Radial Distribution Function. Long-Range Corrections (1) Temperature. 3. Calculation of Equilibrium Properties. Thermodynamics Properties

Size: px
Start display at page:

Download "Radial Distribution Function. Long-Range Corrections (1) Temperature. 3. Calculation of Equilibrium Properties. Thermodynamics Properties"

Transcription

1 . Calculato o qulbrum Prorts. hrmodamc Prorts mratur, Itral rg ad Prssur Fr rg ad tro. Calculato o Damc Prorts Duso Coct hrmal Coductvt Shar scost Irard Absorto Coct k k k mratur m v Rmmbr hrmodamcs or mooatomcmolculs 5 or datomc molculs For Moatomc Molcul Ktc rg or ach rdom hrmodamcs Prorts Itral rg or otal rg U k + k + φ( rj j Rmmbr hrmodamcs or Idal Gas U k Ak R r mol U R u R r mass m' m' k : oltzma Costat,.66 - J/K A : Avogadro umbr, 6.5 /mol R : Uvrsal Gas Costat,. J/(mol K m : Molcular Wght (kg/mol Prssur b vral thorm P k j φ rj r hrmodamcs or Idal Gas P k P k R A j For mol A P k ρk ρ: umbr dst Radal Dstrbuto Fucto Radal Dstrbuto Fucto (Par Corrlato Fucto ρ δ[ r r j ] j ( r, ρ ( r, Rato o a local dst ρ( to th sstm dst ρ Log-Rag Corrctos ( πρ φ( rc πρ φ + πρ φ ( ( r rc ~ + LR πρ φ( LR r c dr LR πρ πρ 6 ( rc ( rc For Lard-Jos ( rc

2 Log-Rag Corrctos ( For Prssur mrcal Rlatos( mratur Dst ~ + LR LR LR πρ 6πρ ( r c r c dφ( r dr 6πρ 6 ( rc ( r c For Lard-Jos Prssur Hlmholtz Fr rg Gbbs Fr rg Pottal rg Itral rg tro R corrlato ad colas t al. corrlato mrcal Rlatos( P ρ + P ρ + ( ρ, A ρ P dρ ρ ρ ( / A ρ ρ P U P d ρ ρ ρ S µ st Partcl Mthod( k l Ω µ µ µ / U t g k l ( k x / k k k µ k l /( g /( k [ ρλ ] Istataous mratur Chmcal Pottal or Idal Gas P S U + µ ρ Λ U U t h /(πmk t, MD 6π ρ rc hrmal D rogl Wavlgth wc log-rag corrcto xcss chmcal ottal µ.5 st Partcl Mthod ( 56 5 colas t al. xcss chmcal ottal µ.6.. st Partcl Mthod ( t 96 t 6 mratur R colas mratur.5 6 m (s 6. m (s

3 st Partcl Mthod ( st Partcl Mthod (5 xcss chmcal ottal umbr o tst artcls xcss chmcal ottal µ ρ. ρ mratur ρ.6 R colas t al. Damc Prorts Damc Prorts (Sl Dusvt, Fck s Law D hrmal Coductvt, Fourr s Law q λ scost, wto Fluds u F µ ts o mthod: qulbrum Molcular Damcs :Corrlato uctos oqulbrum Molcular Damcs :Fctous Fld Drct Molcular Damcs :oudar Codto qulbrum Molcular Damcs (Sl Dusvt D < v( t v( dt < vx vx ( + v v ( + vz vz ( dt st s quato: γ < A A ( dt tγ < ( A( t A( t D < r r ( 6t For larg t Duso Coct <(r (t r ( [A ] 6 D. A /s.x 9 m /s Grad.5 A /s m τ [s]

4 qulbrum Molcular Damcs hrmal Coductvt D k < j j ( dt λ < ( δ( t δ ( k t δ δx δ r ( < r ( < x r ( < / m + j v( r j hrmal Coductvt [ 5 ] <(δ (t δ ( /(k [J/Km] 5 λ.5 mw/km Grad.99x 6 J/(Km / s 5 m τ [s] qulbrum Molcular Damcs Shar scost < ( ( dt µ Pβ t Pβ k < ( Dβ Dβ ( k t D β r β Prort Dto Statstcal Mchacal Gr-Kubo Formula Duso D coct v v ( dt hrmal q λ coductvt q ~ q~ ( dt k Shar U F µ t dt vscost k ~ ( ~ β ( Wth st Rlato For larg t 6t r r ( ( δ ( t k t ( δ ~ ~ k t ( Dβ ( β D β q~ dδ, δ ( dt r, mv + φ( rj, x,, z j ol. ~ mv v + rj j, D ~ m β β β r v, β x, z, β β j zx Damc Prorts.57 A 55. A A sold } lars Lqud aor Lqud sold } lars Coolg Hatg mratur [K] Dst [/Å ] loct [m/s]... 5 s 5 s s 5 s mratur jum: 6. K 5. K <v z Posto [Å] hrmal oudar Rsstac ovr Lqud-Sold Itrac: m K/W hrm. hrm. Sc. Sc. gg., 999, 999, vol. vol. 7, 7, o. o.,, mratur [K] Dst [/Å ] loct [m/s]... 5 s 5 s s 5 s mratur jum: 6. K 5. K <v z Posto [Å] hrmal Coductvt λ L q W /( / z. W/m K Hadbook valu.97 W/m K at th saturatd tmratur o K hrmal Rsstac ovr Lqud-Sold Itrac

