Cooper and McGillem Chapter 4: Moments Linear Regression

Size: px
Start display at page:

Download "Cooper and McGillem Chapter 4: Moments Linear Regression"

Transcription

1 Cooper d McGllem Chpter 4: Momets Ler Regresso Chpter 4: lemets of Sttstcs 4-6 Curve Fttg d Ler Regresso 4-7 Correlto Betwee Two Sets of Dt Cocepts How close re the smple vlues to the uderlg pdf vlues? Prctcl curve fttg, usg NTC resstor to mesure temperture. Sttstcs Defto: The scece of ssemlg, clssfg, tultg, d lzg dt or fcts: Descrptve sttstcs the collectg, groupg d presetg dt w tht c e esl uderstood or ssmlted. Iductve sttstcs or sttstcl ferece use dt to drw coclusos out or estmte prmeters of, the evromet from whch the dt cme from. Notes d fgures re sed o or tke from mterls the course tetook: Prolstc Methods of Sgl d Sstem Alss (3rd ed.) George R. Cooper d Clre D. McGllem; Oford Press, 999. ISBN: B.J. Bzu, Fll 06 of 7 C 3800

2 4-6 Curve Fttg d Ler Regresso Fttg les/curves to sctter plots. Dt provded s (,) prs. Is there fucto tht goes through ll the pots? es If ou wt to use poloml of degree - for prs! But we usull wt smple curves to represet the dt, lke les or prols, etc. where or c To ft the curve we wt to mmze the followg fucto (the squred error): For ler regresso ( le), we hve err To mmze for the vlues d, tke the dervtves d set them equl to zero. The solve for d : Solvg results d err d d err d 0 0 d Wht hppes whe we tke epected vlues? Notce sttstcl terms? Notes d fgures re sed o or tke from mterls the course tetook: Prolstc Methods of Sgl d Sstem Alss (3rd ed.) George R. Cooper d Clre D. McGllem; Oford Press, 999. ISBN: B.J. Bzu, Fll 06 of 7 C 3800

3 Notes d fgures re sed o or tke from mterls the course tetook: Prolstc Methods of Sgl d Sstem Alss (3rd ed.) George R. Cooper d Clre D. McGllem; Oford Press, 999. ISBN: B.J. Bzu, Fll 06 3 of 7 C 3800 Proof: 0 d err d Workg o 0 d err d Susttutg for the computto for Isoltg Now tht s determed sed o the vlues, retur to Susttutg for the computto for to

4 Notes d fgures re sed o or tke from mterls the course tetook: Prolstc Methods of Sgl d Sstem Alss (3rd ed.) George R. Cooper d Clre D. McGllem; Oford Press, 999. ISBN: B.J. Bzu, Fll 06 4 of 7 C 3800 Therefore ecomes Alterte formulto usg the computed smple mes of d Notce covrce d vrce terms s well.

5 Ler regresso emple p. 80. Fgure 4-5. %% % Fgure 4_5 % cler; close ll; =(0:0.5:0)'; % Ler Curve vlues =*c+ =; =4; ref = +*; % Rdom ose dded to the le dt = ref + 5*rd(sze()); fgure plot(,dt,'',,ref) leged('dt','ref Le') me=me(); me=me(dt); mesq = me(.^); mesq = me(dt.^); mecorr = me(.*dt); Notes d fgures re sed o or tke from mterls the course tetook: Prolstc Methods of Sgl d Sstem Alss (3rd ed.) George R. Cooper d Clre D. McGllem; Oford Press, 999. ISBN: B.J. Bzu, Fll 06 5 of 7 C 3800

6 est_equ = (me*mesq-me*mecorr)/(mesq-me^); est_equ = (mecorr-me*me)/(mesq-me^); est_equ = est_equ + est_equ*; p=polft(,dt,); est = p(); est = p(); est = polvl(p,); fgure plot(,dt,'o',,ref,'k',,est,'r',,est_equ,'m'); leged('dt','ref Le','Polft Le','qu Le') fprtf('computto error\') m(s(est_equ-est)) r = mecorr/sqrt(mesq*mesq) Notes d fgures re sed o or tke from mterls the course tetook: Prolstc Methods of Sgl d Sstem Alss (3rd ed.) George R. Cooper d Clre D. McGllem; Oford Press, 999. ISBN: B.J. Bzu, Fll 06 6 of 7 C 3800

7 No-ler estmto usg poloml ft mple: Tkg the dt from Tle 4-3 o p T, VB, Fgure V= * * Brekdow Voltge Temperture ( C) p=polft(,,); = p(3); = p(); c = p(); z = + * + c*.^; fgure plot(,,'o',,z,'r'); lel('temperture ( C)') lel('brekdow Voltge') ttle('fgure 4-6') grd tt=sprtf('v=%g+%g*+%g*^',,,c); tet(50,375,tt); Notes d fgures re sed o or tke from mterls the course tetook: Prolstc Methods of Sgl d Sstem Alss (3rd ed.) George R. Cooper d Clre D. McGllem; Oford Press, 999. ISBN: B.J. Bzu, Fll 06 7 of 7 C 3800

8 Notes d fgures re sed o or tke from mterls the course tetook: Prolstc Methods of Sgl d Sstem Alss (3rd ed.) George R. Cooper d Clre D. McGllem; Oford Press, 999. ISBN: B.J. Bzu, Fll 06 8 of 7 C Correlto of dscrete rdom vrles For sgle rdom vrle, we hve defed mesures of the reltoshp of oe smple or evet d the et. These re the mes d momets d the vrce. Me or st Momet d Momet d f, d f, d d R d Cetrl Momet d f, d C The vrce s mesure of the smlrt of successve smples or evets wth ech other. How close or correlted wth the others would evet e epected to e? R C

9 Notes d fgures re sed o or tke from mterls the course tetook: Prolstc Methods of Sgl d Sstem Alss (3rd ed.) George R. Cooper d Clre D. McGllem; Oford Press, 999. ISBN: B.J. Bzu, Fll 06 9 of 7 C 3800 Correlto etwee dscrete rdom vrles d For two sequeces or pred groupgs (,). If we ssume tht ever (,) pr s equll lkel, the pmf of the fuctos hs the sme vlue for ever pr. Repeted prs smpl sum the prolt t the pot. So, for correlto, d d f, for, prs, to we c defe pmf for ech smple pot s /. Therefore, R Defg the cross correlto d d f, C

