Department of Economics University of Toronto. ECO2408F M.A. Econometrics. Lecture Notes on Simple Regression Model

Size: px
Start display at page:

Download "Department of Economics University of Toronto. ECO2408F M.A. Econometrics. Lecture Notes on Simple Regression Model"

Transcription

1 Deprtmet f Ecmc Uvert f Trt ECO48F M.A. Ecmetrc Lecture Nte Smple Regre Mdel

2 Smple Regre Mdel I the frt lecture we lked t fttg le t ctter f pt. I th chpter we eme regre methd f eplrg the prbbltc tructure f the dt dd rdm dturbce t the mdel. ε depedet ble (rdm ble) epltr r depedet ble (fed-tchtc) ε rdm errr term Wh de rdm errr term re?. Mdel mplfct f relt. Meuremet errr ble Fr ever vlue f Χ there et prbblt dtrbut f ε d hece prbblt dtrbut f the Υ ' INSERT GRAPH Imprtt umpt uderlg tw ble regre mdel Remrk: ) The relthp betwee Υ d Χ ler ) Χ tchtc ble whe vlue fed 3) The errr h zer epected vlue Ε( ε ) 4) The errr term h ctt ce fr ll bervt Ε ε ε 5) The rdm bleε re tttcll depedet ll d j 6) Errr term rmll dtrbuted Ε fr ε ε j 3 - mde fr cveece 4 - therefre me ce hmkedtc hwever f the ce t ctt the hve heterkedtct (re fte cr-ect) E.g. INSERT GRAPH

3 5 - me tht errr tht re rdm d d t hve crrelt betwee them INSERT GRAPH Crrlr t umpt d 3 tht Χ depedet f the redul Ε Χ ε Χ Ε ε C l tte umpt term f Υ 3 - The rdm ble Υ h epected vlue α Ε( Υ ) Ε( α Χ ε ) α Χ Ε( ε ) α Χ 4 - The rdm ble Υ h ctt ce 5 - The rdm ble Υ re depedet Regre: Ordr Let Squre Regre methd re pprch tht lk t the crrelt betwee tw (r mre) ble. Wth regre mdel; teret fcue lkg t the cllect f dtrbut whch ll hve the me ce d hve me le lg trght le. Te prblem t etmte the le (lpe d tercept). Regre Mdel ε ε ~ Ν - prmeter f mdel (eed t be etmted) the epltr bledepedet ble ( fed t rdm) depedet ble (epled b ) ε rdm errr term gve uepled prt f regre The bve cdt mpl tht rdm ble gve wth. Ε ( ) ( ) rml 3

4 d d Χ re ll ctt ther ce re ll zer d cce wth ε re zer jut leveε Gve bervt Χ d Υ hw d we etmte the prmeter f the mdel d gve... d... Ν Ν The rdrl let qure (OLS) prceed b fdg etmte f d b mmzg ( N.B. pck tht errr ε re mll If jut ue ( ) lrge errr mpl lmted frmt but ue rule tht rule ut lrge errr (e.g. qurg) ( )( ) The lut f the OLS prblem wll be d ( ) Nte the frmul fr mple tht the etmted regre le wll ru thrugh the mple me f d The etmt fr gve b ( ) Hve me reult the ptmlt f the etmte f Gu Mrkv Therem: OLS BLUE The rdr let qure etmtr Therem: ce) ler (ler B Q U E... d f d ) ubed etmtr re the bet (mmum 4

5 The etmtr f the bet (mmum) ce qudrtc (qudrtc ) ubed etmtr... Smplg dtrbut fr the etmtr d Therem: The mplg dtrbut f d Ν ~.e. hve bte rml dtrbut wth the gve me d cce mtr Wht th me the fllwg d cv. Sce ukw we replce t wth prctce. Therem: Smplg dtrbut f ~ Χ Ad depedetl f d 5

6 We c ue the ce f t cduct tet the tercept d lpe prmeter. We eed the etmte f the cce betwee the lpe d tercept term f we re tereted cductg tet me ler cmbt f the lpe r tercept. Oce we kw the mplg dtrbut f d we c d hpthe tetg. TEST ON t-tet ~ t TEST ON ~ t TESTS ON ARBITRARY LINEAR COMBINATION OF AND e.g. Recll - cv ~ t Predct Itervl Ctruct the α tervl whch ct the et bervt gve α α α Pr t p t b 6

