UNCERTAINTY OF AIRCRAFT NOISE MEASUREMENTS: EVALUATION FOR AN AIRCRAFT NOISE MONITORING NETWORK

Size: px
Start display at page:

Download "UNCERTAINTY OF AIRCRAFT NOISE MEASUREMENTS: EVALUATION FOR AN AIRCRAFT NOISE MONITORING NETWORK"

Transcription

1 UNCERTAINTY OF AIRCRAFT NOISE MEASUREMENTS: EVALUATION FOR AN AIRCRAFT NOISE MONITORING NETWORK Chrstophe Rosn Acostcs Department, Aéroports de Pars 03 Aérogare Sd CS90055, Orly aérogare cedex, France ABSTRACT Generally, an arcraft nose montorng network comples wth Class templates from the standard IEC 667- abot specfcatons on sond level meters. In addton, some specfc standards sch as ISO 0906 or NF EN 3-90 provde ways to evalate ncertanty. Aéroports de Pars operates 50 nose montorng systems on several arports. Specfcally at Pars Charles de Galle arport, the nose management s based on a nose bdget ndex calclated from measred maxmm nose level of all arcraft movements on the arport. In ths context, t s mportant to know exactly the nstrments metrology n order to exceed the standards templates and to assess ts own ncertanty. To do ths, t s necessary to know all the parameters whch can nflence the measre. The objectve s to mprove the robstness of measrements. Ths artcle lsts all the nflence factors and presents an ncertanty calclaton sng specfc vales, standards vales or docmented vales. Ths approach s consstent wth the French standard draft abot the evalaton of the ncertantes of envronmental nose. INTRODUCTION Generally, the ncertanty n acostcs that goes along wth a measre s set to 3dB. Laboratores wshng to be accredted accordng to the referental ISO 705 mst comply wth the specfc reqrements and evalate the ncertantes n measrement. However, there s no standard of reference or gde to assess or calclate ncertanty n envronmental acostcs. Informatons on ncertantes are ncreasngly reqested by cstomers, especally by resdents n the case of nose montorng arond arports. Frthermore, n accordance wth French reglatons, the IGMP ndex (measred and calclated from the weghted sond levels LA,eq,s,max of each arcraft overflght) specfc to Pars Charles de Galle arport s calclated each year. Ths global ndex s sed to pt a pper lmt to the nmber of movements n the arport. The lmt s set by the average of three years consdered nosy. For several years, the Laboratory try to mprove the robstness of the calclaton (ncreasng the operang rate, mprovng the detecton of flyng arcraft, mproved detecton of arcraft nose by a method ncldng ado mltvaldaton arcraft recognton, ncreasng the correlaton rate...). It s now necessary to know the ncertanty srrondng the vale of ths ndex. Chrstophe.rosn@adp.fr

2 . GENERALITIES ABOUT THE UNCERTAINTIES The ncertanty calclaton s done wth the followng standards: ISO/IEC Gde 98-3:008. Uncertanty of measrement - Part 3: Gde to the expresson of ncertanty n measrement (GUM:995). ISO/IEC 667-:000: Electroacostcs Sond level meters Part Specfcatons ISO 0906:009: Acostcs Unattended montorng of arcraft sond n the vcnty of arports.. Measrand The prpose of the measre s not to measre nose level at sorce bt to measre the nose level receved on the grond by local resdents. It s therefore ndependent of the propagaton condtons. As a reslt, the physcal qantty beng measred, also called measrand s the sond pressre level of the fne layer of ar n contact wth the membrane of the mcrophone, expressed n dba.. Ishkawa dagram Ths method allocates the ncertantes of the varos parameters nto fve famles accordng to ther sorces: Method of measrement operators (Man) measrng devce (Machne) Envronnement prodct to be measred (Materal). It can be otlned as an Ishkawa dagram, also called "fshbone dagram". Ths dagram s shown n Fgre. Method Man Machne Uncertanty of measrement Envronment Materal Fgre - Ishkawa dagram Frst, we classfy the dfferent sorces of ncertanty n each category of famles (Table ). Method Man Machne Envronment Materal calbraton, lnearty temperatre calbraton wth fttng drft of eqpment hmdty procedres A-weghted flter resolton of eqpment electromagnetc capactaton tolerance freqency response felds sensblty bandwth pressre Table - Orgn of ncertanty for the elementary vale measrement the sond

3 .3 Prncple of ncertanty calclaton To perform the calclaton of the ncertanty assocated wth a measrement, t s necessary to defne the standard ncertanty of each nflence affectng a measre to calclate the overall ncertanty from the standard ncertantes, and defne the ncertanty expanded wth a confdence ndex. In the prevos secton, the varables that affect the measrement of elementary levels were lsted. The measrand s the LAeqs level. It s calclated from two LAeq,0.5s (elementary vale measred by a montorng nose staton). Consder the followng hypothess : L Aeq,0.5s =μ + e + e + wth μ the rght vale, not known and e, e errors arond the tre vale. It s also assmed that the average vale of possble errors s centered on 0 (no bas correcton of the measred vale). In ths scheme, error s noted e : δ 0 e Standard ncertanty s defned as : = δ k Wth k vale whch depend on the probablty dstrbton. For a rectanglar probablty dstrbton, standard ncertanty s : 3 In a dfferent scheme, the standard ncertanty de to +e or e error calclated wth a rectanglar probablty s : -e 0 +e 3 After calclatng the standard ncertantes, the combned standard ncertanty of the elementary vale s sed. Combned standard ncertanty s defned as : = =

