3 Implementation and validation of analysis methods

Size: px
Start display at page:

Download "3 Implementation and validation of analysis methods"

Transcription

1 3 Implementaton and valdaton of anal method 3. Preface When mplementng new method bacall three cae can be dfferentated: - Implementaton of offcal method (nternatonall approved, valdated method, e.g. method of the 64 LFGB / German Food, Feed and Commodte Act, the Analtca EBC or the MEBAK) - Modfcaton of alread etng method - Implementaton of new method that requre comprehenve valdaton. In trace anal the approach dffer from the other method lke content determnaton, phcal meaurement etc. In addton, content determnaton can be dfferentated n calbrateable method, at whch pecfc reference ubtance are avalable for addton tet and uch, for whch that not poble, e.g. foam meaurement or vcot determnaton. 3. Term, Defnton Work area The work area defned a concentraton area of an analte n an anal oluton, n whch the tet procedure provde relable reult n contant accurac. Calbraton functon Functonal correlaton between meaurable dmenon and concentraton to be determned. 30

2 The regreon anal provde a calbraton functon ( = a + b n cae of a lnear frt degree functon) wth the followng charactertc: - Slope b (meaure for the entvt of a method) - A ntercept a - Redual tandard devaton (dperon of the meaured data around the regreon lne) - Method dependng tandard devaton (abolute accurac meaure) - Method dependng coeffcent of varaton (relatve accurac meaure) Sentvt The entvt pecfe, to what etent the gnal of the tem to be meaured vare when changng one concentraton value. Recover rate The recover rate equal the quotent of recovered and added quantt multpled b 00 (pecfcaton n percent). If a recover rate of 00 % determned, the method free of devaton. Accurac Accurac a qualtatve term for the etent of appromaton of tet reult on a reference value, e.g. the true value. Accurac a uperordnate term for precon and truene. Precon Precon a qualtatve term for the etent of the mutual appromaton of tet reult ndependent from another n multple applcaton of a defned tet method under predefned condton. A dtncton drawn between repeatablt and reproducblt. Repeatablt Repeatablt the qualtatve term for the etent of mutual appromaton of tet reult under repettve condton. The et when under dentcal condton (ame peron n charge, ame equpment and ame laborator) wthn hort nterval a predefned anal method carred out everal tme ung dentcal ample. 303

3 Reproducblt The reproducblt a qualtatve term for the etent of mutual appromaton of tet reult under comparatve condton. The et, f meaurement reult are obtaned ung a defned anal method ung dentcal ample at varng nterval under dfferent condton (dfferent peron n charge, dfferent equpment, and varou laboratore) Truene Truene a qualtatve term for the etent of appromaton of the epected value of the tet reult on the reference value, wherea th can be the true or proper value. Detecton lmt The detecton lmt (DL) the lowet content of an analte n a ample that can be detected wth hgh predefned probablt. Lmt of quanttaton The lmt of quanttaton (LO) correpond wth the lowet analte content that can be quantfed wth a predefned precon. Specfct The analtcal pecfct the ablt of a tet method to olel detect the ought analte, wherea other element preent n the ample do not nterfere wth the tet reult. The MADEL goodne- of- ft- tet Calculator check of the calbraton data on lneart under conderaton of the redual tandard devaton a well a the varance of the lnear calbraton functon and the econd-degree calbraton functon. Outler tet b redual anal Calculator check of the calbraton data on outler under conderaton of the redual tandard devaton. 304

4 3.3 Implementaton of new method: approach ew method Modfcaton of etng method offcal method Content determnaton Trace anal Content determnaton Trace anal Content determnaton Trace anal ca nca ca nca Specfct Lneart Truene Precon Recover Lmt of quanttaton Detecton lmt Robutne ca = calbrateable method nca = non calbrateable method 3.4 Montorng/ualt aurance For montorng the qualt of anal method varou ntrument can be ued. Interlaborator tet/comparon anal On one hand the hall how tematc devaton; on the other hand the hall conve ecurt for nvolved emploee. The reult of the nterlaborator tet are beng evaluated and repectve meaure are beng deduced. 305

5 Control chart The prncple of control chart an optcal llutraton of meaured data takng a ba - ualt objectve (pecfed value of the reult of control ample) - ualt bound The latter are dfferentated between warnng lmt, whoe ndvdual eceedance beng tolerated, and control or acton lmt, whoe eceedance caue meaure. Control chart are manl ued there, where the anal precon and the truene of a reult of ver hgh mportance, e.g. crtera of ale anal, that form the ba for bllng. Control ample Varou categore of control ample can be ued: - Blank tet that defntel do not contan the ought analte - Reference materal wth defned concentraton of ndvdual analte, e.g. CRM-ample (certfed reference materal) - Reference materal wth defned properte, e.g. calbraton ol for calbraton of the vcometer or tandard malt for calbratng the malt Frablmeter. 306

