Means. Ed Stanek. In order to collapse the expanded random variables to weighted sample and remainder

Collapsing to Sample and Remainder Means. Ed Stanek. In order to collapse the expanded random variables to weighted sample and remainder Collapg to Saple ad Reader Mea Ed Staek Collapg to Saple ad Reader Average order to collape the expaded rado varable to weghted aple ad reader average, we pre-ultpled by ( M C C ( ( M C ( M M M ( M M M,

More information

( ) ( ) ( ( )) ( ) ( ) ( ) ( ) ( ) = ( ) ( ) + ( ) ( ) = ( ( )) ( ) + ( ( )) ( ) Review. Second Derivatives for f : y R. Let A be an m n matrix.

( ) ( ) ( ( )) ( ) ( ) ( ) ( ) ( ) = ( ) ( ) + ( ) ( ) = ( ( )) ( ) + ( ( )) ( ) Review. Second Derivatives for f : y R. Let A be an m n matrix. Revew + v, + y = v, + v, + y, + y, Cato! v, + y, + v, + y geeral Let A be a atr Let f,g : Ω R ( ) ( ) R y R Ω R h( ) f ( ) g ( ) ( ) ( ) ( ( )) ( ) dh = f dg + g df A, y y A Ay = = r= c= =, : Ω R he Proof

More information

1. Linear second-order circuits

1. Linear second-order circuits ear eco-orer crcut Sere R crcut Dyamc crcut cotag two capactor or two uctor or oe uctor a oe capactor are calle the eco orer crcut At frt we coer a pecal cla of the eco-orer crcut, amely a ere coecto of

More information

State Feedback Control Block Diagram

State Feedback Control Block Diagram State Feedback Cotrol Block Dagra r B C -K lt-it I Ste t Cotrollablt:,B cotrollable ff rakp, P[B B - B]: Pck -learl deedet col of P gog fro left to rght ad rearrage a b b b b b : col of B Potve teger o

More information

Non-degenerate Perturbation Theory

Non-degenerate Perturbation Theory No-degeerate Perturbato Theory Proble : H E ca't solve exactly. But wth H H H' H" L H E Uperturbed egevalue proble. Ca solve exactly. E Therefore, kow ad. H ' H" called perturbatos Copyrght Mchael D. Fayer,

More information

Signal Recovery - Prof. S. Cova - Exam 2016/02/16 - P1 pag.1

Signal Recovery - Prof. S. Cova - Exam 2016/02/16 - P1 pag.1 gal Recovery - Pro.. Cova - Exam 06/0/6 - P pag. PROBEM Data ad Note Appled orce F rt cae: tep ple ecod cae: rectaglar ple wth drato p = 5m Pezoelectrc orce eor A q =0pC/N orce-to-charge covero C = 500pF

More information

ROOT-LOCUS ANALYSIS. Lecture 11: Root Locus Plot. Consider a general feedback control system with a variable gain K. Y ( s ) ( ) K

ROOT-LOCUS ANALYSIS. Lecture 11: Root Locus Plot. Consider a general feedback control system with a variable gain K. Y ( s ) ( ) K ROOT-LOCUS ANALYSIS Coder a geeral feedback cotrol yte wth a varable ga. R( Y( G( + H( Root-Locu a plot of the loc of the pole of the cloed-loop trafer fucto whe oe of the yte paraeter ( vared. Root locu

More information

On the energy of complement of regular line graphs

On the energy of complement of regular line graphs MATCH Coucato Matheatcal ad Coputer Chetry MATCH Cou Math Coput Che 60 008) 47-434 ISSN 0340-653 O the eergy of copleet of regular le graph Fateeh Alaghpour a, Baha Ahad b a Uverty of Tehra, Tehra, Ira

More information

Solutions to problem set ); (, ) (

Solutions to problem set ); (, ) ( Solutos to proble set.. L = ( yp p ); L = ( p p ); y y L, L = yp p, p p = yp p, + p [, p ] y y y = yp + p = L y Here we use for eaple that yp, p = yp p p yp = yp, p = yp : factors that coute ca be treated

More information

Stationary states of atoms and molecules

Stationary states of atoms and molecules Statoary states of atos ad olecules I followg wees the geeral aspects of the eergy level structure of atos ad olecules that are essetal for the terpretato ad the aalyss of spectral postos the rotatoal

More information

Overview. Review Superposition Solution. Review Superposition. Review x and y Swap. Review General Superposition

Overview. Review Superposition Solution. Review Superposition. Review x and y Swap. Review General Superposition ylcal aplace Soltos ebay 6 9 aplace Eqato Soltos ylcal Geoety ay aetto Mechacal Egeeg 5B Sea Egeeg Aalyss ebay 6 9 Ovevew evew last class Speposto soltos tocto to aal cooates Atoal soltos of aplace s eqato

More information

3.1 Introduction to Multinomial Logit and Probit

3.1 Introduction to Multinomial Logit and Probit ES3008 Ecooetrcs Lecture 3 robt ad Logt - Multoal 3. Itroducto to Multoal Logt ad robt 3. Estato of β 3. Itroducto to Multoal Logt ad robt The ultoal Logt odel s used whe there are several optos (ad therefore