4 Expanded ncertanty s calclated wth the k coverage factor,to defne a confdence. k= for a 95% confdence k=.58 for a 99% confdence k=3 for a 99.7% confdence U = k. CALCULATION OF UNCERTAINTY. Combnaton from LAeq,0.5s to LAeq,s The evalaton of the ncertanty concerns the elementary vale second stored n database. Eqpment delvers a measre every 0.5 second. Ths elementary vale reslts of a calclaton made from two vales LAeq,0.5s. The nt vale stored n a database, and commncated to the cstomer and wth all ndcators are constrcted s the LAeq,s. It s recalclated by the central server from LAeq0, 5s measred onste by each nose montorng eqpment. The translaton of LAeq,0.5second to Laeq,second occrs n two consectve energetcally smmng vales LAeq,0.5second. W and W are the energy assocated wth the two LAeq,0.5second and Laeq,second.The mathematcal expresson of LAeqsecond s as follows: W W LAeq s 0 log (4) wth : W P P 0 ; W P P 0 (5) and P 0 the reference pressre level : P 0 =.0-5 Pa. 0 W 0 and L Aeq 0.5s The expresson (4) can be smplfed, whch leads to: L 0 logw W 3 0 W 0 (6) L Aeq 0.5s Aeq s (7) Calclaton of ncertanty n L aeqs As we saw above, the LAeq,0.5s are sed to calclate LAeq,s, whch s the elementary vale sed to calclate varos ndcators of arcraft nose. Also, ths calclaton leads to ncertanty abot the LAeq,s vale. Ths ncertanty s determned from the formla (7) whch allow to calclate the LAeq,s. The varances combnaton law (see formla (3)) provdes the analytcal expresson: Aeqs Aeqs L W W Aeqs L L W W (8) 4

5 Dervatves are L Aeqs W L L et W Aeqs W L Aeqs W. The calclaton s detaled only for one dervatve: 0 log W W Aeqs 0 W ln 0 W W 3 (9) Wth the second dervatve, the calclaton s the same: LAeq s 0 W ln 0 W W (0) It remans to calclate the standard ncertanty and W. For ease of readng, W and W are wrtten W wth =;. The varances combnaton law carres ot to the followng mathematcal expresson: W P W () P P0 P0 W P P Now, expressons et P have ncertantes becase they are based on L aeq0.5s (formla 0 P0 (6)). The combned ncertanty assocated wth LAeq,0.5s takes part n the calclaton of the combned ncertanty on LAeq,s. Ths acton reslted n: W L ) W ( Aeq 0. 5s () L Aeq 0.5s After several steps of calclaton, we fnd W ln 0 0 L Aeq 0.5s 0 0 L Aeq 0.5s However, for one gven type of staton, ncertantes L aeq. 5s L L. Aeq 0.5s Aeq 0.5s (3). 0 are eqal. So we can wrte 5

6 It s now possble to gve the fnal expresson of the combned ncertanty of the vale of each Laeqs from expressons (8), (9), (0) and (3). After smplfcatons, the fnal reslt s wrtten as below : L Aeq 0.5s Aeq 0.5s 5 5 L 0 0 L Aeqs L Aeq 0..5s (4) W W Ths expresson shows that the ncertanty LAeq,s depends on the dfference of 0.5second consectve levels. For vales of LAeq,0.5s dentcal or very smlar, the ncertanty s less than LAeq,s on LAeq,0.5s. However, for vales of LAeq,0.5s havng a very bg dfference, especally the extreme vales of the dynamc range of statons (30-40 db), we see that the ncertanty Laeqs s eqal to the ncertanty LAeq,0.5s. L L Aeqs Aeq 0. 5s To cover the fll dynamc range, the ncertanty s LAeqs s: In the case of an ncrease: L L Aeq s Aeq 0. 5s. Spectrm of arcraft nose The nose sorce measred by the nose montorng statons s the arcraft flyng-over. Therefore, the spectrm measrement s lmted to an average spectrm of arcraft nose. Bandwdth s 50Hz - 4kHz. It s defned by observng the nflence of each one-thrd octave band on the reconstrcton of the LAeq n dba. 3. INFLUENCING FACTORS In the followng docment, the dfferent ncertanty factors are classfed by famly: calbraton otdoor wndscreen protecton wth antbrd spkes pstonphone calbrator measrement system: analyzer and mcrophone In each famly, nflencng factors are descrbed precsely. 3. Calbraton Ths tem otlnes the varos errors made drng the calbraton phase adjstment wth a nose montorng staton. 3.. Tolerance of calbraton Ths s the permssble devaton between the theoretcal vale and the vale read drng calbraton. The nflence qantty s 0. dba. The probablty dstrbton for ths tem s a rectangle probablty dstrbton. =5.8E Calbraton drft Ths s the extent of the vale allowed for the calbraton drft. Ths ensres that the vales measred by atomatc checks performed every sx hors (nserton voltage and actator) are wthn the template +/-0.5 dba. The probablty dstrbton for ths tem s a rectangle probablty dstrbton. =.9E-0 6

