Numerical Differentiation

Size: px
Start display at page:

Download "Numerical Differentiation"

Transcription

1 College o Egeerg ad Computer Scece Mecacal Egeerg Departmet Numercal Aalyss Notes November 4, 7 Istructor: Larry Caretto Numercal Deretato Itroducto Tese otes provde a basc troducto to umercal deretato usg te- derece grds. Tey cosder te terplay betwee trucato error ad roudo error. Fte-derece grds I a te-derece grd, a rego s subdvded to a set o dscrete pots. Te spacg betwee te pots may be uorm or o-uorm. For eample, a grd te drecto, m ma may be wrtte as ollows. Frst, we place a seres o N+ odes umbered rom zero to N ts rego. Te coordate o te rst ode, equals m. Te al grd ode, N = ma. Te spacg betwee ay two grd odes, ad -, as te symbol Δ. Tese relatos are summarzed as equato []. = m N = ma - = Δ [] A o-uorm grd, wt deret spacg betwee deret odes, s llustrated below ~ ~ N- N- N For a uorm grd, all values o Δ are te same. I ts case, te uorm grd spacg, a oedmesoal problem s usually gve te symbol. I.e., = - or all values o. I tese otes, we wll lmt our cosderato to oe-dmesoal te-derece problems. However, advaced courses cosder multple space dmesos dscussed equatos [] ad [] below. I two space dmesos a grd s requred or bot te ad y, drectos, wc results te ollowg grd ad geometry detos, assumg tat tere are M+ grd odes te y drecto. = m N = ma - = Δ y = ymj ym = ymay yj yj- = Δyj [] For a tree-dmesoal traset problem tere would be our depedet varables: te tree space dmesos,, y ad z, ad tme. Eac o tese varables would be deed at dscrete pots,.e. = m N = ma - = Δ y = ymj ym = ymay yj yj- = Δyj [] z = zmk zk = zmaz zk zk- = Δzk t = tm tl = tmay t t- = Δt Jacarada Hall 4 Mal Code Poe: N/A Emal: lcaretto@csu.edu 848 Fa:

2 Numercal Deretato L. S. Caretto, November 4, 7 Page Ay depedet varable suc as u(,y,z,t) a cotuous represetato would be deed oly at dscrete grd pots a te-derece represetato. Te ollowg otato s used or a oedmesoal problem. ) [4] k Ts otato ca be eteded to problems more ta oe dmeso cludg traset problems. I te most comple case te otato u, y, z, t ) ( k u s used to deote te jk ( j k value o te depedet at a partcular pot te rego, (, yj, zk, t) were te varable s deed. Fte-derece Epressos Derved rom Taylor Seres Te Taylor seres provdes a smple tool or dervg te-derece appromatos. It also gves a dcato o te error caused by te te derece epresso. Recall tat te Taylor seres or a ucto o oe varable, (), epaded about some pot = a, s gve by te te seres, ( ) ( a) d a ( a)! d a ( - a)! d a ( - a)... [5] Te = a subscrpt o te dervatves reorces te act tat tese dervatves are evaluated at te epaso pot, = a. We ca wrte te te seres usg a summato otato as ollows: d ( ) ( - a) []! a I te equato above, we use te detos o! =! = ad te deto o te zerot dervatve as te ucto tsel. I.e., d / =a = (a). I te seres s trucated ater some te umber o terms, say m terms, te omtted terms are called te trucato error. Tese omtted terms are also a te seres. Ts s llustrated below. ( ) m d d ( - a) a ( - )! m a! a Terms used Trucato error [7] I ts equato te secod sum represets te trucato error, εm, rom trucatg te seres ater m terms. d m ( - a) [8]! m Te teorem o te mea ca be used to sow tat te te-seres trucato error ca be epressed terms o te rst term te trucato error, tat s a

3 Numercal metods ME 9, L. S. Caretto, November 4, 7 Page m d m ( - a) m )! m [9] ( m Here te subscrpt, = ξ, o te dervatve dcates tat ts dervatve s o loger evaluated at te kow pot = a, but s to be evaluated at = ξ, a ukow pot betwee ad a. Tus, te prce we pay or reducg te te seres or te trucato error to a sgle term s tat we lose te certaty about te pot were te dervatve s evaluated. I prcple, ts would allow us to compute a boud o te error by dg te value o ξ, betwee ad a, tat made te error computed by equato [9] a mamum. I practce, we do ot usually kow te eact uctoal orm, (), let aloe ts (m+) t dervatve. I usg Taylor seres to derve te basc te-derece epressos, we start wt uorm oedmesoal grd spacg. Te derece, Δ, betwee ay two grd pots s te same ad s gve te symbol,. Ts uorm grd ca be epressed as ollows. Δ = - = or = + or all =,,N [] Varous cremets at ay pot alog te grd ca be wrtte as ollows: + - = + = - = + = - + = + = [] Usg te cremets deed above ad te otato = () te ollowg Taylor seres ca be wrtte usg epaso about te pot = to epress te values o at some specc grd pots, +, -, + ad -. Te covetoal Taylor seres epresso or () equato [5] ca be adapted or use te dereces by wrtg a epaso equato about a partcular grd pot, =, to determe te value o () at aoter grd pot, +k. From equato [], we see tat +k = + k so tat (+k) = ( + k). Te derece,, te depedet varable,, betwee te evaluato pot, + k, ad te epaso pot,, s equal to k. Usg = a as te epaso pot ad k as allows us to rewrte equato [5] as sow below. d d d ( k) ( ) k ( k) ( k)... []!! Te et step s to use te otato tat ( + k) = +k, ad te ollowg otato or te t dervatve, evaluated at =. d d... d [] Wt tese otatoal cages, te Taylor seres equato [] ca be wrtte as ollows. ( k)! ( k)! k k... [4] Fte-derece epressos or varous dervatves ca be obtaed by wrtg te Taylor seres sow above or deret values o k, combg te results, ad solvg or te dervatve. Te smplest eample o ts s to use oly te seres or k =.