5 Absorto Cross Scto (ω ( ω πω{ x( ω / k } c rasto Rat I(ω I( ω, I ( ω µ t π x( ωtdt µ ( ( Classcal Lmt πω ω / k ( ω I( ω k c Quatum Mchacs Prturbato hor Powr Sctrum o Dol Momt Irard Absorto Sctrum Radom umbr Cogurato o Molculs Wghtd Avrag or Statstcal Prort Radom Chag o Cogurato Slct or ot b th Probablt Dstrbuto ad Radom umbr costat (umbr, volum ad rg: mcrocaocal costat (umbr, volum ad tmratur: caocal costat P (umbr, rssur ad tmratur costat µ (chmcal ottal, volum ad tmratur: grad-caocal Mot Carlo Smulato (Mtrools Mthod 5

Statistical Thermodynamics Essential Concepts. (Boltzmann Population, Partition Functions, Entropy, Enthalpy, Free Energy) - lecture 5 -

Statistical Thermodynamics Essential Concepts. (Boltzmann Population, Partition Functions, Entropy, Enthalpy, Free Energy) - lecture 5 - Statstcal Thrmodyamcs sstal Cocpts (Boltzma Populato, Partto Fuctos, tropy, thalpy, Fr rgy) - lctur 5 - uatum mchacs of atoms ad molculs STATISTICAL MCHANICS ulbrum Proprts: Thrmodyamcs MACROSCOPIC Proprts

More information

Math Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1)

Math Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1) Math Trcks r! Combato - umbr o was to group r o objcts, ordr ot mportat r! r! ar 0 a r a s costat, 0 < r < k k! k 0 EX E[XX-] + EX Basc Probablt 0 or d Pr[X > ] - Pr[X ] Pr[ X ] Pr[X ] - Pr[X ] Proprts

More information

The real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k.

The real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k. Modr Smcoductor Dvcs for Itgratd rcuts haptr. lctros ad Hols Smcoductors or a bad ctrd at k=0, th -k rlatoshp ar th mmum s usually parabolc: m = k * m* d / dk d / dk gatv gatv ffctv mass Wdr small d /

More information

Relate p and T at equilibrium between two phases. An open system where a new phase may form or a new component can be added

Relate p and T at equilibrium between two phases. An open system where a new phase may form or a new component can be added 4.3, 4.4 Phas Equlbrum Dtrmn th slops of th f lns Rlat p and at qulbrum btwn two phass ts consdr th Gbbs functon dg η + V Appls to a homognous systm An opn systm whr a nw phas may form or a nw componnt

More information

Lecture #11. A Note of Caution

Lecture #11. A Note of Caution ctur #11 OUTE uctos rvrs brakdow dal dod aalyss» currt flow (qualtatv)» morty carrr dstrbutos Radg: Chatr 6 Srg 003 EE130 ctur 11, Sld 1 ot of Cauto Tycally, juctos C dvcs ar formd by coutr-dog. Th quatos

More information

Pion Production via Proton Synchrotron Radiation in Strong Magnetic Fields in Relativistic Quantum Approach

Pion Production via Proton Synchrotron Radiation in Strong Magnetic Fields in Relativistic Quantum Approach Po Producto va Proto Sychrotro Radato Strog Magtc Flds Rlatvstc Quatum Approach Partcl Productos TV Ergy Rgo Collaborators Toshtaka Kajo Myog-K Chou Grad. J. MATHEWS Tomoyuk Maruyama BRS. Nho Uvrsty NaO,

More information

Chp6. pn Junction Diode: I-V Characteristics II

Chp6. pn Junction Diode: I-V Characteristics II Ch6. Jucto od: -V Charactrstcs 147 6. 1. 3 rvato Pror 163 Hols o th quas utral -sd For covc s sak, df coordat as, - Th, d h d' ' B.C. 164 1 ) ' ( ' / qv L P qv P P P P L q d d q J '/ / 1) ( ' ' 같은방법으로

More information

EQUATION SHEET Quiz 2

EQUATION SHEET Quiz 2 MASSACHUSETTS INSTITUTE OF TECHNOLOGY DEPARTMENT OF MECHANICAL ENGINEERING CAMBRIDGE, MASSACHUSETTS 039.9 NUMERICAL FLUID MECHANICS SPRING 05 EQUATION SHEET Quz Nubr Rrstato - Floatg Nubr Rrstato: = b,

More information

Chapter 4 NUMERICAL METHODS FOR SOLVING BOUNDARY-VALUE PROBLEMS

Chapter 4 NUMERICAL METHODS FOR SOLVING BOUNDARY-VALUE PROBLEMS Chaptr 4 NUMERICL METHODS FOR SOLVING BOUNDRY-VLUE PROBLEMS 00 4. Varatoal formulato two-msoal magtostatcs Lt th followg magtostatc bouar-valu problm b cosr ( ) J (4..) 0 alog ΓD (4..) 0 alog ΓN (4..)

More information

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty 3 Bary Choc LPM logt logstc rgrso probt Multpl Choc Multomal Logt (c Pogsa Porchawssul,

More information

Chapter 6. pn-junction diode: I-V characteristics

Chapter 6. pn-junction diode: I-V characteristics Chatr 6. -jucto dod: -V charactrstcs Tocs: stady stat rsos of th jucto dod udr ald d.c. voltag. ucto udr bas qualtatv dscusso dal dod quato Dvatos from th dal dod Charg-cotrol aroach Prof. Yo-S M Elctroc

More information

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D { 2 2... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data

More information

Correlation in tree The (ferromagnetic) Ising model

Correlation in tree The (ferromagnetic) Ising model 5/3/00 :\ jh\slf\nots.oc\7 Chaptr 7 Blf propagato corrlato tr Corrlato tr Th (frromagtc) Isg mol Th Isg mol s a graphcal mol or par ws raom Markov fl cosstg of a urct graph wth varabls assocat wth th vrtcs.