More information

Chapter 2: Descriptive Statistics

Chapter 2: Descriptive Statistics Chapte : Decptve Stattc Peequte: Chapte. Revew of Uvaate Stattc The cetal teecy of a oe o le yetc tbuto of a et of teval, o hghe, cale coe, ofte uaze by the athetc ea, whch efe a We ca ue the ea to ceate

More information

Nuclear and Particle Physics - Lecture 16 Neutral kaon decays and oscillations

Nuclear and Particle Physics - Lecture 16 Neutral kaon decays and oscillations 1 Introction Nclear an Particle Phyic - Lectre 16 Netral kaon ecay an ocillation e have alreay een that the netral kaon will have em-leptonic an haronic ecay. However, they alo exhibit the phenomenon of

More information

Reaction Time VS. Drug Percentage Subject Amount of Drug Times % Reaction Time in Seconds 1 Mary John Carl Sara William 5 4

Reaction Time VS. Drug Percentage Subject Amount of Drug Times % Reaction Time in Seconds 1 Mary John Carl Sara William 5 4 CHAPTER Smple Lear Regreo EXAMPLE A expermet volvg fve ubject coducted to determe the relatohp betwee the percetage of a certa drug the bloodtream ad the legth of tme t take the ubject to react to a tmulu.

More information

Layered structures: transfer matrix formalism

Layered structures: transfer matrix formalism Layered tructure: trafer matrx formalm Iterface betwee LI meda Trafer matrx formalm Petr Kužel Practcally oly oe formula to be kow order to calculate ay tructure Applcato: Atreflectve coatg Delectrc mrror,

More information

Some Different Perspectives on Linear Least Squares

Some Different Perspectives on Linear Least Squares Soe Dfferet Perspectves o Lear Least Squares A stadard proble statstcs s to easure a respose or depedet varable, y, at fed values of oe or ore depedet varables. Soetes there ests a deterstc odel y f (,,

More information

Solutions to Odd-Numbered End-of-Chapter Exercises: Chapter 17

Solutions to Odd-Numbered End-of-Chapter Exercises: Chapter 17 Itroucto to Ecoometrcs (3 r Upate Eto) by James H. Stock a Mark W. Watso Solutos to O-Numbere E-of-Chapter Exercses: Chapter 7 (Ths erso August 7, 04) 05 Pearso Eucato, Ic. Stock/Watso - Itroucto to Ecoometrcs

More information

UNIT 7 RANK CORRELATION

UNIT 7 RANK CORRELATION UNIT 7 RANK CORRELATION Rak Correlato Structure 7. Itroucto Objectves 7. Cocept of Rak Correlato 7.3 Dervato of Rak Correlato Coeffcet Formula 7.4 Te or Repeate Raks 7.5 Cocurret Devato 7.6 Summar 7.7

More information

Theory study about quarter-wave-stack dielectric mirrors

Theory study about quarter-wave-stack dielectric mirrors Theor tud about quarter-wave-tack delectrc rror Stratfed edu tratted reflected reflected Stratfed edu tratted cdet cdet T T Frt, coder a wave roagato a tratfed edu. A we kow, a arbtrarl olared lae wave

More information

Simple Linear Regression Analysis

Simple Linear Regression Analysis LINEAR REGREION ANALYSIS MODULE II Lecture - 5 Smple Lear Regreo Aaly Dr Shalabh Departmet of Mathematc Stattc Ida Ittute of Techology Kapur Jot cofdece rego for A jot cofdece rego for ca alo be foud Such

More information

for each of its columns. A quick calculation will verify that: thus m < dim(v). Then a basis of V with respect to which T has the form: A

for each of its columns. A quick calculation will verify that: thus m < dim(v). Then a basis of V with respect to which T has the form: A Desty of dagoalzable square atrces Studet: Dael Cervoe; Metor: Saravaa Thyagaraa Uversty of Chcago VIGRE REU, Suer 7. For ths etre aer, we wll refer to V as a vector sace over ad L(V) as the set of lear

More information

CS 2750 Machine Learning Lecture 8. Linear regression. Supervised learning. a set of n examples

CS 2750 Machine Learning Lecture 8. Linear regression. Supervised learning. a set of n examples CS 75 Mache Learg Lecture 8 Lear regresso Mlos Hauskrecht los@cs.tt.eu 59 Seott Square Suervse learg Data: D { D D.. D} a set of eales D s a ut vector of sze s the esre outut gve b a teacher Obectve: lear

More information

Hamilton s principle for non-holonomic systems

Hamilton s principle for non-holonomic systems Das Hamltosche Przp be chtholoome Systeme, Math. A. (935), pp. 94-97. Hamlto s prcple for o-holoomc systems by Georg Hamel Berl Traslate by: D. H. Delphech I the paper Le prcpe e Hamlto et l holoomsme,

