The dimensionless compressibility factor, Z, for a gaseous species is defined as the ratio

Size: px
Start display at page:

Download "The dimensionless compressibility factor, Z, for a gaseous species is defined as the ratio"

Transcription

1 Chater The Comressibility Fator Equatio of State The dimesioless omressibility fator, Z, for a gaseous seies is defied as the ratio Z = (3.4-1) If the gas behaes ideally Z = 1. The extet to whih Z differs from 1 is a measure of the extet to whih the gas is behaig oideally. The omressibility a be determied from exerimetal data where Z is lotted ersus a dimesioless redued ressure R ad redued temerature T R, defied as R = / ad T R = T/T I these exressios, ad T deote the ritial ressure ad temerature, resetiely. A geeralized omressibility hart of the form Z = f( R, T R ) is show i Figure for 10 differet gases. The solid lies rereset the best ures fitted to the data. Figure Geeralized omressibility hart for arious gases 10. It a be see from Figure that the alue of Z teds to uity for all temeratures as ressure aroah zero ad Z also aroahes uity for all ressure at ery high temerature. If the,, ad T data are aailable i table format or omuter software the you should ot use the geeralized omressibility hart to ealuate,, ad T sie usig Z is just aother aroximatio to the real data. 10 Mora, M. J. ad Shairo H. N., Fudametals of Egieerig Thermodyamis, Wiley, 008, g

2 Examle A losed, rigid tak filled with water aor, iitially at 0 MPa, 50 o C, is ooled util its temerature reahes 400 o C. Usig the omressibility hart, determie (a) the seifi olume of the water aor i m 3 /kg at the iitial state. (b) the ressure i MPa at the fial state. Comare the results of arts (a) ad (b) with alues obtaied from the thermodyami table or software 11. Solutio (a) The seifi olume of the water aor i m 3 /kg at the iitial state. Look u the ritial temerature T ad ritial ressure of water: Substae Chemial Formula M (kg/kmol) T (K) (bar) Water H O Ealuate the redue ressured R ad redue temeratured T R R1 = 0/.09 = 0.91, T R1 = ( )/647.3 = 1.3 Figure E3.4- Geeralized Comressibility Chart With these alues for the redued ressure ad redued temerature, the alue of Z from Figure E3.4- is aroximately 0.83 Z = = M 1 1 = Z 1 M 1 `` 8314 N m/kmol K K 1 = kg/kmol 0 10 N/m 6 = m3 /kg 11 Mora, M. J. ad Shairo H. N., Fudametals of Egieerig Thermodyamis, Wiley, 008, g

3 This alue is i good agreemet with the seifi olume of m 3 /kg from CATT rogram (Table E3.4-). Table E3.4- Water roerties from CATT rogram Seifi Iteral Tem Pressure Volume Eergy State C MPa m 3 /kg kj/kg (b) The ressure i MPa at the fial state. Sie both mass ad olume remai ostat, the water aor ools at ostat seifi olume ad thus at redued seifi olume ' R = ( 3 )( 6 ) = m /kg N/m 8314 N m K 18.0 kg K ( ) = 1.1 The redued temerature at the fial state is T R = ( )/647.3 = 1.04 Figure E3.4- Geeralized Comressibility Chart Loatig the oit o the omressibility hart where ' R = 1.1 ad T R = 1.04, the orresodig alue for R is about The fial ressure is the = ( R ) = (.09 MPa)(0.69) = 15.4 MPa This alue is i good agreemet with the ressure of 15.1 MPa from CATT rogram (Table E3.4-). 3-1

4 3.4-3 Virial Equatio of State A irial equatio of state exresses the quatity olume. as a ower series i the ierse of molar = 1 + B( T ) C( T ) + D( T ) (3.4-8) I this equatio, B, C, ad D are alled irial oeffiiet ad are futios of temerature. We will show the use of a truated irial equatio with two terms. = 1 + B( T ) (3.4-9) I this equatio, B(T) a be estimated from the followig equatios: B(T) = (B 0 + ωb 1 ) (3.4-10) 0.4 B 0 = T R 0.17, B 1 = T R I equatio (3.4-10), ω is the Pitzer aetri fator, a arameter that reflets the geometry ad olarity of a moleule. The aetri fator for oer 1000 omouds a be obtaied from om4.exe rogram writte by T.K. Nguye. This rogram is aailable i the Distributio Folder for CHE30 ourse. Figure 3.4- Aetri fator for itroge from om4.exe rogram. 3-

5 Examle A three-liter tak otais two gram-moles of itroge at o C. Estimate the tak ressure usig the ideal gas equatio of state ad the usig the irial equatio of state truated after the seod term. Takig the seod estimate to be orret, alulate the eretage error that results from the use of the ideal gas equatio at the system oditios. Data for itroge: T = 16. K, = 33.5 atm, ad ω = Solutio From the ideal gas law, = 3.0 L/ mol = 1.5 L/mol, T = = 1.4 K ideal = = ( ) L atm/mol K (1.4 K) 1.50 L/mol = atm From the truated irial equatio, = 1 + B( T ) T R = 1.4/16. = B 0 = T R 0.4 = = B 1 = T R 0.17 = B 1 = = B(T) = (B 0 + ωb 1 ) B(T) = ( ) L atm/mol K (16. K) [ (0.04)( )] = 0.11 L/mol 33.5 atm = = ( ) 1.50 L/mol L atm/mol K (1.4 K) (0.953) = atm Error i usig ideal gas law ε = ideal 100 = 8.07 % 1 Felder R. M., Rousseau R. W., Elemetary Priiles of Chemial Proesses, Wiley, 005, g

