Experimental Errors and Error Analysis

Size: px
Start display at page:

Download "Experimental Errors and Error Analysis"

Transcription

1 Expermental Errors and Error Analss Rajee Prabhakar Unerst of Texas at Astn, Astn, TX Freeman Research Grop Meetng March 10, 004

2 Topcs coered Tpes of expermental errors Redcng errors Descrbng errors qanttatel Measred arables Qanttes calclated from measred arables (error propagaton) Least-sqares ft straght lne polnomal arbtrar fncton Ref: Bengton and Robnson, Data Redcton and Error Analss for the Phscal Scences, nd edton, McGraw Hll, NY 199

3 Qalt of Experments and Expermental Error Accrac Measre of correctness of reslt Precson Measre of reprodcblt of reslt Tpes of errors Mstakes Wrong membrane sed Nmber recorded ncorrectl, 1.9 wrtten as 1.9 Sstematc error Change n membrane castng condtons Incorrect calbraton Random error Flctatons n temperatre, pressre etc.

4 Redcng Errors Plannng experments carefll Objecte s to nderstand and redce sorces of sstematc error Don t ge n to the t s alwas been done ths wa mentalt Use more accrate nstrments Repeat the measrement, f possble

5 Uncertantes n Measred Varables Flctatons Often ndependent of actal ale Readng an analog nstrment ½ of smallest scale dson/least cont Uncertant estmate standard deaton, ½ of least cont Repeat measrement f possble and take standard deaton of the data

6 Parameters calclated from measred arables Propagaton of Errors V e.g. Volme measrement = LW H o o o o If the measred ales are L, W and H, V V V V( L, W, H) V + L W H o + + L W H WoHo LoHo LoWo Error, V = V V o N N ( ) ( ) 1 1 = V o = N = 1 = 1 Varance, V V lm V V N N

7 Propagaton of errors general formla ( ) 1 ( ) ( ) ( ( ) ( ) ( (, ) ) 1 lm 1 lm ) N N N f N N = = = + = = + + = +, Neglectng the co - arance + + (1) () co-arance

8 Volme example smplfed eqaton + V V V + + V L W H L W H SnceV = LWH, ( WH ) + ( HL) + ( LW ) V L W H V V V + + V L W H L W H V L W H V + + L W H Smplfed form of eqaton ald when the dependent arable s a prodct of ndependent arables to the 1 or -1 power onl

9 Example - Lmtatons of smplfed eqaton If V = π R H, V V V R + H R H ( RH ) + ( R ) π π V R H V V V R + H R H V R H V + R H

10 Least-sqares ft to a straght lne = a + b*x Objecte s to fnd the ales of a and b that prode the best predcton of for a gen x Goodness-of-ft parameter, χ 1 χ = ( a bx ) (3) To mnmze the error, χ χ = 0 = a b Resltng eqatons for a and b pgs. 104 and 113 of Bengton Errors n a and b - Eqatons on pgs. 109 & 114 of Bengton

11 Least-sqares ft to an eqaton lnear n the coeffcents Example, Polnomal: = a + b*x + c*x +.. Other fnctons: = a + b*e x Smlar mathematcal treatment as for straght lne. Howeer, determnants get bgger and more nmeros. See pg 117 (last 3 lnes) and 118 for example of qadratc eqaton. Alternate method Matrx solton (pg. 11)

12 Matrx Method for eqaton lnear n coeffcents x ( ) = af( x) k k k = 1 The coeffcent matrx a s gen b, a = βα 1 m where 1 β = f ( x ) ; row matrx k k α 1 = f ( x ) f ( x) ; smmetrc matrx lk l k Error n the coeffcents are elements of the nerse α matrx - Dagonal elements are arances - Off-dagonal elements are co-arances = α 1 ak kk = α 1 aa l k lk

13 Example Qadratc ft to Permeablt data P= b+ c p+ d( p) Compare wth, m x ( ) = af( x) = af( x) + af( x) + af( x) k k k = 1 Therefore, a = b; a = c; a = d; f = 1; f = p; f = ( p) Sole for a, b and c sng matrx method Yo wll also obtan arances and coarances (preos slde) Then, calclate error n P sng eqs. (1) or (). Example n Excel fle: Error analss demo.xls; sheet: P For more detals, ncldng an example, Bengton: pgs

14 Least-sqares ft to an fncton Best-ft for fnctons that are not lnear n the coeffcents Tral-and-error methods (Ch. 8, Bengton) Fnd coeffcent ales b sng SOLVER fncton n Excel To get errors, ar one coeffcent at a tme to ncrease b 1 χ The change n the coeffcent s t s standard deaton

15 Example S ft to dal mode eqaton f * g S = e+ 1 + g* p Determne coeffcents b mnmzng χ, N 1 S S calc expt χ = N = 1 b arng the coeffcents e, f and g. Change ntal gess ales of the coeffcents to confrm best - ft ales. Then determne for each coeffcent, b the method descrbed on the preos slde. Example n Excel fle: Error analss demo.xls; sheet: S

16 Calclatng D eff from best ft eqatons of P and S ( ) dp dp = + D C eff P p d p p dc p + ( ) + 3 ( ) = = fg e + (1 + gp) b c p d p NUMR DENR Calclate NUMR & DENR and ther errors. Calclate D eff. Error n D eff, D eff NUMR DENR = + D NUMR DENR eff

17 References Bengton Pgs. 1-6: Accrac, precson and ncertantes Pgs : Propagaton of errors wth specfc examples Pgs : Least-sqares ft to a straght lne Pgs : Least-sqares ft to a fncton lnear n the coeffcents, ncldng an example for a qadratc ft Mcrosoft Excel Help MINVERSE worksheet fncton MMULT worksheet fncton