7 3.3 Otdoor wndscreen protecton wth antbrd spkes For these tems of ncertanty, the manfactrer has not provded any nformaton. Experments n an anechoc room of a Natonal Metrology Laboratory were condcted to determne the nflence of the otdoor wndscreen protecton, wth or wthot waterloggng Otdoor wndscreen protecton dry (50Hz-4kHz) It s the nflence of otdoor wndscreen protecton dry (wthot water nsde) on the measre. In bandwdth 50Hz-4kHz, the nflence qantty s 0. dba. The probablty dstrbton for ths tem s a rectangle probablty dstrbton. =.E Otdoor wndscreen protecton wet (50Hz-4kHz) Ths s the nflence of the water nsde the foam of the otdoor wndscreen protecton on the measre. The maxmm water mpregnaton sed corresponds to an eqvalent of 40mm of ran, wth mm/mn rates and 3mm/mn. In bandwdth 50Hz-4kHz, the nflence qantty s 0.5 dba. The probablty dstrbton for ths tem s a rectangle probablty dstrbton. =.9E Pstonphone calbrator The vales sed are from the docmentaton provded by the hardware manfactrer or the vales n reports calbraton of eqpment made n the Natonal Metrology Laboratory Tolerance n reference condtons (calbraton n natonal laboratory) The vale comes from the calbraton report of the pstonphone calbrator. The vale s an expanded ncertanty, the standard ncertanty s therefore half the extended ncertanty vale. =3.5E Drft of pstonphone calbrator Before each calbraton, the nose level generated by the pstonphone s compared to the nose of another pstonphone. The tolerance for ths comparson s 0. dba. The probablty dstrbton for ths tem s a rectangle probablty dstrbton. =5.8E Tolerance on operatng condtons wth the mcrophone Ths s the vale gven n the manfactrer docment based on AtoCorrect temperatre and hmdty accordng to the mcrophone sed. Tolerance s 0. dba. The probablty dstrbton for ths tem s a rectangle probablty dstrbton. =5.8E Measrement system: analyzer and mcrophone 3.5. Resolton The sond level measred LAeq0.5s s gven wth /0th of db (A). The probablty dstrbton for ths tem s a rectangle probablty dstrbton. =5.8E-0 7

8 3.5. Freqency weghtng (A-weghted flter 50Hz-4kHz) The vales come from calbratons certfcates performed by a natonal metrology laboratory for all measrng eqpments. For each freqency band, the mnmm and maxmm devatons are selected. From a spectrm of plane type, the calclaton of overall levels LAeq,max and LAeq,mn s calclated n dba. The nflence qantty s the dfference between the mnmm vale and maxmm vale, t s 0.8 dba. The probablty dstrbton for ths tem s a rectangle probablty dstrbton. =.3E Level lnearty of the measrement system Accordng to IEC 667-, the maxmm vale of the expanded ncertanty s 0.3 db. Ths fxed vale s chosen as the varable nflence. The vale s an expanded ncertanty, the standard ncertanty s therefore half the extended vale. =.5E Drectvty of mcrophone The nose measrement statons are nstalled perpendclar to the overflghts. Accordng to ISO 0906:009, the angle θ s zero or well below 30. Annex B.3. of the ISO 0906 standard specfes that n ths case, the ncertanty s db to the vales of the IEC The probablty dstrbton for ths poston s a rectangle law. =.9E Response to brsts of 4-kHz tones Accordng to IEC 667-, the maxmm vale of the expanded ncertanty s 0.3 db. Ths fxed vale s chosen as the varable nflence. The vale s an expanded ncertanty, the standard ncertanty s therefore half the extended vale. =.5E Temperatre Accordng to IEC 667-, the maxmm vale of the expanded ncertanty s 0.3 db for operatng temperatres between -0 C and +50 C. Ths fxed vale s chosen as the varable nflence. The vale s an expanded ncertanty; the standard ncertanty s therefore half the extended vale. =.5E Hmdty Accordng to IEC 667-, the maxmm vale of the expanded ncertanty s 0.3 db for a relatve hmdty between 5 and 90%. Ths fxed vale s chosen as the varable nflence. The vale s an expanded ncertanty, the standard ncertanty s therefore half the extended vale. =.5E Statc ar pressre Accordng to IEC 667-, the maxmm vale of the expanded ncertanty s 0.3 db for a statc ar pressre between 850 and 080 hpa. Ths fxed vale s chosen as the varable nflence. The vale s an expanded ncertanty, the standard ncertanty s therefore half the extended vale. =.5E Cable The cable that connects the mcrophone to analyzer s consdered as new n each nstallaton ste. The factor of ncertanty assocated wth the cable s consdered neglgble compared wth other ncertantes tems. 8