6 3.4-4 Soae-Redlik-Kwog (SRK) Equatio The Soae-Redlik-Kwog (SRK) equatio belogs to a lass of ubi equatios of state beause, whe exaded, they yield third-degree equatios for the seifi olume. The SRK equatio of state is = b αa ( + b) (3.4-11) I this equatio, the arameter a, b, ad α are emirial futios of the ritial temerature ad ressure, the Pitzer aetri fator, ad the system temerature. The followig orrelatios a be used to estimate these arameters: ( a = ) b = m = ω ω α = + m( T ) 1 1 R Examle A gas ylider with a olume of.50 m 3 otais 1.00 kmol of arbo dioxide at T = 300 K. Use the SRK equatio of state to estimate the gas ressure i atm. Data for arbo dioxide: T = 304. K, = 7.9 atm, ad ω = Solutio T R = 300/304. = = ( L atm/mol K)(304. K) = 4.96 L atm/mol ( a = ) = ( 4.96 L atm/mol ) 7.9 atm = L atm/mol b = = L atm/mol 7.9 atm = L/mol m = ω ω = Felder R. M., Rousseau R. W., Elemetary Priiles of Chemial Proesses, Wiley, 005, g

7 α = 1+ m( 1 T ) R = ( ) = = b αa ( + b) L atm/mol K (300 K) = ( ) = 9.38 atm ( ) L/mol ( ) ( ) (3.654 L atm/mol ).50 L/mol ( ) L/mol Examle A stream of roae at temerature T = 43 K ad ressure (atm) flows at a rate of kmol/hr. Use the SRK equatio of state to estimate the olumetri flow rate V of the stream for = 0.7 atm, 7 atm, ad 70 atm. I eah ase, alulate the eretage differees betwee the reditios of the SRK equatio ad the ideal gas equatio of state. Data for roae: T = K, = 4.0 atm, ad ω = Solutio We first alulate a, b, ad α from the followig exressios: flow ( a = ), b = m = ω ω, α = m( T ) The SRK equatio is writte i the form 1+ 1 R f( ) = b + αa ( + b) = 0 is the alulated usig Newto s method: = f ( ) f '( ) = d, where f ( ) = ( ) b α a( + b) ( + b) The iitial alue for is obtaied from ideal gas law: ideal =. The iteratio roess stos whe /d is less tha The eretage differee betwee SRK ad ideal is 14 Felder R. M., Rousseau R. W., Elemetary Priiles of Chemial Proesses, Wiley, 005, g

8 ideal 100% Oe is kow for a gie, the olumetri flow rate orresodig to a molar flow rate of kmol/hr is obtaied as V flow (m mol /hr) = (L/mol) kmol 1 m L (100 kmol/hr) = 100 (L/mol) The alulatios are erformed usig the followig Matlab rogram: % Examle T=369.9; % K =4.0; % atm w=0.15; % aetri fator Rg=0.0806; % L*atm/(mol*K) T=43; % K =iut('(atm) = '); Tr=T/T; a=0.4747*(rg*t)^/; b= *(rg*t)/; m = *w *w^; alfa=(1+m*(1-tr^0.5))^; ideal=rg*t/;=ideal; for i=1:0; f=-rg*t/(-b)+alfa*a/(*(+b)); df=rg*t/(-b)^-alfa*a*(*+b)/(*(+b))^; d=f/df; =-d; if abs(d/)<1e-4, break, ed ed Di=(ideal-)/*100; Flowrate=100*; fritf('ideal = %6.f, (L/mol) = %6.f, Peretage Differee = %6.3f\',ideal,,Di) fritf('flow rate (m3/hr) = %6.1f\', Flowrate) >> ex3d4d5 (atm) =.7 ideal = , (L/mol) = , Peretage Differee = 0.37 Flow rate (m3/hr) = >> ex3d4d5 (atm) = 7 ideal = 4.959, (L/mol) = 4.775, Peretage Differee = 3.86 Flow rate (m3/hr) =

9 >> ex3d4d5 (atm) = 70 ideal = 0.496, (L/mol) = 0.89, Peretage Differee = Flow rate (m3/hr) = 8.9 The SRK equatio of state (ad eery other equatio of state) is itself a aroximatio. At 43 K ad 70 atm, the atual alue for is L/mol. The eretage error i the SRK estimate ( = 0.89 L/mol) is 1%, ad that i the ideal gas estimate ( = 0.50 L/mol) is 9%. Reiew: The Newto-Rahso Method The Newto-Rahso method ad its modifiatio is robably the most widely used of all root-fidig methods. Startig with a iitial guess x 1 at the root, the ext guess x is the itersetio of the taget from the oit [x 1, f(x 1 )] to the x-axis. The ext guess x 3 is the itersetio of the taget from the oit [x, f(x )] to the x-axis as show i Figure The roess a be reeated util the desired tolerae is attaied. f(x) f(x ) 1 B x 3 x x 1 Figure R-1 Grahial deitio of the Newto-Rahso method. The Newto-Rahso method a be deried from the defiitio of a sloe f (x 1 ) = f ( x x x 1 1) 0 x = x 1 f ( x1) f ' ( x ) 1 I geeral, from the oit [x, f(x )], the ext guess is alulated as x +1 = x f ( x ) f ' ( x ) 3-7

10 The deriatie or sloe f(x ) a be aroximated umerially as Examle f (x ) = f ( x + x) f ( x ) x Sole f(x) = x 3 + 4x 10 usig the the Newto-Rahso method for a root i the iteral [1, ]. Solutio From the formula x +1 = x f ( x ) f ' ( x ) f(x ) = 3 x + 4 x 10 f (x ) = 3 x + 8x x +1 = x 3 x + 4x 10 3x + 8x Usig the iitial guess, x = 1.5, x +1 is estimated as x +1 = = The ext estimate is x +1 = The ext estimate is x +1 = = = This alue is lose to the guessed alue of , therefore we a aet it as the solutio x =

