Chapter 5: Incompressible Flow over Finite Wings

Size: px
Start display at page:

Download "Chapter 5: Incompressible Flow over Finite Wings"

Transcription

1 Chapter 5: Icompressle Flow over Fte Wgs

2 5. Itroducto fte wgs, dowwash, duced drag 5. ortex Theor prcple: the vortex flamet Bot-Savart law Helmholtz s vortex theorems 5.3 The Classcal ftg-e Theor ellptcal ad geeral lft dstruto the effect of spect Rato Extesos: umercal mplemetato lftg-surfacevortex-lattce REFERECE MTERI: see 5. umercal Example of the Wg Equato

3 rfol : D flow c l, c d Real Wg: 3D flow C, C D fte extet varato of sectos alog the wg spa The flow over fte wgs I what respect s the flow aroud a true wg dfferet from a arfol a fte wg? spawse flow compoet due to leakage flow aroud the tps

4 Tralg vortces ad dowwash Tralg vortces tp vortces Results: tralg vortces tp vortces ad dowwash vertcal flow compoet dowwash Upflow tp vortex

5 Flg geese

6 Tp ortex 3D rfol ftg e theor

7 Dowwash ad the effectve flow drecto. The dowwash modfes the effectve flow drecto ad reduces : effectve agle of attack - geometrc agle of attack - duced agle of attack eff Iduced Drag D. The lft vector s cled ackwards: duced drag D' ' ote: total drag = duced drag + profle drag flght drecto Effectve gle of ttack Iduced eloct

8 DRG FOR SUBSOIC -D IRFOI D THE FIITE WIG For susoc -D arfol: D f : sk frcto drag, C f =D f S D p : prssure darg D f >> D p small agle of attack Profle drag coeffcet C d = D f + D p S d d For susoc fte wg: D : duced drag Total drag coeffcet C D = D f + D p + D S

9 = total lft of the wg Dstruto of lft = sectoal lft, local lft per ut spa log the wg spa varato of: chord c arfol propertes aerodamc twst geometrc geometrc twst duced Hece, also varato of: lft coeffcet sectoal lft crculato c c C q ' cl q c cl c l ote: S eff ' ~ ' d ~ c l c erodamc twst s defed as "the agle etwee the zero-lft agle of a arfol ad the zero-lft agle of the root arfol." I essece, ths meas that the arfol of the wg would actuall chage shape as t moved farther awa from the fuselage.

10 Twsted wg geometr twst & dfferet arfol cross sectos alog the spa aerodamc twst

11 Dstruto of lft ote: ft s zero at the tps pressure equalzato Cetral suject of wg theor: Relato etwee wg shape ad lft dstruto. alss: determe the lft dstruto for gve wg shape. Desg: determe wg shape for desred lft dstruto ftg le theor: the wg s replaced a vortex flamet wth varale crculato at the quarter-chord le + free vortces

12 OUTIE FOR Chapter 5 Helmholtz ortex Theorem Cosder the moto of a vscd flud uder the acto of coservatve od force e.g. gravt, for whch G=gz D Dt

13 HEMHOTZ ORTEX THEOREM for Curved ortex Flamet ortex le vortct veloct q ortex flamet: a ftesmal vortex tue. D Dt ortex tue Referece: ow Speed erodamcs From Wg Theor to Pael method Katz aad Plotk Chapter.9 arale defto: - volume; q - veloct; - vortct

14 3-D ortex Theor: the vortex flamet flow aroud a real wg uform flow + vortces D: Straght vortex le: 3D geeral: curved vortex le r P duced veloct r

15 3-D ortex Theor: Helmholtz s vortex theorems compare the veloct duced the vortex flamet to the magetc feld duced a electrcal curret The crculato stregth remas costat alog the flamet a vortex flamet caot ed the flow, ut: exteds to ft eds at a oudar forms a closed loop cosequece:

16 3-D ortex Theor: The Bot-Savart aw The cotruto d of a flamet secto dl to the duced veloct P: d dl r θ 3 4 r The Bot-Savart aw Drecto: d s perpedcular to dl ad r ote: s the agle: dl r Magtude: s d dl 4 r

17 Propertes of a straght vortex flamet segmet B B dl r s 4 P h r l B θ -θ s ta s h dl h l h r cos cos 4 s 4 B B h d h Fte segmet B, costat

18 Propertes of a straght vortex flamet segmet B θ B B B 4h cos cos B 4h cos cos B h P ote: ad B are the teral agles of BP θ Specal cases: fte vortex flamet : = B = : 4h h same as D vortex sem-fte flamet: =9º; B = : 4h 4h P

19 5.3 The ftg-e Theor The Horseshoe vortex as a smple model of a fte wg the wg tself a oud vortex at the 4-chord le s fxed, hece, expereces lft = the tp vortces free-tralg vortces free to adjust to the local flow drecto, o lft ll vortces have the same crculato stregth ; the free tralg vortces exted to ft dowstream

20 The sgle horseshoe vortex rght tp vortex Sem-fte flamet: h w P W 4h Dowwash duced alog the wg the two tralg wg tp vortces w 4 4 left tp vortex rght tp vortex left tp vortex w 4 Remarks: w < whe > : the duced flow s deed dowwards for postve lft Prolems wth the smple horseshoe-vortex model of a wg: = costat lft dstruto w at the tps ot realstc!