More information

Partition Functions and Ideal Gases

Partition Functions and Ideal Gases Partitio Fuctios ad Idal Gass PFIG- You v lard about partitio fuctios ad som uss ow w ll xplor tm i mor dpt usig idal moatomic diatomic ad polyatomic gass! for w start rmmbr: Q( N ( N! N Wat ar N ad? W

More information

ln x = n e = 20 (nearest integer)

ln x = n e = 20 (nearest integer) H JC Prlim Solutios 6 a + b y a + b / / dy a b 3/ d dy a b at, d Giv quatio of ormal at is y dy ad y wh. d a b () (,) is o th curv a+ b () y.9958 Qustio Solvig () ad (), w hav a, b. Qustio d.77 d d d.77

More information

Rarefied Gas Flow in Microtubes at Low Reynolds Numbers

Rarefied Gas Flow in Microtubes at Low Reynolds Numbers Darko R. Radkovć Tachg Assstat Uvrsty of Blgrad Faculty of Mchacal Egrg Sžaa S. Mlćv Assstat Profssor Uvrsty of Blgrad Faculty of Mchacal Egrg Nva D. Stvaovć Assocat Profssor Uvrsty of Blgrad Faculty of

More information

MODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f

MODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f MODEL QUESTION Statstcs (Thory) (Nw Syllabus) GROUP A d θ. ) Wrt dow th rsult of ( ) ) d OR, If M s th mod of a dscrt robablty dstrbuto wth mass fucto f th f().. at M. d d ( θ ) θ θ OR, f() mamum valu

More information

Notation for Mixed Models for Finite Populations

Notation for Mixed Models for Finite Populations 30- otato for d odl for Ft Populato Smpl Populato Ut ad Rpo,..., Ut Labl for,..., Epctd Rpo (ovr rplcatd maurmt for,..., Rgro varabl (Luz r for,...,,,..., p Aular varabl for ut (Wu z μ for,...,,,..., p

More information

Chapter 5 Special Discrete Distributions. Wen-Guey Tzeng Computer Science Department National Chiao University

Chapter 5 Special Discrete Distributions. Wen-Guey Tzeng Computer Science Department National Chiao University Chatr 5 Scal Dscrt Dstrbutos W-Guy Tzg Comutr Scc Dartmt Natoal Chao Uvrsty Why study scal radom varabls Thy aar frqutly thory, alcatos, statstcs, scc, grg, fac, tc. For aml, Th umbr of customrs a rod

More information

Entropy Equation for a Control Volume

Entropy Equation for a Control Volume Fudamtals of Thrmodyamcs Chaptr 7 Etropy Equato for a Cotrol Volum Prof. Syoug Jog Thrmodyamcs I MEE2022-02 Thrmal Egrg Lab. 2 Q ds Srr T Q S2 S1 1 Q S S2 S1 Srr T t t T t S S s m 1 2 t S S s m tt S S

More information

Reliability of time dependent stress-strength system for various distributions

Reliability of time dependent stress-strength system for various distributions IOS Joural of Mathmatcs (IOS-JM ISSN: 78-578. Volum 3, Issu 6 (Sp-Oct., PP -7 www.osrjourals.org lablty of tm dpdt strss-strgth systm for varous dstrbutos N.Swath, T.S.Uma Mahswar,, Dpartmt of Mathmatcs,

More information

Lecture 1: Empirical economic relations

Lecture 1: Empirical economic relations Ecoomcs 53 Lctur : Emprcal coomc rlatos What s coomtrcs? Ecoomtrcs s masurmt of coomc rlatos. W d to kow What s a coomc rlato? How do w masur such a rlato? Dfto: A coomc rlato s a rlato btw coomc varabls.

More information

Course 10 Shading. 1. Basic Concepts: Radiance: the light energy. Light Source:

Course 10 Shading. 1. Basic Concepts: Radiance: the light energy. Light Source: Cour 0 Shadg Cour 0 Shadg. Bac Coct: Lght Sourc: adac: th lght rg radatd from a ut ara of lght ourc or urfac a ut old agl. Sold agl: $ # r f lght ourc a ot ourc th ut ara omttd abov dfto. llumato: lght

More information

Byeong-Joo Lee

Byeong-Joo Lee OSECH - MSE calphad@postch.ac.kr Equipartition horm h avrag nrgy o a particl pr indpndnt componnt o motion is ε ε ' ε '' ε ''' U ln Z Z ε < ε > U ln Z β ( ε ' ε '' ε ''' / Z' Z translational kintic nrgy

More information

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES TRANSFORMATION OF FUNCTION OF A RANDOM VARIABLE UNIVARIATE TRANSFORMATIONS TRANSFORMATION OF RANDOM VARIABLES If s a rv wth cdf F th Y=g s also a rv. If w wrt

More information

Transparency and stability of low density stellar plasma related to Boltzmann statistics, inverse stimulated bremsstrahlung and to dark matter

Transparency and stability of low density stellar plasma related to Boltzmann statistics, inverse stimulated bremsstrahlung and to dark matter Trasparcy ad stablty of low dsty stllar plasma rlatd to oltzma statstcs, vrs stmulatd brmsstrahlug ad to dark mattr Y. -Aryh Tcho-Isral Isttut of Tchology, Physc Dpartmt, Isral, Hafa, Emal: phr65yb@tcho.physcs.ac.l

More information

Aotomorphic Functions And Fermat s Last Theorem(4)

Aotomorphic Functions And Fermat s Last Theorem(4) otomorphc Fuctos d Frmat s Last Thorm(4) Chu-Xua Jag P. O. Box 94 Bg 00854 P. R. Cha agchuxua@sohu.com bsract 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral

More information

3.4 Properties of the Stress Tensor

3.4 Properties of the Stress Tensor cto.4.4 Proprts of th trss sor.4. trss rasformato Lt th compots of th Cauchy strss tsor a coordat systm wth bas vctors b. h compots a scod coordat systm wth bas vctors j,, ar gv by th tsor trasformato