More information

Linear Approximating to Integer Addition

Linear Approximating to Integer Addition Lear Approxmatg to Iteger Addto L A-Pg Bejg 00085, P.R. Cha apl000@a.com Abtract The teger addto ofte appled cpher a a cryptographc mea. I th paper we wll preet ome reult about the lear approxmatg for

More information

CS473-Algorithms I. Lecture 12b. Dynamic Tables. CS 473 Lecture X 1

CS473-Algorithms I. Lecture 12b. Dynamic Tables. CS 473 Lecture X 1 CS473-Algorthm I Lecture b Dyamc Table CS 473 Lecture X Why Dyamc Table? I ome applcato: We do't kow how may object wll be tored a table. We may allocate pace for a table But, later we may fd out that

More information

( t) ( t) ( t) ρ ψ ψ. (9.1)

( t) ( t) ( t) ρ ψ ψ. (9.1) Adre Toaoff, MT Departet of Cestry, 3/19/29 p. 9-1 9. THE DENSTY MATRX Te desty atrx or desty operator s a alterate represetato of te state of a quatu syste for wc we ave prevously used te wavefucto. Altoug

More information

ENGI 4421 Propagation of Error Page 8-01

ENGI 4421 Propagation of Error Page 8-01 ENGI 441 Propagato of Error Page 8-01 Propagato of Error [Navd Chapter 3; ot Devore] Ay realstc measuremet procedure cotas error. Ay calculatos based o that measuremet wll therefore also cota a error.

More information

Sensorless A.C. Drive with Vector Controlled Synchronous Motor

Sensorless A.C. Drive with Vector Controlled Synchronous Motor Seole A.C. Dve wth Vecto Cotolle Sychoo Moto Ořej Fše VŠB-echcal Uvety of Otava, Faclty of Electcal Egeeg a Ifomatc, Deatmet of Powe Electoc a Electcal Dve, 17.ltoa 15, 78 33 Otava-Poba, Czech eblc oej.fe@vb.cz

More information

A scalar t is an eigenvalue of A if and only if t satisfies the characteristic equation of A: det (A ti) =0

A scalar t is an eigenvalue of A if and only if t satisfies the characteristic equation of A: det (A ti) =0 Chapter 5 a glace: Let e a lear operator whose stadard matrx s wth sze x. he, a ozero vector x s sad to e a egevector of ad f there exsts a scalar sch that (x) x x. he scalar s called a egevale of (or

More information

Physics 114 Exam 2 Fall Name:

Physics 114 Exam 2 Fall Name: Physcs 114 Exam Fall 015 Name: For gradg purposes (do ot wrte here): Questo 1. 1... 3. 3. Problem Aswer each of the followg questos. Pots for each questo are dcated red. Uless otherwse dcated, the amout

More information

Addition of angular momentum. C4 Lecture 2 - Jim Libby 1

Addition of angular momentum. C4 Lecture 2 - Jim Libby 1 Aition of anglar oent C4 Lectre - i Libby C4 Lectre - i Libby Aition of anglar oent Let an be anglar oent operators i.e. The orbital anglar oent an the spin of the sae particle The anglar oent of spinless

More information

loft man s spline is a flexible strip of material, which can be clamped or weighted so it

loft man s spline is a flexible strip of material, which can be clamped or weighted so it . Parametrc B-ple. Sple Cre Sple cre were frt e a a raftg tool for arcraft a hp blg tre. A loft ma ple a flexble trp of materal, whch ca be clampe or weghte o t wll pa throgh ay mber of pot wth mooth eformato.

More information

Coherent Potential Approximation

Coherent Potential Approximation Coheret Potetal Approxato Noveber 29, 2009 Gree-fucto atrces the TB forals I the tght bdg TB pcture the atrx of a Haltoa H s the for H = { H j}, where H j = δ j ε + γ j. 2 Sgle ad double uderles deote

More information

Chapter Newton-Raphson Method of Solving Simultaneous Nonlinear Equations

Chapter Newton-Raphson Method of Solving Simultaneous Nonlinear Equations Chapter 7 Newto-Rapho Method o Solg Smltaeo Nolear Eqato Ater readg th chapter o hold be able to: dere the Newto-Rapho method ormla or mltaeo olear eqato deelop the algorthm o the Newto-Rapho method or

More information

18.413: Error Correcting Codes Lab March 2, Lecture 8

18.413: Error Correcting Codes Lab March 2, Lecture 8 18.413: Error Correctg Codes Lab March 2, 2004 Lecturer: Dael A. Spelma Lecture 8 8.1 Vector Spaces A set C {0, 1} s a vector space f for x all C ad y C, x + y C, where we take addto to be compoet wse

More information

The theoretical background of

The theoretical background of he theoretcal backgroud of -echologes he theoretcal backgroud of FactSage he followg sldes gve a abrdged overvew of the ajor uderlyg prcples of the calculatoal odules of FactSage. -echologes he bbs Eergy

More information

C.11 Bang-bang Control

C.11 Bang-bang Control Itroucto to Cotrol heory Iclug Optmal Cotrol Nguye a e -.5 C. Bag-bag Cotrol. Itroucto hs chapter eals wth the cotrol wth restrctos: s boue a mght well be possble to have scotutes. o llustrate some of