21 Exteso of the horseshoe vortex model towards the lftg-le model Istead of a sgle horseshoe vortex: superposto of ma vortex sstems Each vortex has a dfferet spa ut the oud vortex segmets cocde o the same le ad form the lftg le = the wg The crculato alog the lftg le s o loger costat, ut t vares alog the spa a stepwse fasho Extrapolate to fte umer of horseshoe vortces to ota cotuous

22 Prcple of the lftg le + d d The wg s replaced a oud vortex wth cotuousl varg crculato The tralg vortces create a vortex wake the form of a cotuous vortex sheet local stregth of the tralg vortex at posto s gve the chage : d = dd d the vortex sheet s assumed to rema flat o deformato aldt: good approxmato for straght, sleder wgs at moderate lft

23 Determg the dowwash of the lftg le I Stregth of the tralg vortex at posto alog the wg spa: Take small segmet of the lftg le, d, at posto Over ths segmet the chage crculato of the lftg le s: d = dd d Ths s equal to the stregth of the tralg vortex The cotruto dw to the duced veloct at posto : Total veloct at posto duced the etre vortex wake: w - d d = dd d d dw 4 w 4 d d d

24 Determg the duced agle of attack of the lftg le duced agle of attack: d d d w w 4 ta Total veloct at posto duced the etre vortex wake: d d d w 4

25 The relato etwee crculato ad wg shape Use D arfol theor, ut modfed the effectve flow drecto: From the relato etwee lft ad crculato: comato: ] [ ] [ eff eff l l a a c c ' c c c c l l a c d d d c a 4 d d d w 4 The fudametal equato of Pradtl s lftg-le theor d dc a l = cost

26 Pradtl s lftg-le equato the wg equato a c 4 d d d Some remarks:. Ths equato descres the relato etwee crculato ad wg propertes. It s lear 3. The crculato s proportoal to ft ~ ~ 4. For a wg wthout twst ad = are costat: crculato s proportoal to = for ever value of the lft dstruto has the same form whch depeds o a, c ad, therefore, o the wg shape the total lft s zero whe = = ad the: alog the spawse o drecto 5. For a wg wth twst ad = are ot costat: THIS IS OT SO partcular: total zero lft s geeral ot accompaed : alog the spawse o drecto

27 Wg propertes for gve crculato. ft dstruto:. Total lft: 3. Iduced agle of attack: 4. Iduced drag: ' ' d d d S S q C d d d 4 ' ' d d d D D D d S S q D C

28 The ellptcal lft dstruto Cosder the followg ellptcal lft dstruto: w 4 Compute the dowwash veloct from: d d d = max.crculato - coordate trasformato: cos d s d s d d cos w d d cos cos cos cos w = Dowwash ad duced agle of attack are costat over the spa of the wg!

29 The ellptcal lft dstruto s Calculato of the total lft: 4 s s d d d d d s C S S C 4 4 C S C = S: s called the aspect rato R of the wg slakhed tpcal values: 6-8 for susoc arcraft - for glder arcraft The duced agle of attack Relato etwee ad C :

30 The ellptcal lft dstruto 3 Calculato of the duced drag: D ' d ' d ote that C s costat here C D C C Coclusos: The ducd drag s the drag due to lft Rememer : total drag C D c d C D ~ C : quadratc depedece C D C : large R decreases duced drag D ~

31 The ellptcal lft dstruto - wg shape What wg shape ca geerate a ellptcal lft dstruto? assume: o twst: so ad - are costat assume: lft slope a = dc l d s costat cosequece: wth also costat c l a [ ] costat C c l C Remark: Proof: C cl c d cl S S c d c l requred varato of the chord: ' cl q c ' c ~ ' ~ q c l The wg must have a ellptcal plaform

32 The ellptcal wg shape ellptcal wg plaform: ote straght 4-chord le 4-chord le ellptc lft dstruto, a ellptc wg plaform ad a costat dowwash

33 The Supermare Sptfre

34 erodamc propertes of the ellptc wg We foud that: = costat Comg: solve for C : C C = costat c l c l c l C a a C [ ] where: a dcl d [ a ] a a ote: C = whe = = ad: for a ellptc wg for a geeral wg C dc a d a

35 Effect of spect Rato o the lft-curve C for a ellptc wg: a dc a d a The lft slope s reduced. phscal explaato: the dowwash reduces the effectve agle of attack: dc d dcl d dcl d eff deff. d d a d

36 The ellptcal lft dstruto - summar Costat dowwash alog the spa Iduced drag: ft slope: a C D C dc a d a C effect of creasg the wg aspect rato: - duced drag smaller - lft-slope larger a a Practcal sgfcace of the ellptcal wg: optmum wg shape: mmal duced drag for gve lft referece wg: reasoale approxmato for real wgs C

37 Geeral lft dstruto For the ellptcal wg: wth: ad: s C cos Descre the crculato of a geeral wg wth a Fourer se seres: ote: s The umer of terms should e take suffcetl large = at the tps Questos to e aswered: a costat depedg learl o C, hece, o what are the aerodamc propertes lft, duced drag? what s the relato etwee the coeffcets ad the wg geometr? costats that deped o Ellptcal wg: =; =C

38 Geeral lft dstruto: total lft s s d S d S S q C.. s s s s d S d S Stadard tegrals: = whe = whe = C. Depeds ol o the frst coeffcet Calculato of the lft coeffcet:

39 Geeral lft dstruto: dowwash s Calculato of the duced agle of attack: d d cos 4 d d d d d d cos cos cos cos cos d Stadard tegrals: s s s s

40 Geeral lft dstruto: duced drag s s d S d S S q D C D s s s s d S = whe m = whe = m D C Calculato of the duced-drag coeffcet: s s s s d m S m m

41 Geeral lft dstruto: summar ad coclusos C. Cocluso: the ellptc wg =, e = gves the lowest possle duced drag for gve lft ad aspect rato D C where D C C the "spa effcec factor" where : or e e C C D

42 The relato etwee the ad the wg geometr Solve Pradtl s wg equato: susttute: c a l a c s s s 4 a s s c s umercal soluto method: Take a trucated seres wth ukow coeffcets:,, Take dfferet spawse locatos o the wg where the equato s to e satsfed:,,.. ; ut ot at the tps, so: < < Sstem of equatos wth ukows Solve matx ote: t s ot possle to solve for ol oe coeffcet, as Chapter 4!