6 Mathematcal Bac 3.5. Regreon anal Aular quantte ) ( = ) ( = = ) ( = 3 3 = 4 ) ( 4 = ) ( ) ( = = Frt degree regreon Slope b = A ntercept b a = Correlaton coeffcent ( )( ) ( ) ( ) = ) ( r Redual tandard devaton = Method tandard devaton b o =

7 Method coeffcent of o varaton Vo = 00% Lower lmt of work area p = o t ( p ) + + b Second degree regreon A ntercept a = b c Coeffcent b = c 3 Coeffcent Redual tandard devaton c = ( ) ( a b c ) = Sentvt E = b + c Method tandard devaton o = E 308

8 3.5. Lneart tet: MADEL adaptaton tet Redual tandard devaton for frt degree calbraton functon Redual tandard devaton for econd degree calbraton functon DS = ( ) ( 3) Degree of freedom f = Tet value DS PW = Comparon wth table value F (f =, f = 3, P = 99 %) If PW F, then no gnfcantl better adaptaton obtaned b the econd degree calbraton functon. The calbraton functon lnear Outler tet va F-tet of redual varance A a bac prncple, calbraton data mut be outler-free. The redual anal can be ued for detecton of outler. For th purpoe at frt the calbraton lne calculated va redual tandard devaton A baed on all par of varate. After elmnaton of a upect outler par of the data a new calbraton lne calculated wth the redual varance. The verfcaton conducted wth the F-tet. A 309

9 A Redual tandard devaton for calbraton functon ung all value A Redual tandard devaton for calbraton functon wthout upect outler value Tet value ( PW = A A ) ( ) A Comparon wth table value F (f =, f = A -, P = 95 %) If PW < F, then no outler et. A A 3.6 Lterature. W. Funk, V. Dammann und G. Donnewert, ualtätcherung n der Analtchen Cheme, VCH Verlaggeellchaft mbh, Wenhem, German 99 30

10 t-table f t (P = 95 %) t (P = 99 %) f = degree of freedom F-table for f = f F (P = 95 %) F (P = 99 %) f, f = degree of freedom 3

11 FOR Method valdaton Tpe of ample: Valdaton on: Method: Stattc charactertc Calbraton functon lnear econd degree polnomal other Precon (.3) r: R: Recover Addtve:.. % Addtve:.. % Lmt of quanttaton Meaurng range... Approved: Date/Sgnature.... 3

12 . Specfct Ye o Date/Sgnature.. Anal of ample olvent.. Anal of contamnaton/degradaton product.3. Comparon of peak form, retenton tme and UV-pectrum of tandard and ample.4. Precon Ye o Date/Sgnature.. Precon of devce: 6 tme determnaton of tandard.. Precon of devce: 6 tme determnaton of ample.3. Precon of method: 6 tme recondtonng of homogenou pattern.4. 33

13 3. Truene/Recover 3.. Recover, addtve equvalent to L determnaton tme. 3.. Recover, addtve equvalent to 0 L determnaton tme 3.3. Recover, addtve equvalent to. determnaton tme 3.4. Anal of certfed reference materal, CRM. determnaton tme 3.5 Comparon of varou etracton technque 3.6. Ye o Date/Sgnature 4. Lneart 4.. Lneart tandard: 5 concentraton between... and Lneart ample preparaton: 5 concentraton between... and Ye o Date/Sgnature 34

14 5. Meaurng range Ye o Date/Sgnature Detecton lmt Ye o Date/Sgnature 6.. Detecton lmt (Sgnal/noe 3:): Lmt of quanttaton Ye o Date/Sgnature 7.. Lmt of quanttaton (Sgnal/noe 0:):. 8. Robutne Ye o Date/Sgnature

15 9. Stem qualfng eamnaton Ye o Date/Sgnature 9.. Retenton tme of 9... Injecton precon of 9.3. Anal of blank tet

Specification -- Assumptions of the Simple Classical Linear Regression Model (CLRM) 1. Introduction

Specification -- Assumptions of the Simple Classical Linear Regression Model (CLRM) 1. Introduction ECONOMICS 35* -- NOTE ECON 35* -- NOTE Specfcaton -- Aumpton of the Smple Clacal Lnear Regreon Model (CLRM). Introducton CLRM tand for the Clacal Lnear Regreon Model. The CLRM alo known a the tandard lnear

More information

Properties of Umass Boston

Properties of Umass Boston Fle name hould be LatName_labNumber.doc or LatName_labNumber.doc.l. 0 pont wll be taken for wrong fle name. Follow the format of report and data heet. Both are poted n the web. MS Word and Ecel 003 format.