10 Notes d fgures re sed o or tke from mterls the course tetook: Prolstc Methods of Sgl d Sstem Alss (3rd ed.) George R. Cooper d Clre D. McGllem; Oford Press, 999. ISBN: B.J. Bzu, Fll 06 0 of 7 C 3800 The Dscrete Correlto coeffcet For two sequeces or pred groupgs (,). If we ssume tht ever (,) pr s equll lkel, the pmf of the fuctos hs the sme vlue for ever pr, /. Repeted prs smpl sum the prolt t the pot. So, d d f, r C r or mkg t full dt drve r The tet defes ths s the Perso s r sttstcl mesure, the ler correlto coeffcet etwee two sets of dt! from Wkped Perso product-momet correlto coeffcet:

11 Bsed o the dscrete terms, ler estmto ecomes The, R R R R C R d R R C C Pvlov codtog for smpled dt lws compute the followg wth dt! : Me, d momet, vrce (, R, d ) : Me, d momet, vrce (, R, d ) d : For more formto: R, C, d R C R R C C Alerto Leo-Grc, Prolt, Sttstcs, d Rdom Processes For lectrcl geerg, 3rd ed., Perso Pretce Hll, Upper Sddle Rver, NJ, 008. Chp. 8. Notes d fgures re sed o or tke from mterls the course tetook: Prolstc Methods of Sgl d Sstem Alss (3rd ed.) George R. Cooper d Clre D. McGllem; Oford Press, 999. ISBN: B.J. Bzu, Fll 06 of 7 C 3800

12 Prctcl mple: NTC Resstor Temperture Mesuremets Suseeker Bsed o Vsh BCCompoets, Resstor Products Applcto Note, Documet Numer: 9053, 4 M, 0. Notes d fgures re sed o or tke from mterls the course tetook: Prolstc Methods of Sgl d Sstem Alss (3rd ed.) George R. Cooper d Clre D. McGllem; Oford Press, 999. ISBN: B.J. Bzu, Fll 06 of 7 C 3800

13 Note: The B costt wll e clled K costt the followg mterl. Notes d fgures re sed o or tke from mterls the course tetook: Prolstc Methods of Sgl d Sstem Alss (3rd ed.) George R. Cooper d Clre D. McGllem; Oford Press, 999. ISBN: B.J. Bzu, Fll 06 3 of 7 C 3800

14 The dt s epoetl curve wth respect to temperture. NTC Resstors tpcll refereced to 5 C or 98.5 K. R T R T ep K For the st order ppromto, ssume T T Notes d fgures re sed o or tke from mterls the course tetook: Prolstc Methods of Sgl d Sstem Alss (3rd ed.) George R. Cooper d Clre D. McGllem; Oford Press, 999. ISBN: B.J. Bzu, Fll 06 4 of 7 C 3800

15 Plottg the resst versus temperture sed o the dt d some ppromtos, we hve Suseeker NTC Resstor Temperture Curves Resstce (ohms) Dt Sheet K5/85 K5/ Temperture (deg C) See cel Spred Sheet for vlues Tpcll, dt sheets provde K vlues sed o 5 C d 85 C. K R T l R T T 490 T Notes d fgures re sed o or tke from mterls the course tetook: Prolstc Methods of Sgl d Sstem Alss (3rd ed.) George R. Cooper d Clre D. McGllem; Oford Press, 999. ISBN: B.J. Bzu, Fll 06 5 of 7 C 3800

16 For etter ccurc wth crtcl rego, the K c e computed to oud desred temperture opertg pots. For Suseeker, ke tempertures for tter operto re 45 C d 60 C. Therefore K sed o 5 C d 60 C s suffcet for operto. Ths resulted porto of the spred sheet lss. Desgg wth NTC Thermstor. PCOS NTC Thermstor Applcto Notes, Fe A referece curret or voltge s requred. I ths cse kow voltge s provded to resstor dvder d the output voltge s dctve of the temperture. The resultg curve s hghl o-ler due to the epoetl ture of the devce. To lerze the curve d reduce the steepest prt of the curve, plce the NTC prllel wth lrge resstor. Notes d fgures re sed o or tke from mterls the course tetook: Prolstc Methods of Sgl d Sstem Alss (3rd ed.) George R. Cooper d Clre D. McGllem; Oford Press, 999. ISBN: B.J. Bzu, Fll 06 6 of 7 C 3800

17 For Suseeker: A.5 Vref drves the resstor dvder. The Upper vlue used s 00kΩ d the resstor prllel wth the NTC thermstor s 330kΩ. A vertg op-mp s ot used, we re drectl coected to 4-t ADC. The resultg voltge to temperture curve s NTC See the spred sheet for the epected ADC outputs d hedecml dgtl vlues. Notes d fgures re sed o or tke from mterls the course tetook: Prolstc Methods of Sgl d Sstem Alss (3rd ed.) George R. Cooper d Clre D. McGllem; Oford Press, 999. ISBN: B.J. Bzu, Fll 06 7 of 7 C 3800

Numerical Analysis Topic 4: Least Squares Curve Fitting

Numerical Analysis Topic 4: Least Squares Curve Fitting Numerl Alss Top 4: Lest Squres Curve Fttg Red Chpter 7 of the tetook Alss_Numerk Motvto Gve set of epermetl dt: 3 5. 5.9 6.3 The reltoshp etwee d m ot e ler. Fd futo f tht est ft the dt 3 Alss_Numerk Motvto

More information

Regression. By Jugal Kalita Based on Chapter 17 of Chapra and Canale, Numerical Methods for Engineers

Regression. By Jugal Kalita Based on Chapter 17 of Chapra and Canale, Numerical Methods for Engineers Regresso By Jugl Klt Bsed o Chpter 7 of Chpr d Cle, Numercl Methods for Egeers Regresso Descrbes techques to ft curves (curve fttg) to dscrete dt to obt termedte estmtes. There re two geerl pproches two

More information

Soo King Lim Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7: Figure 8: Figure 9: Figure 10: Figure 11:

Soo King Lim Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7: Figure 8: Figure 9: Figure 10: Figure 11: Soo Kg Lm 1.0 Nested Fctorl Desg... 1.1 Two-Fctor Nested Desg... 1.1.1 Alss of Vrce... Exmple 1... 5 1.1. Stggered Nested Desg for Equlzg Degree of Freedom... 7 1.1. Three-Fctor Nested Desg... 8 1.1..1