3 Energy. 3.3. Non-Flow Energy Equation (NFEE) Internal Energy. MECH 225 Engineering Science 2

3 Energy. 3.3. Non-Flow Energy Equation (NFEE) Internal Energy. MECH 225 Engineering Science 2 MECH 5 Egieerig Sciece 3 Eergy 3.3. No-Flow Eergy Equatio (NFEE) You may have oticed that the term system kees croig u. It is ecessary, therefore, that before we start ay aalysis we defie the system that

More information

Chapter 5 Single Phase Systems

Chapter 5 Single Phase Systems Chapter 5 Single Phase Systems Chemial engineering alulations rely heavily on the availability of physial properties of materials. There are three ommon methods used to find these properties. These inlude

More information

APPLIED THERMODYNAMICS TUTORIAL 2 GAS COMPRESSORS

APPLIED THERMODYNAMICS TUTORIAL 2 GAS COMPRESSORS APPLIED HERMODYNAMICS UORIAL GAS COMPRESSORS I order to omlete this tutorial you should be familiar with gas laws ad olytroi gas roesses. You will study the riiles of reiroatig omressors i detail ad some

More information

SOLID MECHANICS DYNAMICS TUTORIAL DAMPED VIBRATIONS. On completion of this tutorial you should be able to do the following.

SOLID MECHANICS DYNAMICS TUTORIAL DAMPED VIBRATIONS. On completion of this tutorial you should be able to do the following. SOLID MECHANICS DYNAMICS TUTORIAL DAMPED VIBRATIONS This work overs elemets of the syllabus for the Egieerig Couil Eam D5 Dyamis of Mehaial Systems, C05 Mehaial ad Strutural Egieerig ad the Edeel HNC/D

More information

The Reduced van der Waals Equation of State

The Reduced van der Waals Equation of State The Redued van der Waals Equation of State The van der Waals equation of state is na + ( V nb) n (1) V where n is the mole number, a and b are onstants harateristi of a artiular gas, and R the gas onstant

More information

Laws of Exponents. net effect is to multiply with 2 a total of 3 + 5 = 8 times

Laws of Exponents. net effect is to multiply with 2 a total of 3 + 5 = 8 times The Mathematis 11 Competey Test Laws of Expoets (i) multipliatio of two powers: multiply by five times 3 x = ( x x ) x ( x x x x ) = 8 multiply by three times et effet is to multiply with a total of 3

More information

EDEXCEL NATIONAL CERTIFICATE/DIPLOMA. PRINCIPLES AND APPLICATIONS of THERMODYNAMICS NQF LEVEL 3 OUTCOME 1 -EXPANSION AND COMPRESSION OF GASES

EDEXCEL NATIONAL CERTIFICATE/DIPLOMA. PRINCIPLES AND APPLICATIONS of THERMODYNAMICS NQF LEVEL 3 OUTCOME 1 -EXPANSION AND COMPRESSION OF GASES EDEXCEL NAIONAL CERIFICAE/DIPLOMA PRINCIPLES AND APPLICAIONS of HERMODYNAMICS NQF LEEL 3 OUCOME -EXPANSION AND COMPRESSION OF GASES CONEN Be able to aly thermodyamic riciles to the exasio ad comressio

More information

Unit 8: Inference for Proportions. Chapters 8 & 9 in IPS

Unit 8: Inference for Proportions. Chapters 8 & 9 in IPS Uit 8: Iferece for Proortios Chaters 8 & 9 i IPS Lecture Outlie Iferece for a Proortio (oe samle) Iferece for Two Proortios (two samles) Cotigecy Tables ad the χ test Iferece for Proortios IPS, Chater

More information

Soving Recurrence Relations

Soving Recurrence Relations Sovig Recurrece Relatios Part 1. Homogeeous liear 2d degree relatios with costat coefficiets. Cosider the recurrece relatio ( ) T () + at ( 1) + bt ( 2) = 0 This is called a homogeeous liear 2d degree

More information

Definition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean

Definition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean 1 Social Studies 201 October 13, 2004 Note: The examples i these otes may be differet tha used i class. However, the examples are similar ad the methods used are idetical to what was preseted i class.

More information

REVIEW OF INTEGRATION

REVIEW OF INTEGRATION REVIEW OF INTEGRATION Trig Fuctios ad Itegratio by Parts Oeriew I this ote we will reiew how to ealuate the sorts of itegrals we ecouter i ealuatig Fourier series. These will iclude itegratio of trig fuctios

More information

BENEFIT-COST ANALYSIS Financial and Economic Appraisal using Spreadsheets

BENEFIT-COST ANALYSIS Financial and Economic Appraisal using Spreadsheets BENEIT-CST ANALYSIS iacial ad Ecoomic Appraisal usig Spreadsheets Ch. 2: Ivestmet Appraisal - Priciples Harry Campbell & Richard Brow School of Ecoomics The Uiversity of Queeslad Review of basic cocepts

More information

Thermodynamics worked examples

Thermodynamics worked examples An Introduction to Mechanical Engineering Part hermodynamics worked examles. What is the absolute ressure, in SI units, of a fluid at a gauge ressure of. bar if atmosheric ressure is.0 bar? Absolute ressure

More information

An Intelligent E-commerce Recommender System Based on Web Mining

An Intelligent E-commerce Recommender System Based on Web Mining Iteratioal Joural of Busiess ad Maagemet A Itelliget E-ommere Reommeder System Based o We Miig Zimig Zeg Shool of Iformatio Maagemet, Wuha Uiversity Wuha 43007, Chia E-mail: zmzeg1977@yahoo.om. The researh

