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

Size: px
Start display at page:

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

Transcription

1 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 Wate* School of Cvl and Envronmental Engneerng, The Unverty of New South Wale, Sydney, New South Wale 2052, Autrala Southern Cro GeoScence, Southern Cro Unverty, Lmore, New South Wale 2480, Autrala Supportng Informaton contan detal of the knetc model parameter fttng procedure, a table of addtonal reacton n the preence of t-butanol and four fgure contanng further expermental data and knetc model output. S1

2 S1. Addtonal Reacton n the Preence of t-utanol Table S1. Addtonal reacton n the preence of t-butanol Reacton HO + (CH 3 ) 3 COH H 2 O + CH 2 C(CH 3 ) 2 OH Rate Contant (M 1 1 ) a 2 CH 2 C(CH 3 ) 2 OH HO(CH 3 ) 2 CH 2 CH 2 C(CH 3 ) 2 OH 2k = b Fe(II)L + CH 2 C(CH 3 ) 2 OH Fe(III)L + [ CH 2 (CH 3 ) 3 COH] CH 2 C(CH 3 ) 2 OH + O 2 OOCH 2 C(CH 3 ) 2 OH Fe(II)L + OOCH 2 C(CH 3 ) 2 OH Fe(III)L + HOC(CH 3 ) 2 CH 2 OO c d e 2 OOCH 2 C(CH 3 ) 2 OH [HOC(CH 3 ) 2 CH 2 OO] 2 2k = (8 ± 2) 10 8 b [HOC(CH 3 ) 2 CH 2 OO] 2 O 2 + HOC(CH 3 ) 2 CH 2 OH + HOC(CH 3 ) 2 CHO [HOC(CH 3 ) 2 CH 2 OO] 2 H 2 O HOC(CH 3 ) 2 CHO [HOC(CH 3 ) 2 CH 2 OO] 2 O CH 2 O + 2 C(CH 3 ) 2 OH [HOC(CH 3 ) 2 CH 2 OO] 2 O 2 + HO(CH 3 ) 2 CCH 2 OOCH 2 C(CH 3 ) 2 OH C(CH 3 ) 2 OH + O 2 OOC(CH 3 ) 2 OH OOC(CH 3 ) 2 OH (CH 3 ) 2 C=O + HO 2 R = f R = f R = f R = f d d a uxton et al. 1 b Smc et al. 2 c rate contant for Fe(II)EDTA from Croft et al. 3, aumed the ame for Fe(II)L 1 ; although Rahhal and Rchter 4 ugget the reacton doen t occur for Fe(II)DTPA, th reacton wll only be a mnor pathway for the organc radcal and wll not trongly nfluence the model predcton at the Fe(II) concentraton ued here. d Rez et al. 5 e etmate from Khakn et al. 6 f decay of tetroxde ntermedate aumed to be rapd; a value of R wa adopted, wth the proporton (R) taken from Rez et al. 5 S2

3 S2. Model Fttng Procedure The model wa ft by determnng the parameter bet able to reproduce the fnal oberved Phth- OH concentraton () and alo the peudo-frt order rate contant for Phth-OH formaton (k ). Thee parameter were determned for both the expermental data and knetc model output by fttng a functon of the form t ( e k ) [ Phth OH] = 1 (S1) ung a non-lnear leat-quare algorthm. In th way the fnal model wa able to well-contran both the magntude and formaton rate of HO producton. A the two obervable ( and k ) were of dfferent order of magntude a unt dtance calng wa appled to each obervable to enure they were both of equal mportance n the fttng proce. 7 The calng wa appled to both the expermental data and the knetc model output ung calng parameter derved from the expermental data, a outlned below. = n k, k = n (S2) d = ( ) 2, d ( = k k k ) 2 (S3) In thee equaton n the total number of condton examned and and k are value of and k at condton. The caled value were determned a below, where and k are the caled parameter. d = (S4) k k = (S5) d k k S3

4 To determne the model value of and k the knetc model wa run n Kntecu, then a nonlnear leat-quare fttng routne appled to ft eq S1 to the model output. In th way, by runnng the model at varyng rate contant value, numercal etmate of the partal dervatve of and k could be evaluated ung the formula below. ((1+ ) k ) ( (1 ) k ) 2 k (S6) k ((1+ ) k ) k ((1 ) k ) 2 k (S7) where (( 1+ ) and (( 1+ ) )k k )k are the value of and k, repectvely, when the model evaluated for k = (1 + )k. Thee dervatve are requred to form the Jacoban matrx n the parameter fttng proce, however, due to the unt calng appled to the data thee dervatve alo requred calng. ( ) = k d (S8) = 1 (S9) d k mlarly, = 1 d k (S10) The model wa ft ung a non-lnear leat quare approach by adjutng rate contant k 4r and k 9 (number refer to reacton n the man paper), a uch, dervatve wth repect to thee rate contant were determned and the Jacoban formed a below. J J = J k (S11) S4