4 Numercal Deretato L. S. Caretto, November 4, 7 Page 4... [5] We ca rearrage ts equato to solve or te rst dervatve, ; recall tat ts s te rst dervatve at te pot =.... O( ) [] Te rst term to te rgt o te equal sg gves us a smple epresso or te rst dervatve; t s smply te derece te ucto at two pots, (+) (), dvded by, wc s te derece betwee tose two pots. Te remag terms te rst orm o te equato are a te seres. Tat te seres gves us a equato or te error tat we would ave we used te smple te derece epresso to evaluate te rst dervatve. Represetg te trucato error as te order o te error As oted above, we ca replace te te seres or te trucato error by te leadg term tat seres. Remember tat we pay a prce or ts replacemet; we o loger kow te pot at wc te leadg term s to be evaluated. Because o ts we ote wrte te trucato error as sow te secod equato. Here we use a captal o ollowed by te grd sze pareteses. I geeral, te grd sze s rased to some power. (Here we ave te rst power o te grd sze, =.) For a trucato error proportoal to te t power o te step sze we would use te otato, O( ). Ts otato tells us ow te trucato error depeds o te step sze. Te order o te error depedece o te step sze s a mportat cocept. I te error s proportoal to, cuttg al would cut te error al. I te error s proportoal to, te cuttg te step sze al would reduce te error by ¼. We te trucato error s wrtte wt ts O( ) otato, we call te order o te error. I two calculatos, wt step szes ad, we epect te ollowg relato betwee te trucato errors, ε ad ε or te calculatos. [7] We use te appromato sg ( ) rater ta te equalty sg ts equato because te error term also cludes a ukow actor o some ger order dervatve, evaluated at some ukow pot te rego. Te appromato sow equato [7] would be a equalty ts oter actor were te same or bot step szes. Aoter mportat dea about te order o te error s tat a t order te-derece epresso wll gve a eact value or te dervatve o a t order polyomal. Because a Taylor seres s a polyomal seres, t ca represet a polyomal eactly a sucet umber o terms are used. Ts s llustrated urter below. Te epresso or te rst dervatve tat we derved equato [] s sad to ave a rst order error. We ca obta a smlar te derece appromato by wrtg te geeral seres equato [4] or k = -. Ts gves te ollowg result.... [8]

5 Numercal metods ME 9, L. S. Caretto, November 4, 7 Page 5 We ca rearrage ts equato to solve or te rst dervatve, ; recall tat ts s te rst dervatve at te pot =.... O( ) [9] Here aga, as equato [], we ave a smple te-derece epresso or te rst dervatve tat as a rst-order error. Te epresso equato [] s called a orward derece. It gves a appromato to te dervatve at pot terms o values at tat pot ad pots orward ( te + drecto) o tat pot. Te epresso equato [9] s called a backwards derece or smlar reasos. Dervatve epressos wt ger order errors A epresso or te rst dervatve tat as a secod-order error ca be oud by subtractg equato [8] rom equato [5]. We ts s doe, terms wt eve powers o cacel gvg te ollowg result [] Solvg ts equato or te rst dervatve gves te ollowg result O( ) [] Te te-derece epresso or te rst dervatve equato [] s called a cetral derece. Te pot at wc te dervatve s evaluated,, s cetral to te two pots (+ ad -) at wc te ucto s evaluated. Te cetral derece epresso provdes a ger order (more accurate) epresso or te rst dervatve as compared to te orward or backward dervatves. Tere s oly a small amout o etra work (a dvso by ) gettg ts more accurate result. Because o ter ger accuracy, cetral dereces are usually preerred te derece epressos. Cetral derece epressos are ot possble at te start o ed o a boudary. It s possble to get ger order te derece epressos or suc pots by usg more comple epressos. For eample, at te start o a rego, =, we ca wrte te Taylor seres equato [4] or te rst two pots rom te boudary, ad, epadg aroud te boudary pot,.... [] () () ()... [] Tese equatos ca be combed to elmate te terms. To start, we multply equato [] by 4 ad subtract t rom equato [].

6 Numercal Deretato L. S. Caretto, November 4, 7 Page 4 () () Ts equato ca be smpled as ollows ()... 4 ( ) ( ) ( ).. ( ) 4 () 4... [4] We ts equato s solved or te rst dervatve at te start o te rego a secod order accurate epresso s obtaed O( ) [5] A smlar equato ca be oud at te ed o te rego, = N, by obtag te Taylor seres epasos about te pot = N, or te values o () at = N- ad = N-. Ts dervato parallels te dervato used to obta equato [5]. Te result s sow below. N N 4 N N N 4 N N N... O( ) [] Equatos [5] ad [] gve secod-order accurate epressos or te rst dervatve. Te epresso equato [5] s a orward derece; te oe equato [] s a backwards derece. Te evaluato o tree epressos or te rst dervatve s sow Table. Tese are () te secod-order, cetral-derece epresso rom equato [], () te rst-order, orwardderece rom equato [], ad () te secod-order, orward-derece rom equato [5]. Te rst dervatve s evaluated or () = e. For ts ucto, te rst dervatve, d/ = e. Sce we kow te eact value o te rst dervatve, we ca calculate te error te te derece results. I Table, te results are computed or tree deret step szes: =.4, =. ad =.. Te table also sows te rato o te error as te step sze s caged. Te et-to-last colum sows te rato o te error or =.4 to te error or =.. Te al colum sows te rato o te error or =. to te error or =.. Table Tests o Fte-Derece Formulae to Compute te Frst Dervatve () = ep() () =.4 =. =. Error Ratos Eact () () Error () Error () Error (=.4)/ (=.)/ (=.) (=.) Results usg secod-order cetral dereces