43 umercal example of the wg equato Cosder: rectagular wg: c = costat; spa = ; c = ; wthout twst: = costat; = = evaluate the wg equato at the cotrol pots at : 4 a s s The wg s smmetrcal, 4, are zero s s If If s s eve : s s odd : s for s eve umer s s s for s odd umer s,,... s s

44 umercal example of the wg equato evaluate the wg equato at the cotrol pots at : 4 s a s,,... The wg s smmetrcal, 4, are zero take ol, 3, as ukows take ol cotrol pots o half of the wg: < Example for =3: take, 3, 5 as ukows take cotrol pots equdstat : = 6, = 3, 3 = take lft-slope of the arfols a =, ad wg aspect rato =

45 umercal example of the wg equato 3 =, = 6, =, = 3, =3, 3 = a s s 4...,, 6 s5 6 s s3 6 s s 6 s a a a 3 s5 3 s s3 3 s s 3 s a a a s5 s 5 4 s3 s 3 4 s s a a a

46 umercal example of the wg equato 3 4 a =, = 6, s s ,, =, = 3, =3, 3 =

47 umercal example: the rectagular wg =3 The set of equatos ecomes: wth soluto: Evaluato of the propertes of the rectagular wg wth = a = : ote: wth.5: ol 5% more duced drag tha ellptcal wg! C d dc a e =3 =

48 C ~ CD C where

49 Effect of wg plaform ad aspect rato C C a D a alues of deped o plaform ad aspect rato of the wg a Effect of wg plaform o for a tapered wg example tapered wg wth taper rato c t c r =.3 s almost as good as a ellptcal wg!

50 Fal coclusos the effect of wg plaform o the duced drag C D C I order to reduce the duced drag t s more mportat to crease the aspect rato tha trg to approach the ellptc lft dstruto accuratel tapered wg wth taper rato c t c r =.3 s almost as good as a ellptcal wg ad s much easer to maufacture ote that the parameter s a costat.e., depedet of ol for a wg wthout twst! Rememer: total drag = duced drag + profle drag ~ vscost

51 ftg-le theor: Wg theor - a summar The wg s replaced a oud vortex at the 4-chord le of the wg wth varg crculato : the lftg le The tralg vortces form a flat sheet of dstruted vortct: the vortex wake mtatos of the classcal theor: sleder wgs large aspect rato, or: spa>>chord straght wgs o wg sweep moderate aerodamc loadg o deformato of the vortex wake lear relato Extesos: c l ~ eff 5.4 o-lear lftg-le theor: c l eff 5.5 methods where the wg s represeted a vortex-sheet stead of a le: lftg-surface vortex-lattce methods

52 5.4 umercal olear lftg-le method Gve the wg shape ad the agle of attack :. Dvde the wg spawse postos:. ssume a tal crculato dstruto =, e.g. ellptcal 3. Calculate the duced agle of attack: 4. Calculate: 4 eff d d d evaluate the tegral umercall 5. Calculate lft coeffcet: cl cl eff c 6. Update crculato: cl terate utl covergece uder relaxato

53 5.5 ftg-surface theor prcple wg wake streamwse vortct ftg le: wg represeted a vortex flamet ol spawse vortct vald ol for sleder wgs ftg surface: wg represeted a vortex sheet wth dstruted spawse ad chordwse vortct

54 ftg-surface theor - umercal mplemetato 3D vortex-pael methods: the wg s represeted paels wth dstruted vortct three-dmesoal exteso of the vortex-pael method secto 4.9 ortex-attce methods: dstruted vortct s cocetrated to a lattce of horseshoe vortces sgle horseshoe vortex The vortex-lattce sstem o a fte wg

Chapter 5: Incompressible Flow over Finite Wings

Chapter 5: Incompressible Flow over Finite Wings 683 hapter 5: Iompressle Flow over Fte Wgs 5. Itrouto fte wgs, owwash, ue rag 5. ortex Theor prple: the vortex flamet Bot-avart law Helmholtz s vortex theorems 5.3 The lassal ftg-e Theor ellptal a geeral

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

L5 Polynomial / Spline Curves

L5 Polynomial / Spline Curves L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a

More information

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses Johs Hopks Uverst Departmet of Bostatstcs Math Revew for Itroductor Courses Ratoale Bostatstcs courses wll rel o some fudametal mathematcal relatoshps, fuctos ad otato. The purpose of ths Math Revew s

More information

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses Johs Hopks Uverst Departmet of Bostatstcs Math Revew for Itroductor Courses Ratoale Bostatstcs courses wll rel o some fudametal mathematcal relatoshps, fuctos ad otato. The purpose of ths Math Revew s

More information

MMJ 1113 FINITE ELEMENT METHOD Introduction to PART I

MMJ 1113 FINITE ELEMENT METHOD Introduction to PART I MMJ FINITE EEMENT METHOD Cotut requremets Assume that the fuctos appearg uder the tegral the elemet equatos cota up to (r) th order To esure covergece N must satsf Compatblt requremet the fuctos must have