9 4. SUMMARY TABLE OF UNCERTAINTY CALCULATION The followng table smmarzes all the nflencng factors lsted above: INFLUENCING FACTORS nflence qantty Standard ncertanty Varance ² Calbraton Tolerance of calbraton E Calbraton drft 0.5.9E Otdoor wndscreen protecton wth antbrd spkes Otdoor wndscreen protecton dry (50Hz-4kHz) 0..E Otdoor wndscreen protecton wet (50Hz-4kHz) 0.5.9E Pstonphone calbrator Tolerance n reference condtons (calbraton n natonal laboratory) E Drft of pstonphone calbrator E Tolerance on operatng condtons wth the mcrophone E Measrement system: analyzer and mcrophone Resolton E Freqency weghtng (A-weghted flter 50Hz-4kHz) 0.8.3E Level lnearty of the measrement system 0.3.5E Drectvty of mcrophone.9e Response to brsts of 4-kHz tones 0.3.5E Temperatre 0.3.5E Hmdty 0.3.5E Statc ar pressre 0.3.5E Cable 0 0.0E Table - Smmary table of ncertanty calclaton The combned standard ncertanty s 0.666dBA. The expanded ncertanty U wth confdence level of 95% (k = ) s.3dba. The expanded ncertanty U wth confdence level of 99% (k =,58) s.7dba. 9

10 CONCLUSION In ths paper, the ncertanty calclaton s performed for the elementary vales measred by the arcraft nose montorng network. The ncertanty assessment presented here s an overall ncertanty that can be assocated wth all measrements for the entre stock of eqpment operated by Aéroports de Pars laboratory, regardless of the measrement eqpment sed. For each nflencng factor, the ncertanty s sometmes determned from the vales of the calbraton certfcates measred by a natonal metrology laboratory, or sometmes determned from fxed vales of the normatve docments, or sometmes qantfed from vales provded by the hardware manfactrer. The se of vales from the calbraton certfcates can be sed to redce the standard ncertantes, and therefore n some cases can redce the overall ncertanty attached to a measre. Work mst be contned to lead to an assessment of the ncertantes assocated wth nose ndcators sed n envronmental acostcs. REFERENCES [] Internatonal Standard ISO/IEC 667-:00: Electroacostcs - Sond level meters - Part : Specfcatons. [] Eropean Standard IEC 6094:003: Electroacostcs Sond calbrators, [3] Internatonal Standard ISO/IEC Gde 98-3:008. Uncertanty of measrement -- Part 3: Gde to the expresson of ncertanty n measrement (GUM:995). [4] Internatonal Standard ISO 0906:009: Acostcs Unattended montorng of arcraft sond n the vcnty of arports. [5] French Standard NF S 3-90 NF S 3-90:008: Acostqe - Caractérsaton d brt d'aéronefs perçs dans l'envronnement, AFNOR [6] French Standard NF S 3-00:008: Acostqe - Caractérsaton et mesrage des brts de l'envronnement - Méthode partclère de mesrage, AFNOR [7] Otdoor Mcrophone System Type 4AM, Instrcton Manal. Docment avalable onlne on the GRAS ste, accessed : jne_00.pdf [8] Otdoor Mcrophone System Type 4AM on the GRAS ste, accessed : [9] Pstonphone Type 4AP, Instrcton Manal. Docment avalable onlne on the GRAS ste, accessed : [0] Pstonphone Type 4AP Docment avalable onlne on the GRAS ste, accessed : [] French Standard Pr S 3-5. Gde por l évalaton des ncerttdes de mesrage en acostqe de l envronnement, Pars, French Assocaton for Standardzaton, Pars, France;. to be pblshed n 03. 0

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

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

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

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

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

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

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

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

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

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

CLOSED-FORM CHARACTERIZATION OF THE CHANNEL CAPACITY OF MULTI-BRANCH MAXIMAL RATIO COMBINING OVER CORRELATED NAKAGAMI FADING CHANNELS

CLOSED-FORM CHARACTERIZATION OF THE CHANNEL CAPACITY OF MULTI-BRANCH MAXIMAL RATIO COMBINING OVER CORRELATED NAKAGAMI FADING CHANNELS CLOSED-FORM CHARACTERIZATION OF THE CHANNEL CAPACITY OF MULTI-BRANCH MAXIMAL RATIO COMBINING OVER CORRELATED NAKAGAMI FADING CHANNELS Yawgeng A. Cha and Karl Yng-Ta Hang Department of Commncaton Engneerng,