5 where: J = M 4r 1 4r n 9 M 1 9 n (S12) J k 4r = M 4r 1 n 9 M 1 9 n (S13) A weghtng matrx wa alo requred n the model fttng proce, contructed ung the nvere of the varance of and k (after accountng for calng). Under ome condton the value of k could not be relably determned due to the rapdty of the reacton and nablty to ample fat enough to obtan data able to determne th parameter accurately. In thee cae the value of the tandard error of k wa et to an arbtrarly hgh value of 10 1 to enure that th data pont dd not unduly nfluence the fttng proce. Fnally, the weghtng matrx (W) wa further caled uch that the um of weght for wa equvalent to the um of weght for k, enurng both parameter had equal mpact upon the fttng proce. The fttng proce wa conducted followng Manthey, 8 wth an adjutment matrx X calculated ung the formula below, where X a column vector contanng the parameter that are beng ftted (.e., k 4r and k 9 ). A new etmate for X wa obtaned from X + X and the proce repeated untl convergence. T 1 T ( J WJ) J WK X= (S14) After convergence, the varance-covarance matrx (Q xx ) wa computed and the value of the tandard error for the ftted parameter determned by takng the quare root of the dagonal entre of the approprately caled varance-covarance matrx (Q xx ) S5

6 xx T ( J ) 1 Q = WJ (S15) Q Q xx = (S16) S xx 2 0 where: 2 K T WK S0 = (S17) n p n = number of datapont p = number of parameter beng ftted S6

7 S3. Plot of Knetc Model Output and Expermental Data Fgure S1. Comparon of expermental data and knetc model output for [SRFA] = 10 mg L 1. In all panel [Fe(II)] 0 wa 2 µm, H 2 O 2 concentraton were a labeled, ymbol repreent expermental data (from at leat two eparate experment), error bar are one tandard error from the calbraton and old lne how the knetc model output. S7

8 S8 Fgure S2. Comparon of expermental data and knetc model output for [SRFA] = 20 mg L 1. In all panel [Fe(II)] 0 wa 2 µm, H 2 O 2 concentraton were a labeled, ymbol repreent expermental data (from at leat two eparate experment), error bar are one tandard error from the calbraton and old lne how the knetc model output.

9 S9 Fgure S3. Comparon of expermental data and knetc model output for [SRFA] = 30 mg L 1. In all panel [Fe(II)] 0 wa 2 µm, H 2 O 2 concentraton were a labeled, ymbol repreent expermental data (from three eparate experment), error bar are one tandard error from the calbraton and old lne how the knetc model output.

10 S10 Fgure S4. Comparon of expermental data and knetc model output for [SRFA] = 40 mg L 1. In all panel [Fe(II)] 0 wa 2 µm, H 2 O 2 concentraton were a labeled, ymbol repreent expermental data (from at leat two eparate experment), error bar are one tandard error from the calbraton and old lne how the knetc model output.

11 S4. Reference (1) uxton, G. V.; Greentock, C. L.; Helman, W. P.; Ro, A.. Crtcal revew of rate contant for reacton of hydrated electron, hydrogen atom and hydroxyl radcal ( OH/ O ) n aqueou oluton. J. Phy. Chem. Ref. Data 1988, 17 (2), (2) Smc, M.; Neta, P.; Hayon, E. Pule radoly tudy of alcohol n aqueou oluton. J. Phy. Chem. 1969, 73 (11), (3) Croft, S.; Glbert,. C.; Smth, J. R. L.; Whtwood, A. C. An E.S.R. nvetgaton of the reactve ntermedate generated n the reacton between Fe II and H 2 O 2 n aqueou oluton. Drect evdence for the formaton of the hydroxyl radcal. Free Radcal Re. 1992, 17 (1), (4) Rahhal, S.; Rchter, H. W. Reacton of hydroxyl radcal wth the ferrou and ferrc ron chelate of dethylenetramne-n,n,n',n'',n''-pentaacetate. Free Radcal Re. 1989, 6 (6), (5) Rez, E.; Schmdt, W.; Schuchmann, H. P.; von Sonntag, C. Photoly of ozone n aqueou oluton n the preence of tertary butanol. Envron. Sc. Technol. 2003, 37 (9), (6) Khakn, G. I.; Alfa, Z..; Hue, R. E.; Neta, P. Oxdaton of ferrou and ferrocyande on by peroxyl radcal. J. Phy. Chem. 1996, 100 (17), (7) Gunt, R. F.; Maon, R. L. Regreon Analy and t Applcaton: A Data-Orented Approach. Marcel Dekker, nc.: New York and ael, S11