18 Qestons

ESS 265 Spring Quarter 2005 Time Series Analysis: Error Analysis

ESS 265 Spring Quarter 2005 Time Series Analysis: Error Analysis ESS 65 Sprng Qarter 005 Tme Seres Analyss: Error Analyss Lectre 9 May 3, 005 Some omenclatre Systematc errors Reprodcbly errors that reslt from calbraton errors or bas on the part of the obserer. Sometmes

More information

STRATEGIES FOR BUILDING AN AC-DC TRANSFER SCALE

STRATEGIES FOR BUILDING AN AC-DC TRANSFER SCALE Smposo de Metrología al 7 de Octbre de 00 STRATEGIES FOR BUILDING AN AC-DC TRANSFER SCALE Héctor Laz, Fernando Kornblt, Lcas D Lllo Insttto Naconal de Tecnología Indstral (INTI) Avda. Gral Paz, 0 San Martín,

More information

EMISSION MEASUREMENTS IN DUAL FUELED INTERNAL COMBUSTION ENGINE TESTS

EMISSION MEASUREMENTS IN DUAL FUELED INTERNAL COMBUSTION ENGINE TESTS XVIII IEKO WORLD NGRESS etrology for a Sstanable Development September, 17, 006, Ro de Janero, Brazl EISSION EASUREENTS IN DUAL FUELED INTERNAL BUSTION ENGINE TESTS A.F.Orlando 1, E.Santos, L.G.do Val

More information

C PLANE ELASTICITY PROBLEM FORMULATIONS

C PLANE ELASTICITY PROBLEM FORMULATIONS C M.. Tamn, CSMLab, UTM Corse Content: A ITRODUCTIO AD OVERVIEW mercal method and Compter-Aded Engneerng; Phscal problems; Mathematcal models; Fnte element method. B REVIEW OF -D FORMULATIOS Elements and

More information

The Impact of Instrument Transformer Accuracy Class on the Accuracy of Hybrid State Estimation

The Impact of Instrument Transformer Accuracy Class on the Accuracy of Hybrid State Estimation 1 anel Sesson: Addressng Uncertanty, Data alty and Accracy n State Estmaton The mpact of nstrment Transformer Accracy Class on the Accracy of Hybrd State Estmaton Elas Kyrakdes and Markos Aspro KOS Research

More information

AE/ME 339. K. M. Isaac. 8/31/2004 topic4: Implicit method, Stability, ADI method. Computational Fluid Dynamics (AE/ME 339) MAEEM Dept.

AE/ME 339. K. M. Isaac. 8/31/2004 topic4: Implicit method, Stability, ADI method. Computational Fluid Dynamics (AE/ME 339) MAEEM Dept. AE/ME 339 Comptatonal Fld Dynamcs (CFD) Comptatonal Fld Dynamcs (AE/ME 339) Implct form of dfference eqaton In the prevos explct method, the solton at tme level n,,n, depended only on the known vales of,

More information

Investigation of Uncertainty Sources in the Determination of Beta Emitting Tritium in the UAL

Investigation of Uncertainty Sources in the Determination of Beta Emitting Tritium in the UAL Investgaton of Uncertanty Sorces n the Determnaton of Beta Emttng Trtm n the UL. Specfcaton lqd scntllaton conter LSC s sed to determne the actvty concentraton n Bq/dm 3 of the beta emttng trtm n rne samples.

More information

Fall 2012 Analysis of Experimental Measurements B. Eisenstein/rev. S. Errede

Fall 2012 Analysis of Experimental Measurements B. Eisenstein/rev. S. Errede Fall 0 Analyss of Expermental easurements B. Esensten/rev. S. Errede We now reformulate the lnear Least Squares ethod n more general terms, sutable for (eventually extendng to the non-lnear case, and also

More information

Regression Analysis. Regression Analysis

Regression Analysis. Regression Analysis Regresson Analyss Smple Regresson Multvarate Regresson Stepwse Regresson Replcaton and Predcton Error 1 Regresson Analyss In general, we "ft" a model by mnmzng a metrc that represents the error. n mn (y

More information

Reading Assignment. Panel Data Cross-Sectional Time-Series Data. Chapter 16. Kennedy: Chapter 18. AREC-ECON 535 Lec H 1

Reading Assignment. Panel Data Cross-Sectional Time-Series Data. Chapter 16. Kennedy: Chapter 18. AREC-ECON 535 Lec H 1 Readng Assgnment Panel Data Cross-Sectonal me-seres Data Chapter 6 Kennedy: Chapter 8 AREC-ECO 535 Lec H Generally, a mxtre of cross-sectonal and tme seres data y t = β + β x t + β x t + + β k x kt + e

More information

BAR & TRUSS FINITE ELEMENT. Direct Stiffness Method

BAR & TRUSS FINITE ELEMENT. Direct Stiffness Method BAR & TRUSS FINITE ELEMENT Drect Stness Method FINITE ELEMENT ANALYSIS AND APPLICATIONS INTRODUCTION TO FINITE ELEMENT METHOD What s the nte element method (FEM)? A technqe or obtanng approxmate soltons

More information

8/25/17. Data Modeling. Data Modeling. Data Modeling. Patrice Koehl Department of Biological Sciences National University of Singapore

8/25/17. Data Modeling. Data Modeling. Data Modeling. Patrice Koehl Department of Biological Sciences National University of Singapore 8/5/17 Data Modelng Patrce Koehl Department of Bologcal Scences atonal Unversty of Sngapore http://www.cs.ucdavs.edu/~koehl/teachng/bl59 koehl@cs.ucdavs.edu Data Modelng Ø Data Modelng: least squares Ø