More information

Centroids & Moments of Inertia of Beam Sections

Centroids & Moments of Inertia of Beam Sections RCH 614 Note Set 8 S017ab Cetrods & Momets of erta of Beam Sectos Notato: b C d d d Fz h c Jo L O Q Q = ame for area = ame for a (base) wdth = desgato for chael secto = ame for cetrod = calculus smbol

More information

PHYS Look over. examples 2, 3, 4, 6, 7, 8,9, 10 and 11. How To Make Physics Pay PHYS Look over. Examples: 1, 4, 5, 6, 7, 8, 9, 10,

PHYS Look over. examples 2, 3, 4, 6, 7, 8,9, 10 and 11. How To Make Physics Pay PHYS Look over. Examples: 1, 4, 5, 6, 7, 8, 9, 10, PHYS Look over Chapter 9 Sectos - Eamples:, 4, 5, 6, 7, 8, 9, 0, PHYS Look over Chapter 7 Sectos -8 8, 0 eamples, 3, 4, 6, 7, 8,9, 0 ad How To ake Phscs Pa We wll ow look at a wa of calculatg where the

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

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

CP 2 Properties of polygonal plane areas

CP 2 Properties of polygonal plane areas CP Propertes of polgoal plae areas Ket D. Hjelmstad Scool for Sustaale Egeerg ad te Bult Evromet Ira. Fulto Scools of Egeerg roa State Uverst Te propertes of cross sectos Cross Secto Cross Secto Logtudal

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

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

Functions of Random Variables

Functions of Random Variables Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,

More information

Lecture 07: Poles and Zeros

Lecture 07: Poles and Zeros Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto

More information

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The

More information

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin Learzato of the Swg Equato We wll cover sectos.5.-.6 ad begg of Secto 3.3 these otes. 1. Sgle mache-fte bus case Cosder a sgle mache coected to a fte bus, as show Fg. 1 below. E y1 V=1./_ Fg. 1 The admttace

More information

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II CEE49b Chapter - Free Vbrato of Mult-Degree-of-Freedom Systems - II We ca obta a approxmate soluto to the fudametal atural frequecy through a approxmate formula developed usg eergy prcples by Lord Raylegh

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

Correlation and Regression Analysis

Correlation and Regression Analysis Chapter V Correlato ad Regresso Aalss R. 5.. So far we have cosdered ol uvarate dstrbutos. Ma a tme, however, we come across problems whch volve two or more varables. Ths wll be the subject matter of the

More information

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation?

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation? Ca we tae the Mstcsm Out of the Pearso Coeffcet of Lear Correlato? Itroducto As the ttle of ths tutoral dcates, our purpose s to egeder a clear uderstadg of the Pearso coeffcet of lear correlato studets

More information

Determination of angle of attack for rotating blades

Determination of angle of attack for rotating blades Determato of agle of attack for rotatg blades Hora DUMITRESCU 1, Vladmr CARDOS*,1, Flor FRUNZULICA 1,, Alexadru DUMITRACHE 1 *Correspodg author *,1 Gheorghe Mhoc-Caus Iacob Isttute of Mathematcal Statstcs

More information

Computational Geometry

Computational Geometry Problem efto omputatoal eometry hapter 6 Pot Locato Preprocess a plaar map S. ve a query pot p, report the face of S cotag p. oal: O()-sze data structure that eables O(log ) query tme. pplcato: Whch state

More information

CS5620 Intro to Computer Graphics

CS5620 Intro to Computer Graphics CS56 Itro to Computer Graphcs Geometrc Modelg art II Geometrc Modelg II hyscal Sples Curve desg pre-computers Cubc Sples Stadard sple put set of pots { } =, No dervatves specfed as put Iterpolate by cubc

More information

AERODYNAMICS I LECTURE 6 AERODYNAMICS OF A WING FUNDAMENTALS OF THE LIFTING-LINE THEORY

AERODYNAMICS I LECTURE 6 AERODYNAMICS OF A WING FUNDAMENTALS OF THE LIFTING-LINE THEORY LECTURE 6 AERODYNAMICS OF A WING FUNDAMENTALS OF THE LIFTING-LINE THEORY The Bot-Savart Law The velocty nduced by the sngular vortex lne wth the crculaton can be determned by means of the Bot- Savart formula

More information

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class)

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class) Assgmet 5/MATH 7/Wter 00 Due: Frday, February 9 class (!) (aswers wll be posted rght after class) As usual, there are peces of text, before the questos [], [], themselves. Recall: For the quadratc form

More information

DIFFERENTIAL GEOMETRIC APPROACH TO HAMILTONIAN MECHANICS

DIFFERENTIAL GEOMETRIC APPROACH TO HAMILTONIAN MECHANICS DIFFERENTIAL GEOMETRIC APPROACH TO HAMILTONIAN MECHANICS Course Project: Classcal Mechacs (PHY 40) Suja Dabholkar (Y430) Sul Yeshwath (Y444). Itroducto Hamltoa mechacs s geometry phase space. It deals

More information

Lecture 3. Sampling, sampling distributions, and parameter estimation

Lecture 3. Sampling, sampling distributions, and parameter estimation Lecture 3 Samplg, samplg dstrbutos, ad parameter estmato Samplg Defto Populato s defed as the collecto of all the possble observatos of terest. The collecto of observatos we take from the populato s called

More information

1 Lyapunov Stability Theory

1 Lyapunov Stability Theory Lyapuov Stablty heory I ths secto we cosder proofs of stablty of equlbra of autoomous systems. hs s stadard theory for olear systems, ad oe of the most mportat tools the aalyss of olear systems. It may