More information

Experimental Errors and Error Analysis

Experimental Errors and Error Analysis Expermental Errors and Error Analss Rajee Prabhakar Unerst of Texas at Astn, Astn, TX Freeman Research Grop Meetng March 10, 004 Topcs coered Tpes of expermental errors Redcng errors Descrbng errors qanttatel

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

Regulation No. 117 (Tyres rolling noise and wet grip adhesion) Proposal for amendments to ECE/TRANS/WP.29/GRB/2010/3

Regulation No. 117 (Tyres rolling noise and wet grip adhesion) Proposal for amendments to ECE/TRANS/WP.29/GRB/2010/3 Transmtted by the expert from France Informal Document No. GRB-51-14 (67 th GRB, 15 17 February 2010, agenda tem 7) Regulaton No. 117 (Tyres rollng nose and wet grp adheson) Proposal for amendments to

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

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

Uncertainty evaluation in field measurements of airborne sound insulation

Uncertainty evaluation in field measurements of airborne sound insulation Uncertanty evaluaton n feld measurements of arborne sound nsulaton R. L. X. N. Mchalsk, D. Ferrera, M. Nabuco and P. Massaran Inmetro / CNPq, Av. N. S. das Graças, 50, Xerém, Duque de Caxas, 25250-020

More information

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U) Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of

More information

CS 3750 Machine Learning Lecture 6. Monte Carlo methods. CS 3750 Advanced Machine Learning. Markov chain Monte Carlo

CS 3750 Machine Learning Lecture 6. Monte Carlo methods. CS 3750 Advanced Machine Learning. Markov chain Monte Carlo CS 3750 Machne Learnng Lectre 6 Monte Carlo methods Mlos Haskrecht mlos@cs.ptt.ed 5329 Sennott Sqare Markov chan Monte Carlo Importance samplng: samples are generated accordng to Q and every sample from

More information

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity Week3, Chapter 4 Moton n Two Dmensons Lecture Quz A partcle confned to moton along the x axs moves wth constant acceleraton from x =.0 m to x = 8.0 m durng a 1-s tme nterval. The velocty of the partcle

More information

DETERMINATION OF UNCERTAINTY ASSOCIATED WITH QUANTIZATION ERRORS USING THE BAYESIAN APPROACH

DETERMINATION OF UNCERTAINTY ASSOCIATED WITH QUANTIZATION ERRORS USING THE BAYESIAN APPROACH Proceedngs, XVII IMEKO World Congress, June 7, 3, Dubrovn, Croata Proceedngs, XVII IMEKO World Congress, June 7, 3, Dubrovn, Croata TC XVII IMEKO World Congress Metrology n the 3rd Mllennum June 7, 3,

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

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

MCM-based Uncertainty Evaluations practical aspects and critical issues

MCM-based Uncertainty Evaluations practical aspects and critical issues C-based Uncertanty Evalatons practcal aspects and crtcal sses H. Hatjea, B. van Dorp,. orel and P.H.J. Schellekens Endhoven Unversty of Technology Contents Introdcton Standard ncertanty bdget de wthot

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

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

Turbulence classification of load data by the frequency and severity of wind gusts. Oscar Moñux, DEWI GmbH Kevin Bleibler, DEWI GmbH

Turbulence classification of load data by the frequency and severity of wind gusts. Oscar Moñux, DEWI GmbH Kevin Bleibler, DEWI GmbH Turbulence classfcaton of load data by the frequency and severty of wnd gusts Introducton Oscar Moñux, DEWI GmbH Kevn Blebler, DEWI GmbH Durng the wnd turbne developng process, one of the most mportant

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

Final Report on the International Comparison of Luminous Responsivity CCPR-K3.b

Final Report on the International Comparison of Luminous Responsivity CCPR-K3.b Fnal Report on the Internatonal Comparson of Lmnos Responsvty CCPR-K3.b R. Köhler, M. Stock, C. Garrea Brea Internatonal des Pods et Mesres Pavllon de Bretel 93 Sèvres Cedex France Janary 004 Abstract

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

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

x = , so that calculated

x = , so that calculated Stat 4, secton Sngle Factor ANOVA notes by Tm Plachowsk n chapter 8 we conducted hypothess tests n whch we compared a sngle sample s mean or proporton to some hypotheszed value Chapter 9 expanded ths to

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 optimal delay of the second test is therefore approximately 210 hours earlier than =2.