More information

Unbalanced Panel Data Models

Unbalanced Panel Data Models Ubalacd Pal Data odls Chaptr 9 from Baltag: Ecoomtrc Aalyss of Pal Data 5 by Adrás alascs 4448 troducto balacd or complt pals: a pal data st whr data/obsrvatos ar avalabl for all crosssctoal uts th tr

More information

signal amplification; design of digital logic; memory circuits

signal amplification; design of digital logic; memory circuits hatr Th lctroc dvc that s caabl of currt ad voltag amlfcato, or ga, cojucto wth othr crcut lmts, s th trasstor, whch s a thr-trmal dvc. Th dvlomt of th slco trasstor by Bard, Bratta, ad chockly at Bll

More information

In 1991 Fermat s Last Theorem Has Been Proved

In 1991 Fermat s Last Theorem Has Been Proved I 99 Frmat s Last Thorm Has B Provd Chu-Xua Jag P.O.Box 94Bg 00854Cha Jcxua00@s.com;cxxxx@6.com bstract I 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral

More information

Cloth Simulation. Simulation in Computer Graphics University of Freiburg WS 05/06

Cloth Simulation. Simulation in Computer Graphics University of Freiburg WS 05/06 Clot Smlato Smlato Comptr Grapcs Urst of Frbrg WS 05/06 D. Baraff A. Wtk. Larg stps clot smlato. Sggrap 98 pp. 43-54 998 Ackoldgmt Ts sld st s basd o t follog sorcs: D. Baraff A. Wtk. Larg stps clot smlato.

More information

Superbosonization meets Free Probability

Superbosonization meets Free Probability Suprbosoato mts Fr Probablty M Zrbaur jot wor wth S Madt Eulr Symposum St Ptrsburg Ju 3 009 Itroducto From momts to cumulats Larg- charactrstc fucto by fr probablty Suprbosoato Applcato to dsordrd scattrg

More information

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution: Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed

More information

Lecture 3 Probability review (cont d)

Lecture 3 Probability review (cont d) STATS 00: Itroducto to Statstcal Iferece Autum 06 Lecture 3 Probablty revew (cot d) 3. Jot dstrbutos If radom varables X,..., X k are depedet, the ther dstrbuto may be specfed by specfyg the dvdual dstrbuto

More information

Improving coverage probabilities of confidence intervals in random effects meta-analysis with publication bias

Improving coverage probabilities of confidence intervals in random effects meta-analysis with publication bias Improvg coverage probabltes of cofdece tervals radom effects meta-aalyss th publcato bas Masayuk Hem The Isttute of Statstcal Mathematcs, Japa Joh B. Copas Uversty of Warck, UK Itroducto Meta-aalyss: statstcal

More information

Statics. Consider the free body diagram of link i, which is connected to link i-1 and link i+1 by joint i and joint i-1, respectively. = r r r.

Statics. Consider the free body diagram of link i, which is connected to link i-1 and link i+1 by joint i and joint i-1, respectively. = r r r. Statcs Th cotact btw a mapulato ad ts vomt sults tactv ocs ad momts at th mapulato/vomt tac. Statcs ams at aalyzg th latoshp btw th actuato dv tous ad th sultat oc ad momt appld at th mapulato dpot wh

More information

Position Control of 2-Link SCARA Robot by using Internal Model Control

Position Control of 2-Link SCARA Robot by using Internal Model Control Mmors of th Faculty of Er, Okayama Uvrsty, Vol, pp 9-, Jauary 9 Posto Cotrol of -Lk SCARA Robot by us Itral Modl Cotrol Shya AKAMASU Dvso of Elctroc ad Iformato Systm Er Graduat School of Natural Scc ad

More information

Weights Interpreting W and lnw What is β? Some Endnotes = n!ω if we neglect the zero point energy then ( )

Weights Interpreting W and lnw What is β? Some Endnotes = n!ω if we neglect the zero point energy then ( ) Sprg Ch 35: Statstcal chacs ad Chcal Ktcs Wghts... 9 Itrprtg W ad lw... 3 What s?... 33 Lt s loo at... 34 So Edots... 35 Chaptr 3: Fudatal Prcpls of Stat ch fro a spl odl (drvato of oltza dstrbuto, also

More information

X ε ) = 0, or equivalently, lim

X ε ) = 0, or equivalently, lim Revew for the prevous lecture Cocepts: order statstcs Theorems: Dstrbutos of order statstcs Examples: How to get the dstrbuto of order statstcs Chapter 5 Propertes of a Radom Sample Secto 55 Covergece

More information

' 1.00, has the form of a rhomb with

' 1.00, has the form of a rhomb with Problm I Rflcto ad rfracto of lght A A trstg prsm Th ma scto of a glass prsm stuatd ar ' has th form of a rhomb wth A th yllow bam of moochromatc lght propagatg towards th prsm paralll wth th dagoal AC

More information

Elements of Statistical Thermodynamics

Elements of Statistical Thermodynamics 24 Elmnts of Statistical Thrmodynamics Statistical thrmodynamics is a branch of knowldg that has its own postulats and tchniqus. W do not attmpt to giv hr vn an introduction to th fild. In this chaptr,

More information

IV. First Law of Thermodynamics. Cooler. IV. First Law of Thermodynamics

IV. First Law of Thermodynamics. Cooler. IV. First Law of Thermodynamics D. Applcatons to stady flow dvcs. Hat xchangrs - xampl: Clkr coolr for cmnt kln Scondary ar 50 C, 57,000 lbm/h Clkr? C, 5 ton/h Coolr Clkr 400 C, 5 ton/h Scondary ar 0 C, 57,000 lbm/h a. Assumptons. changs

More information

Solutions to problem set ); (, ) (

Solutions to problem set ); (, ) ( Solutos to proble set.. L = ( yp p ); L = ( p p ); y y L, L = yp p, p p = yp p, + p [, p ] y y y = yp + p = L y Here we use for eaple that yp, p = yp p p yp = yp, p = yp : factors that coute ca be treated