More information

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades STAT 101 Dr. Kar Lock Morga 11/20/12 Exam 2 Grades Multple Regresso SECTIONS 9.2, 10.1, 10.2 Multple explaatory varables (10.1) Parttog varablty R 2, ANOVA (9.2) Codtos resdual plot (10.2) Trasformatos

More information

Ideal multigrades with trigonometric coefficients

Ideal multigrades with trigonometric coefficients Ideal multgrades wth trgoometrc coeffcets Zarathustra Brady December 13, 010 1 The problem A (, k) multgrade s defed as a par of dstct sets of tegers such that (a 1,..., a ; b 1,..., b ) a j = =1 for all

More information

Objectives of Multiple Regression

Objectives of Multiple Regression Obectves of Multple Regresso Establsh the lear equato that best predcts values of a depedet varable Y usg more tha oe eplaator varable from a large set of potetal predctors {,,... k }. Fd that subset of

More information

ENGI 3423 Simple Linear Regression Page 12-01

ENGI 3423 Simple Linear Regression Page 12-01 ENGI 343 mple Lear Regresso Page - mple Lear Regresso ometmes a expermet s set up where the expermeter has cotrol over the values of oe or more varables X ad measures the resultg values of aother varable

More information

MEASURES OF DISPERSION

MEASURES OF DISPERSION MEASURES OF DISPERSION Measure of Cetral Tedecy: Measures of Cetral Tedecy ad Dsperso ) Mathematcal Average: a) Arthmetc mea (A.M.) b) Geometrc mea (G.M.) c) Harmoc mea (H.M.) ) Averages of Posto: a) Meda

More information

Chapter 10 Two Stage Sampling (Subsampling)

Chapter 10 Two Stage Sampling (Subsampling) Chapter 0 To tage amplg (usamplg) I cluster samplg, all the elemets the selected clusters are surveyed oreover, the effcecy cluster samplg depeds o sze of the cluster As the sze creases, the effcecy decreases

More information

Regression and the LMS Algorithm

Regression and the LMS Algorithm CSE 556: Itroducto to Neural Netorks Regresso ad the LMS Algorthm CSE 556: Regresso 1 Problem statemet CSE 556: Regresso Lear regresso th oe varable Gve a set of N pars of data {, d }, appromate d b a

More information

CHAPTER VI Statistical Analysis of Experimental Data

CHAPTER VI Statistical Analysis of Experimental Data Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca

More information

Simple Linear Regression

Simple Linear Regression Correlato ad Smple Lear Regresso Berl Che Departmet of Computer Scece & Iformato Egeerg Natoal Tawa Normal Uversty Referece:. W. Navd. Statstcs for Egeerg ad Scetsts. Chapter 7 (7.-7.3) & Teachg Materal

More information

Cubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem

Cubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem Joural of Amerca Scece ;6( Cubc Nopolyomal Sple Approach to the Soluto of a Secod Order Two-Pot Boudary Value Problem W.K. Zahra, F.A. Abd El-Salam, A.A. El-Sabbagh ad Z.A. ZAk * Departmet of Egeerg athematcs

More information

Chapter Two. An Introduction to Regression ( )

Chapter Two. An Introduction to Regression ( ) ubject: A Itroducto to Regresso Frst tage Chapter Two A Itroducto to Regresso (018-019) 1 pg. ubject: A Itroducto to Regresso Frst tage A Itroducto to Regresso Regresso aalss s a statstcal tool for the

More information

Correlation and Simple Linear Regression

Correlation and Simple Linear Regression Correlato ad Smple Lear Regresso Berl Che Departmet of Computer Scece & Iformato Egeerg Natoal Tawa Normal Uverst Referece:. W. Navd. Statstcs for Egeerg ad Scetsts. Chapter 7 (7.-7.3) & Teachg Materal

More information

Ahmed Elgamal. MDOF Systems & Modal Analysis

Ahmed Elgamal. MDOF Systems & Modal Analysis DOF Systems & odal Aalyss odal Aalyss (hese otes cover sectos from Ch. 0, Dyamcs of Structures, Al Chopra, Pretce Hall, 995). Refereces Dyamcs of Structures, Al K. Chopra, Pretce Hall, New Jersey, ISBN

More information

4 Inner Product Spaces

4 Inner Product Spaces 11.MH1 LINEAR ALGEBRA Summary Notes 4 Ier Product Spaces Ier product s the abstracto to geeral vector spaces of the famlar dea of the scalar product of two vectors or 3. I what follows, keep these key

More information

X ε ) = 0, or equivalently, lim

X ε ) = 0, or equivalently, lim Revew for the prevous lecture Cocepts: order statstcs Theorems: Dstrbutos of order statstcs Examples: How to get the dstrbuto of order statstcs Chapter 5 Propertes of a Radom Sample Secto 55 Covergece

More information

Test Paper-II. 1. If sin θ + cos θ = m and sec θ + cosec θ = n, then (a) 2n = m (n 2 1) (b) 2m = n (m 2 1) (c) 2n = m (m 2 1) (d) none of these

Test Paper-II. 1. If sin θ + cos θ = m and sec θ + cosec θ = n, then (a) 2n = m (n 2 1) (b) 2m = n (m 2 1) (c) 2n = m (m 2 1) (d) none of these Test Paer-II. If s θ + cos θ = m ad sec θ + cosec θ =, the = m ( ) m = (m ) = m (m ). If a ABC, cos A = s B, the t s C a osceles tragle a eulateral tragle a rght agled tragle. If cos B = cos ( A+ C), the