More information

AP Statistics Ch 3 Examining Relationships

AP Statistics Ch 3 Examining Relationships Introducton To tud relatonhp between varable, we mut meaure the varable on the ame group of ndvdual. If we thnk a varable ma eplan or even caue change n another varable, then the eplanator varable and

More information

2.3 Least-Square regressions

2.3 Least-Square regressions .3 Leat-Square regreon Eample.10 How do chldren grow? The pattern of growth vare from chld to chld, o we can bet undertandng the general pattern b followng the average heght of a number of chldren. Here

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

MULTIPLE REGRESSION ANALYSIS For the Case of Two Regressors

MULTIPLE REGRESSION ANALYSIS For the Case of Two Regressors MULTIPLE REGRESSION ANALYSIS For the Cae of Two Regreor In the followng note, leat-quare etmaton developed for multple regreon problem wth two eplanator varable, here called regreor (uch a n the Fat Food

More information

Additional File 1 - Detailed explanation of the expression level CPD

Additional File 1 - Detailed explanation of the expression level CPD Addtonal Fle - Detaled explanaton of the expreon level CPD A mentoned n the man text, the man CPD for the uterng model cont of two ndvdual factor: P( level gen P( level gen P ( level gen 2 (.).. CPD factor

More information

Verification of Selected Precision Parameters of the Trimble S8 DR Plus Robotic Total Station

Verification of Selected Precision Parameters of the Trimble S8 DR Plus Robotic Total Station 81 Verfcaton of Selected Precon Parameter of the Trmble S8 DR Plu Robotc Total Staton Sokol, Š., Bajtala, M. and Ježko, J. Slovak Unverty of Technology, Faculty of Cvl Engneerng, Radlnkého 11, 81368 Bratlava,

More information

Answers Problem Set 2 Chem 314A Williamsen Spring 2000

Answers Problem Set 2 Chem 314A Williamsen Spring 2000 Answers Problem Set Chem 314A Wllamsen Sprng 000 1) Gve me the followng crtcal values from the statstcal tables. a) z-statstc,-sded test, 99.7% confdence lmt ±3 b) t-statstc (Case I), 1-sded test, 95%

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

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

Statistical Properties of the OLS Coefficient Estimators. 1. Introduction

Statistical Properties of the OLS Coefficient Estimators. 1. Introduction ECOOMICS 35* -- OTE 4 ECO 35* -- OTE 4 Stattcal Properte of the OLS Coeffcent Etmator Introducton We derved n ote the OLS (Ordnary Leat Square etmator ˆβ j (j, of the regreon coeffcent βj (j, n the mple

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

Introduction to Interfacial Segregation. Xiaozhe Zhang 10/02/2015

Introduction to Interfacial Segregation. Xiaozhe Zhang 10/02/2015 Introducton to Interfacal Segregaton Xaozhe Zhang 10/02/2015 Interfacal egregaton Segregaton n materal refer to the enrchment of a materal conttuent at a free urface or an nternal nterface of a materal.

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

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

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction ECONOMICS 5* -- NOTE (Summary) ECON 5* -- NOTE The Multple Classcal Lnear Regresson Model (CLRM): Specfcaton and Assumptons. Introducton CLRM stands for the Classcal Lnear Regresson Model. The CLRM s also

More information

Small signal analysis

Small signal analysis Small gnal analy. ntroducton Let u conder the crcut hown n Fg., where the nonlnear retor decrbed by the equaton g v havng graphcal repreentaton hown n Fg.. ( G (t G v(t v Fg. Fg. a D current ource wherea

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

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

Chapter 14 Simple Linear Regression

Chapter 14 Simple Linear Regression Chapter 4 Smple Lnear Regresson Chapter 4 - Smple Lnear Regresson Manageral decsons often are based on the relatonshp between two or more varables. Regresson analss can be used to develop an equaton showng

More information

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 31 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 6. Rdge regresson The OLSE s the best lnear unbased

More information

Analytical Chemistry Calibration Curve Handout

Analytical Chemistry Calibration Curve Handout I. Quck-and Drty Excel Tutoral Analytcal Chemstry Calbraton Curve Handout For those of you wth lttle experence wth Excel, I ve provded some key technques that should help you use the program both for problem

More information

EURAMET.M.D-S2 Final Report Final report

EURAMET.M.D-S2 Final Report Final report Fnal report on ERAMET blateral comparson on volume of mass standards Project number: 1356 (ERAMET.M.D-S2) Volume of mass standards of 10g, 20 g, 200 g, 1 kg Zoltan Zelenka 1 ; Stuart Davdson 2 ; Cslla

More information

Scattering of two identical particles in the center-of. of-mass frame. (b)

Scattering of two identical particles in the center-of. of-mass frame. (b) Lecture # November 5 Scatterng of two dentcal partcle Relatvtc Quantum Mechanc: The Klen-Gordon equaton Interpretaton of the Klen-Gordon equaton The Drac equaton Drac repreentaton for the matrce α and