7 p Vr Iterpretg OLS Ceffcet e Wht? Hw Iterpreted? I mplet ce lpe f regre le. repreet the cree whch wuld ccur f were creed b ut. the mrgl cree whch wuld ccur f were creed. Smetme reult re preeted term f eltcte. Recll eltct defed : η Or: η The let qure etmte f etmte f ll we eed t d frm etmte f uull t the me t etmte the eltct ~ η 7

8 Gde f Ft Attempt t meure ft betwee etmted regre le d the le Gd etmte f the regre le epl lrge prt f the ble f Υ Lrge redul pr ft; mll redul gd ft Drwbck: Th deped ut f meure f depedt ble Need ut free meure Defe the t f Υ but t me Vrt Υ Υ Υ Gl t dvde t f Υ t tw prt:. Accuted fr regre equt. The uepled cmpet If the lpe f the regre kw t be zer ft regre b etmtg l tercept.e. me f Υ The bet predct fr mple me f Υ Υ α Χ α Υ Υ cted fr Χ the gve b the Whe the lpe -zer c mprve ur predct b ccutg fr Υ beg depedt Χ Υ α Χ The ddtl frmt prt f the t Υ T hw th dd zer clever w Υ Υ Υ Υ Υ Υ Χ c help reduce the uepled ADD SLOPEGRAPH 8

9 T meure: Squre bth de d tke um ver Ν bervt ( Υ Υ) ( Υ Υ ) ( Υ Υ) ( Υ Υ )( Υ Υ) C hw ( Υ )( Υ Υ) Υ Left wth: Υ Υ Υ Υ Υ Υ Ttl Sum Redul Vrt Regre Sum f Squre (Errr Sum f f Squre Squre) TSS ESS RSS Dvde bth de b TSS ESS RSS TSS TSS ESS RSS R TSS TSS R the ttl prprt f the ttl t Υ epled b regre f Υ Χ Sce Errr Sum f Squre le betwee zer d TSS d R wll le betwee zer Th l true whe there tercept. Wthut tercept c get R > r < INSERT GRAPH N ft jut rdm pt D hgh vlue f N t rell! R mpl gd ft d lw vlue f R pr ft? Hgh vlue f R re cmm tme ere dt lw vlue re cmm cr-ectl dt 9

10 Nte pecl relthp fry α X ε.e. mple regre R jut qured crrelt ceffcet betwee Υ d Χ Tetg the Regre Equt Dvdg t Υ t tw cmpet ugget tttcl tet fr the etece f ler relthp betwee Υ d Χ EpledVrce F Ν U epledvrce RSS ESS Ν I geerl th tet tttc wll hve F dtrbut Ν F-tttc zer whe epled t zer Tetg the Regre Equt: Subdvdg the t Υ t tw cmpet ugget tttcl tet fr the etece f relthp betwee Υ d Χ EpledVrce F Ν U epledvrce RSS ESS ( Ν ) Epect trg tttcl relthp betwee Χ d Υ t reult lrge rt f epled t uepled ce tet wll hve F- dtrbut wth d Ν degree f freedm F wll be l whe the epled t the regre Fr ler relthp betwee Χ d Υ eed bg tet tttc Al te tht F t qured vlue f t-tttc fr Χ tw Ν Ν ble regre mdel

CS 4758 Robot Kinematics. Ashutosh Saxena

CS 4758 Robot Kinematics. Ashutosh Saxena CS 4758 Rt Kemt Ahuth Se Kemt tude the mt f de e re tereted tw emt tp Frwrd Kemt (ge t pt ht u re gve: he egth f eh he ge f eh t ht u fd: he pt f pt (.e. t (,, rdte Ivere Kemt (pt t ge ht u re gve: he

More information

The Simple Linear Regression Model: Theory

The Simple Linear Regression Model: Theory Chapter 3 The mple Lear Regress Mdel: Ther 3. The mdel 3.. The data bservats respse varable eplaatr varable : : Plttg the data.. Fgure 3.: Dsplag the cable data csdered b Che at al (993). There are 79

More information

An Introduction to Robot Kinematics. Renata Melamud

An Introduction to Robot Kinematics. Renata Melamud A Itrdut t Rt Kemt Ret Memud Kemt tude the mt f de A Empe -he UMA 56 3 he UMA 56 hsirevute t A revute t h E degree f freedm ( DF tht defed t ge 4 here re tw mre t the ed effetr (the grpper ther t Revute