More information

Find the inverse Laplace transform of the function F (p) = Evaluating the residues at the four simple poles, we find. residue at z = 1 is 4te t

Find the inverse Laplace transform of the function F (p) = Evaluating the residues at the four simple poles, we find. residue at z = 1 is 4te t Homework Solutios. Chater, Sectio 7, Problem 56. Fid the iverse Lalace trasform of the fuctio F () (7.6). À Chater, Sectio 7, Problem 6. Fid the iverse Lalace trasform of the fuctio F () usig (7.6). Solutio:

More information

Sequences and Series

Sequences and Series CHAPTER 9 Sequeces ad Series 9.. Covergece: Defiitio ad Examples Sequeces The purpose of this chapter is to itroduce a particular way of geeratig algorithms for fidig the values of fuctios defied by their

More information

Theorems About Power Series

Theorems About Power Series Physics 6A Witer 20 Theorems About Power Series Cosider a power series, f(x) = a x, () where the a are real coefficiets ad x is a real variable. There exists a real o-egative umber R, called the radius

More information

Infinite Sequences and Series

Infinite Sequences and Series CHAPTER 4 Ifiite Sequeces ad Series 4.1. Sequeces A sequece is a ifiite ordered list of umbers, for example the sequece of odd positive itegers: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29...

More information

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008 I ite Sequeces Dr. Philippe B. Laval Keesaw State Uiversity October 9, 2008 Abstract This had out is a itroductio to i ite sequeces. mai de itios ad presets some elemetary results. It gives the I ite Sequeces

More information

1 Computing the Standard Deviation of Sample Means

1 Computing the Standard Deviation of Sample Means Computig the Stadard Deviatio of Sample Meas Quality cotrol charts are based o sample meas ot o idividual values withi a sample. A sample is a group of items, which are cosidered all together for our aalysis.

More information

CHAPTER 3 THE TIME VALUE OF MONEY

CHAPTER 3 THE TIME VALUE OF MONEY CHAPTER 3 THE TIME VALUE OF MONEY OVERVIEW A dollar i the had today is worth more tha a dollar to be received i the future because, if you had it ow, you could ivest that dollar ad ear iterest. Of all

More information

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is 0_0605.qxd /5/05 0:45 AM Page 470 470 Chapter 6 Additioal Topics i Trigoometry 6.5 Trigoometric Form of a Complex Number What you should lear Plot complex umbers i the complex plae ad fid absolute values

More information

Confidence Intervals for One Mean

Confidence Intervals for One Mean Chapter 420 Cofidece Itervals for Oe Mea Itroductio This routie calculates the sample size ecessary to achieve a specified distace from the mea to the cofidece limit(s) at a stated cofidece level for a

More information

A Capacity Supply Model for Virtualized Servers

A Capacity Supply Model for Virtualized Servers 96 Iformatia Eoomiă vol. 3, o. 3/009 A apaity upply Model for Virtualized ervers Alexader PINNOW, tefa OTERBURG Otto-vo-Guerike-Uiversity, Magdeburg, Germay {alexader.piow stefa.osterburg}@iti.s.ui-magdeburg.de

More information

Fugacity, Activity, and Standard States

Fugacity, Activity, and Standard States Fugacity, Activity, and Standard States Fugacity of gases: Since dg = VdP SdT, for an isothermal rocess, we have,g = 1 Vd. For ideal gas, we can substitute for V and obtain,g = nrt ln 1, or with reference

More information

Convexity, Inequalities, and Norms

Convexity, Inequalities, and Norms Covexity, Iequalities, ad Norms Covex Fuctios You are probably familiar with the otio of cocavity of fuctios. Give a twicedifferetiable fuctio ϕ: R R, We say that ϕ is covex (or cocave up) if ϕ (x) 0 for

More information

Modified Line Search Method for Global Optimization

Modified Line Search Method for Global Optimization Modified Lie Search Method for Global Optimizatio Cria Grosa ad Ajith Abraham Ceter of Excellece for Quatifiable Quality of Service Norwegia Uiversity of Sciece ad Techology Trodheim, Norway {cria, ajith}@q2s.tu.o

More information

CS103A Handout 23 Winter 2002 February 22, 2002 Solving Recurrence Relations

CS103A Handout 23 Winter 2002 February 22, 2002 Solving Recurrence Relations CS3A Hadout 3 Witer 00 February, 00 Solvig Recurrece Relatios Itroductio A wide variety of recurrece problems occur i models. Some of these recurrece relatios ca be solved usig iteratio or some other ad

More information

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth Questio 1: What is a ordiary auity? Let s look at a ordiary auity that is certai ad simple. By this, we mea a auity over a fixed term whose paymet period matches the iterest coversio period. Additioally,

More information

Systems Design Project: Indoor Location of Wireless Devices

Systems Design Project: Indoor Location of Wireless Devices Systems Desig Project: Idoor Locatio of Wireless Devices Prepared By: Bria Murphy Seior Systems Sciece ad Egieerig Washigto Uiversity i St. Louis Phoe: (805) 698-5295 Email: bcm1@cec.wustl.edu Supervised

More information

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method Chapter 6: Variace, the law of large umbers ad the Mote-Carlo method Expected value, variace, ad Chebyshev iequality. If X is a radom variable recall that the expected value of X, E[X] is the average value

More information

S. Tanny MAT 344 Spring 1999. be the minimum number of moves required.

S. Tanny MAT 344 Spring 1999. be the minimum number of moves required. S. Tay MAT 344 Sprig 999 Recurrece Relatios Tower of Haoi Let T be the miimum umber of moves required. T 0 = 0, T = 7 Iitial Coditios * T = T + $ T is a sequece (f. o itegers). Solve for T? * is a recurrece,

More information

Maximum Likelihood Estimators.