7 Numercal metods ME 9, L. S. Caretto, November 4, 7 Page Results usg rst-order orward dereces Results usg secod-order orward dereces For te secod-order ormulae, te error ratos te last two colums o Table - are about 4, sowg tat te secod-order error creases by a actor o 4 as te step sze s doubled. For te rst order epresso, tese ratos are about. Ts sows tat te error creases by te same actor as te step sze or te rst order epressos. Te epected values o te error ratos are oly obtaed te lmt o very small step szes. We see tat te values te last colum o ts table (were te actual values o are smaller ta tey are te et-to-last colum) are closer to te deal error rato. Te tecques tat ave bee used ere to derve orward, backward, ad cetral dervatve epressos wt rst- ad secod-order error ca be epaded to cosder ger order dervatves ad ger order errors. Lsts o varous te-derece ormulas ca be oud umercal aalyss tets. Roudo error Trucato errors are ot te oly kd o error tat we ecouter te derece epressos. As te step szes get very small te terms te umerator o te te derece epressos become very close to eac oter. We lose sgcat gure we we do te subtracto. For eample, cosder te prevous problem o dg te umercal dervatve o () = e. Pck = as te pot were we wat to evaluate te dervatve. Wt =. we ave te ollowg data or calculatg te dervatve by te cetral-derece ormula equato []. ( ) ( ) ( ) (.)

8 Numercal Deretato L. S. Caretto, November 4, 7 Page 8 Sce te rst dervatve o e s e, te correct value o te dervatve at = s e =.788; so te error ts value o te rst dervatve s For =., te umercal value o te rst dervatve s oud as ollows. ( ) ( ) ( ) (.) Here, te error s Ts looks lke our secod-order error. We cut te step sze by a actor o, ad our error decreased by a actor o,,, as we would epect or a secod order error. We are startg to see potetal problems te subtracto o te two umbers te umerator. Because te rst our dgts are te same, we ave lost our sgcat gures dog ts subtracto. Wat appes we decrease by a actor o, aga? Here s te result or = -7. ( ) ( ) ( ) (.) Our trucato aalyss leads us to epect aoter actor o oe mllo te error reducto as we decrease te step sze by,. Ts sould gve us a error o However, we d tat te actual error s We see te reaso or ts te umerator o te te derece epresso. As te derece betwee (+) ad (-) srks, we are takg te derece o early equal umbers. Ts kd o error s called roudo error because t results rom te ecessty o a computer to roud o real umbers to some te sze. (Tese calculatos were doe wt a ecel spreadseet wc as about 5 sgcat gures. Fgure - sows te eect o step sze o error or a large rage o step szes. For te large step szes to te rgt o Fgure -, te plot o error versus step sze appears to be a stragt le o ts log-log plot. Ts s cosstet wt equato [7]. I we take logs o bot sdes o tat equato ad solve or, we get te ollowg result. log log log( ) log( ) log( ) log( ) [7] Equato [7] sows tat te order o te error s just te slope o a log(error) versus log() plot. I we take te slope o te stragt-le rego o te rgt o Fgure -, we get a value o appromately two or te slope, cormg te secod order error or te cetral derece epresso tat we are usg ere. However, we also see tat as te step sze reaces about -5, te error starts to level o ad te crease. At very small step szes te umerator o te te-derece epresso becomes zero o a computer ad te error s just te eact value o te dervatve.

9 Error Numercal metods ME 9, L. S. Caretto, November 4, 7 Page 9 Fgure. Eect o Step Sze o Error.E+.E+.E-.E-.E-.E-4.E-5.E-.E-7.E-8.E-9.E-.E-.E-8.E-.E-4.E-.E-.E-8.E-.E-4.E-.E+ Step Sze Fal Observatos o Fte-Derece Epressos rom Taylor Seres Te otes above ave ocused o te geeral approac to te dervato o te-derece epressos usg Taylor seres. Suc dervatos lead to a epresso or te trucato error. Tat error s due to omttg te ger order terms te Taylor seres. We ave caracterzed tat trucato error by te power or order o te step sze te rst term tat s trucated. Te trucato error s a mportat actor te accuracy o te results. However, we also saw tat very small step szes lead to roudo errors tat ca be eve larger ta trucato errors. Te use o Taylor seres to derve te derece epressos ca be eteded to ger order dervatves ad epressos tat are more comple, but ave a ger order trucato error. Oe epresso tat wll be mportat or subsequet course work s te cetral-derece epresso or te secod dervatve. Ts ca be oud by addg equatos [5] ad [8] [8] We ca solve ts equato to obta a te-derece epresso or te secod dervatve.... O( ) [9] Altoug we ave bee dervg epressos ere or ordary dervatves, we wll apply te same epressos to partal dervatves. For eample, te epresso equato [9] or te secod dervatve could represet d / or /. Te Taylor seres we ave bee usg ere ave cosdered as te depedet varable. However, tese epressos ca be appled to ay coordate drecto or tme.