More information

Numerical Methods. ME Mechanical Lab I. Mechanical Engineering ME Lab I

Numerical Methods. ME Mechanical Lab I. Mechanical Engineering ME Lab I 5 9 Mechancal Engneerng -.30 ME Lab I ME.30 Mechancal Lab I Numercal Methods Volt Sne Seres.5 0.5 SIN(X) 0 3 7 5 9 33 37 4 45 49 53 57 6 65 69 73 77 8 85 89 93 97 0-0.5 Normalzed Squared Functon - 0.07

More information

Traceability and uncertainty for phase measurements

Traceability and uncertainty for phase measurements Traceablty and ncertanty for phase measrements Karel Dražl Czech Metrology Insttte Abstract In recent tme the problems connected wth evalatng and expressng ncertanty n complex S-parameter measrements have

More information

Complex Numbers Practice 0708 & SP 1. The complex number z is defined by

Complex Numbers Practice 0708 & SP 1. The complex number z is defined by IB Math Hgh Leel: Complex Nmbers Practce 0708 & SP Complex Nmbers Practce 0708 & SP. The complex nmber z s defned by π π π π z = sn sn. 6 6 Ale - Desert Academy (a) Express z n the form re, where r and

More information

U-Pb Geochronology Practical: Background

U-Pb Geochronology Practical: Background U-Pb Geochronology Practcal: Background Basc Concepts: accuracy: measure of the dfference between an expermental measurement and the true value precson: measure of the reproducblty of the expermental result

More information

Resource Allocation and Decision Analysis (ECON 8010) Spring 2014 Foundations of Regression Analysis

Resource Allocation and Decision Analysis (ECON 8010) Spring 2014 Foundations of Regression Analysis Resource Allocaton and Decson Analss (ECON 800) Sprng 04 Foundatons of Regresson Analss Readng: Regresson Analss (ECON 800 Coursepak, Page 3) Defntons and Concepts: Regresson Analss statstcal technques

More information

PHYS 450 Spring semester Lecture 02: Dealing with Experimental Uncertainties. Ron Reifenberger Birck Nanotechnology Center Purdue University

PHYS 450 Spring semester Lecture 02: Dealing with Experimental Uncertainties. Ron Reifenberger Birck Nanotechnology Center Purdue University PHYS 45 Sprng semester 7 Lecture : Dealng wth Expermental Uncertantes Ron Refenberger Brck anotechnology Center Purdue Unversty Lecture Introductory Comments Expermental errors (really expermental uncertantes)

More information

A REVIEW OF ERROR ANALYSIS

A REVIEW OF ERROR ANALYSIS A REVIEW OF ERROR AALYI EEP Laborator EVE-4860 / MAE-4370 Updated 006 Error Analss In the laborator we measure phscal uanttes. All measurements are subject to some uncertantes. Error analss s the stud

More information

Bruce A. Draper & J. Ross Beveridge, January 25, Geometric Image Manipulation. Lecture #1 January 25, 2013

Bruce A. Draper & J. Ross Beveridge, January 25, Geometric Image Manipulation. Lecture #1 January 25, 2013 Brce A. Draper & J. Ross Beerdge, Janar 5, Geometrc Image Manplaton Lectre # Janar 5, Brce A. Draper & J. Ross Beerdge, Janar 5, Image Manplaton: Contet To start wth the obos, an mage s a D arra of pels

More information

MEMBRANE ELEMENT WITH NORMAL ROTATIONS

MEMBRANE ELEMENT WITH NORMAL ROTATIONS 9. MEMBRANE ELEMENT WITH NORMAL ROTATIONS Rotatons Mst Be Compatble Between Beam, Membrane and Shell Elements 9. INTRODUCTION { XE "Membrane Element" }The comple natre of most bldngs and other cvl engneerng

More information

Lecture 16 Statistical Analysis in Biomaterials Research (Part II)

Lecture 16 Statistical Analysis in Biomaterials Research (Part II) 3.051J/0.340J 1 Lecture 16 Statstcal Analyss n Bomaterals Research (Part II) C. F Dstrbuton Allows comparson of varablty of behavor between populatons usng test of hypothess: σ x = σ x amed for Brtsh statstcan

More information

OPTIMISATION. Introduction Single Variable Unconstrained Optimisation Multivariable Unconstrained Optimisation Linear Programming

OPTIMISATION. Introduction Single Variable Unconstrained Optimisation Multivariable Unconstrained Optimisation Linear Programming OPTIMIATION Introducton ngle Varable Unconstraned Optmsaton Multvarable Unconstraned Optmsaton Lnear Programmng Chapter Optmsaton /. Introducton In an engneerng analss, sometmes etremtes, ether mnmum or

More information

Statistics for Managers Using Microsoft Excel/SPSS Chapter 13 The Simple Linear Regression Model and Correlation

Statistics for Managers Using Microsoft Excel/SPSS Chapter 13 The Simple Linear Regression Model and Correlation Statstcs for Managers Usng Mcrosoft Excel/SPSS Chapter 13 The Smple Lnear Regresson Model and Correlaton 1999 Prentce-Hall, Inc. Chap. 13-1 Chapter Topcs Types of Regresson Models Determnng the Smple Lnear

More information

Training Course Textbook Emma Woolliams Andreas Hueni Javier Gorroño. Intermediate Uncertainty Analysis

Training Course Textbook Emma Woolliams Andreas Hueni Javier Gorroño. Intermediate Uncertainty Analysis Intermedate Uncertanty Analyss for Earth Observaton Instrment Calbraton Modle Tranng Corse Textbook Emma Woollams Andreas Hen Javer Gorroño Ttle: Intermedate Uncertanty Analyss for Earth Observaton (Instrment

More information

Introduction to elastic wave equation. Salam Alnabulsi University of Calgary Department of Mathematics and Statistics October 15,2012

Introduction to elastic wave equation. Salam Alnabulsi University of Calgary Department of Mathematics and Statistics October 15,2012 Introdcton to elastc wave eqaton Salam Alnabls Unversty of Calgary Department of Mathematcs and Statstcs October 15,01 Otlne Motvaton Elastc wave eqaton Eqaton of moton, Defntons and The lnear Stress-