Maximum Likelihood Estimators. Lecture 2 Maximum Likelihood Estimators. Matlab example. As a motivatio, let us look at oe Matlab example. Let us geerate a radom sample of size 00 from beta distributio Beta(5, 2). We will lear the defiitio

More information

Normal Distribution.

Normal Distribution. Normal Distributio www.icrf.l Normal distributio I probability theory, the ormal or Gaussia distributio, is a cotiuous probability distributio that is ofte used as a first approimatio to describe realvalued

More information

First Law, Heat Capacity, Latent Heat and Enthalpy

First Law, Heat Capacity, Latent Heat and Enthalpy First Law, Heat Caacity, Latent Heat and Enthaly Stehen R. Addison January 29, 2003 Introduction In this section, we introduce the first law of thermodynamics and examine sign conentions. Heat and Work

More information

Math C067 Sampling Distributions

Math C067 Sampling Distributions Math C067 Samplig Distributios Sample Mea ad Sample Proportio Richard Beigel Some time betwee April 16, 2007 ad April 16, 2007 Examples of Samplig A pollster may try to estimate the proportio of voters

More information

Heat (or Diffusion) equation in 1D*

Heat (or Diffusion) equation in 1D* Heat (or Diffusio) equatio i D* Derivatio of the D heat equatio Separatio of variables (refresher) Worked eamples *Kreysig, 8 th Ed, Sectios.4b Physical assumptios We cosider temperature i a log thi wire

More information

On the Production of Homeland Security Under True Uncertainty

On the Production of Homeland Security Under True Uncertainty Uiversity of Massahusetts Aherst Deartet of Resoure Eoois Workig Paer No. 006-5 htt://www.uass.edu/rese/workigaers O the Produtio of Hoelad Seurity Uder True Uertaity Joh K. Stralud 1 ad Barry C. Field

More information

Chapter 7 Methods of Finding Estimators

Chapter 7 Methods of Finding Estimators Chapter 7 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 011 Chapter 7 Methods of Fidig Estimators Sectio 7.1 Itroductio Defiitio 7.1.1 A poit estimator is ay fuctio W( X) W( X1, X,, X ) of

More information

5: Introduction to Estimation

5: Introduction to Estimation 5: Itroductio to Estimatio Cotets Acroyms ad symbols... 1 Statistical iferece... Estimatig µ with cofidece... 3 Samplig distributio of the mea... 3 Cofidece Iterval for μ whe σ is kow before had... 4 Sample

More information

I. Chi-squared Distributions

I. Chi-squared Distributions 1 M 358K Supplemet to Chapter 23: CHI-SQUARED DISTRIBUTIONS, T-DISTRIBUTIONS, AND DEGREES OF FREEDOM To uderstad t-distributios, we first eed to look at aother family of distributios, the chi-squared distributios.

More information

Properties of MLE: consistency, asymptotic normality. Fisher information.

Properties of MLE: consistency, asymptotic normality. Fisher information. Lecture 3 Properties of MLE: cosistecy, asymptotic ormality. Fisher iformatio. I this sectio we will try to uderstad why MLEs are good. Let us recall two facts from probability that we be used ofte throughout

More information

3. Greatest Common Divisor - Least Common Multiple

3. Greatest Common Divisor - Least Common Multiple 3 Greatest Commo Divisor - Least Commo Multiple Defiitio 31: The greatest commo divisor of two atural umbers a ad b is the largest atural umber c which divides both a ad b We deote the greatest commo gcd

More information

P 1 2 V V V T V V. AP Chemistry A. Allan Chapter 5 - Gases

P 1 2 V V V T V V. AP Chemistry A. Allan Chapter 5 - Gases A Chemistry A. Alla Chapter 5 - Gases 5. ressure A. roperties of gases. Gases uiformly fill ay cotaier. Gases are easily compressed 3. Gases mix completely with ay other gas 4. Gases exert pressure o their

More information

Measures of Spread and Boxplots Discrete Math, Section 9.4

Measures of Spread and Boxplots Discrete Math, Section 9.4 Measures of Spread ad Boxplots Discrete Math, Sectio 9.4 We start with a example: Example 1: Comparig Mea ad Media Compute the mea ad media of each data set: S 1 = {4, 6, 8, 10, 1, 14, 16} S = {4, 7, 9,

More information

Hypothesis testing. Null and alternative hypotheses

Hypothesis testing. Null and alternative hypotheses Hypothesis testig Aother importat use of samplig distributios is to test hypotheses about populatio parameters, e.g. mea, proportio, regressio coefficiets, etc. For example, it is possible to stipulate

More information

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES Read Sectio 1.5 (pages 5 9) Overview I Sectio 1.5 we lear to work with summatio otatio ad formulas. We will also itroduce a brief overview of sequeces,

More information

CHAPTER 11 Financial mathematics

CHAPTER 11 Financial mathematics CHAPTER 11 Fiacial mathematics I this chapter you will: Calculate iterest usig the simple iterest formula ( ) Use the simple iterest formula to calculate the pricipal (P) Use the simple iterest formula

More information

Example 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here).