10 Numercal Deretato L. S. Caretto, November 4, 7 Page Altoug we ave used Taylor seres to derve te te-derece epressos, tey could also be derved rom terpolatg polyomals. I ts approac, oe uses umercal metods or developg polyomal appromatos to uctos, te takes te dervatves o te appromatg polyomals to appromate te dervatves o te uctos. A te-derece epresso wt a t order error tat gves te value o ay quatty sould be able to represet te gve quatty eactly or a t order polyomal. * Te epressos tat we ave cosdered are or costat step sze. It s also possble to wrte te Taylor seres or varable step sze ad derve te derece epressos wt varable step szes. Suc epressos ave lower-order trucato error terms or te same amout o work computg te te derece epresso. I solvg deretal equatos by te-derece metods, te deretal equato s replaced by ts te derece equvalet at eac ode. Ts gves a set o smultaeous algebrac equatos tat are solved or te values o te depedet varable at eac grd pot. Fte derece epressos ca be derved rom Taylor seres. Ts approac leads to a epresso or te trucato error tat provdes us wt kowledge o ow ts error depeds o te step sze. Ts s called te order o te error. I te-derece approaces, we eed to be cocered about bot trucato errors ad roudo errors. Roudo errors were more o a cocer earler computer applcatos were lmtatos o avalable computer tme ad memory restrcted te sze o real words, or may practcal applcatos, to bts. Ts correspods to te sgle precso type Fortra or te Sgle type VBA. Wt moder computers, t s possble to do route calculatos usg 4-bt (or ger precso) real words. Ts correspods to te double precso type Fortra * or te double type VBA. Te -bt real word allows about 7 sgcat gures; te 4-bt real word allows almost sgcat gures. * I a secod order polyomal s wrtte as y = a + b + c ; ts rst dervatve at a pot = s gve by te ollowg equato: [dy/]= = b + c. I we use te secod-order cetral-derece epresso equato [] to evaluate te rst dervatve, we get te same result as sow below: dy y( ) y( b c( ) a b( ) c( ) c( ) [ a b( ) b 4c b c ) c( ) * Also kow as real(8) or real(kind=8) Fortra 9 ad later versos; sgle precso s typed as real, real(4) or real(kind=4) tese versos o Fortra. ]

Numerical Differentiation

Numerical Differentiation College o Egeerg ad Computer Scece Mecacal Egeerg Departmet ME 9 Numercal Aalyss Marc 4, 4 Istructor: Larry Caretto Numercal Deretato Itroducto Tese otes provde a basc troducto to umercal deretato usg

More information

Introduction to Numerical Differentiation and Interpolation March 10, !=1 1!=1 2!=2 3!=6 4!=24 5!= 120

Introduction to Numerical Differentiation and Interpolation March 10, !=1 1!=1 2!=2 3!=6 4!=24 5!= 120 Itroducto to Numercal Deretato ad Iterpolato Marc, Itroducto to Numercal Deretato ad Iterpolato Larr Caretto Mecacal Egeerg 9 Numercal Aalss o Egeerg stems Marc, Itroducto Iterpolato s te use o a dscrete

More information

4 Round-Off and Truncation Errors

4 Round-Off and Truncation Errors HK Km Slgtly moded 3//9, /8/6 Frstly wrtte at Marc 5 4 Roud-O ad Trucato Errors Errors Roud-o Errors Trucato Errors Total Numercal Errors Bluders, Model Errors, ad Data Ucertaty Recallg, dv dt Δv v t Δt

More information

Basic Concepts in Numerical Analysis November 6, 2017

Basic Concepts in Numerical Analysis November 6, 2017 Basc Cocepts Nuercal Aalyss Noveber 6, 7 Basc Cocepts Nuercal Aalyss Larry Caretto Mecacal Egeerg 5AB Sear Egeerg Aalyss Noveber 6, 7 Outle Revew last class Mdter Exa Noveber 5 covers ateral o deretal

More information

Outline. Numerical Heat Transfer. Review All Black Surfaces. Review View Factor, F i j or F ij. Review Gray Diffuse Opaque II

Outline. Numerical Heat Transfer. Review All Black Surfaces. Review View Factor, F i j or F ij. Review Gray Diffuse Opaque II umercao Heat raser ay 9 ad, 7 umercal Heat raser arry Caretto ecacal geerg 75 Heat raser ay 9 ad, 7 Outle Wat s umercal aalyss Cosderatos o coducto, covecto ad radato evew umercal aalyss bascs ervatve

More information

1. A real number x is represented approximately by , and we are told that the relative error is 0.1 %. What is x? Note: There are two answers.

1. A real number x is represented approximately by , and we are told that the relative error is 0.1 %. What is x? Note: There are two answers. PROBLEMS A real umber s represeted appromately by 63, ad we are told that the relatve error s % What s? Note: There are two aswers Ht : Recall that % relatve error s What s the relatve error volved roudg

More information

CS475 Parallel Programming

CS475 Parallel Programming CS475 Parallel Programmg Deretato ad Itegrato Wm Bohm Colorado State Uversty Ecept as otherwse oted, the cotet o ths presetato s lcesed uder the Creatve Commos Attrbuto.5 lcese. Pheomea Physcs: heat, low,

More information

General Method for Calculating Chemical Equilibrium Composition

General Method for Calculating Chemical Equilibrium Composition AE 6766/Setzma Sprg 004 Geeral Metod for Calculatg Cemcal Equlbrum Composto For gve tal codtos (e.g., for gve reactats, coose te speces to be cluded te products. As a example, for combusto of ydroge wt

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

Chapter 5. Curve fitting

Chapter 5. Curve fitting Chapter 5 Curve ttg Assgmet please use ecell Gve the data elow use least squares regresso to t a a straght le a power equato c a saturato-growthrate equato ad d a paraola. Fd the r value ad justy whch

More information

Idea is to sample from a different distribution that picks points in important regions of the sample space. Want ( ) ( ) ( ) E f X = f x g x dx

Idea is to sample from a different distribution that picks points in important regions of the sample space. Want ( ) ( ) ( ) E f X = f x g x dx Importace Samplg Used for a umber of purposes: Varace reducto Allows for dffcult dstrbutos to be sampled from. Sestvty aalyss Reusg samples to reduce computatoal burde. Idea s to sample from a dfferet

More information

CHAPTER 4 RADICAL EXPRESSIONS

CHAPTER 4 RADICAL EXPRESSIONS 6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube

More information

Basics of Information Theory: Markku Juntti. Basic concepts and tools 1 Introduction 2 Entropy, relative entropy and mutual information