More information

Long blade vibration model for turbine-generator shafts torsional vibration analysis

Long blade vibration model for turbine-generator shafts torsional vibration analysis Avalable ole www.ocpr.co Joural of Checal ad Pharaceutcal Research, 05, 7(3):39-333 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 Log blade vbrato odel for turbe-geerator shafts torsoal vbrato aalyss

More information

Born-Oppenheimer Approximation. Kaito Takahashi

Born-Oppenheimer Approximation. Kaito Takahashi o-oppehee ppoato Kato Takahah toc Ut Fo quatu yte uch a ecto ad olecule t eae to ue ut that ft the=tomc UNT Ue a of ecto (ot kg) Ue chage of ecto (ot coulob) Ue hba fo agula oetu (ot kg - ) Ue 4pe 0 fo

More information

2.160 System Identification, Estimation, and Learning Lecture Notes No. 17 April 24, 2006

2.160 System Identification, Estimation, and Learning Lecture Notes No. 17 April 24, 2006 .6 System Idetfcato, Estmato, ad Learg Lectre Notes No. 7 Aprl 4, 6. Iformatve Expermets. Persstece of Exctato Iformatve data sets are closely related to Persstece of Exctato, a mportat cocept sed adaptve

More information

2/20/2013. Topics. Power Flow Part 1 Text: Power Transmission. Power Transmission. Power Transmission. Power Transmission

2/20/2013. Topics. Power Flow Part 1 Text: Power Transmission. Power Transmission. Power Transmission. Power Transmission /0/0 Topcs Power Flow Part Text: 0-0. Power Trassso Revsted Power Flow Equatos Power Flow Proble Stateet ECEGR 45 Power Systes Power Trassso Power Trassso Recall that for a short trassso le, the power

More information

Identity of King and Flajolet & al. Formulae for LRU Miss Rate Exact Computation

Identity of King and Flajolet & al. Formulae for LRU Miss Rate Exact Computation detty of g ad laolet & al orlae for LRU M Rate Eact otato hrta BERTHET STMcroelectroc Greoble race Abtract Th hort aer gve a detaled roof of detty betwee two clac forla for the cotato of the eact M Rate

More information

( ) Thermal noise ktb (T is absolute temperature in kelvin, B is bandwidth, k is Boltzamann s constant) Shot noise

( ) Thermal noise ktb (T is absolute temperature in kelvin, B is bandwidth, k is Boltzamann s constant) Shot noise OISE Thermal oe ktb (T abolute temperature kelv, B badwdth, k Boltzama cotat) 3 k.38 0 joule / kelv ( joule /ecod watt) ( ) v ( freq) 4kTB Thermal oe refer to the ketc eergy of a body of partcle a a reult

More information

KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS. Peter J. Wilcoxen. Impact Research Centre, University of Melbourne.

KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS. Peter J. Wilcoxen. Impact Research Centre, University of Melbourne. KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS by Peter J. Wlcoxe Ipact Research Cetre, Uversty of Melboure Aprl 1989 Ths paper descrbes a ethod that ca be used to resolve cossteces

More information

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION I lear regreo, we coder the frequecy dtrbuto of oe varable (Y) at each of everal level of a ecod varable (X). Y kow a the depedet varable. The

More information

Singular Value Decomposition. Linear Algebra (3) Singular Value Decomposition. SVD and Eigenvectors. Solving LEs with SVD

Singular Value Decomposition. Linear Algebra (3) Singular Value Decomposition. SVD and Eigenvectors. Solving LEs with SVD Sgular Value Decomosto Lear Algera (3) m Cootes Ay m x matrx wth m ca e decomosed as follows Dagoal matrx A UWV m x x Orthogoal colums U U I w1 0 0 w W M M 0 0 x Orthoormal (Pure rotato) VV V V L 0 L 0

More information

Journal of Engineering and Natural Sciences Mühendislik ve Fen Bilimleri Dergisi FACTORIZATION PROPERTIES IN POLYNOMIAL EXTENSION OF UFR S

Journal of Engineering and Natural Sciences Mühendislik ve Fen Bilimleri Dergisi FACTORIZATION PROPERTIES IN POLYNOMIAL EXTENSION OF UFR S Joural of Egeerg ad Natural Scece Mühedl ve Fe Bller Derg Sga 25/2 FACTORIZATION PROPERTIES IN POLYNOMIAL EXTENSION OF UFR S Murat ALAN* Yıldız Te Üverte, Fe-Edebyat Faülte, Mateat Bölüü, Davutpaşa-İSTANBUL

More information

Third handout: On the Gini Index

Third handout: On the Gini Index Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The

More information

Simple Linear Regression

Simple Linear Regression Statstcal Methods I (EST 75) Page 139 Smple Lear Regresso Smple regresso applcatos are used to ft a model descrbg a lear relatoshp betwee two varables. The aspects of least squares regresso ad correlato