More information

DATA FITTING. Intensive Computation 2013/2014. Annalisa Massini

DATA FITTING. Intensive Computation 2013/2014. Annalisa Massini DATA FITTING Itesve Computto 3/4 Als Mss Dt fttg Dt fttg cocers the problem of fttg dscrete dt to obt termedte estmtes. There re two geerl pproches two curve fttg: Iterpolto Dt s ver precse. The strteg

More information

CURVE FITTING LEAST SQUARES METHOD

CURVE FITTING LEAST SQUARES METHOD Nuercl Alss for Egeers Ger Jord Uverst CURVE FITTING Although, the for of fucto represetg phscl sste s kow, the fucto tself ot be kow. Therefore, t s frequetl desred to ft curve to set of dt pots the ssued

More information

MTH 146 Class 7 Notes

MTH 146 Class 7 Notes 7.7- Approxmte Itegrto Motvto: MTH 46 Clss 7 Notes I secto 7.5 we lered tht some defte tegrls, lke x e dx, cot e wrtte terms of elemetry fuctos. So, good questo to sk would e: How c oe clculte somethg

More information

PubH 7405: REGRESSION ANALYSIS REGRESSION IN MATRIX TERMS

PubH 7405: REGRESSION ANALYSIS REGRESSION IN MATRIX TERMS PubH 745: REGRESSION ANALSIS REGRESSION IN MATRIX TERMS A mtr s dspl of umbers or umercl quttes ld out rectgulr rr of rows d colums. The rr, or two-w tble of umbers, could be rectgulr or squre could be

More information

Optimality of Strategies for Collapsing Expanded Random Variables In a Simple Random Sample Ed Stanek

Optimality of Strategies for Collapsing Expanded Random Variables In a Simple Random Sample Ed Stanek Optmlt of Strteges for Collpsg Expe Rom Vrles Smple Rom Smple E Stek troucto We revew the propertes of prectors of ler comtos of rom vrles se o rom vrles su-spce of the orgl rom vrles prtculr, we ttempt

More information

Chapter 3 Supplemental Text Material

Chapter 3 Supplemental Text Material S3-. The Defto of Fctor Effects Chpter 3 Supplemetl Text Mterl As oted Sectos 3- d 3-3, there re two wys to wrte the model for sglefctor expermet, the mes model d the effects model. We wll geerlly use

More information

Chapter 2 Intro to Math Techniques for Quantum Mechanics

Chapter 2 Intro to Math Techniques for Quantum Mechanics Wter 3 Chem 356: Itroductory Qutum Mechcs Chpter Itro to Mth Techques for Qutum Mechcs... Itro to dfferetl equtos... Boudry Codtos... 5 Prtl dfferetl equtos d seprto of vrbles... 5 Itroducto to Sttstcs...

More information

Roberto s Notes on Integral Calculus Chapter 4: Definite integrals and the FTC Section 2. Riemann sums

Roberto s Notes on Integral Calculus Chapter 4: Definite integrals and the FTC Section 2. Riemann sums Roerto s Notes o Itegrl Clculus Chpter 4: Defte tegrls d the FTC Secto 2 Rem sums Wht you eed to kow lredy: The defto of re for rectgle. Rememer tht our curret prolem s how to compute the re of ple rego

More information

under the curve in the first quadrant.

under the curve in the first quadrant. NOTES 5: INTEGRALS Nme: Dte: Perod: LESSON 5. AREAS AND DISTANCES Are uder the curve Are uder f( ), ove the -s, o the dom., Prctce Prolems:. f ( ). Fd the re uder the fucto, ove the - s, etwee,.. f ( )

More information

ITERATIVE METHODS FOR SOLVING SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS

ITERATIVE METHODS FOR SOLVING SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS Numercl Alyss for Egeers Germ Jord Uversty ITERATIVE METHODS FOR SOLVING SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS Numercl soluto of lrge systems of ler lgerc equtos usg drect methods such s Mtr Iverse, Guss

More information

Area and the Definite Integral. Area under Curve. The Partition. y f (x) We want to find the area under f (x) on [ a, b ]

Area and the Definite Integral. Area under Curve. The Partition. y f (x) We want to find the area under f (x) on [ a, b ] Are d the Defte Itegrl 1 Are uder Curve We wt to fd the re uder f (x) o [, ] y f (x) x The Prtto We eg y prttog the tervl [, ] to smller su-tervls x 0 x 1 x x - x -1 x 1 The Bsc Ide We the crete rectgles

More information

Random variables and sampling theory

Random variables and sampling theory Revew Rdom vrbles d smplg theory [Note: Beg your study of ths chpter by redg the Overvew secto below. The red the correspodg chpter the textbook, vew the correspodg sldeshows o the webste, d do the strred

More information

In Calculus I you learned an approximation method using a Riemann sum. Recall that the Riemann sum is

In Calculus I you learned an approximation method using a Riemann sum. Recall that the Riemann sum is Mth Sprg 08 L Approxmtg Dete Itegrls I Itroducto We hve studed severl methods tht llow us to d the exct vlues o dete tegrls However, there re some cses whch t s ot possle to evlute dete tegrl exctly I

More information

Mathematics HL and further mathematics HL formula booklet

Mathematics HL and further mathematics HL formula booklet Dplom Progrmme Mthemtcs HL d further mthemtcs HL formul boolet For use durg the course d the emtos Frst emtos 04 Publshed Jue 0 Itertol Bcclurete Orgzto 0 5048 Mthemtcs HL d further mthemtcs formul boolet

More information

The z-transform. LTI System description. Prof. Siripong Potisuk

The z-transform. LTI System description. Prof. Siripong Potisuk The -Trsform Prof. Srpog Potsuk LTI System descrpto Prevous bss fucto: ut smple or DT mpulse The put sequece s represeted s ler combto of shfted DT mpulses. The respose s gve by covoluto sum of the put

More information

Solutions Manual for Polymer Science and Technology Third Edition

Solutions Manual for Polymer Science and Technology Third Edition Solutos ul for Polymer Scece d Techology Thrd Edto Joel R. Fred Uer Sddle Rver, NJ Bosto Idols S Frcsco New York Toroto otrel Lodo uch Prs drd Cetow Sydey Tokyo Sgore exco Cty Ths text s ssocted wth Fred/Polymer

More information

St John s College. UPPER V Mathematics: Paper 1 Learning Outcome 1 and 2. Examiner: GE Marks: 150 Moderator: BT / SLS INSTRUCTIONS AND INFORMATION

St John s College. UPPER V Mathematics: Paper 1 Learning Outcome 1 and 2. Examiner: GE Marks: 150 Moderator: BT / SLS INSTRUCTIONS AND INFORMATION St Joh s College UPPER V Mthemtcs: Pper Lerg Outcome d ugust 00 Tme: 3 hours Emer: GE Mrks: 50 Modertor: BT / SLS INSTRUCTIONS ND INFORMTION Red the followg structos crefull. Ths questo pper cossts of

More information

Objective of curve fitting is to represent a set of discrete data by a function (curve). Consider a set of discrete data as given in table.