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

10.2 Series. , we get. which is called an infinite series ( or just a series) and is denoted, for short, by the symbol. i i n

10.2 Series. , we get. which is called an infinite series ( or just a series) and is denoted, for short, by the symbol. i i n 0. Sere I th ecto, we wll troduce ere tht wll be dcug for the ret of th chpter. Wht ere? If we dd ll term of equece, we get whch clled fte ere ( or jut ere) d deoted, for hort, by the ymbol or Doe t mke

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

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

Basics of heteroskedasticity

Basics of heteroskedasticity Sect 8 Heterskedastcty ascs f heterskedastcty We have assumed up t w ( ur SR ad MR assumpts) that the varace f the errr term was cstat acrss bservats Ths s urealstc may r mst ecmetrc applcats, especally

More information

Measurement and Instrumentation Lecture Note: Strain Measurement

Measurement and Instrumentation Lecture Note: Strain Measurement 0-60 Meurement nd Intrumenttin Lecture Nte: Strin Meurement eview f Stre nd Strin Figure : Structure under tenin Frm Fig., xil tre σ, xil trin, trnvere trin t, Pin' rti ν, nd Yung mdulu E re σ F A, dl

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

Simple Linear Regression Analysis

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

More information

PY3101 Optics. Learning objectives. Wave propagation in anisotropic media Poynting walk-off The index ellipsoid Birefringence. The Index Ellipsoid

PY3101 Optics. Learning objectives. Wave propagation in anisotropic media Poynting walk-off The index ellipsoid Birefringence. The Index Ellipsoid The Ide Ellpsd M.P. Vaugha Learg bjectves Wave prpagat astrpc meda Ptg walk-ff The de ellpsd Brefrgece 1 Wave prpagat astrpc meda The wave equat Relatve permttvt I E. Assumg free charges r currets E. Substtutg

More information

Francis Galton ( ) The Inventor of Modern Regression Analysis

Francis Galton ( ) The Inventor of Modern Regression Analysis Decrptve Stattc The Cure S Far: Prbablty Thery Prbablty Dtrbut Samplg Dtrbut Smple Radm Samplg Symbl f Caual Aaly: Crcle repreetg theretcal (r latet) varable that may be caue r effect (r bth) ur thery.

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

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION

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

More information

Ionization Energies in Si, Ge, GaAs

Ionization Energies in Si, Ge, GaAs Izt erges S, Ge, GAs xtrsc Semcuctrs A extrsc semcuctrs s efe s semcuctr whch ctrlle muts f secfc t r murty tms hve bee e s tht the thermlequlbrum electr hle ccetrt re fferet frm the trsc crrer ccetrt.

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

ALGEBRA 2/TRIGONMETRY TOPIC REVIEW QUARTER 3 LOGS

ALGEBRA 2/TRIGONMETRY TOPIC REVIEW QUARTER 3 LOGS ALGEBRA /TRIGONMETRY TOPIC REVIEW QUARTER LOGS Cnverting frm Epnentil frm t Lgrithmic frm: E B N Lg BN E Americn Ben t French Lg Ben-n Lg Prperties: Lg Prperties lg (y) lg + lg y lg y lg lg y lg () lg

More information

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

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

More information

Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Data Mg: cepts ad Techques 3 rd ed. hapter 10 1 Evaluat f lusterg lusterg evaluat assesses the feasblty f clusterg aalyss a data set ad the qualty f the results geerated by a clusterg methd. Three mar

More information

ECE570 Lecture 14: Qualitative Physics

ECE570 Lecture 14: Qualitative Physics ECE570 Lecture 14: Quttve Physcs Jeffrey Mrk Sskd Sch f Eectrc d Cmuter Egeerg F 2017 Sskd (Purdue ECE) ECE570 Lecture 14: Quttve Physcs F 2017 1 / 20 A Physc System Sskd (Purdue ECE) ECE570 Lecture 14:

More information

Waveshapping Circuits and Data Converters. Lesson #17 Comparators and Schmitt Triggers Section BME 373 Electronics II J.