More information

Probability and Random Variable Primer

Probability and Random Variable Primer B. Maddah ENMG 622 Smulaton 2/22/ Probablty and Random Varable Prmer Sample space and Events Suppose that an eperment wth an uncertan outcome s performed (e.g., rollng a de). Whle the outcome of the eperment

More information

Statistical Evaluation of WATFLOOD

Statistical Evaluation of WATFLOOD tatstcal Evaluaton of WATFLD By: Angela MacLean, Dept. of Cvl & Envronmental Engneerng, Unversty of Waterloo, n. ctober, 005 The statstcs program assocated wth WATFLD uses spl.csv fle that s produced wth

More information

Chapter 8 Indicator Variables

Chapter 8 Indicator Variables Chapter 8 Indcator Varables In general, e explanatory varables n any regresson analyss are assumed to be quanttatve n nature. For example, e varables lke temperature, dstance, age etc. are quanttatve n

More information

Introduction. Modeling Data. Approach. Quality of Fit. Likelihood. Probabilistic Approach

Introduction. Modeling Data. Approach. Quality of Fit. Likelihood. Probabilistic Approach Introducton Modelng Data Gven a et of obervaton, we wh to ft a mathematcal model Model deend on adutable arameter traght lne: m + c n Polnomal: a + a + a + L+ a n Choce of model deend uon roblem Aroach

More information

β0 + β1xi and want to estimate the unknown

β0 + β1xi and want to estimate the unknown SLR Models Estmaton Those OLS Estmates Estmators (e ante) v. estmates (e post) The Smple Lnear Regresson (SLR) Condtons -4 An Asde: The Populaton Regresson Functon B and B are Lnear Estmators (condtonal

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

Lecture 15 Statistical Analysis in Biomaterials Research

Lecture 15 Statistical Analysis in Biomaterials Research 3.05/BE.340 Lecture 5 tatstcal Analyss n Bomaterals Research. Ratonale: Why s statstcal analyss needed n bomaterals research? Many error sources n measurements on lvng systems! Eamples of Measured Values

More information

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4) I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes

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

Real Time Tracking of Geostationary Satellites in Sub Arc Second Accuracy

Real Time Tracking of Geostationary Satellites in Sub Arc Second Accuracy Real Tme Trackng of Geotatonar Satellte n Sub Arc Second Accurac Jzhang Sang 1 and Crag Smth 2 EOS Space Stem Pt Ltd, Weton Creek, ACT 2611, Autrala EOS Space Stem conducted a demontraton proect of optcall

More information

This column is a continuation of our previous column

This column is a continuation of our previous column Comparson of Goodness of Ft Statstcs for Lnear Regresson, Part II The authors contnue ther dscusson of the correlaton coeffcent n developng a calbraton for quanttatve analyss. Jerome Workman Jr. and Howard

More information

Econ Statistical Properties of the OLS estimator. Sanjaya DeSilva

Econ Statistical Properties of the OLS estimator. Sanjaya DeSilva Econ 39 - Statstcal Propertes of the OLS estmator Sanjaya DeSlva September, 008 1 Overvew Recall that the true regresson model s Y = β 0 + β 1 X + u (1) Applyng the OLS method to a sample of data, we estmate

More information

No! Yes! Only if reactions occur! Yes! Ideal Gas, change in temperature or pressure. Survey Results. Class 15. Is the following possible?

No! Yes! Only if reactions occur! Yes! Ideal Gas, change in temperature or pressure. Survey Results. Class 15. Is the following possible? Survey Reult Chapter 5-6 (where we are gong) % of Student 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Hour Spent on ChE 273 1-2 3-4 5-6 7-8 9-10 11+ Hour/Week 2008 2009 2010 2011 2012 2013 2014 2015 2017 F17

More information

Estimation of Finite Population Total under PPS Sampling in Presence of Extra Auxiliary Information

Estimation of Finite Population Total under PPS Sampling in Presence of Extra Auxiliary Information Internatonal Journal of Stattc and Analy. ISSN 2248-9959 Volume 6, Number 1 (2016), pp. 9-16 Reearch Inda Publcaton http://www.rpublcaton.com Etmaton of Fnte Populaton Total under PPS Samplng n Preence

More information

Chapter 6 The Effect of the GPS Systematic Errors on Deformation Parameters

Chapter 6 The Effect of the GPS Systematic Errors on Deformation Parameters Chapter 6 The Effect of the GPS Sytematc Error on Deformaton Parameter 6.. General Beutler et al., (988) dd the frt comprehenve tudy on the GPS ytematc error. Baed on a geometrc approach and aumng a unform

More information

Method Validation of Analytical Procedures

Method Validation of Analytical Procedures 32 Revew Artcle Method Valdaton of Analytcal Procedures Prakash Chanda Gupta QC Executve, Natonal Healthcare Pvt. Ltd., Nepal p_c_gupta@yahoo.com ABSTRACT After the development of an analytcal procedure,

More information

where I = (n x n) diagonal identity matrix with diagonal elements = 1 and off-diagonal elements = 0; and σ 2 e = variance of (Y X).