More information

Chapter 9: Statistical Inference and the Relationship between Two Variables

Chapter 9: Statistical Inference and the Relationship between Two Variables Chapter 9: Statstcal Inference and the Relatonshp between Two Varables Key Words The Regresson Model The Sample Regresson Equaton The Pearson Correlaton Coeffcent Learnng Outcomes After studyng ths chapter,

More information

Lecture 8 Mixed Models, BLUP Breeding Values

Lecture 8 Mixed Models, BLUP Breeding Values Lectre 8 Mxed Models, BLUP Breedng Vales Glherme J. M. Rosa Unverst of Wsconsn-Madson Introdcton to Qanttatve Genetcs SISG, Seattle 6 8 Jl 8 OULINE he General Lnear Model Lnear Mxed Models he Anmal Model

More information

Constraining the Sum of Multivariate Estimates. Behrang Koushavand and Clayton V. Deutsch

Constraining the Sum of Multivariate Estimates. Behrang Koushavand and Clayton V. Deutsch Constranng the Sm of Mltarate Estmates Behrang Koshaand and Clayton V. Detsch Geostatstcans are ncreasngly beng faced wth compostonal data arsng from fll geochemcal samplng or some other sorce. Logratos

More information

Statistics MINITAB - Lab 2

Statistics MINITAB - Lab 2 Statstcs 20080 MINITAB - Lab 2 1. Smple Lnear Regresson In smple lnear regresson we attempt to model a lnear relatonshp between two varables wth a straght lne and make statstcal nferences concernng that

More information

Originated from experimental optimization where measurements are very noisy Approximation can be actually more accurate than

Originated from experimental optimization where measurements are very noisy Approximation can be actually more accurate than Surrogate (approxmatons) Orgnated from expermental optmzaton where measurements are ver nos Approxmaton can be actuall more accurate than data! Great nterest now n applng these technques to computer smulatons

More information

Systematic Error Illustration of Bias. Sources of Systematic Errors. Effects of Systematic Errors 9/23/2009. Instrument Errors Method Errors Personal

Systematic Error Illustration of Bias. Sources of Systematic Errors. Effects of Systematic Errors 9/23/2009. Instrument Errors Method Errors Personal 9/3/009 Sstematc Error Illustraton of Bas Sources of Sstematc Errors Instrument Errors Method Errors Personal Prejudce Preconceved noton of true value umber bas Prefer 0/5 Small over large Even over odd

More information

C PLANE ELASTICITY PROBLEM FORMULATIONS

C PLANE ELASTICITY PROBLEM FORMULATIONS C M.. Tamn, CSMLab, UTM Corse Content: A ITRODUCTIO AD OVERVIEW mercal method and Compter-Aded Engneerng; Phscal problems; Mathematcal models; Fnte element method. B REVIEW OF -D FORMULATIOS Elements and

More information

EE215 FUNDAMENTALS OF ELECTRICAL ENGINEERING

EE215 FUNDAMENTALS OF ELECTRICAL ENGINEERING EE215 FUNDAMENTALS OF ELECTRICAL ENGINEERING TaChang Chen Unersty of Washngton, Bothell Sprng 2010 EE215 1 WEEK 8 FIRST ORDER CIRCUIT RESPONSE May 21 st, 2010 EE215 2 1 QUESTIONS TO ANSWER Frst order crcuts

More information

Lecture 10: Dimensionality reduction

Lecture 10: Dimensionality reduction Lecture : Dmensonalt reducton g The curse of dmensonalt g Feature etracton s. feature selecton g Prncpal Components Analss g Lnear Dscrmnant Analss Intellgent Sensor Sstems Rcardo Guterrez-Osuna Wrght

More information

Laboratory 3: Method of Least Squares

Laboratory 3: Method of Least Squares Laboratory 3: Method of Least Squares Introducton Consder the graph of expermental data n Fgure 1. In ths experment x s the ndependent varable and y the dependent varable. Clearly they are correlated wth

More information

Lecture 20: Hypothesis testing

Lecture 20: Hypothesis testing Lecture : Hpothess testng Much of statstcs nvolves hpothess testng compare a new nterestng hpothess, H (the Alternatve hpothess to the borng, old, well-known case, H (the Null Hpothess or, decde whether

More information

The Bellman Equation

The Bellman Equation The Bellman Eqaton Reza Shadmehr In ths docment I wll rovde an elanaton of the Bellman eqaton, whch s a method for otmzng a cost fncton and arrvng at a control olcy.. Eamle of a game Sose that or states

More information

Laboratory 1c: Method of Least Squares

Laboratory 1c: Method of Least Squares Lab 1c, Least Squares Laboratory 1c: Method of Least Squares Introducton Consder the graph of expermental data n Fgure 1. In ths experment x s the ndependent varable and y the dependent varable. Clearly

More information

Problem Set 1 Issued: Wednesday, February 11, 2015 Due: Monday, February 23, 2015

Problem Set 1 Issued: Wednesday, February 11, 2015 Due: Monday, February 23, 2015 MASSACHUSETTS INSTITUTE OF TECHNOLOGY DEPARTMENT OF MECHANICAL ENGINEERING CAMBRIDGE MASSACHUSETTS 09.9 NUMERICAL FLUID MECHANICS SPRING 05 Problem Set Issed: Wednesday Febrary 05 De: Monday Febrary 05

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Analyss of Varance and Desgn of Experment-I MODULE VII LECTURE - 3 ANALYSIS OF COVARIANCE Dr Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur Any scentfc experment s performed

More information

Measurement and Uncertainties

Measurement and Uncertainties Phs L-L Introducton Measurement and Uncertantes An measurement s uncertan to some degree. No measurng nstrument s calbrated to nfnte precson, nor are an two measurements ever performed under eactl the

More information

I. Decision trees II. Ensamble methods: Mixtures of experts

I. Decision trees II. Ensamble methods: Mixtures of experts CS 75 Machne Learnn Lectre 4 I. Decson trees II. Ensamble methods: Mtres of eperts Mlos Hasrecht mlos@cs.ptt.ed 539 Sennott Sqare CS 75 Machne Learnn Eam: Aprl 8 7 Schedle Term proects & proect presentatons:

More information

Delhi School of Economics Course 001: Microeconomic Theory Solutions to Problem Set 2.