More information

Quiz 1- Linear Regression Analysis (Based on Lectures 1-14)

Quiz 1- Linear Regression Analysis (Based on Lectures 1-14) Quz - Lear Regreo Aaly (Baed o Lecture -4). I the mple lear regreo model y = β + βx + ε, wth Tme: Hour Ε ε = Ε ε = ( ) 3, ( ), =,,...,, the ubaed drect leat quare etmator ˆβ ad ˆβ of β ad β repectvely,

More information

Maximum Walk Entropy Implies Walk Regularity

Maximum Walk Entropy Implies Walk Regularity Maxmum Walk Etropy Imples Walk Regularty Eresto Estraa, a José. e la Peña Departmet of Mathematcs a Statstcs, Uversty of Strathclye, Glasgow G XH, U.K., CIMT, Guaajuato, Mexco BSTRCT: The oto of walk etropy

More information

Consider two masses m 1 at x = x 1 and m 2 at x 2.

Consider two masses m 1 at x = x 1 and m 2 at x 2. Chapte 09 Syste of Patcles Cete of ass: The cete of ass of a body o a syste of bodes s the pot that oes as f all of the ass ae cocetated thee ad all exteal foces ae appled thee. Note that HRW uses co but

More information

DUALITY FOR MINIMUM MATRIX NORM PROBLEMS

DUALITY FOR MINIMUM MATRIX NORM PROBLEMS HE PUBLISHING HOUSE PROCEEDINGS OF HE ROMNIN CDEMY, Seres, OF HE ROMNIN CDEMY Vole 6, Nber /2005,. 000-000 DULIY FOR MINIMUM MRI NORM PROBLEMS Vasle PRED, Crstca FULG Uverst of Bcharest, Faclt of Matheatcs

More information

CS 2750 Machine Learning. Lecture 7. Linear regression. CS 2750 Machine Learning. Linear regression. is a linear combination of input components x

CS 2750 Machine Learning. Lecture 7. Linear regression. CS 2750 Machine Learning. Linear regression. is a linear combination of input components x CS 75 Mache Learg Lecture 7 Lear regresso Mlos Hauskrecht los@cs.ptt.edu 59 Seott Square CS 75 Mache Learg Lear regresso Fucto f : X Y s a lear cobato of put copoets f + + + K d d K k - paraeters eghts

More information

Chapter 11 Systematic Sampling

Chapter 11 Systematic Sampling Chapter stematc amplg The sstematc samplg techue s operatoall more coveet tha the smple radom samplg. It also esures at the same tme that each ut has eual probablt of cluso the sample. I ths method of

More information

Scattering of two identical particles in the center-of. of-mass frame. (b)

Scattering of two identical particles in the center-of. of-mass frame. (b) Lecture # November 5 Scatterng of two dentcal partcle Relatvtc Quantum Mechanc: The Klen-Gordon equaton Interpretaton of the Klen-Gordon equaton The Drac equaton Drac repreentaton for the matrce α and

More information

+ve 10 N. Note we must be careful about writing If mass is not constant: dt dt dt

+ve 10 N. Note we must be careful about writing If mass is not constant: dt dt dt Force /N Moet is defied as the prodct of ass ad elocity. It is therefore a ector qatity. A ore geeral ersio of Newto s Secod Law is that force is the rate of chage of oet. I the absece of ay exteral force,

More information

Econometric Methods. Review of Estimation

Econometric Methods. Review of Estimation Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators

More information

Suggested Answers, Problem Set 4 ECON The R 2 for the unrestricted model is by definition u u u u

Suggested Answers, Problem Set 4 ECON The R 2 for the unrestricted model is by definition u u u u Da Hgerma Fall 9 Sggested Aswers, Problem Set 4 ECON 333 The F-test s defed as ( SSEr The R for the restrcted model s by defto SSE / ( k ) R ( SSE / SST ) so therefore, SSE SST ( R ) ad lkewse SSEr SST

More information

Multiple Choice Test. Chapter Adequacy of Models for Regression

Multiple Choice Test. Chapter Adequacy of Models for Regression Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to

More information

Camera calibration & radiometry

Camera calibration & radiometry Caera calbrato & radoetr Readg: Chapter 2, ad secto 5.4, Forsth & oce Chapter, Hor Optoal readg: Chapter 4, Forsth & oce Sept. 2, 22 MI 6.8/6.866 rofs. Freea ad Darrell Req: F 2, 5.4, H Opt: F 4 Req: F

More information

u(x, t) = u 0 (x ct). This Riemann invariant u is constant along characteristics λ with x = x 0 +ct (u(x, t) = u 0 (x 0 )):

u(x, t) = u 0 (x ct). This Riemann invariant u is constant along characteristics λ with x = x 0 +ct (u(x, t) = u 0 (x 0 )): x, t, h x The Frst-Order Wave Eqato The frst-order wave advecto eqato s c > 0 t + c x = 0, x, t = 0 = 0x. The solto propagates the tal data 0 to the rght wth speed c: x, t = 0 x ct. Ths Rema varat s costat