Objective of curve fitting is to represent a set of discrete data by a function (curve). Consider a set of discrete data as given in table. CURVE FITTING Obectve curve ttg s t represet set dscrete dt b uct curve. Csder set dscrete dt s gve tble. 3 3 = T use the dt eectvel, curve epress s tted t the gve dt set, s = + = + + = e b ler uct plml

More information

Describes techniques to fit curves (curve fitting) to discrete data to obtain intermediate estimates.

Describes techniques to fit curves (curve fitting) to discrete data to obtain intermediate estimates. CURVE FITTING Descbes techques to ft cuves (cuve fttg) to dscete dt to obt temedte estmtes. Thee e two geel ppoches fo cuve fttg: Regesso: Dt ehbt sgfct degee of sctte. The stteg s to deve sgle cuve tht

More information

Advanced Algorithmic Problem Solving Le 3 Arithmetic. Fredrik Heintz Dept of Computer and Information Science Linköping University

Advanced Algorithmic Problem Solving Le 3 Arithmetic. Fredrik Heintz Dept of Computer and Information Science Linköping University Advced Algorthmc Prolem Solvg Le Arthmetc Fredrk Hetz Dept of Computer d Iformto Scece Lköpg Uversty Overvew Arthmetc Iteger multplcto Krtsu s lgorthm Multplcto of polyomls Fst Fourer Trsform Systems of

More information

Chapter 4: Elements of Statistics

Chapter 4: Elements of Statistics Chapter : lemets of tatstcs - Itroducto The amplg Problem Ubased stmators -&3 amplg Theory --The ample Mea ad ace amplg Theorem - amplg Dstrbutos ad Cofdece Itervals tudet s T-Dstrbuto -5 Hypothess Testg

More information

ICS141: Discrete Mathematics for Computer Science I

ICS141: Discrete Mathematics for Computer Science I Uversty o Hw ICS: Dscrete Mthemtcs or Computer Scece I Dept. Iormto & Computer Sc., Uversty o Hw J Stelovsy bsed o sldes by Dr. Be d Dr. Stll Orgls by Dr. M. P. Fr d Dr. J.L. Gross Provded by McGrw-Hll

More information

Chapter 4: Distributions

Chapter 4: Distributions Chpter 4: Dstrbutos Prerequste: Chpter 4. The Algebr of Expecttos d Vrces I ths secto we wll mke use of the followg symbols: s rdom vrble b s rdom vrble c s costt vector md s costt mtrx, d F m s costt

More information

6.6 Moments and Centers of Mass

6.6 Moments and Centers of Mass th 8 www.tetodre.co 6.6 oets d Ceters of ss Our ojectve here s to fd the pot P o whch th plte of gve shpe lces horzotll. Ths pot s clled the ceter of ss ( or ceter of grvt ) of the plte.. We frst cosder

More information

Mathematics HL and further mathematics HL formula booklet

Mathematics HL and further mathematics HL formula booklet Dplom Progrmme Mthemtcs HL d further mthemtcs HL formul boolet For use durg the course d the emtos Frst emtos 04 Edted 05 (verso ) Itertol Bcclurete Orgzto 0 5048 Cotets Pror lerg Core 3 Topc : Algebr

More information

Introduction to mathematical Statistics

Introduction to mathematical Statistics Itroducto to mthemtcl ttstcs Fl oluto. A grou of bbes ll of whom weghed romtely the sme t brth re rdomly dvded to two grous. The bbes smle were fed formul A; those smle were fed formul B. The weght gs

More information

Chapter Linear Regression

Chapter Linear Regression Chpte 6.3 Le Regesso Afte edg ths chpte, ou should be ble to. defe egesso,. use sevel mmzg of esdul cte to choose the ght cteo, 3. deve the costts of le egesso model bsed o lest sques method cteo,. use

More information

Chapter 17. Least Square Regression

Chapter 17. Least Square Regression The Islmc Uvest of Gz Fcult of Egeeg Cvl Egeeg Deptmet Numecl Alss ECIV 336 Chpte 7 Lest que Regesso Assocte Pof. Mze Abultef Cvl Egeeg Deptmet, The Islmc Uvest of Gz Pt 5 - CURVE FITTING Descbes techques

More information

Physics 220: Worksheet5 Name

Physics 220: Worksheet5 Name ocepts: pctce, delectrc costt, resstce, seres/prllel comtos () coxl cle cossts of sultor of er rdus wth chrge/legth +λ d outer sultg cylder of rdus wth chrge/legth -λ. () Fd the electrc feld everywhere

More information

Chapter 2 Intro to Math Techniques for Quantum Mechanics

Chapter 2 Intro to Math Techniques for Quantum Mechanics Fll 4 Chem 356: Itroductory Qutum Mechcs Chpter Itro to Mth Techques for Qutum Mechcs... Itro to dfferetl equtos... Boudry Codtos... 5 Prtl dfferetl equtos d seprto of vrbles... 5 Itroducto to Sttstcs...