Delhi School of Economics Course 001: Microeconomic Theory Solutions to Problem Set 2. Delh School of Economcs Corse 00: Mcroeconomc Theor Soltons to Problem Set.. Propertes of % extend to and. (a) Assme x x x. Ths mples: x % x % x ) x % x. Now sppose x % x. Combned wth x % x and transtvt

More information

Introduction to Regression

Introduction to Regression Introducton to Regresson Dr Tom Ilvento Department of Food and Resource Economcs Overvew The last part of the course wll focus on Regresson Analyss Ths s one of the more powerful statstcal technques Provdes

More information

( ) G. Narsimlu Department of Mathematics, Chaitanya Bharathi Institute of Technology, Gandipet, Hyderabad, India

( ) G. Narsimlu Department of Mathematics, Chaitanya Bharathi Institute of Technology, Gandipet, Hyderabad, India VOL. 3, NO. 9, OCTOBER 8 ISSN 89-668 ARPN Jornal of Engneerng and Appled Scences 6-8 Asan Research Pblshng Networ (ARPN). All rghts reserved. www.arpnornals.com SOLUTION OF AN UNSTEADY FLOW THROUGH POROUS

More information

SIMPLE LINEAR REGRESSION

SIMPLE LINEAR REGRESSION Smple Lnear Regresson and Correlaton Introducton Prevousl, our attenton has been focused on one varable whch we desgnated b x. Frequentl, t s desrable to learn somethng about the relatonshp between two

More information

1 i. δ ε. so all the estimators will be biased; in fact, the less correlated w. (i.e., more correlated to ε. is perfectly correlated to x 4 i.

1 i. δ ε. so all the estimators will be biased; in fact, the less correlated w. (i.e., more correlated to ε. is perfectly correlated to x 4 i. Specal Topcs I. Use Instrmental Varable to F Specfcaton Problem (e.g., omtted varable 3 3 Assme we don't have data on If s correlated to,, or s mssng from the regresson Tradtonal Solton - pro varable:

More information

Linear Approximation with Regularization and Moving Least Squares

Linear Approximation with Regularization and Moving Least Squares Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...

More information

Systems of Equations (SUR, GMM, and 3SLS)

Systems of Equations (SUR, GMM, and 3SLS) Lecture otes on Advanced Econometrcs Takash Yamano Fall Semester 4 Lecture 4: Sstems of Equatons (SUR, MM, and 3SLS) Seemngl Unrelated Regresson (SUR) Model Consder a set of lnear equatons: $ + ɛ $ + ɛ

More information

Navier Stokes Second Exact Transformation

Navier Stokes Second Exact Transformation Unversal Jornal of Appled Mathematcs (3): 136-140, 014 DOI: 1013189/jam01400303 http://wwwhrpborg Naver Stokes Second Eact Transformaton Aleandr Koachok Kev, Ukrane *Correspondng Athor: a-koachok1@andea

More information

Statistics Chapter 4

Statistics Chapter 4 Statstcs Chapter 4 "There are three knds of les: les, damned les, and statstcs." Benjamn Dsrael, 1895 (Brtsh statesman) Gaussan Dstrbuton, 4-1 If a measurement s repeated many tmes a statstcal treatment

More information

Lecture 3 Stat102, Spring 2007

Lecture 3 Stat102, Spring 2007 Lecture 3 Stat0, Sprng 007 Chapter 3. 3.: Introducton to regresson analyss Lnear regresson as a descrptve technque The least-squares equatons Chapter 3.3 Samplng dstrbuton of b 0, b. Contnued n net lecture

More information

Finite Difference Method

Finite Difference Method 7/0/07 Instructor r. Ramond Rump (9) 747 698 rcrump@utep.edu EE 337 Computatonal Electromagnetcs (CEM) Lecture #0 Fnte erence Method Lecture 0 These notes ma contan coprghted materal obtaned under ar use

More information

Refining the evaluation of uncertainties in [UTC UTC (k)]

Refining the evaluation of uncertainties in [UTC UTC (k)] Refnng the evalaton of ncertantes n [UC UC (k] W. ewandowsk Brea Internatonal des Pods et Mesres, Sèvres, France, wlewandowsk@bpm.org D. Matsaks Unted States aval Observatory, USA, matsaks.demetros@sno.navy.ml

More information

Chapter 15 Student Lecture Notes 15-1

Chapter 15 Student Lecture Notes 15-1 Chapter 15 Student Lecture Notes 15-1 Basc Busness Statstcs (9 th Edton) Chapter 15 Multple Regresson Model Buldng 004 Prentce-Hall, Inc. Chap 15-1 Chapter Topcs The Quadratc Regresson Model Usng Transformatons

More information

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 30 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 2 Remedes for multcollnearty Varous technques have

More information

Chapter 12 Analysis of Covariance

Chapter 12 Analysis of Covariance Chapter Analyss of Covarance Any scentfc experment s performed to know somethng that s unknown about a group of treatments and to test certan hypothess about the correspondng treatment effect When varablty

More information

Vectors in Rn un. This definition of norm is an extension of the Pythagorean Theorem. Consider the vector u = (5, 8) in R 2

Vectors in Rn un. This definition of norm is an extension of the Pythagorean Theorem. Consider the vector u = (5, 8) in R 2 MATH 307 Vectors in Rn Dr. Neal, WKU Matrices of dimension 1 n can be thoght of as coordinates, or ectors, in n- dimensional space R n. We can perform special calclations on these ectors. In particlar,

More information

Microscopic Properties of Gases

Microscopic Properties of Gases icroscopic Properties of Gases So far we he seen the gas laws. These came from observations. In this section we want to look at a theory that explains the gas laws: The kinetic theory of gases or The kinetic

More information

Chapter 2 - The Simple Linear Regression Model S =0. e i is a random error. S β2 β. This is a minimization problem. Solution is a calculus exercise.