More information

3D Reconstruction from Image Pairs. Reconstruction from Multiple Views. Computing Scene Point from Two Matching Image Points

3D Reconstruction from Image Pairs. Reconstruction from Multiple Views. Computing Scene Point from Two Matching Image Points D Recostructo fro Iage ars Recostructo fro ultple Ves Dael Deetho Fd terest pots atch terest pots Copute fudaetal atr F Copute caera atrces ad fro F For each atchg age pots ad copute pot scee Coputg Scee

More information

ELEMENTS OF NUMBER THEORY. In the following we will use mainly integers and positive integers. - the set of integers - the set of positive integers

ELEMENTS OF NUMBER THEORY. In the following we will use mainly integers and positive integers. - the set of integers - the set of positive integers ELEMENTS OF NUMBER THEORY I the followg we wll use aly tegers a ostve tegers Ζ = { ± ± ± K} - the set of tegers Ν = { K} - the set of ostve tegers Oeratos o tegers: Ato Each two tegers (ostve tegers) ay

More information

Analysis of error propagation in profile measurement by using stitching

Analysis of error propagation in profile measurement by using stitching Ay o error propgto proe eureet y ug ttchg Ttuy KUME, Kzuhro ENAMI, Yuo HIGASHI, Kej UENO - Oho, Tuu, Ir, 35-8, JAPAN Atrct Sttchg techque whch ee oger eureet rge o proe ro eer eure proe hg prty oerppe

More information

Lecture 3 Probability review (cont d)

Lecture 3 Probability review (cont d) STATS 00: Itroducto to Statstcal Iferece Autum 06 Lecture 3 Probablty revew (cot d) 3. Jot dstrbutos If radom varables X,..., X k are depedet, the ther dstrbuto may be specfed by specfyg the dvdual dstrbuto

More information

Construction of Composite Indices in Presence of Outliers

Construction of Composite Indices in Presence of Outliers Costructo of Coposte dces Presece of Outlers SK Mshra Dept. of Ecoocs North-Easter Hll Uversty Shllog (da). troducto: Oftetes we requre costructg coposte dces by a lear cobato of a uber of dcator varables.

More information

Consumer theory. A. The preference ordering B. The feasible set C. The consumption decision. A. The preference ordering. Consumption bundle

Consumer theory. A. The preference ordering B. The feasible set C. The consumption decision. A. The preference ordering. Consumption bundle Thomas Soesso Mcoecoomcs Lecte Cosme theoy A. The efeece odeg B. The feasble set C. The cosmto decso A. The efeece odeg Cosmto bdle x ( 2 x, x,... x ) x Assmtos: Comleteess 2 Tastvty 3 Reflexvty 4 No-satato

More information

Standard Deviation for PDG Mass Data

Standard Deviation for PDG Mass Data 4 Dec 06 Stadard Devato for PDG Mass Data M. J. Gerusa Retred, 47 Clfde Road, Worghall, HP8 9JR, UK. gerusa@aol.co, phoe: +(44) 844 339754 Abstract Ths paper aalyses the data for the asses of eleetary

More information

Mean is only appropriate for interval or ratio scales, not ordinal or nominal.

Mean is only appropriate for interval or ratio scales, not ordinal or nominal. Mea Same as ordary average Sum all the data values ad dvde by the sample sze. x = ( x + x +... + x Usg summato otato, we wrte ths as x = x = x = = ) x Mea s oly approprate for terval or rato scales, ot

More information

Summary of the lecture in Biostatistics

Summary of the lecture in Biostatistics Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the

More information

THE CONCEPT OF THE ROOT LOCUS. H(s) THE CONCEPT OF THE ROOT LOCUS

THE CONCEPT OF THE ROOT LOCUS. H(s) THE CONCEPT OF THE ROOT LOCUS So far i the tudie of cotrol yte the role of the characteritic equatio polyoial i deteriig the behavior of the yte ha bee highlighted. The root of that polyoial are the pole of the cotrol yte, ad their

More information

b. There appears to be a positive relationship between X and Y; that is, as X increases, so does Y.

b. There appears to be a positive relationship between X and Y; that is, as X increases, so does Y. .46. a. The frst varable (X) s the frst umber the par ad s plotted o the horzotal axs, whle the secod varable (Y) s the secod umber the par ad s plotted o the vertcal axs. The scatterplot s show the fgure

More information

Section l h l Stem=Tens. 8l Leaf=Ones. 8h l 03. 9h 58

Section l h l Stem=Tens. 8l Leaf=Ones. 8h l 03. 9h 58 Secto.. 6l 34 6h 667899 7l 44 7h Stem=Tes 8l 344 Leaf=Oes 8h 5557899 9l 3 9h 58 Ths dsplay brgs out the gap the data: There are o scores the hgh 7's. 6. a. beams cylders 9 5 8 88533 6 6 98877643 7 488

More information

Weak Interactions. Chapter 8 M&S

Weak Interactions. Chapter 8 M&S Some weak interaction baic: Weak force i reponible for β decay e.g. n pev (1930 ). Interaction involve both qark and lepton. Not all qantm nmber are conerved in weak interaction: parity, charge conjgation,