Basics of Information Theory: Markku Juntti. Basic concepts and tools 1 Introduction 2 Entropy, relative entropy and mutual information : Markku Jutt Overvew Te basc cocepts o ormato teory lke etropy mutual ormato ad EP are eeralzed or cotuous-valued radom varables by troduc deretal etropy ource Te materal s maly based o Capter 9 o te

More information

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy Bouds o the expected etropy ad KL-dvergece of sampled multomal dstrbutos Brado C. Roy bcroy@meda.mt.edu Orgal: May 18, 2011 Revsed: Jue 6, 2011 Abstract Iformato theoretc quattes calculated from a sampled

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

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

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation PGE 30: Formulato ad Soluto Geosystems Egeerg Dr. Balhoff Iterpolato Numercal Methods wth MATLAB, Recktewald, Chapter 0 ad Numercal Methods for Egeers, Chapra ad Caale, 5 th Ed., Part Fve, Chapter 8 ad

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

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

. 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

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

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

The equation is sometimes presented in form Y = a + b x. This is reasonable, but it s not the notation we use.

The equation is sometimes presented in form Y = a + b x. This is reasonable, but it s not the notation we use. INTRODUCTORY NOTE ON LINEAR REGREION We have data of the form (x y ) (x y ) (x y ) These wll most ofte be preseted to us as two colum of a spreadsheet As the topc develops we wll see both upper case ad

More information

ENGI 4430 Numerical Integration Page 5-01

ENGI 4430 Numerical Integration Page 5-01 ENGI 443 Numercal Itegrato Page 5-5. Numercal Itegrato I some o our prevous work, (most otaly the evaluato o arc legth), t has ee dcult or mpossle to d the dete tegral. Varous symolc algera ad calculus

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

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

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

Assignment 7/MATH 247/Winter, 2010 Due: Friday, March 19. Powers of a square matrix

Assignment 7/MATH 247/Winter, 2010 Due: Friday, March 19. Powers of a square matrix Assgmet 7/MATH 47/Wter, 00 Due: Frday, March 9 Powers o a square matrx Gve a square matrx A, ts powers A or large, or eve arbtrary, teger expoets ca be calculated by dagoalzg A -- that s possble (!) Namely,

More information

Chapter 3. Differentiation 3.3 Differentiation Rules

Chapter 3. Differentiation 3.3 Differentiation Rules 3.3 Dfferetato Rules 1 Capter 3. Dfferetato 3.3 Dfferetato Rules Dervatve of a Costat Fucto. If f as te costat value f(x) = c, te f x = [c] = 0. x Proof. From te efto: f (x) f(x + ) f(x) o c c 0 = 0. QED

More information

FREQUENCY ANALYSIS OF A DOUBLE-WALLED NANOTUBES SYSTEM

FREQUENCY ANALYSIS OF A DOUBLE-WALLED NANOTUBES SYSTEM Joural of Appled Matematcs ad Computatoal Mecacs 04, 3(4), 7-34 FREQUENCY ANALYSIS OF A DOUBLE-WALLED NANOTUBES SYSTEM Ata Cekot, Stasław Kukla Isttute of Matematcs, Czestocowa Uversty of Tecology Częstocowa,

More information

ESS Line Fitting

ESS Line Fitting ESS 5 014 17. Le Fttg A very commo problem data aalyss s lookg for relatoshpetwee dfferet parameters ad fttg les or surfaces to data. The smplest example s fttg a straght le ad we wll dscuss that here

More information

A stopping criterion for Richardson s extrapolation scheme. under finite digit arithmetic.

A stopping criterion for Richardson s extrapolation scheme. under finite digit arithmetic. A stoppg crtero for cardso s extrapoato sceme uder fte dgt artmetc MAKOO MUOFUSHI ad HIEKO NAGASAKA epartmet of Lbera Arts ad Sceces Poytecc Uversty 4-1-1 Hasmotoda,Sagamara,Kaagawa 229-1196 JAPAN Abstract:

More information

Applied Fitting Theory VII. Building Virtual Particles

Applied Fitting Theory VII. Building Virtual Particles Appled Fttg heory II Paul Avery CBX 98 38 Jue 8, 998 Apr. 7, 999 (rev.) Buldg rtual Partcles I Statemet of the problem I may physcs aalyses we ecouter the problem of mergg a set of partcles to a sgle partcle

More information

Lecture 5: Interpolation. Polynomial interpolation Rational approximation

Lecture 5: Interpolation. Polynomial interpolation Rational approximation Lecture 5: Iterpolato olyomal terpolato Ratoal appromato Coeffcets of the polyomal Iterpolato: Sometme we kow the values of a fucto f for a fte set of pots. Yet we wat to evaluate f for other values perhaps

More information

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions.

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions. Ordary Least Squares egresso. Smple egresso. Algebra ad Assumptos. I ths part of the course we are gog to study a techque for aalysg the lear relatoshp betwee two varables Y ad X. We have pars of observatos

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 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

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

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

Lecture Notes Forecasting the process of estimating or predicting unknown situations

Lecture Notes Forecasting the process of estimating or predicting unknown situations Lecture Notes. Ecoomc Forecastg. Forecastg the process of estmatg or predctg ukow stuatos Eample usuall ecoomsts predct future ecoomc varables Forecastg apples to a varet of data () tme seres data predctg

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

Chapter 3. Differentiation 3.2 Differentiation Rules for Polynomials, Exponentials, Products and Quotients

Chapter 3. Differentiation 3.2 Differentiation Rules for Polynomials, Exponentials, Products and Quotients 3.2 Dfferetato Rules 1 Capter 3. Dfferetato 3.2 Dfferetato Rules for Polyomals, Expoetals, Proucts a Quotets Rule 1. Dervatve of a Costat Fucto. If f as te costat value f(x) = c, te f x = [c] = 0. x Proof.