where I = (n x n) diagonal identity matrix with diagonal elements = 1 and off-diagonal elements = 0; and σ 2 e = variance of (Y X). 11.4.1 Estmaton of Multple Regresson Coeffcents In multple lnear regresson, we essentally solve n equatons for the p unnown parameters. hus n must e equal to or greater than p and n practce n should e

More information

First Year Examination Department of Statistics, University of Florida

First Year Examination Department of Statistics, University of Florida Frst Year Examnaton Department of Statstcs, Unversty of Florda May 7, 010, 8:00 am - 1:00 noon Instructons: 1. You have four hours to answer questons n ths examnaton.. You must show your work to receve

More information

Open Systems: Chemical Potential and Partial Molar Quantities Chemical Potential

Open Systems: Chemical Potential and Partial Molar Quantities Chemical Potential Open Systems: Chemcal Potental and Partal Molar Quanttes Chemcal Potental For closed systems, we have derved the followng relatonshps: du = TdS pdv dh = TdS + Vdp da = SdT pdv dg = VdP SdT For open systems,

More information

STAT 511 FINAL EXAM NAME Spring 2001

STAT 511 FINAL EXAM NAME Spring 2001 STAT 5 FINAL EXAM NAME Sprng Instructons: Ths s a closed book exam. No notes or books are allowed. ou may use a calculator but you are not allowed to store notes or formulas n the calculator. Please wrte

More information

Confidence intervals for the difference and the ratio of Lognormal means with bounded parameters

Confidence intervals for the difference and the ratio of Lognormal means with bounded parameters Songklanakarn J. Sc. Technol. 37 () 3-40 Mar.-Apr. 05 http://www.jt.pu.ac.th Orgnal Artcle Confdence nterval for the dfference and the rato of Lognormal mean wth bounded parameter Sa-aat Nwtpong* Department

More information

Properties of Least Squares

Properties of Least Squares Week 3 3.1 Smple Lnear Regresson Model 3. Propertes of Least Squares Estmators Y Y β 1 + β X + u weekly famly expendtures X weekly famly ncome For a gven level of x, the expected level of food expendtures

More information

ASSOCIATION POUR LA CERTIFICATION DES MATERIAUX ISOLANTS

ASSOCIATION POUR LA CERTIFICATION DES MATERIAUX ISOLANTS Revson ndex Date of mplementaton C 15/03/017 Techncal Specfcaton E ASSOCIATIO POUR LA CERTIFICATIO DES MATERIAUX ISOLATS 4, avenue du Recteur-Poncaré, 7578 Pars Cedex 16 Tel. 33.(0)1.64.68.84.97 Fax. 33.(0)1.64.68.83.45

More information

/ n ) are compared. The logic is: if the two

/ n ) are compared. The logic is: if the two STAT C141, Sprng 2005 Lecture 13 Two sample tests One sample tests: examples of goodness of ft tests, where we are testng whether our data supports predctons. Two sample tests: called as tests of ndependence

More information

[ ] λ λ λ. Multicollinearity. multicollinearity Ragnar Frisch (1934) perfect exact. collinearity. multicollinearity. exact

[ ] λ λ λ. Multicollinearity. multicollinearity Ragnar Frisch (1934) perfect exact. collinearity. multicollinearity. exact Multcollnearty multcollnearty Ragnar Frsch (934 perfect exact collnearty multcollnearty K exact λ λ λ K K x+ x+ + x 0 0.. λ, λ, λk 0 0.. x perfect ntercorrelated λ λ λ x+ x+ + KxK + v 0 0.. v 3 y β + β

More information

Variable Structure Control ~ Basics

Variable Structure Control ~ Basics Varable Structure Control ~ Bac Harry G. Kwatny Department of Mechancal Engneerng & Mechanc Drexel Unverty Outlne A prelmnary example VS ytem, ldng mode, reachng Bac of dcontnuou ytem Example: underea

More information

Statistics for Economics & Business

Statistics for Economics & Business Statstcs for Economcs & Busness Smple Lnear Regresson Learnng Objectves In ths chapter, you learn: How to use regresson analyss to predct the value of a dependent varable based on an ndependent varable

More information

Not at Steady State! Yes! Only if reactions occur! Yes! Ideal Gas, change in temperature or pressure. Yes! Class 15. Is the following possible?