Waveshapping Circuits and Data Converters. Lesson #17 Comparators and Schmitt Triggers Section BME 373 Electronics II J. Waeshappg Crcuts and Data Cnerters Lessn #7 Cmparatrs and Schmtt Trggers Sectn. BME 7 Electrncs II 0 Waeshappg Crcuts and Data Cnerters Cmparatrs and Schmtt Trggers Astable Multbratrs and Tmers ectfers,

More information

Exam-style practice: A Level

Exam-style practice: A Level Exa-tye practce: A Leve a Let X dete the dtrbut ae ad X dete the dtrbut eae The dee the rad varabe Y X X j j The expected vaue Y : E( Y) EX X j j EX EX j j EX E X 7 The varace : Var( Y) VarX VarX j j Var(

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

OVERVIEW Using Similarity and Proving Triangle Theorems G.SRT.4

OVERVIEW Using Similarity and Proving Triangle Theorems G.SRT.4 OVRVIW Using Similrity nd Prving Tringle Therems G.SRT.4 G.SRT.4 Prve therems ut tringles. Therems include: line prllel t ne side f tringle divides the ther tw prprtinlly, nd cnversely; the Pythgren Therem

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

11.2. Infinite Series

11.2. Infinite Series .2 Infinite Series 76.2 Infinite Series An infinite series is the sum f n infinite seuence f numbers + 2 + 3 + Á + n + Á The gl f this sectin is t understnd the mening f such n infinite sum nd t develp

More information

If σis unknown. Properties of t distribution. 6.3 One and Two Sample Inferences for Means. What is the correct multiplier? t

If σis unknown. Properties of t distribution. 6.3 One and Two Sample Inferences for Means. What is the correct multiplier? t /8/009 6.3 Oe a Tw Samle Iferece fr Mea If i kw a 95% Cfiece Iterval i 96 ±.96 96.96 ± But i ever kw. If i ukw Etimate by amle taar eviati The etimate taar errr f the mea will be / Uig the etimate taar

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

Chapter 3.1: Polynomial Functions

Chapter 3.1: Polynomial Functions Ntes 3.1: Ply Fucs Chapter 3.1: Plymial Fuctis I Algebra I ad Algebra II, yu ecutered sme very famus plymial fuctis. I this secti, yu will meet may ther members f the plymial family, what sets them apart

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

Super-efficiency Models, Part II

Super-efficiency Models, Part II Super-efficiec Mdels, Part II Emilia Niskae The 4th f Nvember S steemiaalsi Ctets. Etesis t Variable Returs-t-Scale (0.4) S steemiaalsi Radial Super-efficiec Case Prblems with Radial Super-efficiec Case

More information

20.2. The Transform and its Inverse. Introduction. Prerequisites. Learning Outcomes

20.2. The Transform and its Inverse. Introduction. Prerequisites. Learning Outcomes The Trnform nd it Invere 2.2 Introduction In thi Section we formlly introduce the Lplce trnform. The trnform i only pplied to cul function which were introduced in Section 2.1. We find the Lplce trnform

More information

Linear Regression with One Regressor

Linear Regression with One Regressor Lear Regresso wth Oe Regressor AIM QA.7. Expla how regresso aalyss ecoometrcs measures the relatoshp betwee depedet ad depedet varables. A regresso aalyss has the goal of measurg how chages oe varable,

More information

Goal of the Lecture. Lecture Structure. FWF 410: Analysis of Habitat Data I: Definitions and Descriptive Statistics

Goal of the Lecture. Lecture Structure. FWF 410: Analysis of Habitat Data I: Definitions and Descriptive Statistics FWF : Aalyss f Habtat Data I: Defts ad Descrptve tatstcs Number f Cveys A A B Bur Dsk Bur/Dsk Habtat Treatmet Matthew J. Gray, Ph.D. Cllege f Agrcultural ceces ad Natural Resurces Uversty f Teessee-Kvlle

More information

Chapter #5 EEE Control Systems

Chapter #5 EEE Control Systems Sprig EEE Chpter #5 EEE Cotrol Sytem Deig Bed o Root Locu Chpter / Sprig EEE Deig Bed Root Locu Led Cotrol (equivlet to PD cotrol) Ued whe the tedy tte propertie of the ytem re ok but there i poor performce,

More information

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

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

More information

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

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

Internal vs. external validity. External validity. This section is based on Stock and Watson s Chapter 9.

Internal vs. external validity. External validity. This section is based on Stock and Watson s Chapter 9. Sectin 7 Mdel Assessment This sectin is based n Stck and Watsn s Chapter 9. Internal vs. external validity Internal validity refers t whether the analysis is valid fr the ppulatin and sample being studied.

