Performance of a Queuing System with Exceptional Service

Size: px
Start display at page:

Download "Performance of a Queuing System with Exceptional Service"

Transcription

1 Iteratoal Joural o Eeer ad Matheatcal Sceces Ja.- Jue 0, Volue, Issue, pp ISSN Prt , Ole All rhts reserved IJEMS Abstract Perorace o a Queu Syste wth Exceptoal Servce Dr. Habeer Sh, Dr. Sajay Ja, Dr. A.K. Shrvastav 3 Assocate Proessor, Deptt. o Math, RIET, Fardabad, Haryaa drhabeer@al.co Assstat Proessor, Deptt o Statstcs, St. Joh s Collee, Ara Assstat Proessor, Deptt. o Math, RIET, Fardabad, Haryaa 3 I ths paper a attept has bee ade to calculate the characterstcs o M/G/ odel wth exceptoal arrval/servce.e. statoary wat te, sojour te, queue leth ad soe other characterstcs. A M/G/ odel, whch servce te dstrbuto each busy perod ay deped o the uber o custoers who have bee served the sae busy perod, s called exceptoal servce odel. We use the reeeratve approach; wth ths approach we et characterstcs tractable ors. Itroducto I ths paper we studed a No-Markova cha wth exceptoal servces. I whch arrval ollows Posso asho ad servce dstrbuto ay deped o the uber o custoers who have bee served the curret busy perod. Our odel s also looks lke queu systes wth vacato. By a vacato we ea that the server wll ot wat or custoers ater the copleto o busy perod. By exceptoal servce we eas that servce te dstrbuto each busy perods ay deped the uber o custoers who have bee served the sae busy perod. Kella ad Yechall 988 studed prortes M/G/ queues wth server vacatos. Lee 989 studed M/G//N queue wth vacato te ad lted servce dscple. Heker 990 studed a ote o sojour tes queu etworks wth ult-server odes. Taka 99 studed aalyss o a M/G//N queue wth ultple server vacato ad ts applcatos to a poll odel. Dosh 996 surveyed the queu systes wth vacato. Iak et. al. 998 studed o a eeralzed M/G/ queue wth servce deradato/eorceet Ahahru ad Ftzatrck 999 studed wat te dstrbuto o a Fo/ Lo M/D/ queue. Keaku ad Myazawa 000 also studed a reeeratve cycle approach to a M/G/ queue wth exceptoal servce. Tooyuk ad Myazawa 00 studed a M/G/ queue wth Markov-depedet exceptoal servce tes. Basal 003 studed aalyss o the M/G/ processor-shar queue wth bulk arrvals. Yechal 004 studed O the MX/G/ Queue wth a Wat Server ad Vacato. Perry & Stadje 006 studed a cotrolled M/G/ workload process wth a applcato to pershable vetory systes. Boxa, O.J., Bru, J. 66

2 Dr. Habeer Sh, Dr. Sajay Ja, Dr. A.K. Shrvastav Fralx. B.H. 009 studed wat tes poll systes wth varous servce dscples. es Deso & Seva Sheer 00 also studed Global ad local asyptotcs or the busy perod o a M/G/ queue. I ths preset work we used reeeratve cycle approach ad studed wat te, statoary wat te, sojour te ad queue leths wth soe uercal exaples. Such type o study s useul coputer systes where a server ay eed ore or less te ater coplet all jobs.e. dl to be servce to the ext batch o jobs. Notato Used Mea arrval rate. W The wat te o the -arrv custoer. W + W + S T 0, N > T Iter-arrval betwee th ad +th arrv custoer. N Total uber o custoers that arrves rst busy perod. Laplace trasor o G. Laplace trasor o the statoary wat te dstrbuto. S The servce te o the th arrv custoer the rst busy perod,. G Dstrbuto o S. Nuber o custoers. Nuber o custoers who et exceptoal servce. S Subjects to the dstrbuto G. U Rado varable subject to the statoary sojour te dstrbuto. U Sojour te to the th arrv custoer. h Laplace trasor o the dstrbuto 0,. q + Queue leth just ater the th custoer copletes ths servce. q Rado varable subject to the Statoary queue leth dstrbuto at a arbtrary te stat. Q M / G / k Queue leth ot clud a custoer be served the correspod M/G/ queue. A arbtrary postve teer. Trac testy The Queue Model ad Wat Te We have studed the M/G/, queu odel wth exceptoal servces as the exceptoal servce s related wth busy perod. We have started a busy perod wth a epty state.e. there s o custoer the syste. Ths epty state s cosdered as a reeerato epoch. S s assued to be a sequece o depedet rado varable. That are depedet o arrval process ad ES s te or all,, s a teer, dstrbuto o servce te s eeral G. Suppose that rst custoers who 67