12 (8) Manthey, D. General Leat-Square Drect Soluton and undle Adjutment. S12

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

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

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

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

Improvements on Waring s Problem

Improvements on Waring s Problem Improvement on Warng Problem L An-Png Bejng, PR Chna apl@nacom Abtract By a new recurve algorthm for the auxlary equaton, n th paper, we wll gve ome mprovement for Warng problem Keyword: Warng Problem,

More information

NUMERICAL DIFFERENTIATION

NUMERICAL DIFFERENTIATION NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the

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

Supporting Information

Supporting Information Supportng Informaton The neural network f n Eq. 1 s gven by: f x l = ReLU W atom x l + b atom, 2 where ReLU s the element-wse rectfed lnear unt, 21.e., ReLUx = max0, x, W atom R d d s the weght matrx to

More information

Gasometric Determination of NaHCO 3 in a Mixture

Gasometric Determination of NaHCO 3 in a Mixture 60 50 40 0 0 5 15 25 35 40 Temperature ( o C) 9/28/16 Gasometrc Determnaton of NaHCO 3 n a Mxture apor Pressure (mm Hg) apor Pressure of Water 1 NaHCO 3 (s) + H + (aq) Na + (aq) + H 2 O (l) + CO 2 (g)

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

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

Harmonic oscillator approximation

Harmonic oscillator approximation armonc ocllator approxmaton armonc ocllator approxmaton Euaton to be olved We are fndng a mnmum of the functon under the retrcton where W P, P,..., P, Q, Q,..., Q P, P,..., P, Q, Q,..., Q lnwgner functon

More information

APPROXIMATE FUZZY REASONING BASED ON INTERPOLATION IN THE VAGUE ENVIRONMENT OF THE FUZZY RULEBASE AS A PRACTICAL ALTERNATIVE OF THE CLASSICAL CRI

APPROXIMATE FUZZY REASONING BASED ON INTERPOLATION IN THE VAGUE ENVIRONMENT OF THE FUZZY RULEBASE AS A PRACTICAL ALTERNATIVE OF THE CLASSICAL CRI Kovác, Sz., Kóczy, L.T.: Approxmate Fuzzy Reaonng Baed on Interpolaton n the Vague Envronment of the Fuzzy Rulebae a a Practcal Alternatve of the Clacal CRI, Proceedng of the 7 th Internatonal Fuzzy Sytem

More information

Chem 2A Exam 1. First letter of your last name

Chem 2A Exam 1. First letter of your last name Frst letter of your last name NAME: PERM# INSTRUCTIONS: Fll n your name, perm number and frst ntal of your last name above. Be sure to show all of your work for full credt. Use the back of the page f necessary.

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

Electronic Quantum Monte Carlo Calculations of Energies and Atomic Forces for Diatomic and Polyatomic Molecules

Electronic Quantum Monte Carlo Calculations of Energies and Atomic Forces for Diatomic and Polyatomic Molecules RESERVE HIS SPACE Electronc Quantum Monte Carlo Calculatons of Energes and Atomc Forces for Datomc and Polyatomc Molecules Myung Won Lee 1, Massmo Mella 2, and Andrew M. Rappe 1,* 1 he Maknen heoretcal

More information

An identification algorithm of model kinetic parameters of the interfacial layer growth in fiber composites

An identification algorithm of model kinetic parameters of the interfacial layer growth in fiber composites IOP Conference Seres: Materals Scence and Engneerng PAPER OPE ACCESS An dentfcaton algorthm of model knetc parameters of the nterfacal layer growth n fber compostes o cte ths artcle: V Zubov et al 216

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

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

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

Sensor Calibration Method Based on Numerical Rounding

Sensor Calibration Method Based on Numerical Rounding ensors & Transducers, Vol 164, Issue, February 014, pp 5-30 ensors & Transducers 014 by IFA Publshng, L http://wwwsensorsportalcom ensor Calbraton Method Based on Numercal Roundng Youcheng WU, Jan WANG

More information

is the calculated value of the dependent variable at point i. The best parameters have values that minimize the squares of the errors

is the calculated value of the dependent variable at point i. The best parameters have values that minimize the squares of the errors Multple Lnear and Polynomal Regresson wth Statstcal Analyss Gven a set of data of measured (or observed) values of a dependent varable: y versus n ndependent varables x 1, x, x n, multple lnear regresson