Not at Steady State! Yes! Only if reactions occur! Yes! Ideal Gas, change in temperature or pressure. Yes! Class 15. Is the following possible? Chapter 5-6 (where we are gong) Ideal gae and lqud (today) Dente Partal preure Non-deal gae (next tme) Eqn. of tate Reduced preure and temperature Compreblty chart (z) Vapor-lqud ytem (Ch. 6) Vapor preure

More information

Investigation of Uncertainty Sources in the External Dosimetry Laboratory

Investigation of Uncertainty Sources in the External Dosimetry Laboratory Investgaton of Uncertanty Sources n the External Dosmetry Laboratory Specfcaton.1.1. Analyss of uncertantes Methods for calculatng the overall uncertanty from ndvdual measured uncertantes are gven n the

More information

x yi In chapter 14, we want to perform inference (i.e. calculate confidence intervals and perform tests of significance) in this setting.

x yi In chapter 14, we want to perform inference (i.e. calculate confidence intervals and perform tests of significance) in this setting. The Practce of Statstcs, nd ed. Chapter 14 Inference for Regresson Introducton In chapter 3 we used a least-squares regresson lne (LSRL) to represent a lnear relatonshp etween two quanttatve explanator

More information

Rockefeller College University at Albany

Rockefeller College University at Albany Rockefeller College Unverst at Alban PAD 705 Handout: Maxmum Lkelhood Estmaton Orgnal b Davd A. Wse John F. Kenned School of Government, Harvard Unverst Modfcatons b R. Karl Rethemeer Up to ths pont n

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

Uncertainty in measurements of power and energy on power networks

Uncertainty in measurements of power and energy on power networks Uncertanty n measurements of power and energy on power networks E. Manov, N. Kolev Department of Measurement and Instrumentaton, Techncal Unversty Sofa, bul. Klment Ohrdsk No8, bl., 000 Sofa, Bulgara Tel./fax:

More information

Team. Outline. Statistics and Art: Sampling, Response Error, Mixed Models, Missing Data, and Inference

Team. Outline. Statistics and Art: Sampling, Response Error, Mixed Models, Missing Data, and Inference Team Stattc and Art: Samplng, Repone Error, Mxed Model, Mng Data, and nference Ed Stanek Unverty of Maachuett- Amhert, USA 9/5/8 9/5/8 Outlne. Example: Doe-repone Model n Toxcology. ow to Predct Realzed

More information

Linear Regression Analysis: Terminology and Notation

Linear Regression Analysis: Terminology and Notation ECON 35* -- Secton : Basc Concepts of Regresson Analyss (Page ) Lnear Regresson Analyss: Termnology and Notaton Consder the generc verson of the smple (two-varable) lnear regresson model. It s represented

More information

Supporting Information. Hydroxyl Radical Production by H 2 O 2 -Mediated. Conditions

Supporting Information. Hydroxyl Radical Production by H 2 O 2 -Mediated. Conditions Supportng Informaton Hydroxyl Radcal Producton by H 2 O 2 -Medated Oxdaton of Fe(II) Complexed by Suwannee Rver Fulvc Acd Under Crcumneutral Frehwater Condton Chrtopher J. Mller, Andrew L. Roe, T. Davd

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

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

SUBTRACTION METHOD FOR REMOVING POWERLINE INTERFERENCE

SUBTRACTION METHOD FOR REMOVING POWERLINE INTERFERENCE SUBTRACTION METHOD OR REMOVING POWERLINE INTERERENCE ROM ECG IN CASE O REQUENCY DEVIATION Georgy Slavtchev Mhov, Ratcho Marnov Ivanov, Chavdar Lev Levkov aculty of Electronc Engneerng and Technologe (ETT),

More information

ksi a b 8.1 (a) B = stress = y / 0.1; A = strain = x / 10 Let, E(B A = a) = + a a i Nos. a i b i a i b i

ksi a b 8.1 (a) B = stress = y / 0.1; A = strain = x / 10 Let, E(B A = a) = + a a i Nos. a i b i a i b i 8. (a) B = stress = / 0.; A = stran = / 0 Let, E(B A = a) = + a Nos. a b a b a b 90-4 0 90-3 80-8 00 00-4 0 400-3 4000-8 400 3 80-4 30 840-3 7840-8 900 4 40-4 40 640-3 680-8 600 5 50-4 50 600-3 7040-8

More information

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding Recall: man dea of lnear regresson Lecture 9: Lnear regresson: centerng, hypothess testng, multple covarates, and confoundng Sandy Eckel seckel@jhsph.edu 6 May 8 Lnear regresson can be used to study an

More information

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding Lecture 9: Lnear regresson: centerng, hypothess testng, multple covarates, and confoundng Sandy Eckel seckel@jhsph.edu 6 May 008 Recall: man dea of lnear regresson Lnear regresson can be used to study

More information

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for P Charts. Dr. Wayne A. Taylor

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for P Charts. Dr. Wayne A. Taylor Taylor Enterprses, Inc. Control Lmts for P Charts Copyrght 2017 by Taylor Enterprses, Inc., All Rghts Reserved. Control Lmts for P Charts Dr. Wayne A. Taylor Abstract: P charts are used for count data

More information

Validation of a GC-MS Method for Determination of the Optical Purity of Peptides. J. Gerhardt 1.), G.J. Nicholson 2.)