More information

8.1 Hashing Algorithms

8.1 Hashing Algorithms CS787: Advaced Algorthms Scrbe: Mayak Maheshwar, Chrs Hrchs Lecturer: Shuch Chawla Topc: Hashg ad NP-Completeess Date: September 21 2007 Prevously we looked at applcatos of radomzed algorthms, ad bega

More information

Outline. Finite Difference Grids. Numerical Analysis. Finite Difference Grids II. Finite Difference Grids III

Outline. Finite Difference Grids. Numerical Analysis. Finite Difference Grids II. Finite Difference Grids III Itrodcto to Nmercl Alyss Mrc, 9 Nmercl Metods or PDEs Lrry Cretto Meccl Egeerg 5B Semr Egeerg Alyss Mrc, 9 Otle Revew mdterm soltos Revew bsc mterl o mercl clcls Expressos or dervtves, error d error order

More information

Data Processing Techniques

Data Processing Techniques Uverstas Gadjah Mada Departmet o Cvl ad Evrometal Egeerg Master o Egeerg Natural Dsaster Maagemet Data Processg Techques Curve Fttg: Regresso ad Iterpolato 3Oct7 Curve Fttg Reerece Chapra, S.C., Caale

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

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

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

Outline. Remaining Course Schedule. Review Systems of ODEs. Example. Example Continued. Other Algorithms for Ordinary Differential Equations

Outline. Remaining Course Schedule. Review Systems of ODEs. Example. Example Continued. Other Algorithms for Ordinary Differential Equations ter Nuercal DE Algorts Aprl 8 0 ter Algorts or rdar Deretal Equatos Larr aretto Mecacal Egeerg 09 Nuercal Aalss o Egeerg Sstes Aprl 8 0 utle Scedule Revew sstes o DEs Sprg-ass-daper proble wt two asses

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

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

Numerical Simulations of the Complex Modied Korteweg-de Vries Equation. Thiab R. Taha. The University of Georgia. Abstract

Numerical Simulations of the Complex Modied Korteweg-de Vries Equation. Thiab R. Taha. The University of Georgia. Abstract Numercal Smulatos of the Complex Moded Korteweg-de Vres Equato Thab R. Taha Computer Scece Departmet The Uversty of Georga Athes, GA 002 USA Tel 0-542-2911 e-mal thab@cs.uga.edu Abstract I ths paper mplemetatos

More information

Differential Entropy 吳家麟教授

Differential Entropy 吳家麟教授 Deretl Etropy 吳家麟教授 Deto Let be rdom vrble wt cumultve dstrbuto ucto I F s cotuous te r.v. s sd to be cotuous. Let = F we te dervtve s deed. I te s clled te pd or. Te set were > 0 s clled te support set

More information

STA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1

STA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1 STA 08 Appled Lear Models: Regresso Aalyss Sprg 0 Soluto for Homework #. Let Y the dollar cost per year, X the umber of vsts per year. The the mathematcal relato betwee X ad Y s: Y 300 + X. Ths s a fuctoal

More information

Sampling Theory MODULE V LECTURE - 14 RATIO AND PRODUCT METHODS OF ESTIMATION

Sampling Theory MODULE V LECTURE - 14 RATIO AND PRODUCT METHODS OF ESTIMATION Samplg Theor MODULE V LECTUE - 4 ATIO AND PODUCT METHODS OF ESTIMATION D. SHALABH DEPATMENT OF MATHEMATICS AND STATISTICS INDIAN INSTITUTE OF TECHNOLOG KANPU A mportat objectve a statstcal estmato procedure

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

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

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

( ) 2 2. Multi-Layer Refraction Problem Rafael Espericueta, Bakersfield College, November, 2006

( ) 2 2. Multi-Layer Refraction Problem Rafael Espericueta, Bakersfield College, November, 2006 Mult-Layer Refracto Problem Rafael Espercueta, Bakersfeld College, November, 006 Lght travels at dfferet speeds through dfferet meda, but refracts at layer boudares order to traverse the least-tme path.

More information

Evaluating Polynomials

Evaluating Polynomials Uverst of Nebraska - Lcol DgtalCommos@Uverst of Nebraska - Lcol MAT Exam Expostor Papers Math the Mddle Isttute Partershp 7-7 Evaluatg Polomals Thomas J. Harrgto Uverst of Nebraska-Lcol Follow ths ad addtoal

More information

X X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then

X X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then Secto 5 Vectors of Radom Varables Whe workg wth several radom varables,,..., to arrage them vector form x, t s ofte coveet We ca the make use of matrx algebra to help us orgaze ad mapulate large umbers

More information

EVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM

EVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM EVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM Jose Javer Garca Moreta Ph. D research studet at the UPV/EHU (Uversty of Basque coutry) Departmet of Theoretcal

More information

x y exp λ'. x exp λ 2. x exp 1.

x y exp λ'. x exp λ 2. x exp 1. egecosmcd Egevalue-egevector of the secod dervatve operator d /d hs leads to Fourer seres (se, cose, Legedre, Bessel, Chebyshev, etc hs s a eample of a systematc way of geeratg a set of mutually orthogoal

More information

Midterm Exam 1, section 1 (Solution) Thursday, February hour, 15 minutes

Midterm Exam 1, section 1 (Solution) Thursday, February hour, 15 minutes coometrcs, CON Sa Fracsco State Uversty Mchael Bar Sprg 5 Mdterm am, secto Soluto Thursday, February 6 hour, 5 mutes Name: Istructos. Ths s closed book, closed otes eam.. No calculators of ay kd are allowed..