More information

3. REVIEW OF PROPERTIES OF EIGENVALUES AND EIGENVECTORS

3. REVIEW OF PROPERTIES OF EIGENVALUES AND EIGENVECTORS . REVIEW OF PROPERTIES OF EIGENVLUES ND EIGENVECTORS. EIGENVLUES ND EIGENVECTORS We hll ow revew ome bc fct from mtr theory. Let be mtr. clr clled egevlue of f there et ozero vector uch tht Emle: Let 9

More information

Distributions, spatial statistics and a Bayesian perspective

Distributions, spatial statistics and a Bayesian perspective Distributins, spatial statistics and a Bayesian perspective Dug Nychka Natinal Center fr Atmspheric Research Distributins and densities Cnditinal distributins and Bayes Thm Bivariate nrmal Spatial statistics

More information

Preliminary Examinations: Upper V Mathematics Paper 1

Preliminary Examinations: Upper V Mathematics Paper 1 relmr Emtos: Upper V Mthemtcs per Jul 03 Emer: G Evs Tme: 3 hrs Modertor: D Grgortos Mrks: 50 INSTRUCTIONS ND INFORMTION Ths questo pper sts of 0 pges, cludg swer Sheet pge 8 d Iformto Sheet pges 9 d 0

More information

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

Ch. 1 Introduction to Estimation 1/15

Ch. 1 Introduction to Estimation 1/15 Ch. Itrducti t stimati /5 ample stimati Prblem: DSB R S f M f s f f f ; f, φ m tcsπf t + φ t f lectrics dds ise wt usually white BPF & mp t s t + w t st. lg. f & φ X udi mp cs π f + φ t Oscillatr w/ f

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

Modeling and Predicting Sequences: HMM and (may be) CRF. Amr Ahmed Feb 25

Modeling and Predicting Sequences: HMM and (may be) CRF. Amr Ahmed Feb 25 Modelg d redcg Sequeces: HMM d m be CRF Amr Ahmed 070 Feb 25 Bg cure redcg Sgle Lbel Ipu : A se of feures: - Bg of words docume - Oupu : Clss lbel - Topc of he docume - redcg Sequece of Lbels Noo Noe:

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

ECE 2100 Circuit Analysis

ECE 2100 Circuit Analysis ECE 2100 Circuit Analysis Lessn 25 Chapter 9 & App B: Passive circuit elements in the phasr representatin Daniel M. Litynski, Ph.D. http://hmepages.wmich.edu/~dlitynsk/ ECE 2100 Circuit Analysis Lessn

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

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

Stress Concentrations

Stress Concentrations Stress Cncentrtins A stress cncentrtin refers t n re in bject where stress increses ver very shrt distnce (i.e., where high stress grdient eists Stress cncentrtins typiclly ccur due t sme lclized chnge

More information

1 4 6 is symmetric 3 SPECIAL MATRICES 3.1 SYMMETRIC MATRICES. Defn: A matrix A is symmetric if and only if A = A, i.e., a ij =a ji i, j. Example 3.1.

1 4 6 is symmetric 3 SPECIAL MATRICES 3.1 SYMMETRIC MATRICES. Defn: A matrix A is symmetric if and only if A = A, i.e., a ij =a ji i, j. Example 3.1. SPECIAL MATRICES SYMMETRIC MATRICES Def: A mtr A s symmetr f d oly f A A, e,, Emple A s symmetr Def: A mtr A s skew symmetr f d oly f A A, e,, Emple A s skew symmetr Remrks: If A s symmetr or skew symmetr,

More information

BC Calculus Review Sheet. converges. Use the integral: L 1

BC Calculus Review Sheet. converges. Use the integral: L 1 BC Clculus Review Sheet Whe yu see the wrds.. Fid the re f the uuded regi represeted y the itegrl (smetimes f ( ) clled hriztl imprper itegrl).. Fid the re f differet uuded regi uder f() frm (,], where

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

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

(Communicated at the meeting of January )

(Communicated at the meeting of January ) Physics. - Establishment f an Abslute Scale fr the herm-electric Frce. By G. BOR ELlUS. W. H. KEESOM. C. H. JOHANSSON and J. O. LND E. Supplement N0. 69b t the Cmmunicatins frm the Physical Labratry at

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

Flipping Physics Lecture Notes: Simple Harmonic Motion Introduction via a Horizontal Mass-Spring System