More information

Chapter 8. Inferences about More Than Two Population Central Values

Chapter 8. Inferences about More Than Two Population Central Values Chapter 8. Ifereces about More Tha Two Populato Cetral Values Case tudy: Effect of Tmg of the Treatmet of Port-We tas wth Lasers ) To vestgate whether treatmet at a youg age would yeld better results tha

More information

Numerical Analysis Formulae Booklet

Numerical Analysis Formulae Booklet Numercal Aalyss Formulae Booklet. Iteratve Scemes for Systems of Lear Algebrac Equatos:.... Taylor Seres... 3. Fte Dfferece Approxmatos... 3 4. Egevalues ad Egevectors of Matrces.... 3 5. Vector ad Matrx

More information

16 Homework lecture 16

16 Homework lecture 16 Quees College, CUNY, Departmet of Computer Scece Numercal Methods CSCI 361 / 761 Fall 2018 Istructor: Dr. Sateesh Mae c Sateesh R. Mae 2018 16 Homework lecture 16 Please emal your soluto, as a fle attachmet,

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

Module 7: Probability and Statistics

Module 7: Probability and Statistics Lecture 4: Goodess of ft tests. Itroducto Module 7: Probablty ad Statstcs I the prevous two lectures, the cocepts, steps ad applcatos of Hypotheses testg were dscussed. Hypotheses testg may be used to

More information

PTAS for Bin-Packing

PTAS for Bin-Packing CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,

More information

BERNSTEIN COLLOCATION METHOD FOR SOLVING NONLINEAR DIFFERENTIAL EQUATIONS. Aysegul Akyuz Dascioglu and Nese Isler

BERNSTEIN COLLOCATION METHOD FOR SOLVING NONLINEAR DIFFERENTIAL EQUATIONS. Aysegul Akyuz Dascioglu and Nese Isler Mathematcal ad Computatoal Applcatos, Vol. 8, No. 3, pp. 293-300, 203 BERNSTEIN COLLOCATION METHOD FOR SOLVING NONLINEAR DIFFERENTIAL EQUATIONS Aysegul Ayuz Dascoglu ad Nese Isler Departmet of Mathematcs,

More information

Chapter Business Statistics: A First Course Fifth Edition. Learning Objectives. Correlation vs. Regression. In this chapter, you learn:

Chapter Business Statistics: A First Course Fifth Edition. Learning Objectives. Correlation vs. Regression. In this chapter, you learn: Chapter 3 3- Busess Statstcs: A Frst Course Ffth Edto Chapter 2 Correlato ad Smple Lear Regresso Busess Statstcs: A Frst Course, 5e 29 Pretce-Hall, Ic. Chap 2- Learg Objectves I ths chapter, you lear:

More information

Computer Graphics. Geometric Modeling. Geometric Modeling. Page. Copyright Gotsman, Elber, Barequet, Karni, Sheffer Computer Science - Technion

Computer Graphics. Geometric Modeling. Geometric Modeling. Page. Copyright Gotsman, Elber, Barequet, Karni, Sheffer Computer Science - Technion Computer Graphcs Geometrc Modelg Geometrc Modelg A Example 4 Outle Objectve: Develop methods ad algorthms to mathematcally model shape of real world objects Categores: Wre-Frame Represetato Object s represeted

More information

Newton s Power Flow algorithm

Newton s Power Flow algorithm Power Egeerg - Egll Beedt Hresso ewto s Power Flow algorthm Power Egeerg - Egll Beedt Hresso The ewto s Method of Power Flow 2 Calculatos. For the referece bus #, we set : V = p.u. ad δ = 0 For all other

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

MATH 247/Winter Notes on the adjoint and on normal operators.

MATH 247/Winter Notes on the adjoint and on normal operators. MATH 47/Wter 00 Notes o the adjot ad o ormal operators I these otes, V s a fte dmesoal er product space over, wth gve er * product uv, T, S, T, are lear operators o V U, W are subspaces of V Whe we say

More information

residual. (Note that usually in descriptions of regression analysis, upper-case

residual. (Note that usually in descriptions of regression analysis, upper-case Regresso Aalyss Regresso aalyss fts or derves a model that descres the varato of a respose (or depedet ) varale as a fucto of oe or more predctor (or depedet ) varales. The geeral regresso model s oe of

More information

Mu Sequences/Series Solutions National Convention 2014

Mu Sequences/Series Solutions National Convention 2014 Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed

More information

TESTS BASED ON MAXIMUM LIKELIHOOD

TESTS BASED ON MAXIMUM LIKELIHOOD ESE 5 Toy E. Smth. The Basc Example. TESTS BASED ON MAXIMUM LIKELIHOOD To llustrate the propertes of maxmum lkelhood estmates ad tests, we cosder the smplest possble case of estmatg the mea of the ormal

More information

Applying the condition for equilibrium to this equilibrium, we get (1) n i i =, r G and 5 i

Applying the condition for equilibrium to this equilibrium, we get (1) n i i =, r G and 5 i CHEMICAL EQUILIBRIA The Thermodyamc Equlbrum Costat Cosder a reversble reacto of the type 1 A 1 + 2 A 2 + W m A m + m+1 A m+1 + Assgg postve values to the stochometrc coeffcets o the rght had sde ad egatve

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

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

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Numercal Computg -I UNIT SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Structure Page Nos..0 Itroducto 6. Objectves 7. Ital Approxmato to a Root 7. Bsecto Method 8.. Error Aalyss 9.4 Regula Fals Method

More information

ENGI 4421 Joint Probability Distributions Page Joint Probability Distributions [Navidi sections 2.5 and 2.6; Devore sections