More information

DUE: WEDS FEB 21ST 2018

DUE: WEDS FEB 21ST 2018 HOMEWORK # 1: FINITE DIFFERENCES IN ONE DIMENSION DUE: WEDS FEB 21ST 2018 1. Theory Beam bendng s a classcal engneerng analyss. The tradtonal soluton technque makes smplfyng assumptons such as a constant

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

Physics 120. Exam #1. April 15, 2011

Physics 120. Exam #1. April 15, 2011 Phyc 120 Exam #1 Aprl 15, 2011 Name Multple Choce /16 Problem #1 /28 Problem #2 /28 Problem #3 /28 Total /100 PartI:Multple Choce:Crclethebetanwertoeachqueton.Anyothermark wllnotbegvencredt.eachmultple

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

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

SPANC -- SPlitpole ANalysis Code User Manual

SPANC -- SPlitpole ANalysis Code User Manual Functonal Descrpton of Code SPANC -- SPltpole ANalyss Code User Manual Author: Dale Vsser Date: 14 January 00 Spanc s a code created by Dale Vsser for easer calbratons of poston spectra from magnetc spectrometer

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

Least Squares Fitting of Data

Least Squares Fitting of Data Least Squares Fttng of Data Davd Eberly Geoetrc Tools, LLC http://www.geoetrctools.co/ Copyrght c 1998-2015. All Rghts Reserved. Created: July 15, 1999 Last Modfed: January 5, 2015 Contents 1 Lnear Fttng

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

The multivariate Gaussian probability density function for random vector X (X 1,,X ) T. diagonal term of, denoted

The multivariate Gaussian probability density function for random vector X (X 1,,X ) T. diagonal term of, denoted Appendx Proof of heorem he multvarate Gauan probablty denty functon for random vector X (X,,X ) px exp / / x x mean and varance equal to the th dagonal term of, denoted he margnal dtrbuton of X Gauan wth

More information

Name ID # For relatively dilute aqueous solutions the molality and molarity are approximately equal.

Name ID # For relatively dilute aqueous solutions the molality and molarity are approximately equal. Name ID # 1 CHEMISTRY 212, Lect. Sect. 002 Dr. G. L. Roberts Exam #1/Sprng 2000 Thursday, February 24, 2000 CLOSED BOOK EXM No notes or books allowed. Calculators may be used. tomc masses of nterest are

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

Improvements on Waring s Problem

Improvements on Waring s Problem Imrovement on Warng Problem L An-Png Bejng 85, PR Chna al@nacom Abtract By a new recurve algorthm for the auxlary equaton, n th aer, we wll gve ome mrovement for Warng roblem Keyword: Warng Problem, Hardy-Lttlewood

More information

Method Of Fundamental Solutions For Modeling Electromagnetic Wave Scattering Problems

Method Of Fundamental Solutions For Modeling Electromagnetic Wave Scattering Problems Internatonal Workhop on MehFree Method 003 1 Method Of Fundamental Soluton For Modelng lectromagnetc Wave Scatterng Problem Der-Lang Young (1) and Jhh-We Ruan (1) Abtract: In th paper we attempt to contruct

More information

Please review the following statement: I certify that I have not given unauthorized aid nor have I received aid in the completion of this exam.

Please review the following statement: I certify that I have not given unauthorized aid nor have I received aid in the completion of this exam. Please revew the followng statement: I certfy that I have not gven unauthorzed ad nor have I receved ad n the completon of ths exam. Sgnature: Instructor s Name and Secton: (Crcle Your Secton) Sectons:

More information

36.1 Why is it important to be able to find roots to systems of equations? Up to this point, we have discussed how to find the solution to

36.1 Why is it important to be able to find roots to systems of equations? Up to this point, we have discussed how to find the solution to ChE Lecture Notes - D. Keer, 5/9/98 Lecture 6,7,8 - Rootndng n systems o equatons (A) Theory (B) Problems (C) MATLAB Applcatons Tet: Supplementary notes rom Instructor 6. Why s t mportant to be able to

More information

Report on Image warping

Report on Image warping Report on Image warpng Xuan Ne, Dec. 20, 2004 Ths document summarzed the algorthms of our mage warpng soluton for further study, and there s a detaled descrpton about the mplementaton of these algorthms.