More information

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D {... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data pots

More information

2. THERMODYNAMICS and ENSEMBLES (Part B) R. Bhattacharya, Department of Physics, Jadavpur University, Kolkata 32

2. THERMODYNAMICS and ENSEMBLES (Part B) R. Bhattacharya, Department of Physics, Jadavpur University, Kolkata 32 . THRMODYAMICS ad SMBLS (Pat B R. Battacaya, Dpatmt of Pyscs, Jadavpu Uvsty, Kolkata.4 smbls A smbl s a collcto of plcas of mmb systms wc av dtcal macoscopc paamts, but wos mcoscopc dscptos of ts mmbs

More information

Session : Plasmas in Equilibrium

Session : Plasmas in Equilibrium Sssio : Plasmas i Equilibrium Ioizatio ad Coductio i a High-prssur Plasma A ormal gas at T < 3000 K is a good lctrical isulator, bcaus thr ar almost o fr lctros i it. For prssurs > 0.1 atm, collisio amog

More information

Inner Product Spaces INNER PRODUCTS

Inner Product Spaces INNER PRODUCTS MA4Hcdoc Ir Product Spcs INNER PRODCS Dto A r product o vctor spc V s ucto tht ssgs ubr spc V such wy tht th ollowg xos holds: P : w s rl ubr P : P : P 4 : P 5 : v, w = w, v v + w, u = u + w, u rv, w =

More information

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg

More information

FORMULA SHEET. General formulas:

FORMULA SHEET. General formulas: FORMULA SHEET You may use this formula sheet during the Advanced Transport Phenomena course and it should contain all formulas you need during this course. Note that the weeks are numbered from 1.1 to

More information

Three-Dimensional Theory of Nonlinear-Elastic. Bodies Stability under Finite Deformations

Three-Dimensional Theory of Nonlinear-Elastic. Bodies Stability under Finite Deformations Appld Mathmatcal Sccs ol. 9 5 o. 43 75-73 HKAR Ltd www.m-hkar.com http://dx.do.org/.988/ams.5.567 Thr-Dmsoal Thory of Nolar-Elastc Bods Stablty udr Ft Dformatos Yu.. Dmtrko Computatoal Mathmatcs ad Mathmatcal

More information

4. Standard Regression Model and Spatial Dependence Tests

4. Standard Regression Model and Spatial Dependence Tests 4. Stadard Regresso Model ad Spatal Depedece Tests Stadard regresso aalss fals the presece of spatal effects. I case of spatal depedeces ad/or spatal heterogeet a stadard regresso model wll be msspecfed.

More information

Odd Generalized Exponential Flexible Weibull Extension Distribution

Odd Generalized Exponential Flexible Weibull Extension Distribution Odd Gralzd Epotal Flbl Wbull Etso Dstrbuto Abdlfattah Mustafa Mathmatcs Dpartmt Faculty of Scc Masoura Uvrsty Masoura Egypt abdlfatah mustafa@yahoo.com Bh S. El-Dsouy Mathmatcs Dpartmt Faculty of Scc Masoura

More information

Pressure-Temperature Diagram. Heterogeneous Liquid Droplet Nucleation on Solid Surface Mirror. 5. Nucleation Dynamics

Pressure-Temperature Diagram. Heterogeneous Liquid Droplet Nucleation on Solid Surface Mirror. 5. Nucleation Dynamics . Nuclato Dyamcs A. Homogous Nuclato o Lqud Droplt ad Vapor ubbl. Htrogous Nuclato o Lqud Droplt ad Vapor ubbl. Grato o Spcal Structurs Prssur Stabl Lqud rtcal Pot D Ustabl go E Lqud Saturato F Stabl Vapor

More information

Introduction. Free Electron Fermi Gas. Energy Levels in One Dimension

Introduction. Free Electron Fermi Gas. Energy Levels in One Dimension ree Electro er Gas Eergy Levels Oe Deso Effect of eperature o the er-drac Dstrbuto ree Electro Gas hree Desos Heat Capacty of the Electro Gas Electrcal Coductvty ad Oh s Law Moto Magetc elds heral Coductvty

More information

Parameter, Statistic and Random Samples

Parameter, Statistic and Random Samples Parameter, Statstc ad Radom Samples A parameter s a umber that descrbes the populato. It s a fxed umber, but practce we do ot kow ts value. A statstc s a fucto of the sample data,.e., t s a quatty whose

More information

IYPT 2000 Problem No. 3 PLASMA

IYPT 2000 Problem No. 3 PLASMA IYPT 000 Problm No. 3 PLASMA Tam Austria Invstigat th lctrical conducivity of th flam of a candl. Examin th influnc of rlvant paramtrs, in particular, th shap and polarity of th lctrods. Th xprimnts should

More information

Some Useful Formulae

Some Useful Formulae ME - hrmodynamcs I Som Usful Formula Control Mass Contnuty Equaton m constant Frst Law Comprsson-xpanson wor U U m V V mg Z Z Q W For polytropc procs, PV n c, Scond Law W W PdV P V P V n n P V ln V V n

More information

How many neutrino species?

How many neutrino species? ow may utrio scis? Two mthods for dtrmii it lium abudac i uivrs At a collidr umbr of utrio scis Exasio of th uivrs is ovrd by th Fridma quatio R R 8G tot Kc R Whr: :ubblcostat G :Gravitatioal costat 6.