More information

Asymptotic Formulas Composite Numbers II

Asymptotic Formulas Composite Numbers II Iteratoal Matematcal Forum, Vol. 8, 203, o. 34, 65-662 HIKARI Ltd, www.m-kar.com ttp://d.do.org/0.2988/mf.203.3854 Asymptotc Formulas Composte Numbers II Rafael Jakmczuk Dvsó Matemátca, Uversdad Nacoal

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

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

, so the next 5 terms are 31, 4, 37, 12, and The constant is π. Ptolemy used a 360-gon to approximate π as

, so the next 5 terms are 31, 4, 37, 12, and The constant is π. Ptolemy used a 360-gon to approximate π as . The umber chose was, guessg as e tes dgt ad as e oes dgt each ears hal a pot.. The best way to do s s rough brute orce o a computer. The rst two are 6867 ad 68. The rd s + 7 + 6 867767.. The eve term

More information

å 1 13 Practice Final Examination Solutions - = CS109 Dec 5, 2018

å 1 13 Practice Final Examination Solutions - = CS109 Dec 5, 2018 Chrs Pech Fal Practce CS09 Dec 5, 08 Practce Fal Examato Solutos. Aswer: 4/5 8/7. There are multle ways to obta ths aswer; here are two: The frst commo method s to sum over all ossbltes for the rak of

More information

1 Review and Overview

1 Review and Overview CS9T/STATS3: Statstcal Learg Teory Lecturer: Tegyu Ma Lecture #7 Scrbe: Bra Zag October 5, 08 Revew ad Overvew We wll frst gve a bref revew of wat as bee covered so far I te frst few lectures, we stated

More information

Chapter 13, Part A Analysis of Variance and Experimental Design. Introduction to Analysis of Variance. Introduction to Analysis of Variance

Chapter 13, Part A Analysis of Variance and Experimental Design. Introduction to Analysis of Variance. Introduction to Analysis of Variance Chapter, Part A Aalyss of Varace ad Epermetal Desg Itroducto to Aalyss of Varace Aalyss of Varace: Testg for the Equalty of Populato Meas Multple Comparso Procedures Itroducto to Aalyss of Varace Aalyss

More information

Chapter 3. Linear Equations and Matrices

Chapter 3. Linear Equations and Matrices Vector Spaces Physcs 8/6/05 hapter Lear Equatos ad Matrces wde varety of physcal problems volve solvg systems of smultaeous lear equatos These systems of lear equatos ca be ecoomcally descrbed ad effcetly

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

DKA method for single variable holomorphic functions

DKA method for single variable holomorphic functions DKA method for sgle varable holomorphc fuctos TOSHIAKI ITOH Itegrated Arts ad Natural Sceces The Uversty of Toushma -, Mamhosama, Toushma, 770-8502 JAPAN Abstract: - Durad-Kerer-Aberth (DKA method for

More information

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur

More information

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations Dervato of -Pot Block Method Formula for Solvg Frst Order Stff Ordary Dfferetal Equatos Kharul Hamd Kharul Auar, Kharl Iskadar Othma, Zara Bb Ibrahm Abstract Dervato of pot block method formula wth costat

More information

Theoretical Physics. Course codes: Phys2325 Course Homepage:

Theoretical Physics. Course codes: Phys2325 Course Homepage: Theoretcal Phscs Course codes: Phs35 Course Homepage: http://bohr.phscs.hku.hk/~phs35/ Lecturer: Z.D.Wag, Oce: Rm58, Phscs Buldg Tel: 859 96 E-mal: wag@hkucc.hku.hk Studet Cosultato hours: :3-4:3pm Tuesda

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

5 Short Proofs of Simplified Stirling s Approximation

5 Short Proofs of Simplified Stirling s Approximation 5 Short Proofs of Smplfed Strlg s Approxmato Ofr Gorodetsky, drtymaths.wordpress.com Jue, 20 0 Itroducto Strlg s approxmato s the followg (somewhat surprsg) approxmato of the factoral,, usg elemetary fuctos:

More information

Evaluation of uncertainty in measurements

Evaluation of uncertainty in measurements Evaluato of ucertaty measuremets Laboratory of Physcs I Faculty of Physcs Warsaw Uversty of Techology Warszawa, 05 Itroducto The am of the measuremet s to determe the measured value. Thus, the measuremet

More information

Overview of the weighting constants and the points where we evaluate the function for The Gaussian quadrature Project two

Overview of the weighting constants and the points where we evaluate the function for The Gaussian quadrature Project two Overvew of the weghtg costats ad the pots where we evaluate the fucto for The Gaussa quadrature Project two By Ashraf Marzouk ChE 505 Fall 005 Departmet of Mechacal Egeerg Uversty of Teessee Koxvlle, TN

More information

Introduction to Computer Design. Standard Forms for Boolean Functions. Sums and Products. Standard Forms for Boolean Functions (cont ) CMPT-150

Introduction to Computer Design. Standard Forms for Boolean Functions. Sums and Products. Standard Forms for Boolean Functions (cont ) CMPT-150 CMPT- Itroducto to Computer Desg SFU Harbour Cetre Sprg 7 Lecture : Ja. 6 7 Stadard orms or boolea uctos Sum o Products Product o Sums Stadard Forms or Boolea Fuctos (cot ) It s useul to spec Boolea uctos

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

Evolution Operators and Boundary Conditions for Propagation and Reflection Methods

Evolution Operators and Boundary Conditions for Propagation and Reflection Methods voluto Operators ad for Propagato ad Reflecto Methods Davd Yevck Departmet of Physcs Uversty of Waterloo Physcs 5/3/9 Collaborators Frak Schmdt ZIB Tlma Frese ZIB Uversty of Waterloo] atem l-refae Nortel

More information

The conformations of linear polymers

The conformations of linear polymers The coformatos of lear polymers Marc R. Roussel Departmet of Chemstry ad Bochemstry Uversty of Lethbrdge February 19, 9 Polymer scece s a rch source of problems appled statstcs ad statstcal mechacs. I