Flipping Physics Lecture Notes: Simple Harmonic Motion Introduction via a Horizontal Mass-Spring System Flipping Physics Lecture Ntes: Simple Harmnic Mtin Intrductin via a Hrizntal Mass-Spring System A Hrizntal Mass-Spring System is where a mass is attached t a spring, riented hrizntally, and then placed

More information

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b CS 70 Dscrete Mathematcs ad Probablty Theory Fall 206 Sesha ad Walrad DIS 0b. Wll I Get My Package? Seaky delvery guy of some compay s out delverg packages to customers. Not oly does he had a radom package

More information

Quantum Mechanics for Scientists and Engineers. David Miller

Quantum Mechanics for Scientists and Engineers. David Miller Quatum Mechaics fr Scietists ad Egieers David Miller Time-depedet perturbati thery Time-depedet perturbati thery Time-depedet perturbati basics Time-depedet perturbati thery Fr time-depedet prblems csider

More information

4-4 E-field Calculations using Coulomb s Law

4-4 E-field Calculations using Coulomb s Law 1/11/5 ection_4_4_e-field_clcultion_uing_coulomb_lw_empty.doc 1/1 4-4 E-field Clcultion uing Coulomb Lw Reding Aignment: pp. 9-98 Specificlly: 1. HO: The Uniform, Infinite Line Chrge. HO: The Uniform Dik

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

Fall 2012 Analysis of Experimental Measurements B. Eisenstein/rev. S. Errede. with respect to λ. 1. χ λ χ λ ( ) λ, and thus:

Fall 2012 Analysis of Experimental Measurements B. Eisenstein/rev. S. Errede. with respect to λ. 1. χ λ χ λ ( ) λ, and thus: More on χ nd errors : uppose tht we re fttng for sngle -prmeter, mnmzng: If we epnd The vlue χ ( ( ( ; ( wth respect to. χ n Tlor seres n the vcnt of ts mnmum vlue χ ( mn χ χ χ χ + + + mn mnmzes χ, nd

More information

8. INVERSE Z-TRANSFORM

8. INVERSE Z-TRANSFORM 8. INVERSE Z-TRANSFORM The proce by whch Z-trnform of tme ere, nmely X(), returned to the tme domn clled the nvere Z-trnform. The nvere Z-trnform defned by: Computer tudy Z X M-fle trn.m ued to fnd nvere

More information

Lecture 2. Basic Semiconductor Physics

Lecture 2. Basic Semiconductor Physics Lecture Basc Semcductr Physcs I ths lecture yu wll lear: What are semcductrs? Basc crystal structure f semcductrs Electrs ad hles semcductrs Itrsc semcductrs Extrsc semcductrs -ded ad -ded semcductrs Semcductrs

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

CHAPTER 5 ENTROPY GENERATION Instructor: Prof. Dr. Uğur Atikol

CHAPTER 5 ENTROPY GENERATION Instructor: Prof. Dr. Uğur Atikol CAPER 5 ENROPY GENERAION Istructr: Pr. Dr. Uğur Atkl Chapter 5 Etrpy Geerat (Exergy Destruct Outle st Avalable rk Cycles eat ege cycles Rergerat cycles eat pump cycles Nlw Prcesses teady-flw Prcesses Exergy

More information

MAT 1275: Introduction to Mathematical Analysis

MAT 1275: Introduction to Mathematical Analysis MAT 75: Intrdutin t Mthemtil Anlysis Dr. A. Rzenlyum Trignmetri Funtins fr Aute Angles Definitin f six trignmetri funtins Cnsider the fllwing girffe prlem: A girffe s shdw is 8 meters. Hw tll is the girffe

More information

Exercises H /OOA> f Wo AJoTHS l^»-l S. m^ttrt /A/ ?C,0&L6M5 INFERENCE FOR DISTRIBUTIONS OF CATEGORICAL DATA. tts^e&n tai-ns 5 2%-cas-hews^, 27%

Exercises H /OOA> f Wo AJoTHS l^»-l S. m^ttrt /A/ ?C,0&L6M5 INFERENCE FOR DISTRIBUTIONS OF CATEGORICAL DATA. tts^e&n tai-ns 5 2%-cas-hews^, 27% /A/ mttrt?c,&l6m5 INFERENCE FOR DISTRIBUTIONS OF CATEGORICAL DATA Exercses, nuts! A cmpany clams that each batch f ttse&n ta-ns 5 2%-cas-hews, 27% almnds, 13% macadama nuts, and 8% brazl nuts. T test ths