More information

3 Implementation and validation of analysis methods

3 Implementation and validation of analysis methods 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

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

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

Using Spectrophotometric Methods to Determine an Equilibrium Constant Prelab

Using Spectrophotometric Methods to Determine an Equilibrium Constant Prelab Usng Spectrophotometrc Methods to Determne an Equlbrum Constant Prelab 1. What s the purpose of ths experment? 2. Wll the absorbance of the ulbrum mxture (at 447 nm) ncrease or decrease as Fe soluton s

More information

CSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography

CSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography CSc 6974 and ECSE 6966 Math. Tech. for Vson, Graphcs and Robotcs Lecture 21, Aprl 17, 2006 Estmatng A Plane Homography Overvew We contnue wth a dscusson of the major ssues, usng estmaton of plane projectve

More information

Module 5. Cables and Arches. Version 2 CE IIT, Kharagpur

Module 5. Cables and Arches. Version 2 CE IIT, Kharagpur odule 5 Cable and Arche Veron CE IIT, Kharagpur Leon 33 Two-nged Arch Veron CE IIT, Kharagpur Intructonal Objectve: After readng th chapter the tudent wll be able to 1. Compute horzontal reacton n two-hnged

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

Least squares cubic splines without B-splines S.K. Lucas

Least squares cubic splines without B-splines S.K. Lucas Least squares cubc splnes wthout B-splnes S.K. Lucas School of Mathematcs and Statstcs, Unversty of South Australa, Mawson Lakes SA 595 e-mal: stephen.lucas@unsa.edu.au Submtted to the Gazette of the Australan

More information

Be true to your work, your word, and your friend.

Be true to your work, your word, and your friend. Chemstry 13 NT Be true to your work, your word, and your frend. Henry Davd Thoreau 1 Chem 13 NT Chemcal Equlbrum Module Usng the Equlbrum Constant Interpretng the Equlbrum Constant Predctng the Drecton

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

Physics 207: Lecture 20. Today s Agenda Homework for Monday

Physics 207: Lecture 20. Today s Agenda Homework for Monday Physcs 207: Lecture 20 Today s Agenda Homework for Monday Recap: Systems of Partcles Center of mass Velocty and acceleraton of the center of mass Dynamcs of the center of mass Lnear Momentum Example problems

More information

Solutions Review Worksheet

Solutions Review Worksheet Solutons Revew Worksheet NOTE: Namng acds s ntroduced on pages 163-4 and agan on pages 208-9.. You learned ths and were quzzed on t, but snce acd names are n the Data Booklet you wll not be tested on ths

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

modeling of equilibrium and dynamic multi-component adsorption in a two-layered fixed bed for purification of hydrogen from methane reforming products

modeling of equilibrium and dynamic multi-component adsorption in a two-layered fixed bed for purification of hydrogen from methane reforming products modelng of equlbrum and dynamc mult-component adsorpton n a two-layered fxed bed for purfcaton of hydrogen from methane reformng products Mohammad A. Ebrahm, Mahmood R. G. Arsalan, Shohreh Fatem * Laboratory

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

Parameter Estimation for Dynamic System using Unscented Kalman filter

Parameter Estimation for Dynamic System using Unscented Kalman filter Parameter Estmaton for Dynamc System usng Unscented Kalman flter Jhoon Seung 1,a, Amr Atya F. 2,b, Alexander G.Parlos 3,c, and Klto Chong 1,4,d* 1 Dvson of Electroncs Engneerng, Chonbuk Natonal Unversty,

More information

Parametric fractional imputation for missing data analysis. Jae Kwang Kim Survey Working Group Seminar March 29, 2010

Parametric fractional imputation for missing data analysis. Jae Kwang Kim Survey Working Group Seminar March 29, 2010 Parametrc fractonal mputaton for mssng data analyss Jae Kwang Km Survey Workng Group Semnar March 29, 2010 1 Outlne Introducton Proposed method Fractonal mputaton Approxmaton Varance estmaton Multple mputaton

More information

Number of cases Number of factors Number of covariates Number of levels of factor i. Value of the dependent variable for case k

Number of cases Number of factors Number of covariates Number of levels of factor i. Value of the dependent variable for case k ANOVA Model and Matrx Computatons Notaton The followng notaton s used throughout ths chapter unless otherwse stated: N F CN Y Z j w W Number of cases Number of factors Number of covarates Number of levels

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

Lecture 21: Numerical methods for pricing American type derivatives

Lecture 21: Numerical methods for pricing American type derivatives Lecture 21: Numercal methods for prcng Amercan type dervatves Xaoguang Wang STAT 598W Aprl 10th, 2014 (STAT 598W) Lecture 21 1 / 26 Outlne 1 Fnte Dfference Method Explct Method Penalty Method (STAT 598W)

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

Pythagorean triples. Leen Noordzij.