Validation of a GC-MS Method for Determination of the Optical Purity of Peptides. J. Gerhardt 1.), G.J. Nicholson 2.) Valdaton of a GC-MS Method for Determnaton of the Optcal Purty of Peptdes. J. Gerhardt 1.), G.J. Ncholson 2.) 1.) C.A.T. GmbH & Co. KG, D-72070 Tübngen 2.) Unverstät Tübngen, Insttute für Organsche Cheme,

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

RELIABILITY ASSESSMENT

RELIABILITY ASSESSMENT CHAPTER Rsk Analyss n Engneerng and Economcs RELIABILITY ASSESSMENT A. J. Clark School of Engneerng Department of Cvl and Envronmental Engneerng 4a CHAPMAN HALL/CRC Rsk Analyss for Engneerng Department

More information

Chapter 11. Supplemental Text Material. The method of steepest ascent can be derived as follows. Suppose that we have fit a firstorder

Chapter 11. Supplemental Text Material. The method of steepest ascent can be derived as follows. Suppose that we have fit a firstorder S-. The Method of Steepet cent Chapter. Supplemental Text Materal The method of teepet acent can be derved a follow. Suppoe that we have ft a frtorder model y = β + β x and we wh to ue th model to determne

More information

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers Psychology 282 Lecture #24 Outlne Regresson Dagnostcs: Outlers In an earler lecture we studed the statstcal assumptons underlyng the regresson model, ncludng the followng ponts: Formal statement of assumptons.

More information

j) = 1 (note sigma notation) ii. Continuous random variable (e.g. Normal distribution) 1. density function: f ( x) 0 and f ( x) dx = 1

j) = 1 (note sigma notation) ii. Continuous random variable (e.g. Normal distribution) 1. density function: f ( x) 0 and f ( x) dx = 1 Random varables Measure of central tendences and varablty (means and varances) Jont densty functons and ndependence Measures of assocaton (covarance and correlaton) Interestng result Condtonal dstrbutons

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

Multinomial logit regression

Multinomial logit regression 07/0/6 Multnomal logt regresson Introducton We now turn our attenton to regresson models for the analyss of categorcal dependent varables wth more than two response categores: Y car owned (many possble

More information

Lecture 6: Introduction to Linear Regression

Lecture 6: Introduction to Linear Regression Lecture 6: Introducton to Lnear Regresson An Manchakul amancha@jhsph.edu 24 Aprl 27 Lnear regresson: man dea Lnear regresson can be used to study an outcome as a lnear functon of a predctor Example: 6

More information

Research of Dependence of Heat Conductivity of The Clap-Raw And Its Components From Temperature And Humidity

Research of Dependence of Heat Conductivity of The Clap-Raw And Its Components From Temperature And Humidity ISSN: 35-38 Internatonal Journal of AdvancedReearch n Scence, Engneerng and Technology Vol. 5, Iue, December 8 Reearch of Dependence of Heat Conductvty of The Clap-Raw And It Component From Temperature

More information

A NUMERICAL MODELING OF MAGNETIC FIELD PERTURBATED BY THE PRESENCE OF SCHIP S HULL

A NUMERICAL MODELING OF MAGNETIC FIELD PERTURBATED BY THE PRESENCE OF SCHIP S HULL A NUMERCAL MODELNG OF MAGNETC FELD PERTURBATED BY THE PRESENCE OF SCHP S HULL M. Dennah* Z. Abd** * Laboratory Electromagnetc Sytem EMP BP b Ben-Aknoun 606 Alger Algera ** Electronc nttute USTHB Alger

More information

Development of Pedotransfer Functions for Saturated Hydraulic Conductivity

Development of Pedotransfer Functions for Saturated Hydraulic Conductivity Open Journal of Modern Hydrology, 03, 3, 54-64 http://dx.do.org/0.436/ojmh.03.3309 Publhed Onlne July 03 (http://www.crp.org/journal/ojmh) Development of Pedotranfer Functon for Saturated Hydraulc Conductvty

More information

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for U Charts. Dr. Wayne A. Taylor

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for U Charts. Dr. Wayne A. Taylor Taylor Enterprses, Inc. Adjusted Control Lmts for U Charts Copyrght 207 by Taylor Enterprses, Inc., All Rghts Reserved. Adjusted Control Lmts for U Charts Dr. Wayne A. Taylor Abstract: U charts are used

More information

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal Inner Product Defnton 1 () A Eucldean space s a fnte-dmensonal vector space over the reals R, wth an nner product,. Defnton 2 (Inner Product) An nner product, on a real vector space X s a symmetrc, blnear,

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

PubH 7405: REGRESSION ANALYSIS. SLR: INFERENCES, Part II

PubH 7405: REGRESSION ANALYSIS. SLR: INFERENCES, Part II PubH 7405: REGRESSION ANALSIS SLR: INFERENCES, Part II We cover te topc of nference n two sessons; te frst sesson focused on nferences concernng te slope and te ntercept; ts s a contnuaton on estmatng

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

The Geometry of Logit and Probit

The Geometry of Logit and Probit The Geometry of Logt and Probt Ths short note s meant as a supplement to Chapters and 3 of Spatal Models of Parlamentary Votng and the notaton and reference to fgures n the text below s to those two chapters.