Chapter 2 - The Simple Linear Regression Model S =0. e i is a random error. S β2 β. This is a minimization problem. Solution is a calculus exercise. Chapter - The Smple Lnear Regresson Model The lnear regresson equaton s: where y + = β + β e for =,..., y and are observable varables e s a random error How can an estmaton rule be constructed for the

More information

Uncertainty Analysis Principles and Methods, RCC Document , September 2007 UNCERTAINTY ANALYSIS PRINCIPLES AND METHODS

Uncertainty Analysis Principles and Methods, RCC Document , September 2007 UNCERTAINTY ANALYSIS PRINCIPLES AND METHODS Uncertanty Analyss Prncples and Methods, RCC ocment -7, September 7 OCUMENT -7 TEEMETRY GROUP UNCERTAINTY ANAYSIS PRINCIPES AN METHOS WHITE SANS MISSIE RANGE REAGAN TEST SITE YUMA PROVING GROUN UGWAY PROVING

More information

e i is a random error

e i is a random error Chapter - The Smple Lnear Regresson Model The lnear regresson equaton s: where + β + β e for,..., and are observable varables e s a random error How can an estmaton rule be constructed for the unknown

More information

Chapter 13: Multiple Regression

Chapter 13: Multiple Regression Chapter 13: Multple Regresson 13.1 Developng the multple-regresson Model The general model can be descrbed as: It smplfes for two ndependent varables: The sample ft parameter b 0, b 1, and b are used to

More information

Stochastic Structural Dynamics

Stochastic Structural Dynamics Stochastc Structural Dynamcs Lecture-1 Defnton of probablty measure and condtonal probablty Dr C S Manohar Department of Cvl Engneerng Professor of Structural Engneerng Indan Insttute of Scence angalore

More information

SUMMARY OF STOICHIOMETRIC RELATIONS AND MEASURE OF REACTIONS' PROGRESS AND COMPOSITION FOR MULTIPLE REACTIONS

SUMMARY OF STOICHIOMETRIC RELATIONS AND MEASURE OF REACTIONS' PROGRESS AND COMPOSITION FOR MULTIPLE REACTIONS UMMAY OF TOICHIOMETIC ELATION AND MEAUE OF EACTION' POGE AND COMPOITION FO MULTIPLE EACTION UPDATED 0/4/03 - AW APPENDIX A. In case of multple reactons t s mportant to fnd the number of ndependent reactons.

More information

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9 Chapter 9 Correlaton and Regresson 9. Correlaton Correlaton A correlaton s a relatonshp between two varables. The data can be represented b the ordered pars (, ) where s the ndependent (or eplanator) varable,

More information

SIO 224. m(r) =(ρ(r),k s (r),µ(r))

SIO 224. m(r) =(ρ(r),k s (r),µ(r)) SIO 224 1. A bref look at resoluton analyss Here s some background for the Masters and Gubbns resoluton paper. Global Earth models are usually found teratvely by assumng a startng model and fndng small

More information

A Modeling System to Combine Optimization and Constraint. Programming. INFORMS, November Carnegie Mellon University.

A Modeling System to Combine Optimization and Constraint. Programming. INFORMS, November Carnegie Mellon University. A Modelng Sstem to Combne Optmzaton and Constrant Programmng John Hooker Carnege Mellon Unverst INFORMS November 000 Based on ont work wth Ignaco Grossmann Hak-Jn Km Mara Axlo Osoro Greger Ottosson Erlendr

More information

Statistics for Business and Economics

Statistics for Business and Economics Statstcs for Busness and Economcs Chapter 11 Smple Regresson Copyrght 010 Pearson Educaton, Inc. Publshng as Prentce Hall Ch. 11-1 11.1 Overvew of Lnear Models n An equaton can be ft to show the best lnear

More information

Lecture notes on Computational Fluid Dynamics

Lecture notes on Computational Fluid Dynamics Lectre notes on Comptatonal Fld Dnamcs Dan S. Hennngson Martn Berggren Janar 3, 5 Contents Dervaton of the Naver-Stokes eqatons 7. Notaton............................................... 7. Knematcs.............................................

More information

Exact Solutions for Nonlinear D-S Equation by Two Known Sub-ODE Methods

Exact Solutions for Nonlinear D-S Equation by Two Known Sub-ODE Methods Internatonal Conference on Compter Technology and Scence (ICCTS ) IPCSIT vol. 47 () () IACSIT Press, Sngapore DOI:.7763/IPCSIT..V47.64 Exact Soltons for Nonlnear D-S Eqaton by Two Known Sb-ODE Methods

More information

UNIVERSITY OF TORONTO Faculty of Arts and Science. December 2005 Examinations STA437H1F/STA1005HF. Duration - 3 hours

UNIVERSITY OF TORONTO Faculty of Arts and Science. December 2005 Examinations STA437H1F/STA1005HF. Duration - 3 hours UNIVERSITY OF TORONTO Faculty of Arts and Scence December 005 Examnatons STA47HF/STA005HF Duraton - hours AIDS ALLOWED: (to be suppled by the student) Non-programmable calculator One handwrtten 8.5'' x