Example 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here). BEGINNING ALGEBRA Roots ad Radicals (revised summer, 00 Olso) Packet to Supplemet the Curret Textbook - Part Review of Square Roots & Irratioals (This portio ca be ay time before Part ad should mostly

More information

A probabilistic proof of a binomial identity

A probabilistic proof of a binomial identity A probabilistic proof of a biomial idetity Joatho Peterso Abstract We give a elemetary probabilistic proof of a biomial idetity. The proof is obtaied by computig the probability of a certai evet i two

More information

c b 5.00 10 5 N/m 2 (0.120 m 3 0.200 m 3 ), = 4.00 10 4 J. W total = W a b + W b c 2.00

c b 5.00 10 5 N/m 2 (0.120 m 3 0.200 m 3 ), = 4.00 10 4 J. W total = W a b + W b c 2.00 Chter 19, exmle rolems: (19.06) A gs undergoes two roesses. First: onstnt volume @ 0.200 m 3, isohori. Pressure inreses from 2.00 10 5 P to 5.00 10 5 P. Seond: Constnt ressure @ 5.00 10 5 P, isori. olume

More information

Escola Federal de Engenharia de Itajubá

Escola Federal de Engenharia de Itajubá Escola Federal de Egeharia de Itajubá Departameto de Egeharia Mecâica Pós-Graduação em Egeharia Mecâica MPF04 ANÁLISE DE SINAIS E AQUISÇÃO DE DADOS SINAIS E SISTEMAS Trabalho 02 (MATLAB) Prof. Dr. José

More information

5.4 Amortization. Question 1: How do you find the present value of an annuity? Question 2: How is a loan amortized?

5.4 Amortization. Question 1: How do you find the present value of an annuity? Question 2: How is a loan amortized? 5.4 Amortizatio Questio 1: How do you fid the preset value of a auity? Questio 2: How is a loa amortized? Questio 3: How do you make a amortizatio table? Oe of the most commo fiacial istrumets a perso

More information

Lecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009)

Lecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009) 18.409 A Algorithmist s Toolkit October 27, 2009 Lecture 13 Lecturer: Joatha Keler Scribe: Joatha Pies (2009) 1 Outlie Last time, we proved the Bru-Mikowski iequality for boxes. Today we ll go over the

More information

The following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles

The following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles The followig eample will help us uderstad The Samplig Distributio of the Mea Review: The populatio is the etire collectio of all idividuals or objects of iterest The sample is the portio of the populatio

More information

SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx

SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx SAMPLE QUESTIONS FOR FINAL EXAM REAL ANALYSIS I FALL 006 3 4 Fid the followig usig the defiitio of the Riema itegral: a 0 x + dx 3 Cosider the partitio P x 0 3, x 3 +, x 3 +,......, x 3 3 + 3 of the iterval

More information

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling Taig DCOP to the Real World: Efficiet Complete Solutios for Distributed Multi-Evet Schedulig Rajiv T. Maheswara, Milid Tambe, Emma Bowrig, Joatha P. Pearce, ad Pradeep araatham Uiversity of Souther Califoria

More information

Solving Logarithms and Exponential Equations

Solving Logarithms and Exponential Equations Solvig Logarithms ad Epoetial Equatios Logarithmic Equatios There are two major ideas required whe solvig Logarithmic Equatios. The first is the Defiitio of a Logarithm. You may recall from a earlier topic:

More information

Basic Elements of Arithmetic Sequences and Series

Basic Elements of Arithmetic Sequences and Series MA40S PRE-CALCULUS UNIT G GEOMETRIC SEQUENCES CLASS NOTES (COMPLETED NO NEED TO COPY NOTES FROM OVERHEAD) Basic Elemets of Arithmetic Sequeces ad Series Objective: To establish basic elemets of arithmetic

More information

Estimating Probability Distributions by Observing Betting Practices

Estimating Probability Distributions by Observing Betting Practices 5th Iteratioal Symposium o Imprecise Probability: Theories ad Applicatios, Prague, Czech Republic, 007 Estimatig Probability Distributios by Observig Bettig Practices Dr C Lych Natioal Uiversity of Irelad,

More information

ECE606: Solid State Devices Lecture 16 p-n diode AC Response

ECE606: Solid State Devices Lecture 16 p-n diode AC Response ECE66: Solid State Devices Lecture 16 - diode C esose Gerhard Klimeck gekco@urdue.edu Klimeck ECE66 Fall 1 otes adoted from lam Toic Ma Equilibrium DC Small sigal Large Sigal Circuits Diode Schottky Diode

More information

RECIPROCATING COMPRESSORS

RECIPROCATING COMPRESSORS RECIPROCATING COMPRESSORS There are various compressor desigs: Rotary vae; Cetrifugal & Axial flow (typically used o gas turbies); Lobe (Roots blowers), ad Reciprocatig. The mai advatages of the reciprocatig

More information

Chapter 5: Inner Product Spaces

Chapter 5: Inner Product Spaces Chapter 5: Ier Product Spaces Chapter 5: Ier Product Spaces SECION A Itroductio to Ier Product Spaces By the ed of this sectio you will be able to uderstad what is meat by a ier product space give examples

More information

Z-TEST / Z-STATISTIC: used to test hypotheses about. µ when the population standard deviation is unknown

Z-TEST / Z-STATISTIC: used to test hypotheses about. µ when the population standard deviation is unknown Z-TEST / Z-STATISTIC: used to test hypotheses about µ whe the populatio stadard deviatio is kow ad populatio distributio is ormal or sample size is large T-TEST / T-STATISTIC: used to test hypotheses about

More information

Ideal Gas and Real Gases

Ideal Gas and Real Gases Ideal Gas and Real Gases Lectures in Physical Chemistry 1 Tamás Turányi Institute of Chemistry, ELTE State roerties state roerty: determines the macroscoic state of a hysical system state roerties of single

More information

5 Boolean Decision Trees (February 11)

5 Boolean Decision Trees (February 11) 5 Boolea Decisio Trees (February 11) 5.1 Graph Coectivity Suppose we are give a udirected graph G, represeted as a boolea adjacecy matrix = (a ij ), where a ij = 1 if ad oly if vertices i ad j are coected