More information

Linear Algebra Concepts

Linear Algebra Concepts Ler Algebr Cocepts Ke Kreutz-Delgdo (Nuo Vscocelos) ECE 75A Wter 22 UCSD Vector spces Defto: vector spce s set H where ddto d sclr multplcto re defed d stsf: ) +( + ) (+ )+ 5) l H 2) + + H 6) 3) H, + 7)

More information

Mathematics HL and further mathematics HL formula booklet

Mathematics HL and further mathematics HL formula booklet Dplom Progrmme Mthemtcs HL d further mthemtcs HL formul boolet For use durg the course d the emtos Frst emtos 04 Publshed Jue 0 Itertol Bcclurete Orgzto 0 5048 Cotets Pror lerg Core Topc : Algebr Topc

More information

this is the indefinite integral Since integration is the reverse of differentiation we can check the previous by [ ]

this is the indefinite integral Since integration is the reverse of differentiation we can check the previous by [ ] Atervtves The Itegrl Atervtves Ojectve: Use efte tegrl otto for tervtves. Use sc tegrto rules to f tervtves. Aother mportt questo clculus s gve ervtve f the fucto tht t cme from. Ths s the process kow

More information

Linear Algebra Concepts

Linear Algebra Concepts Ler Algebr Cocepts Nuo Vscocelos (Ke Kreutz-Delgdo) UCSD Vector spces Defto: vector spce s set H where ddto d sclr multplcto re defed d stsf: ) +( + ) = (+ )+ 5) H 2) + = + H 6) = 3) H, + = 7) ( ) = (

More information

Limit of a function:

Limit of a function: - Limit of fuctio: We sy tht f ( ) eists d is equl with (rel) umer L if f( ) gets s close s we wt to L if is close eough to (This defiitio c e geerlized for L y syig tht f( ) ecomes s lrge (or s lrge egtive

More information

STA261H1.doc. i 1 X n be a random sample. The sample mean is defined by i= 1 X 1 + ( ) X has a N ( σ ) 1 n. N distribution. Then n. distribution.

STA261H1.doc. i 1 X n be a random sample. The sample mean is defined by i= 1 X 1 + ( ) X has a N ( σ ) 1 n. N distribution. Then n. distribution. TA6Hdoc tttcl Iferece RADOM AMPLE Defto: Rdom mple clled rdom mple from dtruto wth pdf f ( (or pf ( depedet d hve detcl dtruto wth pdf f (or pf P (depedet-detcll-dtruted P f re It ofte deoted d ote Let

More information

Integral Equations and their Relationship to Differential Equations with Initial Conditions

Integral Equations and their Relationship to Differential Equations with Initial Conditions Scece Refleco SR Vol 6 wwwscecereflecocom Geerl Leers Mhemcs GLM 6 3-3 Geerl Leers Mhemcs GLM Wese: hp://wwwscecereflecocom/geerl-leers--mhemcs/ Geerl Leers Mhemcs Scece Refleco Iegrl Equos d her Reloshp

More information

The definite Riemann integral

The definite Riemann integral Roberto s Notes o Itegrl Clculus Chpter 4: Defte tegrls d the FTC Secto 4 The defte Rem tegrl Wht you eed to kow lredy: How to ppromte the re uder curve by usg Rem sums. Wht you c ler here: How to use

More information

Lecture 3-4 Solutions of System of Linear Equations

Lecture 3-4 Solutions of System of Linear Equations Lecture - Solutos of System of Ler Equtos Numerc Ler Alger Revew of vectorsd mtrces System of Ler Equtos Guss Elmto (drect solver) LU Decomposto Guss-Sedel method (tertve solver) VECTORS,,, colum vector

More information

Density estimation II

Density estimation II CS 750 Mche Lerg Lecture 6 esty estmto II Mlos Husrecht mlos@tt.edu 539 Seott Squre t: esty estmto {.. } vector of ttrute vlues Ojectve: estmte the model of the uderlyg rolty dstruto over vrles X X usg

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

Statistical Modeling and Analysis of the Correlation between the Gross Domestic Product per Capita and the Life Expectancy

Statistical Modeling and Analysis of the Correlation between the Gross Domestic Product per Capita and the Life Expectancy Als of Dure de Jos Uerst of Glt Fsccle. Ecoocs d Appled fortcs Yers XX o /5 N-L 584-49 N-Ole 44-44X www.e.fe.ugl.ro ttstcl Mode d Alss of the Correlto etwee the Gross Doestc Product per Cpt d the Lfe Epectc

More information

Section 7.2 Two-way ANOVA with random effect(s)

Section 7.2 Two-way ANOVA with random effect(s) Secto 7. Two-wy ANOVA wth rdom effect(s) 1 1. Model wth Two Rdom ffects The fctors hgher-wy ANOVAs c g e cosdered fxed or rdom depedg o the cotext of the study. or ech fctor: Are the levels of tht fctor

More information

Sequences and summations

Sequences and summations Lecture 0 Sequeces d summtos Istructor: Kgl Km CSE) E-ml: kkm0@kokuk.c.kr Tel. : 0-0-9 Room : New Mleum Bldg. 0 Lb : New Egeerg Bldg. 0 All sldes re bsed o CS Dscrete Mthemtcs for Computer Scece course

More information

Chapter Unary Matrix Operations

Chapter Unary Matrix Operations Chpter 04.04 Ury trx Opertos After redg ths chpter, you should be ble to:. kow wht ury opertos mes,. fd the trspose of squre mtrx d t s reltoshp to symmetrc mtrces,. fd the trce of mtrx, d 4. fd the ermt

More information

8.6 The Hyperbola. and F 2. is a constant. P F 2. P =k The two fixed points, F 1. , are called the foci of the hyperbola. The line segments F 1

8.6 The Hyperbola. and F 2. is a constant. P F 2. P =k The two fixed points, F 1. , are called the foci of the hyperbola. The line segments F 1 8. The Hperol Some ships nvigte using rdio nvigtion sstem clled LORAN, which is n cronm for LOng RAnge Nvigtion. A ship receives rdio signls from pirs of trnsmitting sttions tht send signls t the sme time.

More information

INTERPOLATION(2) ELM1222 Numerical Analysis. ELM1222 Numerical Analysis Dr Muharrem Mercimek

INTERPOLATION(2) ELM1222 Numerical Analysis. ELM1222 Numerical Analysis Dr Muharrem Mercimek ELM Numerl Alss Dr Murrem Merme INTEROLATION ELM Numerl Alss Some of te otets re dopted from Luree V. Fusett Appled Numerl Alss usg MATLAB. rete Hll I. 999 ELM Numerl Alss Dr Murrem Merme Tod s leture

More information

Week 13 Notes: 1) Riemann Sum. Aim: Compute Area Under a Graph. Suppose we want to find out the area of a graph, like the one on the right:

Week 13 Notes: 1) Riemann Sum. Aim: Compute Area Under a Graph. Suppose we want to find out the area of a graph, like the one on the right: Week 1 Notes: 1) Riem Sum Aim: Compute Are Uder Grph Suppose we wt to fid out the re of grph, like the oe o the right: We wt to kow the re of the red re. Here re some wys to pproximte the re: We cut the