More information

Fast Fourier Transform 1) Legendre s Interpolation 2) Vandermonde Matrix 3) Roots of Unity 4) Polynomial Evaluation

Fast Fourier Transform 1) Legendre s Interpolation 2) Vandermonde Matrix 3) Roots of Unity 4) Polynomial Evaluation Algorithm Desig d Alsis Victor Admchi CS 5-45 Sprig 4 Lecture 3 J 7, 4 Cregie Mello Uiversit Outlie Fst Fourier Trsform ) Legedre s Iterpoltio ) Vdermode Mtri 3) Roots of Uit 4) Polomil Evlutio Guss (777

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

2. Elementary Linear Algebra Problems

2. Elementary Linear Algebra Problems . Eleety e lge Pole. BS: B e lge Suoute (Pog pge wth PCK) Su of veto opoet:. Coputto y f- poe: () () () (3) N 3 4 5 3 6 4 7 8 Full y tee Depth te tep log()n Veto updte the f- poe wth N : ) ( ) ( ) ( )

More information

Cambridge Assessment International Education Cambridge Ordinary Level. Published

Cambridge Assessment International Education Cambridge Ordinary Level. Published Cambridge Assessment Internatinal Educatin Cambridge Ordinary Level ADDITIONAL MATHEMATICS 4037/1 Paper 1 Octber/Nvember 017 MARK SCHEME Maximum Mark: 80 Published This mark scheme is published as an aid

More information

ECE 5318/6352 Antenna Engineering. Spring 2006 Dr. Stuart Long. Chapter 6. Part 7 Schelkunoff s Polynomial

ECE 5318/6352 Antenna Engineering. Spring 2006 Dr. Stuart Long. Chapter 6. Part 7 Schelkunoff s Polynomial ECE 538/635 Antenna Engineering Spring 006 Dr. Stuart Lng Chapter 6 Part 7 Schelkunff s Plynmial 7 Schelkunff s Plynmial Representatin (fr discrete arrays) AF( ψ ) N n 0 A n e jnψ N number f elements in

More information

Modern Physics. Unit 6: Hydrogen Atom - Radiation Lecture 6.1: The Radial Probability Density. Ron Reifenberger Professor of Physics Purdue University

Modern Physics. Unit 6: Hydrogen Atom - Radiation Lecture 6.1: The Radial Probability Density. Ron Reifenberger Professor of Physics Purdue University Mdern Physics Unit 6: Hydrgen Atm - Rditin Lecture 6.1: The Rdil Prbbility Density Rn Reifenberger Prfessr f Physics Purdue University 1 Prbbility Density Prbbility Density * ΨΨ = Ψ In 1-D, the prbbility

More information

Math 153: Lecture Notes For Chapter 1

Math 153: Lecture Notes For Chapter 1 Mth : Lecture Notes For Chpter Sectio.: Rel Nubers Additio d subtrctios : Se Sigs: Add Eples: = - - = - Diff. Sigs: Subtrct d put the sig of the uber with lrger bsolute vlue Eples: - = - = - Multiplictio

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

BIO752: Advanced Methods in Biostatistics, II TERM 2, 2010 T. A. Louis. BIO 752: MIDTERM EXAMINATION: ANSWERS 30 November 2010

BIO752: Advanced Methods in Biostatistics, II TERM 2, 2010 T. A. Louis. BIO 752: MIDTERM EXAMINATION: ANSWERS 30 November 2010 BIO752: Advaced Methds i Bistatistics, II TERM 2, 2010 T. A. Luis BIO 752: MIDTERM EXAMINATION: ANSWERS 30 Nvember 2010 Questi #1 (15 pits): Let X ad Y be radm variables with a jit distributi ad assume

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

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

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

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

Solutions to Problems. Then, using the formula for the speed in a parabolic orbit (equation ), we have

Solutions to Problems. Then, using the formula for the speed in a parabolic orbit (equation ), we have Slutins t Prblems. Nttin: V speed f cmet immeditely befre cllisin. V speed f cmbined bject immeditely fter cllisin, mmentum is cnserved. V, becuse liner + k q perihelin distnce f riginl prblic rbit, s

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

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

MA123, Chapter 9: Computing some integrals (pp )