More information

LINEAR REGRESSION ANALYSIS

LINEAR REGRESSION ANALYSIS LINEAR REGRESSION ANALYSIS MODULE V Lecture - Correctg Model Iadequaces Through Trasformato ad Weghtg Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur Aalytcal methods for

More information

ECON 5360 Class Notes GMM

ECON 5360 Class Notes GMM ECON 560 Class Notes GMM Geeralzed Method of Momets (GMM) I beg by outlg the classcal method of momets techque (Fsher, 95) ad the proceed to geeralzed method of momets (Hase, 98).. radtoal Method of Momets

More information

F A. Review1 7/1/2014. How to prepare for exams. Chapter 10 - GASES PRESSURE IS THE FORCE ACTING ON AN OBJECT PER UNIT AREA MEASUREMENT OF PRESSURE

F A. Review1 7/1/2014. How to prepare for exams. Chapter 10 - GASES PRESSURE IS THE FORCE ACTING ON AN OBJECT PER UNIT AREA MEASUREMENT OF PRESSURE How to prepare for exams 1. Uderstad EXAMLES chapter(s). Work RACICE EXERCISES 3. Work oe problem from each class of problems at ed of chapter 4. Aswer as may questos as tme permts from text web: www.prehall.com/brow

More information

The Mathematical Appendix

The Mathematical Appendix The Mathematcal Appedx Defto A: If ( Λ, Ω, where ( λ λ λ whch the probablty dstrbutos,,..., Defto A. uppose that ( Λ,,..., s a expermet type, the σ-algebra o λ λ λ are defed s deoted by ( (,,...,, σ Ω.

More information

IAEA-CN-184/61 Y. GOTO, T. KATO, K.NIDAIRA. Nuclear Material Control Center, Tokai-mura Japan.

IAEA-CN-184/61 Y. GOTO, T. KATO, K.NIDAIRA. Nuclear Material Control Center, Tokai-mura Japan. IAEA-CN-84/6 Establshmt of accurat calbrato curv for atoal vrfcato at a larg scal ut accoutablt tak RRP - For strgthg stat sstm for mtg safguards oblgato. GOO. KAO K.NIDAIRA Nuclar Matral Cotrol Ctr oka-mura

More information

ρ < 1 be five real numbers. The

ρ < 1 be five real numbers. The Lecture o BST 63: Statstcal Theory I Ku Zhag, /0/006 Revew for the prevous lecture Deftos: covarace, correlato Examples: How to calculate covarace ad correlato Theorems: propertes of correlato ad covarace

More information

Continuous Distributions

Continuous Distributions 7//3 Cotuous Dstrbutos Radom Varables of the Cotuous Type Desty Curve Percet Desty fucto, f (x) A smooth curve that ft the dstrbuto 3 4 5 6 7 8 9 Test scores Desty Curve Percet Probablty Desty Fucto, f

More information

Lecture 2 - What are component and system reliability and how it can be improved?

Lecture 2 - What are component and system reliability and how it can be improved? Lecture 2 - What are compoet ad system relablty ad how t ca be mproved? Relablty s a measure of the qualty of the product over the log ru. The cocept of relablty s a exteded tme perod over whch the expected

More information

The translational oscillations of a cylindrical bubble in a bounded volume of a liquid with free deformable interface

The translational oscillations of a cylindrical bubble in a bounded volume of a liquid with free deformable interface Joural of Physcs: Cofrc Srs PAPER OPEN ACCESS Th traslatoal oscllatos of a cyldrcal bubbl a boudd volum of a lqud wth fr dformabl trfac To ct ths artcl: A A Alabuzhv ad M I Kaysa 6 J. Phys.: Cof. Sr. 68

More information

Manipulator Dynamics. Amirkabir University of Technology Computer Engineering & Information Technology Department

Manipulator Dynamics. Amirkabir University of Technology Computer Engineering & Information Technology Department Mapulator Dyamcs mrkabr Uversty of echology omputer Egeerg formato echology Departmet troducto obot arm dyamcs deals wth the mathematcal formulatos of the equatos of robot arm moto. hey are useful as:

More information

CME 599 / MSE 620 Fall 2008 Statistical Thermodynamics and Introductory Simulation Concepts

CME 599 / MSE 620 Fall 2008 Statistical Thermodynamics and Introductory Simulation Concepts CM 599 / MS 60 Fall 008 Statstcal hrmodynamcs and Introductory Smulaton Concpts S.. Rann ssocat Profssor Chmcal and Matrals ngnrng Unvrsty of Kntucy, Lxngton Sptmbr 9, 008 Outln Introducton nd for statstcal

More information

1D Lagrangian Gas Dynamics. g t

1D Lagrangian Gas Dynamics. g t Te KT Dfferece Sceme for Te KT Dfferece Sceme for D Laraa Gas Damcs t 0 t 0 0 0 t 0 Dfferece Sceme for D Dfferece Sceme for D Laraa Gas Damcs 0 t m 0 / / F F t t 0 / / F F t 0 / F F t Dfferece Sceme for

More information

FOURIER SERIES. Series expansions are a ubiquitous tool of science and engineering. The kinds of

FOURIER SERIES. Series expansions are a ubiquitous tool of science and engineering. The kinds of Do Bgyoko () FOURIER SERIES I. INTRODUCTION Srs psos r ubqutous too o scc d grg. Th kds o pso to utz dpd o () th proprts o th uctos to b studd d (b) th proprts or chrctrstcs o th systm udr vstgto. Powr

More information

Equil. Properties of Reacting Gas Mixtures. So far have looked at Statistical Mechanics results for a single (pure) perfect gas

Equil. Properties of Reacting Gas Mixtures. So far have looked at Statistical Mechanics results for a single (pure) perfect gas Shool of roa Engnrng Equl. Prort of Ratng Ga Mxtur So far hav lookd at Stattal Mhan rult for a ngl (ur) rft ga hown how to gt ga rort (,, h, v,,, ) from artton funton () For nonratng rft ga mxtur, gt mxtur