The optimal delay of the second test is therefore approximately 210 hours earlier than =2. THE IEC 61508 FORMULAS 223 The optmal delay of the second test s therefore approxmately 210 hours earler than =2. 8.4 The IEC 61508 Formulas IEC 61508-6 provdes approxmaton formulas for the PF for smple

More information

The Analysis and the Performance Simulation of the Capacity of Bit-interleaved Coded Modulation System

The Analysis and the Performance Simulation of the Capacity of Bit-interleaved Coded Modulation System Sensors & Transdcers, Vol. 79, Isse 9, September 4, pp. 5-57 Sensors & Transdcers 4 by IFSA Pblshng, S. L. http://www.sensorsportal.com The Analyss and the Performance Smlaton of the Capacty of Bt-nterleaved

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

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

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

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

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

BIPM comparison BIPM.RI(II)-K1.Eu-155 of the activity measurements of the radionuclide 155 Eu. G. Ratel and C. Michotte BIPM

BIPM comparison BIPM.RI(II)-K1.Eu-155 of the activity measurements of the radionuclide 155 Eu. G. Ratel and C. Michotte BIPM BIPM comparson BIPM.RI(II)-K1.Eu-155 of the actvty measurements of the radonuclde 155 Eu G. Ratel and C. Mchotte BIPM Abstract In 1993, a natonal metrology nsttute, the NPL (UK), submtted a sample of known

More information

KULLBACK-LEIBER DIVERGENCE MEASURE IN CORRELATION OF GRAY-SCALE OBJECTS

KULLBACK-LEIBER DIVERGENCE MEASURE IN CORRELATION OF GRAY-SCALE OBJECTS The Second Internatonal Conference on Innovatons n Informaton Technology (IIT 05) KULLBACK-LEIBER DIVERGENCE MEASURE IN CORRELATION OF GRAY-SCALE OBJECTS M. Sohal Khald Natonal Unversty of Scences and

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

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:

More information

Pop-Click Noise Detection Using Inter-Frame Correlation for Improved Portable Auditory Sensing

Pop-Click Noise Detection Using Inter-Frame Correlation for Improved Portable Auditory Sensing Advanced Scence and Technology Letters, pp.164-168 http://dx.do.org/10.14257/astl.2013 Pop-Clc Nose Detecton Usng Inter-Frame Correlaton for Improved Portable Audtory Sensng Dong Yun Lee, Kwang Myung Jeon,

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

PHYS 705: Classical Mechanics. Calculus of Variations II

PHYS 705: Classical Mechanics. Calculus of Variations II 1 PHYS 705: Classcal Mechancs Calculus of Varatons II 2 Calculus of Varatons: Generalzaton (no constrant yet) Suppose now that F depends on several dependent varables : We need to fnd such that has a statonary

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

Limited Dependent Variables

Limited Dependent Variables Lmted Dependent Varables. What f the left-hand sde varable s not a contnuous thng spread from mnus nfnty to plus nfnty? That s, gven a model = f (, β, ε, where a. s bounded below at zero, such as wages

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

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

More information

Generalized Linear Methods

Generalized Linear Methods Generalzed Lnear Methods 1 Introducton In the Ensemble Methods the general dea s that usng a combnaton of several weak learner one could make a better learner. More formally, assume that we have a set

More information

Inductance Calculation for Conductors of Arbitrary Shape

Inductance Calculation for Conductors of Arbitrary Shape CRYO/02/028 Aprl 5, 2002 Inductance Calculaton for Conductors of Arbtrary Shape L. Bottura Dstrbuton: Internal Summary In ths note we descrbe a method for the numercal calculaton of nductances among conductors

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

} Often, when learning, we deal with uncertainty:

} Often, when learning, we deal with uncertainty: Uncertanty and Learnng } Often, when learnng, we deal wth uncertanty: } Incomplete data sets, wth mssng nformaton } Nosy data sets, wth unrelable nformaton } Stochastcty: causes and effects related non-determnstcally

More information

Measurement of the Activity Concentration of the Radionuclide Am-241 in a Solution COOMET PROJECT 359/RU/06

Measurement of the Activity Concentration of the Radionuclide Am-241 in a Solution COOMET PROJECT 359/RU/06 Measurement of the Actvty Concentraton of the Radonuclde Am-4 n a Soluton COOMET PROJECT 359/RU/06 I.A. Khartonov, A.V. Zanevsky ), V. Mlevsk, A. Ivanukovch ), P. Oropesa and Y. Moreno 3) ), ) BelGIM,

More information

Comparative Studies of Law of Conservation of Energy. and Law Clusters of Conservation of Generalized Energy

Comparative Studies of Law of Conservation of Energy. and Law Clusters of Conservation of Generalized Energy Comparatve Studes of Law of Conservaton of Energy and Law Clusters of Conservaton of Generalzed Energy No.3 of Comparatve Physcs Seres Papers Fu Yuhua (CNOOC Research Insttute, E-mal:fuyh1945@sna.com)

More information

FUZZY FINITE ELEMENT METHOD

FUZZY FINITE ELEMENT METHOD FUZZY FINITE ELEMENT METHOD RELIABILITY TRUCTURE ANALYI UING PROBABILITY 3.. Maxmum Normal tress Internal force s the shear force, V has a magntude equal to the load P and bendng moment, M. Bendng moments

More information

AGC Introduction

AGC Introduction . Introducton AGC 3 The prmary controller response to a load/generaton mbalance results n generaton adjustment so as to mantan load/generaton balance. However, due to droop, t also results n a non-zero

More information

χ x B E (c) Figure 2.1.1: (a) a material particle in a body, (b) a place in space, (c) a configuration of the body