More information

A Brief Introduction to Olympiad Inequalities

A Brief Introduction to Olympiad Inequalities Ev Che Aprl 0, 04 The gol of ths documet s to provde eser troducto to olympd equltes th the stdrd exposto Olympd Iequltes, by Thoms Mldorf I ws motvted to wrte t by feelg gulty for gettg free 7 s o problems

More information

Stats & Summary

Stats & Summary Stts 443.3 & 85.3 Summr The Woodbur Theorem BCD B C D B D where the verses C C D B, d est. Block Mtrces Let the m mtr m q q m be rttoed to sub-mtrces,,,, Smlrl rtto the m k mtr B B B mk m B B l kl Product

More information

ME 501A Seminar in Engineering Analysis Page 1

ME 501A Seminar in Engineering Analysis Page 1 Mtr Trsformtos usg Egevectors September 8, Mtr Trsformtos Usg Egevectors Lrry Cretto Mechcl Egeerg A Semr Egeerg Alyss September 8, Outle Revew lst lecture Trsformtos wth mtr of egevectors: = - A ermt

More information

Chapter Simpson s 1/3 Rule of Integration. ( x)

Chapter Simpson s 1/3 Rule of Integration. ( x) Cpter 7. Smpso s / Rule o Itegrto Ater redg ts pter, you sould e le to. derve te ormul or Smpso s / rule o tegrto,. use Smpso s / rule t to solve tegrls,. develop te ormul or multple-segmet Smpso s / rule

More information

10.2 The Ellipse and the Hyperbola

10.2 The Ellipse and the Hyperbola CHAPTER 0 Conic Sections Solve. 97. Two surveors need to find the distnce cross lke. The plce reference pole t point A in the digrm. Point B is meters est nd meter north of the reference point A. Point

More information

14.2 Line Integrals. determines a partition P of the curve by points Pi ( xi, y

14.2 Line Integrals. determines a partition P of the curve by points Pi ( xi, y 4. Le Itegrls I ths secto we defe tegrl tht s smlr to sgle tegrl except tht sted of tegrtg over tervl [ ] we tegrte over curve. Such tegrls re clled le tegrls lthough curve tegrls would e etter termology.

More information

CS321. Numerical Analysis

CS321. Numerical Analysis CS3 Nuercl Alss Lecture 7 Lest Sures d Curve Fttg Professor Ju Zhg Deprtet of Coputer Scece Uverst of Ketuc Legto KY 456 633 Deceer 4 Method of Lest Sures Coputer ded dt collectos hve produced treedous

More information

Mathematically, integration is just finding the area under a curve from one point to another. It is b

Mathematically, integration is just finding the area under a curve from one point to another. It is b Numerl Metods or Eg [ENGR 9] [Lyes KADEM 7] CHAPTER VI Numerl Itegrto Tops - Rem sums - Trpezodl rule - Smpso s rule - Rrdso s etrpolto - Guss qudrture rule Mtemtlly, tegrto s just dg te re uder urve rom

More information

PROPERTIES OF AREAS In general, and for an irregular shape, the definition of the centroid at position ( x, y) is given by

PROPERTIES OF AREAS In general, and for an irregular shape, the definition of the centroid at position ( x, y) is given by PROPERTES OF RES Centroid The concept of the centroid is prol lred fmilir to ou For plne shpe with n ovious geometric centre, (rectngle, circle) the centroid is t the centre f n re hs n is of smmetr, the

More information

MATH2999 Directed Studies in Mathematics Matrix Theory and Its Applications

MATH2999 Directed Studies in Mathematics Matrix Theory and Its Applications MATH999 Drected Studes Mthemtcs Mtr Theory d Its Applctos Reserch Topc Sttory Probblty Vector of Hgher-order Mrkov Ch By Zhg Sho Supervsors: Prof. L Ch-Kwog d Dr. Ch Jor-Tg Cotets Abstrct. Itroducto: Bckgroud.

More information

Chapter Gauss-Seidel Method

Chapter Gauss-Seidel Method Chpter 04.08 Guss-Sedel Method After redg ths hpter, you should be ble to:. solve set of equtos usg the Guss-Sedel method,. reogze the dvtges d ptflls of the Guss-Sedel method, d. determe uder wht odtos

More information

3/20/2013. Splines There are cases where polynomial interpolation is bad overshoot oscillations. Examplef x. Interpolation at -4,-3,-2,-1,0,1,2,3,4

3/20/2013. Splines There are cases where polynomial interpolation is bad overshoot oscillations. Examplef x. Interpolation at -4,-3,-2,-1,0,1,2,3,4 // Sples There re ses where polyoml terpolto s d overshoot oslltos Emple l s Iterpolto t -,-,-,-,,,,,.... - - - Ide ehd sples use lower order polyomls to oet susets o dt pots mke oetos etwee djet sples

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

: : 8.2. Test About a Population Mean. STT 351 Hypotheses Testing Case I: A Normal Population with Known. - null hypothesis states 0

: : 8.2. Test About a Population Mean. STT 351 Hypotheses Testing Case I: A Normal Population with Known. - null hypothesis states 0 8.2. Test About Popultio Me. Cse I: A Norml Popultio with Kow. H - ull hypothesis sttes. X1, X 2,..., X - rdom smple of size from the orml popultio. The the smple me X N, / X X Whe H is true. X 8.2.1.

More information

Discrete Mathematics and Probability Theory Spring 2016 Rao and Walrand Lecture 17

Discrete Mathematics and Probability Theory Spring 2016 Rao and Walrand Lecture 17 CS 70 Discrete Mthemtics d Proility Theory Sprig 206 Ro d Wlrd Lecture 7 Vrice We hve see i the previous ote tht if we toss coi times with is p, the the expected umer of heds is p. Wht this mes is tht

More information

MATRIX AND VECTOR NORMS

MATRIX AND VECTOR NORMS Numercl lyss for Egeers Germ Jord Uversty MTRIX ND VECTOR NORMS vector orm s mesure of the mgtude of vector. Smlrly, mtr orm s mesure of the mgtude of mtr. For sgle comoet etty such s ordry umers, the

More information

Module 2: Introduction to Numerical Analysis

Module 2: Introduction to Numerical Analysis CY00 Itroducto to Computtol Chemtr Autum 00-0 Module : Itroducto to umercl Al Am of the preet module. Itroducto to c umercl l. Developg mple progrm to mplemet the umercl method opc of teret. Iterpolto:

More information

Math 1313 Final Exam Review

Math 1313 Final Exam Review Mth 33 Fl m Revew. The e Compy stlled ew mhe oe of ts ftores t ost of $0,000. The mhe s depreted lerly over 0 yers wth srp vlue of $,000. Fd the vlue of the mhe fter 5 yers.. mufturer hs mothly fed ost

More information

Linear Open Loop Systems

Linear Open Loop Systems Colordo School of Me CHEN43 Trfer Fucto Ler Ope Loop Sytem Ler Ope Loop Sytem... Trfer Fucto for Smple Proce... Exmple Trfer Fucto Mercury Thermometer... 2 Derblty of Devto Vrble... 3 Trfer Fucto for Proce

More information

GRADE 12 SEPTEMBER 2016 MATHEMATICS P1

GRADE 12 SEPTEMBER 2016 MATHEMATICS P1 NATIONAL SENIOR CERTIFICATE GRADE SEPTEMBER 06 MATHEMATICS P MARKS: 50 TIME: 3 hours *MATHE* This questio pper cosists of pges icludig iformtio sheet MATHEMATICS P (EC/SEPTEMBER 06 INSTRUCTIONS AND INFORMATION

More information

6. Chemical Potential and the Grand Partition Function

6. Chemical Potential and the Grand Partition Function 6. Chemcl Potetl d the Grd Prtto Fucto ome Mth Fcts (see ppedx E for detls) If F() s lytc fucto of stte vrles d such tht df d pd the t follows: F F p lso sce F p F we c coclude: p I other words cross dervtves

More information

Statistics. Correlational. Dr. Ayman Eldeib. Simple Linear Regression and Correlation. SBE 304: Linear Regression & Correlation 1/3/2018

Statistics. Correlational. Dr. Ayman Eldeib. Simple Linear Regression and Correlation. SBE 304: Linear Regression & Correlation 1/3/2018 /3/08 Sstems & Bomedcal Egeerg Departmet SBE 304: Bo-Statstcs Smple Lear Regresso ad Correlato Dr. Ama Eldeb Fall 07 Descrptve Orgasg, summarsg & descrbg data Statstcs Correlatoal Relatoshps Iferetal Geeralsg

More information

INFINITE SERIES. ,... having infinite number of terms is called infinite sequence and its indicated sum, i.e., a 1

INFINITE SERIES. ,... having infinite number of terms is called infinite sequence and its indicated sum, i.e., a 1 Appedix A.. Itroductio As discussed i the Chpter 9 o Sequeces d Series, sequece,,...,,... hvig ifiite umber of terms is clled ifiite sequece d its idicted sum, i.e., + + +... + +... is clled ifite series

More information

Approximate Integration

Approximate Integration Study Sheet (7.7) Approimte Itegrtio I this sectio, we will ler: How to fid pproimte vlues of defiite itegrls. There re two situtios i which it is impossile to fid the ect vlue of defiite itegrl. Situtio:

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

More Regression Lecture Notes CE 311K - McKinney Introduction to Computer Methods Department of Civil Engineering The University of Texas at Austin

More Regression Lecture Notes CE 311K - McKinney Introduction to Computer Methods Department of Civil Engineering The University of Texas at Austin More Regresso Lecture Notes CE K - McKe Itroducto to Coputer Methods Deprtet of Cvl Egeerg The Uverst of Tes t Aust Polol Regresso Prevousl, we ft strght le to os dt (, ), (, ), (, ) usg the lest-squres

More information

UNIVERSITI KEBANGSAAN MALAYSIA PEPERIKSAAN AKHIR SEMESTER I SESI AKADEMIK 2007/2008 IJAZAH SARJANAMUDA DENGAN KEPUJIAN NOVEMBER 2007 MASA : 3 JAM

UNIVERSITI KEBANGSAAN MALAYSIA PEPERIKSAAN AKHIR SEMESTER I SESI AKADEMIK 2007/2008 IJAZAH SARJANAMUDA DENGAN KEPUJIAN NOVEMBER 2007 MASA : 3 JAM UNIVERSITI KEBANGSAAN MALAYSIA PEPERIKSAAN AKHIR SEMESTER I SESI AKADEMIK 7/8 IJAZAH SARJANAMUDA DENGAN KEPUJIAN NOVEMBER 7 MASA : 3 JAM KOD KURSUS : KKKQ33/KKKF33 TAJUK : PENGIRAAN BERANGKA ARAHAN :.

More information

Strategies for the AP Calculus Exam

Strategies for the AP Calculus Exam Strteges for the AP Clculus Em Strteges for the AP Clculus Em Strtegy : Kow Your Stuff Ths my seem ovous ut t ees to e metoe. No mout of cochg wll help you o the em f you o t kow the mterl. Here s lst

More information

Maximum Likelihood Estimation

Maximum Likelihood Estimation Marquette Uverst Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Coprght 08 b Marquette Uverst Maxmum Lkelhood Estmato We have bee sag that ~

More information

Graphing Review Part 3: Polynomials

Graphing Review Part 3: Polynomials Grphig Review Prt : Polomils Prbols Recll, tht the grph of f ( ) is prbol. It is eve fuctio, hece it is smmetric bout the bout the -is. This mes tht f ( ) f ( ). Its grph is show below. The poit ( 0,0)

More information

Lecture 2: The Simple Regression Model

Lecture 2: The Simple Regression Model Lectre Notes o Advaced coometrcs Lectre : The Smple Regresso Model Takash Yamao Fall Semester 5 I ths lectre we revew the smple bvarate lear regresso model. We focs o statstcal assmptos to obta based estmators.