ENGI 4421 Joint Probability Distributions Page Joint Probability Distributions [Navidi sections 2.5 and 2.6; Devore sections ENGI 441 Jot Probablty Dstrbutos Page 7-01 Jot Probablty Dstrbutos [Navd sectos.5 ad.6; Devore sectos 5.1-5.] The jot probablty mass fucto of two dscrete radom quattes, s, P ad p x y x y The margal probablty

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

Beam Warming Second-Order Upwind Method

Beam Warming Second-Order Upwind Method Beam Warmg Secod-Order Upwd Method Petr Valeta Jauary 6, 015 Ths documet s a part of the assessmet work for the subject 1DRP Dfferetal Equatos o Computer lectured o FNSPE CTU Prague. Abstract Ths documet

More information

MA/CSSE 473 Day 27. Dynamic programming

MA/CSSE 473 Day 27. Dynamic programming MA/CSSE 473 Day 7 Dyamc Programmg Bomal Coeffcets Warshall's algorthm (Optmal BSTs) Studet questos? Dyamc programmg Used for problems wth recursve solutos ad overlappg subproblems Typcally, we save (memoze)

More information

1 Onto functions and bijections Applications to Counting

1 Onto functions and bijections Applications to Counting 1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of

More information

EECE 301 Signals & Systems

EECE 301 Signals & Systems EECE 01 Sgals & Systems Prof. Mark Fowler Note Set #9 Computg D-T Covoluto Readg Assgmet: Secto. of Kame ad Heck 1/ Course Flow Dagram The arrows here show coceptual flow betwee deas. Note the parallel

More information

CSE 5526: Introduction to Neural Networks Linear Regression

CSE 5526: Introduction to Neural Networks Linear Regression CSE 556: Itroducto to Neural Netorks Lear Regresso Part II 1 Problem statemet Part II Problem statemet Part II 3 Lear regresso th oe varable Gve a set of N pars of data , appromate d by a lear fucto

More information

STK4011 and STK9011 Autumn 2016

STK4011 and STK9011 Autumn 2016 STK4 ad STK9 Autum 6 Pot estmato Covers (most of the followg materal from chapter 7: Secto 7.: pages 3-3 Secto 7..: pages 3-33 Secto 7..: pages 35-3 Secto 7..3: pages 34-35 Secto 7.3.: pages 33-33 Secto

More information

Review Exam II Complex Analysis

Review Exam II Complex Analysis Revew Exam II Complex Aalyss Uderled Propostos or Theorems: Proofs May Be Asked for o Exam Chapter 3. Ifte Seres Defto: Covergece Defto: Absolute Covergece Proposto. Absolute Covergece mples Covergece

More information

St John s College. Preliminary Examinations July 2014 Mathematics Paper 1. Examiner: G Evans Time: 3 hrs Moderator: D Grigoratos Marks: 150

St John s College. Preliminary Examinations July 2014 Mathematics Paper 1. Examiner: G Evans Time: 3 hrs Moderator: D Grigoratos Marks: 150 St Joh s College Prelmar Eamatos Jul 04 Mathematcs Paper Eamer: G Evas Tme: 3 hrs Moderator: D Grgoratos Marks: 50 PLEASE READ THE FOLLOWING INSTRUCTIONS CAREFULLY. Ths questo paper cossts of pages, cludg

More information

Analysis of Lagrange Interpolation Formula

Analysis of Lagrange Interpolation Formula P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal

More information

Chapter 5 Properties of a Random Sample

Chapter 5 Properties of a Random Sample Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample

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

A NEW LOG-NORMAL DISTRIBUTION

A NEW LOG-NORMAL DISTRIBUTION Joural of Statstcs: Advaces Theory ad Applcatos Volume 6, Number, 06, Pages 93-04 Avalable at http://scetfcadvaces.co. DOI: http://dx.do.org/0.864/jsata_700705 A NEW LOG-NORMAL DISTRIBUTION Departmet of

More information

Extreme Value Theory: An Introduction

Extreme Value Theory: An Introduction (correcto d Extreme Value Theory: A Itroducto by Laures de Haa ad Aa Ferrera Wth ths webpage the authors ted to form the readers of errors or mstakes foud the book after publcato. We also gve extesos for

More information

Chapter 14 Logistic Regression Models

Chapter 14 Logistic Regression Models Chapter 4 Logstc Regresso Models I the lear regresso model X β + ε, there are two types of varables explaatory varables X, X,, X k ad study varable y These varables ca be measured o a cotuous scale as

More information

Aerodynamics. Finite Wings Lifting line theory Glauert s method

Aerodynamics. Finite Wings Lifting line theory Glauert s method α ( y) l Γ( y) r ( y) V c( y) β b 4 V Glauert s method b ( y) + r dy dγ y y dy Soluton procedure that transforms the lftng lne ntegro-dfferental equaton nto a system of algebrac equatons - Restrcted to

More information

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632

More information

Lecture 12 APPROXIMATION OF FIRST ORDER DERIVATIVES

Lecture 12 APPROXIMATION OF FIRST ORDER DERIVATIVES FDM: Appromato of Frst Order Dervatves Lecture APPROXIMATION OF FIRST ORDER DERIVATIVES. INTRODUCTION Covectve term coservato equatos volve frst order dervatves. The smplest possble approach for dscretzato

More information

ECON 482 / WH Hong The Simple Regression Model 1. Definition of the Simple Regression Model

ECON 482 / WH Hong The Simple Regression Model 1. Definition of the Simple Regression Model ECON 48 / WH Hog The Smple Regresso Model. Defto of the Smple Regresso Model Smple Regresso Model Expla varable y terms of varable x y = β + β x+ u y : depedet varable, explaed varable, respose varable,