Pythagorean triples. Leen Noordzij. Pythagorean trple. Leen Noordz Dr.l.noordz@leennoordz.nl www.leennoordz.me Content A Roadmap for generatng Pythagorean Trple.... Pythagorean Trple.... 3 Dcuon Concluon.... 5 A Roadmap for generatng Pythagorean

More information

Application of B-Spline to Numerical Solution of a System of Singularly Perturbed Problems

Application of B-Spline to Numerical Solution of a System of Singularly Perturbed Problems Mathematca Aeterna, Vol. 1, 011, no. 06, 405 415 Applcaton of B-Splne to Numercal Soluton of a System of Sngularly Perturbed Problems Yogesh Gupta Department of Mathematcs Unted College of Engneerng &

More information

Review: Fit a line to N data points

Review: Fit a line to N data points Revew: Ft a lne to data ponts Correlated parameters: L y = a x + b Orthogonal parameters: J y = a (x ˆ x + b For ntercept b, set a=0 and fnd b by optmal average: ˆ b = y, Var[ b ˆ ] = For slope a, set

More information

The Exact Formulation of the Inverse of the Tridiagonal Matrix for Solving the 1D Poisson Equation with the Finite Difference Method

The Exact Formulation of the Inverse of the Tridiagonal Matrix for Solving the 1D Poisson Equation with the Finite Difference Method Journal of Electromagnetc Analyss and Applcatons, 04, 6, 0-08 Publshed Onlne September 04 n ScRes. http://www.scrp.org/journal/jemaa http://dx.do.org/0.46/jemaa.04.6000 The Exact Formulaton of the Inverse

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

Energy, Entropy, and Availability Balances Phase Equilibria. Nonideal Thermodynamic Property Models. Selecting an Appropriate Model

Energy, Entropy, and Availability Balances Phase Equilibria. Nonideal Thermodynamic Property Models. Selecting an Appropriate Model Lecture 4. Thermodynamcs [Ch. 2] Energy, Entropy, and Avalablty Balances Phase Equlbra - Fugactes and actvty coeffcents -K-values Nondeal Thermodynamc Property Models - P-v-T equaton-of-state models -

More information

Operating conditions of a mine fan under conditions of variable resistance

Operating conditions of a mine fan under conditions of variable resistance Paper No. 11 ISMS 216 Operatng condtons of a mne fan under condtons of varable resstance Zhang Ynghua a, Chen L a, b, Huang Zhan a, *, Gao Yukun a a State Key Laboratory of Hgh-Effcent Mnng and Safety

More information

Solution Thermodynamics

Solution Thermodynamics Soluton hermodynamcs usng Wagner Notaton by Stanley. Howard Department of aterals and etallurgcal Engneerng South Dakota School of nes and echnology Rapd Cty, SD 57701 January 7, 001 Soluton hermodynamcs

More information

Please review the following statement: I certify that I have not given unauthorized aid nor have I received aid in the completion of this exam.

Please review the following statement: I certify that I have not given unauthorized aid nor have I received aid in the completion of this exam. ME 270 Sprng 2017 Exam 1 NAME (Last, Frst): Please revew the followng statement: I certfy that I have not gven unauthorzed ad nor have I receved ad n the completon of ths exam. Sgnature: Instructor s Name

More information

Bias-corrected nonparametric correlograms for geostatistical radar-raingauge combination

Bias-corrected nonparametric correlograms for geostatistical radar-raingauge combination ERAD 00 - THE SIXTH EUROPEA COFERECE O RADAR I METEOROLOGY AD HYDROLOGY Ba-corrected nonparametrc correlogram for geotattcal radar-rangauge combnaton Renhard Schemann, Rebekka Erdn, Marco Wll, Chrtoph

More information

THE EFFECT OF TORSIONAL RIGIDITY BETWEEN ELEMENTS ON FREE VIBRATIONS OF A TELESCOPIC HYDRAULIC CYLINDER SUBJECTED TO EULER S LOAD

THE EFFECT OF TORSIONAL RIGIDITY BETWEEN ELEMENTS ON FREE VIBRATIONS OF A TELESCOPIC HYDRAULIC CYLINDER SUBJECTED TO EULER S LOAD Journal of Appled Mathematcs and Computatonal Mechancs 7, 6(3), 7- www.amcm.pcz.pl p-issn 99-9965 DOI:.75/jamcm.7.3. e-issn 353-588 THE EFFECT OF TORSIONAL RIGIDITY BETWEEN ELEMENTS ON FREE VIBRATIONS