More information

Transforms that are commonly used are separable

Transforms that are commonly used are separable Trasforms s Trasforms that are commoly used are separable Eamples: Two-dmesoal DFT DCT DST adamard We ca the use -D trasforms computg the D separable trasforms: Take -D trasform of the rows > rows ( )

More information

Generalized Linear Regression with Regularization

Generalized Linear Regression with Regularization Geeralze Lear Regresso wth Regularzato Zoya Bylsk March 3, 05 BASIC REGRESSION PROBLEM Note: I the followg otes I wll make explct what s a vector a what s a scalar usg vec t or otato, to avo cofuso betwee

More information

PRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION

PRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION PRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION Bars Erkus, 4 March 007 Itroducto Ths docuet provdes a revew of fudaetal cocepts structural dyacs ad soe applcatos hua-duced vbrato aalyss ad tgato of

More information

Is RHIC-Produced Matter More Like Milk or Honey?

Is RHIC-Produced Matter More Like Milk or Honey? Is RHIC-Prodced Matter More Lke Mlk or Hoey? Joe Kapsta Uversty of Mesota ESQGP, Stoy Brook, 008 What has RHIC told s abot the eqato of state? How does RHIC coect to other felds lke cosmology ad codesed

More information

European Journal of Mathematics and Computer Science Vol. 5 No. 2, 2018 ISSN

European Journal of Mathematics and Computer Science Vol. 5 No. 2, 2018 ISSN Europea Joural of Mathematc ad Computer Scece Vol. 5 o., 018 ISS 059-9951 APPLICATIO OF ASYMPTOTIC DISTRIBUTIO OF MA-HITEY STATISTIC TO DETERMIE THE DIFFERECE BETEE THE SYSTOLIC BLOOD PRESSURE OF ME AD

More information

12.2 Estimating Model parameters Assumptions: ox and y are related according to the simple linear regression model

12.2 Estimating Model parameters Assumptions: ox and y are related according to the simple linear regression model 1. Estmatg Model parameters Assumptos: ox ad y are related accordg to the smple lear regresso model (The lear regresso model s the model that says that x ad y are related a lear fasho, but the observed

More information

Debabrata Dey and Atanu Lahiri

Debabrata Dey and Atanu Lahiri RESEARCH ARTICLE QUALITY COMPETITION AND MARKET SEGMENTATION IN THE SECURITY SOFTWARE MARKET Debabrata Dey ad Atau Lahr Mchael G. Foster School of Busess, Uersty of Washgto, Seattle, Seattle, WA 9895 U.S.A.

More information

9 U-STATISTICS. Eh =(m!) 1 Eh(X (1),..., X (m ) ) i.i.d

9 U-STATISTICS. Eh =(m!) 1 Eh(X (1),..., X (m ) ) i.i.d 9 U-STATISTICS Suppose,,..., are P P..d. wth CDF F. Our goal s to estmate the expectato t (P)=Eh(,,..., m ). Note that ths expectato requres more tha oe cotrast to E, E, or Eh( ). Oe example s E or P((,

More information

COMPUTATION OF THE EIGENVALUES AND EIGENFUNCTION OF GENERALIZED STURM-LIOUVILLE PROBLEMS VIA THE DIFFERENTIAL TRANSFORMATION METHOD

COMPUTATION OF THE EIGENVALUES AND EIGENFUNCTION OF GENERALIZED STURM-LIOUVILLE PROBLEMS VIA THE DIFFERENTIAL TRANSFORMATION METHOD IJRRAS 5 3 Je 03 www.arpapress.co/voles/vol5isse3/ijrras_5_3_08.p COMPTATION O THE EIGENVAES AND EIGENNCTION O GENERAIZED STRM-IOVIE PROBEMS VIA THE DIERENTIA TRANSORMATION METHOD Mohae El-Gael & Maho

More information

AN ALGEBRAIC APPROACH TO M-BAND WAVELETS CONSTRUCTION

AN ALGEBRAIC APPROACH TO M-BAND WAVELETS CONSTRUCTION AN ALGEBRAIC APPROACH TO -BAN WAELETS CONSTRUCTION Toy L Qy S Pewe Ho Ntol Lotoy o e Peeto Pe Uety Be 8 P. R. C Att T e eet le o to ott - otool welet e. A yte of ott eto ote fo - otool flte te olto e o

More information

u(x, t) = u 0 (x ct). This Riemann invariant u is constant along characteristics λ with x = x 0 +ct (u(x, t) = u 0 (x 0 )):

u(x, t) = u 0 (x ct). This Riemann invariant u is constant along characteristics λ with x = x 0 +ct (u(x, t) = u 0 (x 0 )): x, t ), h x The Frst-Order Wave Eqato The frst-order wave advecto) eqato s c > 0) t + c x = 0, x, t = 0) = 0x). The solto propagates the tal data 0 to the rght wth speed c: x, t) = 0 x ct). Ths Rema varat