More information

Analyzing Two-Dimensional Data. Analyzing Two-Dimensional Data

Analyzing Two-Dimensional Data. Analyzing Two-Dimensional Data /7/06 Aalzg Two-Dmesoal Data The most commo aaltcal measuremets volve the determato of a ukow cocetrato based o the respose of a aaltcal procedure (usuall strumetal). Such a measuremet requres calbrato,

More information

CS 2750 Machine Learning. Lecture 8. Linear regression. CS 2750 Machine Learning. Linear regression. is a linear combination of input components x

CS 2750 Machine Learning. Lecture 8. Linear regression. CS 2750 Machine Learning. Linear regression. is a linear combination of input components x CS 75 Mache Learg Lecture 8 Lear regresso Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square CS 75 Mache Learg Lear regresso Fucto f : X Y s a lear combato of put compoets f + + + K d d K k - parameters

More information

Supervised learning: Linear regression Logistic regression

Supervised learning: Linear regression Logistic regression CS 57 Itroducto to AI Lecture 4 Supervsed learg: Lear regresso Logstc regresso Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square CS 57 Itro to AI Data: D { D D.. D D Supervsed learg d a set of eamples s

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

Centers of Gravity - Centroids

Centers of Gravity - Centroids RCH Note Set 9. S205ab Ceters of Gravt - Cetrods Notato: C Fz L O Q Q t tw = ame for area = desgato for chael secto = ame for cetrod = force compoet the z drecto = ame for legth = ame for referece org

More information

Analysis of System Performance IN2072 Chapter 5 Analysis of Non Markov Systems

Analysis of System Performance IN2072 Chapter 5 Analysis of Non Markov Systems Char for Network Archtectures ad Servces Prof. Carle Departmet of Computer Scece U Müche Aalyss of System Performace IN2072 Chapter 5 Aalyss of No Markov Systems Dr. Alexader Kle Prof. Dr.-Ig. Georg Carle

More information

A Remark on the Uniform Convergence of Some Sequences of Functions

A Remark on the Uniform Convergence of Some Sequences of Functions Advaces Pure Mathematcs 05 5 57-533 Publshed Ole July 05 ScRes. http://www.scrp.org/joural/apm http://dx.do.org/0.436/apm.05.59048 A Remark o the Uform Covergece of Some Sequeces of Fuctos Guy Degla Isttut

More information

Statistics: Unlocking the Power of Data Lock 5

Statistics: Unlocking the Power of Data Lock 5 STAT 0 Dr. Kar Lock Morga Exam 2 Grades: I- Class Multple Regresso SECTIONS 9.2, 0., 0.2 Multple explaatory varables (0.) Parttog varablty R 2, ANOVA (9.2) Codtos resdual plot (0.2) Exam 2 Re- grades Re-

More information

STA302/1001-Fall 2008 Midterm Test October 21, 2008

STA302/1001-Fall 2008 Midterm Test October 21, 2008 STA3/-Fall 8 Mdterm Test October, 8 Last Name: Frst Name: Studet Number: Erolled (Crcle oe) STA3 STA INSTRUCTIONS Tme allowed: hour 45 mutes Ads allowed: A o-programmable calculator A table of values from

More information

THE COMPLETE ENUMERATION OF FINITE GROUPS OF THE FORM R 2 i ={R i R j ) k -i=i

THE COMPLETE ENUMERATION OF FINITE GROUPS OF THE FORM R 2 i ={R i R j ) k -i=i ENUMERATON OF FNTE GROUPS OF THE FORM R ( 2 = (RfR^'u =1. 21 THE COMPLETE ENUMERATON OF FNTE GROUPS OF THE FORM R 2 ={R R j ) k -= H. S. M. COXETER*. ths paper, we vestgate the abstract group defed by

More information

Generalization of the Dissimilarity Measure of Fuzzy Sets

Generalization of the Dissimilarity Measure of Fuzzy Sets Iteratoal Mathematcal Forum 2 2007 o. 68 3395-3400 Geeralzato of the Dssmlarty Measure of Fuzzy Sets Faramarz Faghh Boformatcs Laboratory Naobotechology Research Ceter vesa Research Isttute CECR Tehra

More information

Chapter 9 Jordan Block Matrices

Chapter 9 Jordan Block Matrices Chapter 9 Jorda Block atrces I ths chapter we wll solve the followg problem. Gve a lear operator T fd a bass R of F such that the matrx R (T) s as smple as possble. f course smple s a matter of taste.

More information

Log1 Contest Round 2 Theta Complex Numbers. 4 points each. 5 points each

Log1 Contest Round 2 Theta Complex Numbers. 4 points each. 5 points each 01 Log1 Cotest Roud Theta Complex Numbers 1 Wrte a b Wrte a b form: 1 5 form: 1 5 4 pots each Wrte a b form: 65 4 4 Evaluate: 65 5 Determe f the followg statemet s always, sometmes, or ever true (you may

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

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits Block-Based Compact hermal Modelg of Semcoductor Itegrated Crcuts Master s hess Defese Caddate: Jg Ba Commttee Members: Dr. Mg-Cheg Cheg Dr. Daqg Hou Dr. Robert Schllg July 27, 2009 Outle Itroducto Backgroud

More information

Convergence of the Desroziers scheme and its relation to the lag innovation diagnostic

Convergence of the Desroziers scheme and its relation to the lag innovation diagnostic Covergece of the Desrozers scheme ad ts relato to the lag ovato dagostc chard Méard Evromet Caada, Ar Qualty esearch Dvso World Weather Ope Scece Coferece Motreal, August 9, 04 o t t O x x x y x y Oservato

More information