χ x B E (c) Figure 2.1.1: (a) a material particle in a body, (b) a place in space, (c) a configuration of the body Secton.. Moton.. The Materal Body and Moton hyscal materals n the real world are modeled usng an abstract mathematcal entty called a body. Ths body conssts of an nfnte number of materal partcles. Shown

More information

Physics 207 Lecture 6

Physics 207 Lecture 6 Physcs 207 Lecture 6 Agenda: Physcs 207, Lecture 6, Sept. 25 Chapter 4 Frames of reference Chapter 5 ewton s Law Mass Inerta s (contact and non-contact) Frcton (a external force that opposes moton) Free

More information

Winter 2008 CS567 Stochastic Linear/Integer Programming Guest Lecturer: Xu, Huan

Winter 2008 CS567 Stochastic Linear/Integer Programming Guest Lecturer: Xu, Huan Wnter 2008 CS567 Stochastc Lnear/Integer Programmng Guest Lecturer: Xu, Huan Class 2: More Modelng Examples 1 Capacty Expanson Capacty expanson models optmal choces of the tmng and levels of nvestments

More information

ERROR RESEARCH ON A HEPA FILTER MEDIA TESTING SYSTEM OF MPPS(MOST PENETRATION PARTICLE SIZE) EFFICIENCY

ERROR RESEARCH ON A HEPA FILTER MEDIA TESTING SYSTEM OF MPPS(MOST PENETRATION PARTICLE SIZE) EFFICIENCY Proceedngs: Indoor Ar 2005 ERROR RESEARCH ON A HEPA FILTER MEDIA TESTING SYSTEM OF MPPS(MOST PENETRATION PARTICLE SIZE) EFFICIENCY S Lu, J Lu *, N Zhu School of Envronmental Scence and Technology, Tanjn

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

Temperature. Chapter Heat Engine

Temperature. Chapter Heat Engine Chapter 3 Temperature In prevous chapters of these notes we ntroduced the Prncple of Maxmum ntropy as a technque for estmatng probablty dstrbutons consstent wth constrants. In Chapter 9 we dscussed the

More information

Homework Assignment 3 Due in class, Thursday October 15

Homework Assignment 3 Due in class, Thursday October 15 Homework Assgnment 3 Due n class, Thursday October 15 SDS 383C Statstcal Modelng I 1 Rdge regresson and Lasso 1. Get the Prostrate cancer data from http://statweb.stanford.edu/~tbs/elemstatlearn/ datasets/prostate.data.

More information

An (almost) unbiased estimator for the S-Gini index

An (almost) unbiased estimator for the S-Gini index An (almost unbased estmator for the S-Gn ndex Thomas Demuynck February 25, 2009 Abstract Ths note provdes an unbased estmator for the absolute S-Gn and an almost unbased estmator for the relatve S-Gn for

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

Appendix B: Resampling Algorithms

Appendix B: Resampling Algorithms 407 Appendx B: Resamplng Algorthms A common problem of all partcle flters s the degeneracy of weghts, whch conssts of the unbounded ncrease of the varance of the mportance weghts ω [ ] of the partcles

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

9 Derivation of Rate Equations from Single-Cell Conductance (Hodgkin-Huxley-like) Equations

9 Derivation of Rate Equations from Single-Cell Conductance (Hodgkin-Huxley-like) Equations Physcs 171/271 - Chapter 9R -Davd Klenfeld - Fall 2005 9 Dervaton of Rate Equatons from Sngle-Cell Conductance (Hodgkn-Huxley-lke) Equatons We consder a network of many neurons, each of whch obeys a set

More information

Math 426: Probability MWF 1pm, Gasson 310 Homework 4 Selected Solutions

Math 426: Probability MWF 1pm, Gasson 310 Homework 4 Selected Solutions Exercses from Ross, 3, : Math 26: Probablty MWF pm, Gasson 30 Homework Selected Solutons 3, p. 05 Problems 76, 86 3, p. 06 Theoretcal exercses 3, 6, p. 63 Problems 5, 0, 20, p. 69 Theoretcal exercses 2,

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

Physics 5153 Classical Mechanics. D Alembert s Principle and The Lagrangian-1

Physics 5153 Classical Mechanics. D Alembert s Principle and The Lagrangian-1 P. Guterrez Physcs 5153 Classcal Mechancs D Alembert s Prncple and The Lagrangan 1 Introducton The prncple of vrtual work provdes a method of solvng problems of statc equlbrum wthout havng to consder the

More information

Cathy Walker March 5, 2010

Cathy Walker March 5, 2010 Cathy Walker March 5, 010 Part : Problem Set 1. What s the level of measurement for the followng varables? a) SAT scores b) Number of tests or quzzes n statstcal course c) Acres of land devoted to corn

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 Notes on Linear Regression

Lecture Notes on Linear Regression Lecture Notes on Lnear Regresson Feng L fl@sdueducn Shandong Unversty, Chna Lnear Regresson Problem In regresson problem, we am at predct a contnuous target value gven an nput feature vector We assume