More information

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution: Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed

More information

Chapter 4 Multiple Random Variables

Chapter 4 Multiple Random Variables Revew for the prevous lecture: Theorems ad Examples: How to obta the pmf (pdf) of U = g (, Y) ad V = g (, Y) Chapter 4 Multple Radom Varables Chapter 44 Herarchcal Models ad Mxture Dstrbutos Examples:

More information

2SLS Estimates ECON In this case, begin with the assumption that E[ i

2SLS Estimates ECON In this case, begin with the assumption that E[ i SLS Estmates ECON 3033 Bll Evas Fall 05 Two-Stage Least Squares (SLS Cosder a stadard lear bvarate regresso model y 0 x. I ths case, beg wth the assumto that E[ x] 0 whch meas that OLS estmates of wll

More information

An Expansion of the Derivation of the Spline Smoothing Theory Alan Kaylor Cline

An Expansion of the Derivation of the Spline Smoothing Theory Alan Kaylor Cline A Epaso of the Derato of the Sple Smoothg heory Ala Kaylor Cle he classc paper "Smoothg by Sple Fctos", Nmersche Mathematk 0, 77-83 967) by Chrsta Resch showed that atral cbc sples were the soltos to a

More information

Introduction. Free Electron Fermi Gas. Energy Levels in One Dimension

Introduction. Free Electron Fermi Gas. Energy Levels in One Dimension ree Electro er Gas Eergy Levels Oe Deso Effect of eperature o the er-drac Dstrbuto ree Electro Gas hree Desos Heat Capacty of the Electro Gas Electrcal Coductvty ad Oh s Law Moto Magetc elds heral Coductvty

More information

C-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory

C-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory ROAD MAP... AE301 Aerodyamcs I UNIT C: 2-D Arfols C-1: Aerodyamcs of Arfols 1 C-2: Aerodyamcs of Arfols 2 C-3: Pael Methods C-4: Th Arfol Theory AE301 Aerodyamcs I Ut C-3: Lst of Subects Problem Solutos?

More information

ELEC 6041 LECTURE NOTES WEEK 3 Dr. Amir G. Aghdam Concordia University

ELEC 6041 LECTURE NOTES WEEK 3 Dr. Amir G. Aghdam Concordia University ecre Noe Prepared b r G. ghda EE 64 ETUE NTE WEE r. r G. ghda ocorda Uer eceraled orol e - Whe corol heor appled o a e ha co of geographcall eparaed copoe or a e cog of a large ber of p-op ao ofe dered

More information

MOLECULAR VIBRATIONS

MOLECULAR VIBRATIONS MOLECULAR VIBRATIONS Here we wsh to vestgate molecular vbratos ad draw a smlarty betwee the theory of molecular vbratos ad Hückel theory. 1. Smple Harmoc Oscllator Recall that the eergy of a oe-dmesoal

More information

European Journal of Mathematics and Computer Science Vol. 5 No. 2, 2018 ISSN

European Journal of Mathematics and Computer Science Vol. 5 No. 2, 2018 ISSN Europea Joural of Mathematc ad Computer Scece Vol. 5 o., 018 ISS 059-9951 APPLICATIO OF ASYMPTOTIC DISTRIBUTIO OF MA-HITEY STATISTIC TO DETERMIE THE DIFFERECE BETEE THE SYSTOLIC BLOOD PRESSURE OF ME AD

More information

Alternating Direction Implicit Method

Alternating Direction Implicit Method Alteratg Drecto Implct Method Whle dealg wth Ellptc Eqatos the Implct form the mber of eqatos to be solved are N M whch are qte large mber. Thogh the coeffcet matrx has may zeros bt t s ot a baded system.

More information

Lecture 25 Highlights Phys 402

Lecture 25 Highlights Phys 402 Lecture 5 Hhlht Phy 40 e are ow o to coder the tattcal mechac of quatum ytem. I partcular we hall tudy the macrocopc properte of a collecto of may (N ~ 0 detcal ad dtuhable Fermo ad Boo wth overlapp wavefucto.

More information

Estimators for Finite Population Variance Using Mean and Variance of Auxiliary Variable

Estimators for Finite Population Variance Using Mean and Variance of Auxiliary Variable Itratoal Jal o Probablt a tattc 5 : - DOI:.59/j.jp.5. tmat Ft Poplato Varac U Ma a Varac o Alar Varabl Ph Mra * R. Kara h Dpartmt o tattc Lcow Urt Lcow Ia Abtract F tmat t poplato arac mato o l alar arabl

More information

. The set of these sums. be a partition of [ ab, ]. Consider the sum f( x) f( x 1)

. The set of these sums. be a partition of [ ab, ]. Consider the sum f( x) f( x 1) Chapter 7 Fuctos o Bouded Varato. Subject: Real Aalyss Level: M.Sc. Source: Syed Gul Shah (Charma, Departmet o Mathematcs, US Sargodha Collected & Composed by: Atq ur Rehma (atq@mathcty.org, http://www.mathcty.org

More information