More information

Topic- 11 The Analysis of Variance

Topic- 11 The Analysis of Variance Topc- 11 The Analyss of Varance Expermental Desgn The samplng plan or expermental desgn determnes the way that a sample s selected. In an observatonal study, the expermenter observes data that already

More information

Statistics for Managers Using Microsoft Excel/SPSS Chapter 14 Multiple Regression Models

Statistics for Managers Using Microsoft Excel/SPSS Chapter 14 Multiple Regression Models Statstcs for Managers Usng Mcrosoft Excel/SPSS Chapter 14 Multple Regresson Models 1999 Prentce-Hall, Inc. Chap. 14-1 Chapter Topcs The Multple Regresson Model Contrbuton of Indvdual Independent Varables

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

Exam 1. Sept. 22, 8:00-9:30 PM EE 129. Material: Chapters 1-8 Labs 1-3

Exam 1. Sept. 22, 8:00-9:30 PM EE 129. Material: Chapters 1-8 Labs 1-3 Eam ept., 8:00-9:30 PM EE 9 Mateal: Chapte -8 Lab -3 tandadzaton and Calbaton: Ttaton: ue of tandadzed oluton to detemne the concentaton of an unknown. Rele on a eacton of known tochomet, a oluton wth

More information

Lecture Notes for STATISTICAL METHODS FOR BUSINESS II BMGT 212. Chapters 14, 15 & 16. Professor Ahmadi, Ph.D. Department of Management

Lecture Notes for STATISTICAL METHODS FOR BUSINESS II BMGT 212. Chapters 14, 15 & 16. Professor Ahmadi, Ph.D. Department of Management Lecture Notes for STATISTICAL METHODS FOR BUSINESS II BMGT 1 Chapters 14, 15 & 16 Professor Ahmad, Ph.D. Department of Management Revsed August 005 Chapter 14 Formulas Smple Lnear Regresson Model: y =

More information

A note on almost sure behavior of randomly weighted sums of φ-mixing random variables with φ-mixing weights

A note on almost sure behavior of randomly weighted sums of φ-mixing random variables with φ-mixing weights ACTA ET COMMENTATIONES UNIVERSITATIS TARTUENSIS DE MATHEMATICA Volume 7, Number 2, December 203 Avalable onlne at http://acutm.math.ut.ee A note on almost sure behavor of randomly weghted sums of φ-mxng

More information

Indeterminate pin-jointed frames (trusses)

Indeterminate pin-jointed frames (trusses) Indetermnate pn-jonted frames (trusses) Calculaton of member forces usng force method I. Statcal determnacy. The degree of freedom of any truss can be derved as: w= k d a =, where k s the number of all

More information

n α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0

n α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0 MODULE 2 Topcs: Lnear ndependence, bass and dmenson We have seen that f n a set of vectors one vector s a lnear combnaton of the remanng vectors n the set then the span of the set s unchanged f that vector

More information

LECTURE 9 CANONICAL CORRELATION ANALYSIS

LECTURE 9 CANONICAL CORRELATION ANALYSIS LECURE 9 CANONICAL CORRELAION ANALYSIS Introducton he concept of canoncal correlaton arses when we want to quantfy the assocatons between two sets of varables. For example, suppose that the frst set of

More information

Sampling Theory MODULE V LECTURE - 17 RATIO AND PRODUCT METHODS OF ESTIMATION

Sampling Theory MODULE V LECTURE - 17 RATIO AND PRODUCT METHODS OF ESTIMATION Samplng Theory MODULE V LECTURE - 7 RATIO AND PRODUCT METHODS OF ESTIMATION DR. SHALABH DEPARTMENT OF MATHEMATICS AND STATISTICS INDIAN INSTITUTE OF TECHNOLOG KANPUR Propertes of separate rato estmator:

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

728. Mechanical and electrical elements in reduction of vibrations

728. Mechanical and electrical elements in reduction of vibrations 78. Mechancal and electrcal element n reducton of vbraton Katarzyna BIAŁAS The Slean Unverty of Technology, Faculty of Mechancal Engneerng Inttute of Engneerng Procee Automaton and Integrated Manufacturng

More information

Limited Dependent Variables and Panel Data. Tibor Hanappi

Limited Dependent Variables and Panel Data. Tibor Hanappi Lmted Dependent Varables and Panel Data Tbor Hanapp 30.06.2010 Lmted Dependent Varables Dscrete: Varables that can take onl a countable number of values Censored/Truncated: Data ponts n some specfc range

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