More information

1 0, x? x x. 1 Root finding. 1.1 Introduction. Solve[x^2-1 0,x] {{x -1},{x 1}} Plot[x^2-1,{x,-2,2}] 3

1 0, x? x x. 1 Root finding. 1.1 Introduction. Solve[x^2-1 0,x] {{x -1},{x 1}} Plot[x^2-1,{x,-2,2}] 3 Adrew Powuk - http://www.powuk.com- Math 49 (Numercal Aalyss) Root fdg. Itroducto f ( ),?,? Solve[^-,] {{-},{}} Plot[^-,{,-,}] Cubc equato https://e.wkpeda.org/wk/cubc_fucto Quartc equato https://e.wkpeda.org/wk/quartc_fucto

More information

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg

More information

UNIT 1 MEASURES OF CENTRAL TENDENCY

UNIT 1 MEASURES OF CENTRAL TENDENCY UIT MEASURES OF CETRAL TEDECY Measures o Cetral Tedecy Structure Itroducto Objectves Measures o Cetral Tedecy 3 Armetc Mea 4 Weghted Mea 5 Meda 6 Mode 7 Geometrc Mea 8 Harmoc Mea 9 Partto Values Quartles

More information

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971)) art 4b Asymptotc Results for MRR usg RESS Recall that the RESS statstc s a specal type of cross valdato procedure (see Alle (97)) partcular to the regresso problem ad volves fdg Y $,, the estmate at the

More information

Homework Assignment Number Eight Solutions

Homework Assignment Number Eight Solutions D Keer MSE 0 Dept o Materals Scece & Egeerg Uversty o Teessee Kovlle Homework Assgmet Number Eght Solutos Problem Fd the soluto to the ollowg system o olear algebrac equatos ear () Soluto: s Sce ths s

More information

Chapter -2 Simple Random Sampling

Chapter -2 Simple Random Sampling Chapter - Smple Radom Samplg Smple radom samplg (SRS) s a method of selecto of a sample comprsg of umber of samplg uts out of the populato havg umber of samplg uts such that every samplg ut has a equal

More information

n -dimensional vectors follow naturally from the one

n -dimensional vectors follow naturally from the one B. Vectors ad sets B. Vectors Ecoomsts study ecoomc pheomea by buldg hghly stylzed models. Uderstadg ad makg use of almost all such models requres a hgh comfort level wth some key mathematcal sklls. I

More information

Taylor s Series and Interpolation. Interpolation & Curve-fitting. CIS Interpolation. Basic Scenario. Taylor Series interpolates at a specific

Taylor s Series and Interpolation. Interpolation & Curve-fitting. CIS Interpolation. Basic Scenario. Taylor Series interpolates at a specific CIS 54 - Iterpolato Roger Crawfs Basc Scearo We are able to prod some fucto, but do ot kow what t really s. Ths gves us a lst of data pots: [x,f ] f(x) f f + x x + August 2, 25 OSU/CIS 54 3 Taylor s Seres

More information

Nonlinear Piecewise-Defined Difference Equations with Reciprocal Quadratic Terms

Nonlinear Piecewise-Defined Difference Equations with Reciprocal Quadratic Terms Joural of Matematcs ad Statstcs Orgal Researc Paper Nolear Pecewse-Defed Dfferece Equatos wt Recprocal Quadratc Terms Ramada Sabra ad Saleem Safq Al-Asab Departmet of Matematcs, Faculty of Scece, Jaza

More information

Outline. Point Pattern Analysis Part I. Revisit IRP/CSR

Outline. Point Pattern Analysis Part I. Revisit IRP/CSR Pot Patter Aalyss Part I Outle Revst IRP/CSR, frst- ad secod order effects What s pot patter aalyss (PPA)? Desty-based pot patter measures Dstace-based pot patter measures Revst IRP/CSR Equal probablty:

More information

Assignment #7 - Solutions

Assignment #7 - Solutions hem 453/544 Fall 003 /05/03 Assgmet #7 - olutos. M& #0. 0.4: 0.: Euler s theorem says that... s homogeeous the....... rove Euler s theorem by deretatg the equato roblem 0- wth respect to ad the settg.

More information

Initial-Value Problems for ODEs. numerical errors (round-off and truncation errors) Consider a perturbed system: dz dt

Initial-Value Problems for ODEs. numerical errors (round-off and truncation errors) Consider a perturbed system: dz dt Ital-Value Problems or ODEs d GIVEN: t t,, a FIND: t or atb umercal errors (roud-o ad trucato errors) Cosder a perturbed sstem: dz t, z t, at b z a a Does z(t) (t)? () (uqueess) a uque soluto (t) exsts

More information

Lecture Note to Rice Chapter 8

Lecture Note to Rice Chapter 8 ECON 430 HG revsed Nov 06 Lecture Note to Rce Chapter 8 Radom matrces Let Y, =,,, m, =,,, be radom varables (r.v. s). The matrx Y Y Y Y Y Y Y Y Y Y = m m m s called a radom matrx ( wth a ot m-dmesoal dstrbuto,

More information

Lecture 9: Tolerant Testing

Lecture 9: Tolerant Testing Lecture 9: Tolerat Testg Dael Kae Scrbe: Sakeerth Rao Aprl 4, 07 Abstract I ths lecture we prove a quas lear lower boud o the umber of samples eeded to do tolerat testg for L dstace. Tolerat Testg We have

More information

2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen.

2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen. .5 x 54.5 a. x 7. 786 7 b. The raked observatos are: 7.4, 7.5, 7.7, 7.8, 7.9, 8.0, 8.. Sce the sample sze 7 s odd, the meda s the (+)/ 4 th raked observato, or meda 7.8 c. The cosumer would more lkely

More information