More information

Lecture 3: Probability Distributions

Lecture 3: Probability Distributions Lecture 3: Probablty Dstrbutons Random Varables Let us begn by defnng a sample space as a set of outcomes from an experment. We denote ths by S. A random varable s a functon whch maps outcomes nto the

More information

ANOVA. The Observations y ij

ANOVA. The Observations y ij ANOVA Stands for ANalyss Of VArance But t s a test of dfferences n means The dea: The Observatons y j Treatment group = 1 = 2 = k y 11 y 21 y k,1 y 12 y 22 y k,2 y 1, n1 y 2, n2 y k, nk means: m 1 m 2

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

Testing for seasonal unit roots in heterogeneous panels

Testing for seasonal unit roots in heterogeneous panels Testng for seasonal unt roots n heterogeneous panels Jesus Otero * Facultad de Economía Unversdad del Rosaro, Colomba Jeremy Smth Department of Economcs Unversty of arwck Monca Gulett Aston Busness School

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

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number

More information

Professor Terje Haukaas University of British Columbia, Vancouver The Q4 Element

Professor Terje Haukaas University of British Columbia, Vancouver  The Q4 Element Professor Terje Haukaas Unversty of Brtsh Columba, ancouver www.nrsk.ubc.ca The Q Element Ths document consders fnte elements that carry load only n ther plane. These elements are sometmes referred to

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

Numerical Heat and Mass Transfer

Numerical Heat and Mass Transfer Master degree n Mechancal Engneerng Numercal Heat and Mass Transfer 06-Fnte-Dfference Method (One-dmensonal, steady state heat conducton) Fausto Arpno f.arpno@uncas.t Introducton Why we use models and

More information

PARTIALLY BALANCED INCOMPLETE BLOCK DESIGNS

PARTIALLY BALANCED INCOMPLETE BLOCK DESIGNS PARTIALLY BALANCED INCOMPLETE BLOCK DESIGNS V.K. Sharma I.A.S.R.I., Lbrary Avenue, New Delh-00. Introducton Balanced ncomplete block desgns, though have many optmal propertes, do not ft well to many expermental

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

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

CS 2750 Machine Learning. Lecture 5. Density estimation. CS 2750 Machine Learning. Announcements

CS 2750 Machine Learning. Lecture 5. Density estimation. CS 2750 Machine Learning. Announcements CS 750 Machne Learnng Lecture 5 Densty estmaton Mlos Hauskrecht mlos@cs.ptt.edu 539 Sennott Square CS 750 Machne Learnng Announcements Homework Due on Wednesday before the class Reports: hand n before

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

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

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

= z 20 z n. (k 20) + 4 z k = 4

= z 20 z n. (k 20) + 4 z k = 4 Problem Set #7 solutons 7.2.. (a Fnd the coeffcent of z k n (z + z 5 + z 6 + z 7 + 5, k 20. We use the known seres expanson ( n+l ( z l l z n below: (z + z 5 + z 6 + z 7 + 5 (z 5 ( + z + z 2 + z + 5 5

More information

Markov Chain Monte Carlo Lecture 6

Markov Chain Monte Carlo Lecture 6 where (x 1,..., x N ) X N, N s called the populaton sze, f(x) f (x) for at least one {1, 2,..., N}, and those dfferent from f(x) are called the tral dstrbutons n terms of mportance samplng. Dfferent ways

More information

Chapter 7 Channel Capacity and Coding

Chapter 7 Channel Capacity and Coding Chapter 7 Channel Capacty and Codng Contents 7. Channel models and channel capacty 7.. Channel models Bnary symmetrc channel Dscrete memoryless channels Dscrete-nput, contnuous-output channel Waveform

More information

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system Transfer Functons Convenent representaton of a lnear, dynamc model. A transfer functon (TF) relates one nput and one output: x t X s y t system Y s The followng termnology s used: x y nput output forcng

More information

Expected Value and Variance

Expected Value and Variance MATH 38 Expected Value and Varance Dr. Neal, WKU We now shall dscuss how to fnd the average and standard devaton of a random varable X. Expected Value Defnton. The expected value (or average value, or

More information

Prof. Dr. I. Nasser Phys 630, T Aug-15 One_dimensional_Ising_Model

Prof. Dr. I. Nasser Phys 630, T Aug-15 One_dimensional_Ising_Model EXACT OE-DIMESIOAL ISIG MODEL The one-dmensonal Isng model conssts of a chan of spns, each spn nteractng only wth ts two nearest neghbors. The smple Isng problem n one dmenson can be solved drectly n several

More information

Week 9 Chapter 10 Section 1-5

Week 9 Chapter 10 Section 1-5 Week 9 Chapter 10 Secton 1-5 Rotaton Rgd Object A rgd object s one that s nondeformable The relatve locatons of all partcles makng up the object reman constant All real objects are deformable to some extent,

More information