More information

On the SO 2 Problem in Thermal Power Plants. 2.Two-steps chemical absorption modeling

On the SO 2 Problem in Thermal Power Plants. 2.Two-steps chemical absorption modeling Internatonal Journal of Engneerng Reearch ISSN:39-689)(onlne),347-53(prnt) Volume No4, Iue No, pp : 557-56 Oct 5 On the SO Problem n Thermal Power Plant Two-tep chemcal aborpton modelng hr Boyadjev, P

More information

Chapter.4 MAGNETIC CIRCUIT OF A D.C. MACHINE

Chapter.4 MAGNETIC CIRCUIT OF A D.C. MACHINE Chapter.4 MAGNETIC CIRCUIT OF A D.C. MACHINE The dfferent part of the dc machne manetc crcut / pole are yoke, pole, ar ap, armature teeth and armature core. Therefore, the ampere-turn /pole to etablh the

More information

CHAPTER 9 LINEAR MOMENTUM, IMPULSE AND COLLISIONS

CHAPTER 9 LINEAR MOMENTUM, IMPULSE AND COLLISIONS CHAPTER 9 LINEAR MOMENTUM, IMPULSE AND COLLISIONS 103 Phy 1 9.1 Lnear Momentum The prncple o energy conervaton can be ued to olve problem that are harder to olve jut ung Newton law. It ued to decrbe moton

More information

Introduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law:

Introduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law: CE304, Sprng 2004 Lecture 4 Introducton to Vapor/Lqud Equlbrum, part 2 Raoult s Law: The smplest model that allows us do VLE calculatons s obtaned when we assume that the vapor phase s an deal gas, and

More information

If the solution does not follow a logical thought process, it will be assumed in error.

If the solution does not follow a logical thought process, it will be assumed in error. Group # Please revew the followng statement: I certfy that I have not gven unauthorzed ad nor have I receved ad n the completon of ths exam. Sgnature: INSTRUCTIONS Begn each problem n the space provded

More information

For now, let us focus on a specific model of neurons. These are simplified from reality but can achieve remarkable results.

For now, let us focus on a specific model of neurons. These are simplified from reality but can achieve remarkable results. Neural Networks : Dervaton compled by Alvn Wan from Professor Jtendra Malk s lecture Ths type of computaton s called deep learnng and s the most popular method for many problems, such as computer vson

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

Finite Element Modelling of truss/cable structures

Finite Element Modelling of truss/cable structures Pet Schreurs Endhoven Unversty of echnology Department of Mechancal Engneerng Materals echnology November 3, 214 Fnte Element Modellng of truss/cable structures 1 Fnte Element Analyss of prestressed structures

More information

CONDITIONAL MOMENT CLOSURE MODELLING OF SOOT FORMATION IN TURBULENT NON-PREMIXED, ETHYLENE-AIR FLAMES

CONDITIONAL MOMENT CLOSURE MODELLING OF SOOT FORMATION IN TURBULENT NON-PREMIXED, ETHYLENE-AIR FLAMES CONDITIONAL MOMENT CLOSURE MODELLING OF SOOT FORMATION IN TURBULENT NON-PREMIXED, ETHYLENE-AIR FLAMES Yunard, Robert M. Woolley and Mchael Farweather Energy and Reource Reearch Inttute, School of Proce,

More information

COMPOSITE BEAM WITH WEAK SHEAR CONNECTION SUBJECTED TO THERMAL LOAD

COMPOSITE BEAM WITH WEAK SHEAR CONNECTION SUBJECTED TO THERMAL LOAD COMPOSITE BEAM WITH WEAK SHEAR CONNECTION SUBJECTED TO THERMAL LOAD Ákos Jósef Lengyel, István Ecsed Assstant Lecturer, Professor of Mechancs, Insttute of Appled Mechancs, Unversty of Mskolc, Mskolc-Egyetemváros,

More information

Citation for published version (APA): Gambardella, F. (2005). NO and O2 absorption in FeII(EDTA) solutions [Groningen]: s.n.

Citation for published version (APA): Gambardella, F. (2005). NO and O2 absorption in FeII(EDTA) solutions [Groningen]: s.n. Unversty of Gronngen NO and O absorpton n FeII(EDTA) solutons Gambardella, Francesca IMPORTANT NOTE: You are advsed to consult the publsher's verson (publsher's PDF) f you wsh to cte from t. Please check

More information

Error Bars in both X and Y

Error Bars in both X and Y Error Bars n both X and Y Wrong ways to ft a lne : 1. y(x) a x +b (σ x 0). x(y) c y + d (σ y 0) 3. splt dfference between 1 and. Example: Prmordal He abundance: Extrapolate ft lne to [ O / H ] 0. [ He

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

Structure and Drive Paul A. Jensen Copyright July 20, 2003

Structure and Drive Paul A. Jensen Copyright July 20, 2003 Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.