More information

Chapter 7: Confidence Interval and Sample Size

Chapter 7: Confidence Interval and Sample Size Chapter 7: Cofidece Iterval ad Sample Size Learig Objectives Upo successful completio of Chapter 7, you will be able to: Fid the cofidece iterval for the mea, proportio, ad variace. Determie the miimum

More information

Elementary Theory of Russian Roulette

Elementary Theory of Russian Roulette Elemetary Theory of Russia Roulette -iterestig patters of fractios- Satoshi Hashiba Daisuke Miematsu Ryohei Miyadera Itroductio. Today we are goig to study mathematical theory of Russia roulette. If some

More information

RF Engineering Continuing Education Introduction to Traffic Planning

RF Engineering Continuing Education Introduction to Traffic Planning RF Egieerig otiuig Educatio Itroductio to Traffic Plaig Queuig Systems Figure. shows a schematic reresetatio of a queuig system. This reresetatio is a mathematical abstractio suitable for may differet

More information

FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. 1. Powers of a matrix

FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. 1. Powers of a matrix FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. Powers of a matrix We begi with a propositio which illustrates the usefuless of the diagoalizatio. Recall that a square matrix A is diogaalizable if

More information

CS103X: Discrete Structures Homework 4 Solutions

CS103X: Discrete Structures Homework 4 Solutions CS103X: Discrete Structures Homewor 4 Solutios Due February 22, 2008 Exercise 1 10 poits. Silico Valley questios: a How may possible six-figure salaries i whole dollar amouts are there that cotai at least

More information

The analysis of the Cournot oligopoly model considering the subjective motive in the strategy selection

The analysis of the Cournot oligopoly model considering the subjective motive in the strategy selection The aalysis of the Courot oligopoly model cosiderig the subjective motive i the strategy selectio Shigehito Furuyama Teruhisa Nakai Departmet of Systems Maagemet Egieerig Faculty of Egieerig Kasai Uiversity

More information

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n We will cosider the liear regressio model i matrix form. For simple liear regressio, meaig oe predictor, the model is i = + x i + ε i for i =,,,, This model icludes the assumptio that the ε i s are a sample

More information

Finding the circle that best fits a set of points

Finding the circle that best fits a set of points Fidig the circle that best fits a set of poits L. MAISONOBE October 5 th 007 Cotets 1 Itroductio Solvig the problem.1 Priciples............................... Iitializatio.............................

More information

NEW HIGH PERFORMANCE COMPUTATIONAL METHODS FOR MORTGAGES AND ANNUITIES. Yuri Shestopaloff,

NEW HIGH PERFORMANCE COMPUTATIONAL METHODS FOR MORTGAGES AND ANNUITIES. Yuri Shestopaloff, NEW HIGH PERFORMNCE COMPUTTIONL METHODS FOR MORTGGES ND NNUITIES Yuri Shestopaloff, Geerally, mortgage ad auity equatios do ot have aalytical solutios for ukow iterest rate, which has to be foud usig umerical

More information

Virtual sound barrier for indoor transformers

Virtual sound barrier for indoor transformers Virtual soud barrier for idoor trasformers Jiaheg TAO 1 ; Shupig WANG 2 ; Xiaoju QIU 3 ; Nig HAN 4 ; Like ZHANG 5 1,2,3 Key Laboratory of Moder Aoustis ad Istitute of Aoustis, Najig Uiversity, Najig, 210093,

More information

1. C. The formula for the confidence interval for a population mean is: x t, which was

1. C. The formula for the confidence interval for a population mean is: x t, which was s 1. C. The formula for the cofidece iterval for a populatio mea is: x t, which was based o the sample Mea. So, x is guarateed to be i the iterval you form.. D. Use the rule : p-value

More information

Confidence Intervals for Two Proportions

Confidence Intervals for Two Proportions PASS Samle Size Software Chater 6 Cofidece Itervals for Two Proortios Itroductio This routie calculates the grou samle sizes ecessary to achieve a secified iterval width of the differece, ratio, or odds

More information

Your organization has a Class B IP address of 166.144.0.0 Before you implement subnetting, the Network ID and Host ID are divided as follows:

Your organization has a Class B IP address of 166.144.0.0 Before you implement subnetting, the Network ID and Host ID are divided as follows: Subettig Subettig is used to subdivide a sigle class of etwork i to multiple smaller etworks. Example: Your orgaizatio has a Class B IP address of 166.144.0.0 Before you implemet subettig, the Network

More information

INFINITE SERIES KEITH CONRAD

INFINITE SERIES KEITH CONRAD INFINITE SERIES KEITH CONRAD. Itroductio The two basic cocepts of calculus, differetiatio ad itegratio, are defied i terms of limits (Newto quotiets ad Riema sums). I additio to these is a third fudametal

More information

UC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Solutions 9 Spring 2006

UC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Solutions 9 Spring 2006 Exam format UC Bereley Departmet of Electrical Egieerig ad Computer Sciece EE 6: Probablity ad Radom Processes Solutios 9 Sprig 006 The secod midterm will be held o Wedesday May 7; CHECK the fial exam

More information

Guidelines for a Good Presentation. Luis M. Correia

Guidelines for a Good Presentation. Luis M. Correia Guidelies for a Good Presetatio Luis M. Correia Outlie Basic riciles. Structure. Sizes ad cotrast. Style. Examles. Coclusios. Basic Priciles The resetatio of a work is iteded to show oly its major asects,

More information

Lecture 4: Cheeger s Inequality

Lecture 4: Cheeger s Inequality Spectral Graph Theory ad Applicatios WS 0/0 Lecture 4: Cheeger s Iequality Lecturer: Thomas Sauerwald & He Su Statemet of Cheeger s Iequality I this lecture we assume for simplicity that G is a d-regular

More information

Lesson 17 Pearson s Correlation Coefficient

Lesson 17 Pearson s Correlation Coefficient Outlie Measures of Relatioships Pearso s Correlatio Coefficiet (r) -types of data -scatter plots -measure of directio -measure of stregth Computatio -covariatio of X ad Y -uique variatio i X ad Y -measurig

More information

University of California, Los Angeles Department of Statistics. Distributions related to the normal distribution

University of California, Los Angeles Department of Statistics. Distributions related to the normal distribution Uiversity of Califoria, Los Ageles Departmet of Statistics Statistics 100B Istructor: Nicolas Christou Three importat distributios: Distributios related to the ormal distributio Chi-square (χ ) distributio.