MA123, Chapter 9: Computing some integrals (pp ) MA13, Chpter 9: Computig some itegrls (pp. 189-05) Dte: Chpter Gols: Uderstd how to use bsic summtio formuls to evlute more complex sums. Uderstd how to compute its of rtiol fuctios t ifiity. Uderstd how

More information

Lead/Lag Compensator Frequency Domain Properties and Design Methods

Lead/Lag Compensator Frequency Domain Properties and Design Methods Lectures 6 and 7 Lead/Lag Cmpensatr Frequency Dmain Prperties and Design Methds Definitin Cnsider the cmpensatr (ie cntrller Fr, it is called a lag cmpensatr s K Fr s, it is called a lead cmpensatr Ntatin

More information

Physics 102. Final Examination. Spring Semester ( ) P M. Fundamental constants. n = 10P

Physics 102. Final Examination. Spring Semester ( ) P M. Fundamental constants. n = 10P ε µ0 N mp M G T Kuwit University hysics Deprtment hysics 0 Finl Exmintin Spring Semester (0-0) My, 0 Time: 5:00 M :00 M Nme.Student N Sectin N nstructrs: Drs. bdelkrim, frsheh, Dvis, Kkj, Ljk, Mrfi, ichler,

More information

ADORO TE DEVOTE (Godhead Here in Hiding) te, stus bat mas, la te. in so non mor Je nunc. la in. tis. ne, su a. tum. tas: tur: tas: or: ni, ne, o:

ADORO TE DEVOTE (Godhead Here in Hiding) te, stus bat mas, la te. in so non mor Je nunc. la in. tis. ne, su a. tum. tas: tur: tas: or: ni, ne, o: R TE EVTE (dhd H Hdg) L / Mld Kbrd gú s v l m sl c m qu gs v nns V n P P rs l mul m d lud 7 súb Fí cón ví f f dó, cru gs,, j l f c r s m l qum t pr qud ct, us: ns,,,, cs, cut r l sns m / m fí hó sn sí

More information

PRINCE SULTAN UNIVERSITY Department of Mathematical Sciences Final Examination First Semester ( ) STAT 271.

PRINCE SULTAN UNIVERSITY Department of Mathematical Sciences Final Examination First Semester ( ) STAT 271. PRINCE SULTAN UNIVERSITY Deprtment f Mthemticl Sciences Finl Exmintin First Semester (007 008) STAT 71 Student Nme: Mrk Student Number: Sectin Number: Techer Nme: Time llwed is ½ hurs. Attendnce Number:

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

Spring Term 1 SPaG Mat 4

Spring Term 1 SPaG Mat 4 Spg Tem 1 SPG Mt Cmplete the tle g sux t ech u t mke jectve Nu Ajectve c e C u vete cmms t ths ect speech setece? Hw u cete tht lvel pctue? ske the cuus gl C u wte et ech these hmphe ws? Use ct t help

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

Chapter 3 Single Random Variables and Probability Distributions (Part 2)

Chapter 3 Single Random Variables and Probability Distributions (Part 2) Chpter 3 Single Rndom Vriles nd Proilit Distriutions (Prt ) Contents Wht is Rndom Vrile? Proilit Distriution Functions Cumultive Distriution Function Proilit Densit Function Common Rndom Vriles nd their

More information

Lecture 13: Markov Chain Monte Carlo. Gibbs sampling

Lecture 13: Markov Chain Monte Carlo. Gibbs sampling Lecture 13: Markv hain Mnte arl Gibbs sampling Gibbs sampling Markv chains 1 Recall: Apprximate inference using samples Main idea: we generate samples frm ur Bayes net, then cmpute prbabilities using (weighted)

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

Chapter 7. , and is unknown and n 30 then X ~ t n

Chapter 7. , and is unknown and n 30 then X ~ t n Chpter 7 Sectio 7. t-ditributio ( 3) Summry: C.L.T. : If the rdom mple of ize 3 come from ukow popultio with me d S.D. where i kow or ukow, the X ~ N,. Note: The hypothei tetig d cofidece itervl re built

More information

**DO NOT ONLY RELY ON THIS STUDY GUIDE!!!**

**DO NOT ONLY RELY ON THIS STUDY GUIDE!!!** Tpics lists: UV-Vis Absrbance Spectrscpy Lab & ChemActivity 3-6 (nly thrugh 4) I. UV-Vis Absrbance Spectrscpy Lab Beer s law Relates cncentratin f a chemical species in a slutin and the absrbance f that

More information