More information

Lecture 2: Numerical Methods for Differentiations and Integrations

Lecture 2: Numerical Methods for Differentiations and Integrations Numercal Smulaton of Space Plasmas (I [AP-4036] Lecture 2 by Lng-Hsao Lyu March, 2018 Lecture 2: Numercal Methods for Dfferentatons and Integratons As we have dscussed n Lecture 1 that numercal smulaton

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

See Book Chapter 11 2 nd Edition (Chapter 10 1 st Edition)

See Book Chapter 11 2 nd Edition (Chapter 10 1 st Edition) Count Data Models See Book Chapter 11 2 nd Edton (Chapter 10 1 st Edton) Count data consst of non-negatve nteger values Examples: number of drver route changes per week, the number of trp departure changes

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

Linear Correlation. Many research issues are pursued with nonexperimental studies that seek to establish relationships among 2 or more variables

Linear Correlation. Many research issues are pursued with nonexperimental studies that seek to establish relationships among 2 or more variables Lnear Correlaton Many research ssues are pursued wth nonexpermental studes that seek to establsh relatonshps among or more varables E.g., correlates of ntellgence; relaton between SAT and GPA; relaton

More information

Department of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution

Department of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution Department of Statstcs Unversty of Toronto STA35HS / HS Desgn and Analyss of Experments Term Test - Wnter - Soluton February, Last Name: Frst Name: Student Number: Instructons: Tme: hours. Ads: a non-programmable

More information

Two-Layered Model of Blood Flow through Composite Stenosed Artery

Two-Layered Model of Blood Flow through Composite Stenosed Artery Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 93-9466 Vol. 4, Iue (December 9), pp. 343 354 (Prevouly, Vol. 4, No.) Applcaton Appled Mathematc: An Internatonal Journal (AAM) Two-ayered Model

More information

Lecture 10 Support Vector Machines II

Lecture 10 Support Vector Machines II Lecture 10 Support Vector Machnes II 22 February 2016 Taylor B. Arnold Yale Statstcs STAT 365/665 1/28 Notes: Problem 3 s posted and due ths upcomng Frday There was an early bug n the fake-test data; fxed

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

Ph.D. Qualifying Examination in Kinetics and Reactor Design

Ph.D. Qualifying Examination in Kinetics and Reactor Design Knetcs and Reactor Desgn Ph.D.Qualfyng Examnaton January 2006 Instructons Ph.D. Qualfyng Examnaton n Knetcs and Reactor Desgn January 2006 Unversty of Texas at Austn Department of Chemcal Engneerng 1.

More information

Electrochemical Equilibrium Electromotive Force

Electrochemical Equilibrium Electromotive Force CHM465/865, 24-3, Lecture 5-7, 2 th Sep., 24 lectrochemcal qulbrum lectromotve Force Relaton between chemcal and electrc drvng forces lectrochemcal system at constant T and p: consder Gbbs free energy

More information

Math 217 Fall 2013 Homework 2 Solutions

Math 217 Fall 2013 Homework 2 Solutions Math 17 Fall 013 Homework Solutons Due Thursday Sept. 6, 013 5pm Ths homework conssts of 6 problems of 5 ponts each. The total s 30. You need to fully justfy your answer prove that your functon ndeed has

More information

Lecture outline. Optimal Experimental Design: Where to find basic information. Theory of D-optimal design

Lecture outline. Optimal Experimental Design: Where to find basic information. Theory of D-optimal design v I N N O V A T I O N L E C T U R E (I N N O l E C) Lecture outlne Bndng and Knetc for Expermental Bologt Lecture 8 Optmal degn of experment The problem: How hould we plan an experment uch we learn the

More information

Curve Fitting with the Least Square Method

Curve Fitting with the Least Square Method WIKI Document Number 5 Interpolaton wth Least Squares Curve Fttng wth the Least Square Method Mattheu Bultelle Department of Bo-Engneerng Imperal College, London Context We wsh to model the postve feedback

More information

Polynomial Regression Models

Polynomial Regression Models LINEAR REGRESSION ANALYSIS MODULE XII Lecture - 6 Polynomal Regresson Models Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur Test of sgnfcance To test the sgnfcance

More information