More information

The Ordinary Least Squares (OLS) Estimator

The Ordinary Least Squares (OLS) Estimator The Ordnary Least Squares (OLS) Estmator 1 Regresson Analyss Regresson Analyss: a statstcal technque for nvestgatng and modelng the relatonshp between varables. Applcatons: Engneerng, the physcal and chemcal

More information

Suppression of Low-frequency Lateral Vibration in Tilting Vehicle Controlled by Pneumatic Power

Suppression of Low-frequency Lateral Vibration in Tilting Vehicle Controlled by Pneumatic Power Challenge D: A world of servces for passengers Sppresson of Low-freqency Lateral Vbraton n ltng Vehcle Controlled by Pnematc Power A. Kazato, S.Kamoshta Ralway echncal Research Insttte, okyo, Japan. Introdcton

More information

Biostatistics. Chapter 11 Simple Linear Correlation and Regression. Jing Li

Biostatistics. Chapter 11 Simple Linear Correlation and Regression. Jing Li Bostatstcs Chapter 11 Smple Lnear Correlaton and Regresson Jng L jng.l@sjtu.edu.cn http://cbb.sjtu.edu.cn/~jngl/courses/2018fall/b372/ Dept of Bonformatcs & Bostatstcs, SJTU Recall eat chocolate Cell 175,

More information

Department of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6

Department of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6 Department of Quanttatve Methods & Informaton Systems Tme Seres and Ther Components QMIS 30 Chapter 6 Fall 00 Dr. Mohammad Zanal These sldes were modfed from ther orgnal source for educatonal purpose only.

More information

Chapter 12. Ordinary Differential Equation Boundary Value (BV) Problems

Chapter 12. Ordinary Differential Equation Boundary Value (BV) Problems Chapter. Ordnar Dfferental Equaton Boundar Value (BV) Problems In ths chapter we wll learn how to solve ODE boundar value problem. BV ODE s usuall gven wth x beng the ndependent space varable. p( x) q(

More information

GPU friendly Fast Poisson Solver for Structured Power Grid Network Analysis

GPU friendly Fast Poisson Solver for Structured Power Grid Network Analysis GPU frendly Fast Posson Solver for Strctred Power Grd Network Analyss Jn Sh, Yc Ca, Xaoy Wang Dept of Compter Scence and Technology, Tsngha Unv. Wentng Ho, Lwe Ma, Pe-Hsn Ho Synopsys Inc. Sheldon X.-D.

More information

Lab 2e Thermal System Response and Effective Heat Transfer Coefficient

Lab 2e Thermal System Response and Effective Heat Transfer Coefficient 58:080 Expermental Engneerng 1 OBJECTIVE Lab 2e Thermal System Response and Effectve Heat Transfer Coeffcent Warnng: though the experment has educatonal objectves (to learn about bolng heat transfer, etc.),

More information

Guidelines on the Estimation of Uncertainty in Hardness Measurements

Guidelines on the Estimation of Uncertainty in Hardness Measurements Eropean Assocaton of Natonal Metrology Instttes Gdelnes on the Estmaton of Uncertanty n ardness Measrements EURAMET cg-16 Verson.0 03/011 Prevosly EA-10/16 Calbraton Gde EURAMET cg-16 Verson.0 03/011 GUIDELINES

More information

ULTRASONIC REFLECTION FROM ROUGH SURFACES IN WATER. James H. Rose and David K. Hsu. Ames Laboratory, USDOE Iowa State University Ames, IA 50011

ULTRASONIC REFLECTION FROM ROUGH SURFACES IN WATER. James H. Rose and David K. Hsu. Ames Laboratory, USDOE Iowa State University Ames, IA 50011 ULTRASONC REFLECTON FROM ROUGH SURFACES N WATER James H. Rose and Davd K. Hs Ames Laboratory, USDOE owa State Unversty Ames, A 50011 NTRODUCTON The se of ltrasond for mmerson mode qanttatve nondestrctve

More information

AP Physics 1 & 2 Summer Assignment

AP Physics 1 & 2 Summer Assignment AP Physcs 1 & 2 Summer Assgnment AP Physcs 1 requres an exceptonal profcency n algebra, trgonometry, and geometry. It was desgned by a select group of college professors and hgh school scence teachers

More information

Negative Binomial Regression

Negative Binomial Regression STATGRAPHICS Rev. 9/16/2013 Negatve Bnomal Regresson Summary... 1 Data Input... 3 Statstcal Model... 3 Analyss Summary... 4 Analyss Optons... 7 Plot of Ftted Model... 8 Observed Versus Predcted... 10 Predctons...

More information

PHY224H1F/324H1S Notes on Error Analysis

PHY224H1F/324H1S Notes on Error Analysis PHY4HF/34HS otes on Error nalss Reerences: J.R. Talor: n Introducton to Error nalss: The Stud o Uncertantes n Phscs Measurements, nd ed., Unverst Scence ooks, 997 P.R. evngton, D.H. Robnson: Data Reducton

More information

Some Comments on Accelerating Convergence of Iterative Sequences Using Direct Inversion of the Iterative Subspace (DIIS)

Some Comments on Accelerating Convergence of Iterative Sequences Using Direct Inversion of the Iterative Subspace (DIIS) Some Comments on Acceleratng Convergence of Iteratve Sequences Usng Drect Inverson of the Iteratve Subspace (DIIS) C. Davd Sherrll School of Chemstry and Bochemstry Georga Insttute of Technology May 1998

More information

STAT 3008 Applied Regression Analysis

STAT 3008 Applied Regression Analysis STAT 3008 Appled Regresson Analyss Tutoral : Smple Lnear Regresson LAI Chun He Department of Statstcs, The Chnese Unversty of Hong Kong 1 Model Assumpton To quantfy the relatonshp between two factors,