More information

Physics 115. Molecular motion and temperature Phase equilibrium, evaporation

Physics 115. Molecular motion and temperature Phase equilibrium, evaporation Physcs 115 General Physcs II Sesson 9 Molecular moton and temperature Phase equlbrum, evaporaton R. J. Wlkes Emal: phy115a@u.washngton.edu Home page: http://courses.washngton.edu/phy115a/ 4/14/14 Physcs

More information

Module 1 : The equation of continuity. Lecture 5: Conservation of Mass for each species. & Fick s Law

Module 1 : The equation of continuity. Lecture 5: Conservation of Mass for each species. & Fick s Law Module : The equato of cotuty Lecture 5: Coservato of Mass for each speces & Fck s Law NPTEL, IIT Kharagpur, Prof. Sakat Chakraborty, Departmet of Chemcal Egeerg 2 Basc Deftos I Mass Trasfer, we usually

More information

Consistency of the Maximum Likelihood Estimator in Logistic Regression Model: A Different Approach

Consistency of the Maximum Likelihood Estimator in Logistic Regression Model: A Different Approach ISSN 168-8 Joural of Statstcs Volum 16, 9,. 1-11 Cosstcy of th Mamum Lklhood Estmator Logstc Rgrsso Modl: A Dffrt Aroach Abstract Mamuur Rashd 1 ad Nama Shfa hs artcl vstgats th cosstcy of mamum lklhood

More information

Statistical modelling and latent variables (2)

Statistical modelling and latent variables (2) Statstcal modellg ad latet varables (2 Mxg latet varables ad parameters statstcal erece Trod Reta (Dvso o statstcs ad surace mathematcs, Departmet o Mathematcs, Uversty o Oslo State spaces We typcally

More information

Homework 1: Solutions

Homework 1: Solutions Howo : Solutos No-a Fals supposto tst but passs scal tst lthouh -f th ta as slowss [S /V] vs t th appaac of laty alty th path alo whch slowss s to b tat to obta tavl ts ps o th ol paat S o V as a cosquc

More information

Petroleum Reservoir Engineering by Non-linear Singular Integral Equations

Petroleum Reservoir Engineering by Non-linear Singular Integral Equations www.ccst.org/mr Mchacal Egrg Rsarch Vol. 1 No. 1; Dcmbr 11 Ptrolm Rsrvor Egrg b No-lar Sglar Itgral Eqatos E. G. Laopolos Itrpapr Rsarch Orgazato 8 Dma Str. Aths GR - 16 7 Grc Rcv: Agst 8 11 Accpt: Sptmbr

More information

On Estimation of Unknown Parameters of Exponential- Logarithmic Distribution by Censored Data

On Estimation of Unknown Parameters of Exponential- Logarithmic Distribution by Censored Data saqartvlos mcrbata rovul akadms moamb, t 9, #2, 2015 BULLETIN OF THE GEORGIAN NATIONAL ACADEMY OF SCIENCES, vol 9, o 2, 2015 Mathmatcs O Estmato of Ukow Paramtrs of Epotal- Logarthmc Dstrbuto by Csord

More information

Introduction to Matrices and Matrix Approach to Simple Linear Regression

Introduction to Matrices and Matrix Approach to Simple Linear Regression Itroducto to Matrces ad Matrx Approach to Smple Lear Regresso Matrces Defto: A matrx s a rectagular array of umbers or symbolc elemets I may applcatos, the rows of a matrx wll represet dvduals cases (people,

More information

THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA

THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA THE ROYAL STATISTICAL SOCIETY 3 EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA PAPER I STATISTICAL THEORY & METHODS The Socety provdes these solutos to assst caddates preparg for the examatos future years ad

More information

Applying the condition for equilibrium to this equilibrium, we get (1) n i i =, r G and 5 i

Applying the condition for equilibrium to this equilibrium, we get (1) n i i =, r G and 5 i CHEMICAL EQUILIBRIA The Thermodyamc Equlbrum Costat Cosder a reversble reacto of the type 1 A 1 + 2 A 2 + W m A m + m+1 A m+1 + Assgg postve values to the stochometrc coeffcets o the rght had sde ad egatve

More information

MILLIKAN OIL DROP EXPERIMENT

MILLIKAN OIL DROP EXPERIMENT 11 Oct 18 Millika.1 MILLIKAN OIL DROP EXPERIMENT This xprimt is dsigd to show th quatizatio of lctric charg ad allow dtrmiatio of th lmtary charg,. As i Millika s origial xprimt, oil drops ar sprayd ito

More information

ECE606: Solid State Devices Lecture 7

ECE606: Solid State Devices Lecture 7 C606: Sold Stat vcs Lctur 7 Grhard Klmck gkco@purdu.du Rfrc: Vol. 6, Ch. 3 & 4 Prstato Outl Itrsc carrr coctrato Pottal, fld, ad charg -k dagram vs. bad-dagram Basc cocpts of doors ad accptors Law of mass-acto

More information

Physics 607 Exam 1. ( ) = 1, Γ( z +1) = zγ( z) x n e x2 dx = 1. e x2

Physics 607 Exam 1. ( ) = 1, Γ( z +1) = zγ( z) x n e x2 dx = 1. e x2 Physcs 607 Exam 1 Please be well-organzed, and show all sgnfcant steps clearly n all problems. You are graded on your wor, so please do not just wrte down answers wth no explanaton! Do all your wor on

More information

Chemistry 350. The take-home least-squares problem will account for 15 possible points on this exam.