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

12 Iterative Methods. Linear Systems: Gauss-Seidel Nonlinear Systems Case Study: Chemical Reactions

12 Iterative Methods. Linear Systems: Gauss-Seidel Nonlinear Systems Case Study: Chemical Reactions HK Km Slghtly moded //9 /8/6 Frstly wrtte t Mrch 5 Itertve Methods er Systems: Guss-Sedel Noler Systems Cse Study: Chemcl Rectos Itertve or ppromte methods or systems o equtos cosst o guessg vlue d the

More information

Line Fitting and Regression

Line Fitting and Regression Marquette Uverst MSCS6 Le Fttg ad Regresso Dael B. Rowe, Ph.D. Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Coprght 8 b Marquette Uverst Least Squares Regresso MSCS6 For LSR we have pots

More information

Level-2 BLAS. Matrix-Vector operations with O(n 2 ) operations (sequentially) BLAS-Notation: S --- single precision G E general matrix M V --- vector

Level-2 BLAS. Matrix-Vector operations with O(n 2 ) operations (sequentially) BLAS-Notation: S --- single precision G E general matrix M V --- vector evel-2 BS trx-vector opertos wth 2 opertos sequetlly BS-Notto: S --- sgle precso G E geerl mtrx V --- vector defes SGEV, mtrx-vector product: r y r α x β r y ther evel-2 BS: Solvg trgulr system x wth trgulr

More information

Review of Linear Algebra

Review of Linear Algebra PGE 30: Forulto d Soluto Geosstes Egeerg Dr. Blhoff Sprg 0 Revew of Ler Alger Chpter 7 of Nuercl Methods wth MATLAB, Gerld Recktewld Vector s ordered set of rel (or cople) uers rrged s row or colu sclr

More information

Fundamental Theorem of Calculus

Fundamental Theorem of Calculus Fundmentl Theorem of Clculus Recll tht if f is nonnegtive nd continuous on [, ], then the re under its grph etween nd is the definite integrl A= f() d Now, for in the intervl [, ], let A() e the re under

More information

CHAPTER 6 CURVE FITTINGS

CHAPTER 6 CURVE FITTINGS CHAPTER 6 CURVE FITTINGS Chpter 6 : TOPIC COVERS CURVE FITTINGS Lest-Squre Regresso - Ler Regresso - Poloml Regresso Iterpolto - Newto s Dvded-Derece Iterpoltg Polomls - Lgrge Iterpoltg Polomls - Sple

More information

The Trapezoidal Rule

The Trapezoidal Rule _.qd // : PM Pge 9 SECTION. Numericl Integrtion 9 f Section. The re of the region cn e pproimted using four trpezoids. Figure. = f( ) f( ) n The re of the first trpezoid is f f n. Figure. = Numericl Integrtion

More information

POWERS OF COMPLEX PERSYMMETRIC ANTI-TRIDIAGONAL MATRICES WITH CONSTANT ANTI-DIAGONALS

POWERS OF COMPLEX PERSYMMETRIC ANTI-TRIDIAGONAL MATRICES WITH CONSTANT ANTI-DIAGONALS IRRS 9 y 04 wwwrppresscom/volumes/vol9issue/irrs_9 05pdf OWERS OF COLE ERSERIC I-RIIGOL RICES WIH COS I-IGOLS Wg usu * Q e Wg Hbo & ue College of Scece versty of Shgh for Scece d echology Shgh Ch 00093

More information

Inference on One Population Mean Hypothesis Testing

Inference on One Population Mean Hypothesis Testing Iferece o Oe Popultio Me ypothesis Testig Scerio 1. Whe the popultio is orml, d the popultio vrice is kow i. i. d. Dt : X 1, X,, X ~ N(, ypothesis test, for istce: Exmple: : : : : : 5'7" (ull hypothesis:

More information

DA 3: The Mean Value Theorem

DA 3: The Mean Value Theorem Differentition pplictions 3: The Men Vlue Theorem 169 D 3: The Men Vlue Theorem Model 1: Pennslvni Turnpike You re trveling est on the Pennslvni Turnpike You note the time s ou pss the Lenon/Lncster Eit

More information

,... are the terms of the sequence. If the domain consists of the first n positive integers only, the sequence is a finite sequence.

,... are the terms of the sequence. If the domain consists of the first n positive integers only, the sequence is a finite sequence. Chpter 9 & 0 FITZGERALD MAT 50/5 SECTION 9. Sequece Defiitio A ifiite sequece is fuctio whose domi is the set of positive itegers. The fuctio vlues,,, 4,...,,... re the terms of the sequece. If the domi

More information

A Technique for Constructing Odd-order Magic Squares Using Basic Latin Squares

A Technique for Constructing Odd-order Magic Squares Using Basic Latin Squares Itertol Jourl of Scetfc d Reserch Publctos, Volume, Issue, My 0 ISSN 0- A Techque for Costructg Odd-order Mgc Squres Usg Bsc Lt Squres Tomb I. Deprtmet of Mthemtcs, Mpur Uversty, Imphl, Mpur (INDIA) tombrom@gml.com

More information

Rank One Update And the Google Matrix by Al Bernstein Signal Science, LLC

Rank One Update And the Google Matrix by Al Bernstein Signal Science, LLC Introducton Rnk One Updte And the Google Mtrx y Al Bernsten Sgnl Scence, LLC www.sgnlscence.net here re two dfferent wys to perform mtrx multplctons. he frst uses dot product formulton nd the second uses

More information

M344 - ADVANCED ENGINEERING MATHEMATICS

M344 - ADVANCED ENGINEERING MATHEMATICS M3 - ADVANCED ENGINEERING MATHEMATICS Lecture 18: Lplce s Eqution, Anltic nd Numericl Solution Our emple of n elliptic prtil differentil eqution is Lplce s eqution, lso clled the Diffusion Eqution. If

More information

Chapter Newton-Raphson Method of Solving a Nonlinear Equation

Chapter Newton-Raphson Method of Solving a Nonlinear Equation Chpter.4 Newton-Rphson Method of Solvng Nonlner Equton After redng ths chpter, you should be ble to:. derve the Newton-Rphson method formul,. develop the lgorthm of the Newton-Rphson method,. use the Newton-Rphson

More information

Interval Estimation. Consider a random variable X with a mean of X. Let X be distributed as X X

Interval Estimation. Consider a random variable X with a mean of X. Let X be distributed as X X ECON 37: Ecoomercs Hypohess Tesg Iervl Esmo Wh we hve doe so fr s o udersd how we c ob esmors of ecoomcs reloshp we wsh o sudy. The queso s how comforble re we wh our esmors? We frs exme how o produce

More information

i+1 by A and imposes Ax

i+1 by A and imposes Ax MASSACHUSETTS INSTITUTE OF TECHNOLOGY DEPARTMENT OF MECHANICAL ENGINEERING CAMBRIDGE, MASSACHUSETTS 09.9 NUMERICAL FLUID MECHANICS FALL 009 Mody, October 9, 009 QUIZ : SOLUTIONS Notes: ) Multple solutos

More information