More information

APPENDIX 2 FITTING A STRAIGHT LINE TO OBSERVATIONS

APPENDIX 2 FITTING A STRAIGHT LINE TO OBSERVATIONS Unversty of Oulu Student Laboratory n Physcs Laboratory Exercses n Physcs 1 1 APPEDIX FITTIG A STRAIGHT LIE TO OBSERVATIOS In the physcal measurements we often make a seres of measurements of the dependent

More information

Comparison of Regression Lines

Comparison of Regression Lines STATGRAPHICS Rev. 9/13/2013 Comparson of Regresson Lnes Summary... 1 Data Input... 3 Analyss Summary... 4 Plot of Ftted Model... 6 Condtonal Sums of Squares... 6 Analyss Optons... 7 Forecasts... 8 Confdence

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Analyss of Varance and Desgn of Exerments-I MODULE III LECTURE - 2 EXPERIMENTAL DESIGN MODELS Dr. Shalabh Deartment of Mathematcs and Statstcs Indan Insttute of Technology Kanur 2 We consder the models

More information

Chapter 11: Simple Linear Regression and Correlation

Chapter 11: Simple Linear Regression and Correlation Chapter 11: Smple Lnear Regresson and Correlaton 11-1 Emprcal Models 11-2 Smple Lnear Regresson 11-3 Propertes of the Least Squares Estmators 11-4 Hypothess Test n Smple Lnear Regresson 11-4.1 Use of t-tests

More information

Unsteady MHD Free Convective Flow Through Porous Media Past on Moving Vertical Plate with Variable Temperature and Viscous Dissipation

Unsteady MHD Free Convective Flow Through Porous Media Past on Moving Vertical Plate with Variable Temperature and Viscous Dissipation ISS 976 4 Avalable onlne at www.nternatonalejornals.com Internatonal ejornals Internatonal ejornal of Mathematcs and Engneerng (7) Vol. 8, Isse, pp Unstead MHD Free Convectve Flow Throgh Poros Meda Past

More information

ISQS 6348 Final Open notes, no books. Points out of 100 in parentheses. Y 1 ε 2

ISQS 6348 Final Open notes, no books. Points out of 100 in parentheses. Y 1 ε 2 ISQS 6348 Fnal Open notes, no books. Ponts out of 100 n parentheses. 1. The followng path dagram s gven: ε 1 Y 1 ε F Y 1.A. (10) Wrte down the usual model and assumptons that are mpled by ths dagram. Soluton:

More information

Numerical solving for optimal control of stochastic nonlinear system based on PF-PSO

Numerical solving for optimal control of stochastic nonlinear system based on PF-PSO 4th Internatonal Conference on Sensors Measrement and Intellgent Materals (ICSMIM 205) mercal solvng for optmal control of stochastc nonlnear sstem based on PF-PSO Xong Yong 2* Xaohao Q 2 School of avgaton

More information

SE Story Shear Frame. Final Project. 2 Story Bending Beam. m 2. u 2. m 1. u 1. m 3. u 3 L 3. Given: L 1 L 2. EI ω 1 ω 2 Solve for m 2.

SE Story Shear Frame. Final Project. 2 Story Bending Beam. m 2. u 2. m 1. u 1. m 3. u 3 L 3. Given: L 1 L 2. EI ω 1 ω 2 Solve for m 2. SE 8 Fnal Project Story Sear Frame Gven: EI ω ω Solve for Story Bendng Beam Gven: EI ω ω 3 Story Sear Frame Gven: L 3 EI ω ω ω 3 3 m 3 L 3 Solve for Solve for m 3 3 4 3 Story Bendng Beam Part : Determnng

More information

DEMO #8 - GAUSSIAN ELIMINATION USING MATHEMATICA. 1. Matrices in Mathematica

DEMO #8 - GAUSSIAN ELIMINATION USING MATHEMATICA. 1. Matrices in Mathematica demo8.nb 1 DEMO #8 - GAUSSIAN ELIMINATION USING MATHEMATICA Obectves: - defne matrces n Mathematca - format the output of matrces - appl lnear algebra to solve a real problem - Use Mathematca to perform

More information

Here is the rationale: If X and y have a strong positive relationship to one another, then ( x x) will tend to be positive when ( y y)

Here is the rationale: If X and y have a strong positive relationship to one another, then ( x x) will tend to be positive when ( y y) Secton 1.5 Correlaton In the prevous sectons, we looked at regresson and the value r was a measurement of how much of the varaton n y can be attrbuted to the lnear relatonshp between y and x. In ths secton,

More information

Econometrics of Panel Data

Econometrics of Panel Data Econometrcs of Panel Data Jakub Mućk Meetng # 8 Jakub Mućk Econometrcs of Panel Data Meetng # 8 1 / 17 Outlne 1 Heterogenety n the slope coeffcents 2 Seemngly Unrelated Regresson (SUR) 3 Swamy s random

More information

1. Inference on Regression Parameters a. Finding Mean, s.d and covariance amongst estimates. 2. Confidence Intervals and Working Hotelling Bands

1. Inference on Regression Parameters a. Finding Mean, s.d and covariance amongst estimates. 2. Confidence Intervals and Working Hotelling Bands Content. Inference on Regresson Parameters a. Fndng Mean, s.d and covarance amongst estmates.. Confdence Intervals and Workng Hotellng Bands 3. Cochran s Theorem 4. General Lnear Testng 5. Measures of

More information

New Method for Solving Poisson Equation. on Irregular Domains

New Method for Solving Poisson Equation. on Irregular Domains Appled Mathematcal Scences Vol. 6 01 no. 8 369 380 New Method for Solvng Posson Equaton on Irregular Domans J. Izadan and N. Karamooz Department of Mathematcs Facult of Scences Mashhad BranchIslamc Azad

More information