More information

INVESTMENT PERFORMANCE COUNCIL (IPC)

INVESTMENT PERFORMANCE COUNCIL (IPC) INVESTMENT PEFOMANCE COUNCIL (IPC) INVITATION TO COMMENT: Global Ivestmet Performace Stadards (GIPS ) Guidace Statemet o Calculatio Methodology The Associatio for Ivestmet Maagemet ad esearch (AIM) seeks

More information

How To Solve The Homewor Problem Beautifully

How To Solve The Homewor Problem Beautifully Egieerig 33 eautiful Homewor et 3 of 7 Kuszmar roblem.5.5 large departmet store sells sport shirts i three sizes small, medium, ad large, three patters plaid, prit, ad stripe, ad two sleeve legths log

More information

Confidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the.

Confidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the. Cofidece Itervals A cofidece iterval is a iterval whose purpose is to estimate a parameter (a umber that could, i theory, be calculated from the populatio, if measuremets were available for the whole populatio).

More information

Incremental calculation of weighted mean and variance

Incremental calculation of weighted mean and variance Icremetal calculatio of weighted mea ad variace Toy Fich faf@cam.ac.uk dot@dotat.at Uiversity of Cambridge Computig Service February 009 Abstract I these otes I eplai how to derive formulae for umerically

More information

HEAT UNIT 1.1 KINETIC THEORY OF GASES. 1.1.1 Introduction. 1.1.2 Postulates of Kinetic Theory of Gases

HEAT UNIT 1.1 KINETIC THEORY OF GASES. 1.1.1 Introduction. 1.1.2 Postulates of Kinetic Theory of Gases UNIT HEAT. KINETIC THEORY OF GASES.. Introduction Molecules have a diameter of the order of Å and the distance between them in a gas is 0 Å while the interaction distance in solids is very small. R. Clausius

More information

When the rate of heat transfer remains constant during a process

When the rate of heat transfer remains constant during a process HERMODYNAMICS It has bee poited out that eergy ca either be created or destroyed; it ca oly chage forms. his priciple is the first law of thermodyamics or the coservatio of eergy priciple. Durig a iteractio

More information

Vladimir N. Burkov, Dmitri A. Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT

Vladimir N. Burkov, Dmitri A. Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT Keywords: project maagemet, resource allocatio, etwork plaig Vladimir N Burkov, Dmitri A Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT The paper deals with the problems of resource allocatio betwee

More information

Drift-Diffusion Model: Introduction

Drift-Diffusion Model: Introduction Drift-Diffusio Model: Itroductio Preared by: Dragica Vasileska Associate Professor Arizoa State Uiversity The oular drift-diffusio curret equatios ca be easily derived from the oltzma trasort equatio by

More information

Partial Di erential Equations

Partial Di erential Equations Partial Di eretial Equatios Partial Di eretial Equatios Much of moder sciece, egieerig, ad mathematics is based o the study of partial di eretial equatios, where a partial di eretial equatio is a equatio

More information

Lesson 15 ANOVA (analysis of variance)

Lesson 15 ANOVA (analysis of variance) Outlie Variability -betwee group variability -withi group variability -total variability -F-ratio Computatio -sums of squares (betwee/withi/total -degrees of freedom (betwee/withi/total -mea square (betwee/withi

More information

Part - I. Mathematics

Part - I. Mathematics Part - I Mathematics CHAPTER Set Theory. Objectives. Itroductio. Set Cocept.. Sets ad Elemets. Subset.. Proper ad Improper Subsets.. Equality of Sets.. Trasitivity of Set Iclusio.4 Uiversal Set.5 Complemet

More information

Statistical inference: example 1. Inferential Statistics

Statistical inference: example 1. Inferential Statistics Statistical iferece: example 1 Iferetial Statistics POPULATION SAMPLE A clothig store chai regularly buys from a supplier large quatities of a certai piece of clothig. Each item ca be classified either

More information

A markovian study of no claim discount system of Insurance Regulatory and Development Authority and its application

A markovian study of no claim discount system of Insurance Regulatory and Development Authority and its application Thailad Statisticia July 214; 12(2): 223-236 htt://statassoc.or.th Cotributed aer A markovia study of o claim discout system of Isurace Regulatory ad Develomet Authority ad its alicatio Dili C. Nath* [a]

More information

PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY AN ALTERNATIVE MODEL FOR BONUS-MALUS SYSTEM

PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY AN ALTERNATIVE MODEL FOR BONUS-MALUS SYSTEM PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY Physical ad Mathematical Scieces 2015, 1, p. 15 19 M a t h e m a t i c s AN ALTERNATIVE MODEL FOR BONUS-MALUS SYSTEM A. G. GULYAN Chair of Actuarial Mathematics

More information

1 Correlation and Regression Analysis

1 Correlation and Regression Analysis 1 Correlatio ad Regressio Aalysis I this sectio we will be ivestigatig the relatioship betwee two cotiuous variable, such as height ad weight, the cocetratio of a ijected drug ad heart rate, or the cosumptio

More information