Chemistry 350. The take-home least-squares problem will account for 15 possible points on this exam. Chmtry 30 Sprg 08 Eam : Chaptr - Nam 00 Pot You mut how your work to rcv crt for problm rqurg math. Rport your awr wth th approprat umbr of gfcat fgur. Th tak-hom lat-quar problm wll accout for pobl pot

More information

MOLECULAR VIBRATIONS

MOLECULAR VIBRATIONS MOLECULAR VIBRATIONS Here we wsh to vestgate molecular vbratos ad draw a smlarty betwee the theory of molecular vbratos ad Hückel theory. 1. Smple Harmoc Oscllator Recall that the eergy of a oe-dmesoal

More information

Likelihood Ratio, Wald, and Lagrange Multiplier (Score) Tests. Soccer Goals in European Premier Leagues

Likelihood Ratio, Wald, and Lagrange Multiplier (Score) Tests. Soccer Goals in European Premier Leagues Lkelhood Rato, Wald, ad Lagrage Multpler (Score) Tests Soccer Goals Europea Premer Leagues - 4 Statstcal Testg Prcples Goal: Test a Hpothess cocerg parameter value(s) a larger populato (or ature), based

More information

Mathematical Modeling of Plasma Assisted Combustion. Tropina A.A., Shneider M.N., Miles R.B.

Mathematical Modeling of Plasma Assisted Combustion. Tropina A.A., Shneider M.N., Miles R.B. Mathmatcal Molg of Plasma Assst Combusto ropa A.A. Shr M.N. Mls R.B. PRESENAION ONLINE INRODUCION PLASMA ASSISED COMBUSION IN ERMS OF CONIUNUUM MECHANICS CRIERIA ANALYIS: LIMIING CASES NANOSECOND PULSED

More information

Blackbody Radiation. All bodies at a temperature T emit and absorb thermal electromagnetic radiation. How is blackbody radiation absorbed and emitted?

Blackbody Radiation. All bodies at a temperature T emit and absorb thermal electromagnetic radiation. How is blackbody radiation absorbed and emitted? All bodis at a tmpratur T mit ad absorb thrmal lctromagtic radiatio Blackbody radiatio I thrmal quilibrium, th powr mittd quals th powr absorbd How is blackbody radiatio absorbd ad mittd? 1 2 A blackbody

More information

ME 501A Seminar in Engineering Analysis Page 1

ME 501A Seminar in Engineering Analysis Page 1 St Ssts o Ordar Drtal Equatos Novbr 7 St Ssts o Ordar Drtal Equatos Larr Cartto Mcacal Er 5A Sar Er Aalss Novbr 7 Outl Mr Rsults Rvw last class Stablt o urcal solutos Stp sz varato or rror cotrol Multstp

More information

ON RANKING OF ALTERNATIVES IN UNCERTAIN GROUP DECISION MAKING MODEL

ON RANKING OF ALTERNATIVES IN UNCERTAIN GROUP DECISION MAKING MODEL IJRRAS (3) Ju 22 www.arpapr.com/volum/voliu3/ijrras 3_5.pdf ON RANKING OF ALRNAIVS IN UNCRAIN GROUP DCISION MAKING MODL Chao Wag * & Lag L Gul Uvrty of chology Gul 544 Cha * mal: wagchao244@63.com llag6666@26.com

More information

1- Summary of Kinetic Theory of Gases

1- Summary of Kinetic Theory of Gases Dr. Kasra Etmad Octobr 5, 011 1- Summary of Kntc Thory of Gass - Radaton 3- E4 4- Plasma Proprts f(v f ( v m 4 ( kt 3/ v xp( mv kt V v v m v 1 rms V kt v m ( m 1/ v 8kT m 3kT v rms ( m 1/ E3: Prcntag of

More information

Neutron Scattering. λ Å. ω = ω ω = Basic properties of neutron and electron. mass charge 0 e. e e. magnetic dipole moment. 2 e. energy

Neutron Scattering. λ Å. ω = ω ω = Basic properties of neutron and electron. mass charge 0 e. e e. magnetic dipole moment. 2 e. energy Nutro Scattrg Basc proprts o utro ad lctro utro lctro 7 1 mass m = 1.675 10 kg m = 9.109 10 kg charg 0 sp s = ½ s = ½ magtc dpol momt µ = gs wth g =.86 µ = gs wth g =.0 m m rgy k π E = k = m λ 81.81 E[

More information

1. Can not explain why certain spectral lines are more intense. 2. many spectral lines actually consist of several separate lines

1. Can not explain why certain spectral lines are more intense. 2. many spectral lines actually consist of several separate lines Chatr 5 Quatu Mchacs ltato of Bohr thory:. Ca ot la why crta sctral ls ar or ts tha othrs.. ay sctral ls actually cosst of svral sarat ls whos λ dffr slghtly. 3. a udrstadg of how dvdual atos tract wth

More information

Comparison of Dual to Ratio-Cum-Product Estimators of Population Mean

Comparison of Dual to Ratio-Cum-Product Estimators of Population Mean Research Joural of Mathematcal ad Statstcal Sceces ISS 30 6047 Vol. 1(), 5-1, ovember (013) Res. J. Mathematcal ad Statstcal Sc. Comparso of Dual to Rato-Cum-Product Estmators of Populato Mea Abstract

More information

MB DISTRIBUTION AND ITS APPLICATION USING MAXIMUM ENTROPY APPROACH

MB DISTRIBUTION AND ITS APPLICATION USING MAXIMUM ENTROPY APPROACH Yugoslav Joural of Opratos Rsarch 6 (06), Numbr, 89-98 DOI: 0.98/YJOR405906B MB DISTRIBUTION AND ITS APPLICATION USING MAXIMUM ENTROPY APPROACH Suma BHADRA Rsarch Scholar Dpartmt of Mathmatcs IIEST, Shbpur

More information

On new theta identities of fermion correlation functions on genus g Riemann surfaces

On new theta identities of fermion correlation functions on genus g Riemann surfaces O w thta dtts o rmo corrlato uctos o us Rma suracs A.G. Tsucha. Oct. 7 Last rvsd o ov. 7 Abstract Thta dtts o us Rma suracs whch dcompos smpl products o rmo corrlato uctos wth a costrat o thr varabls ar

More information