3 Perorace o a Queu Syste wth Exceptoal Servce receved exceptoal servce FIFO.e. rst -rst out asho. G be the servce te dstrbuto o a custoer other tha these -custoer, s depedet o ; ay be te. As we have cosdered that the syste bes wth the epty state zero dee as. E e W ; N, 0, E N... W S T E e, N,... W S w S E e e N...3 W ad S are depedet & W = 0 so, we have By equato 3,...4 Where = e S I = 0, the eq. 4 ples. = 0 + 0,...5 Wth the help o eq. 3 we et,...6 To d Laplace trasor te. o the statoary wat te dstrbuto I case queue s stable, EN s Us cycle orula N - E N W E E N e - W N, Where W s a dscrete te reeeratve process 68

4 Dr. Habeer Sh, Dr. Sajay Ja, Dr. A.K. Shrvastav E N...7 Statoary Wat Te As we are assue that oly rst custoers et exceptoal servce, de...8 For each = 0, ad E S...9 Fro eq. 7 we have, E N...0 Frst we have to calculate. wth the help o eq 5, eq 4 ca be wrtte as, or we have Ad or Su o over ad wth the help o eq, we have, =+, + 0 Substtut to ths orula, we have Also or

5 Perorace o a Queu Syste wth Exceptoal Servce Su eq 3 & 4 or =,,.., Wth act F = leads to Above eq. ples...5 Above eq. 5 wll provde us 0 Hece we calculate statoary wat te by eq. 5 & 6 W Both odels have the sae arrval rate ad the sae dstrbuto or the o- exceptoal servce te s.e. W M / G /...8 W WM G / 0 /...9 Further we studed the statoary probablty wth the help o eq. 7, eq. 4 ples P W 0 E N 70

6 Dr. Habeer Sh, Dr. Sajay Ja, Dr. A.K. Shrvastav Us eq. 9, E S + ad E S k + or =, are te, K s a arbtrary teer. k k k k j E W E W M / G / j E WM / G / j 0 k j 0 k j k j k 0 j 0 E S E S... Above eq. Ca be wrtte as ' 0 0 E S E S E S E W... 0 Where 0 s o eatve, ad calculated as ' 0 l 0 0,,, Sojour Te ad Queue Leth Sojour Te: I ths secto we calculate sojour te.e. the te spet by a custoer the syste ro arrval to al departure ad queue leth that eat the total uber o custoers clud a custoer be served. We assue the teess o the uber o the exceptoal servces ad testablty codto < as the prevous secto. For N, we et u ; N...4 Because S s depedet o W 7

7 Perorace o a Queu Syste wth Exceptoal Servce Slar to recurrece relato U 0 5 Wth the help o eq. eq., we have, 6 By the relato 5, 6 becoes U 0 Put the value o & 0 by eq. 5 & 6 above eq. We et U Ths s the sojour te 0 7 We et the ollow or by the help o eq. 7 W M/G/ the correspod M/G/ queue. Uder the assupto o eq. 8, we have, / u WM / G / 8 0 Queue Leth The uber o custoers who arrved dur the th custoer be the syste.e. q +. E z q ; N W W S S k = z ; N! 0 7

8 Dr. Habeer Sh, Dr. Sajay Ja, Dr. A.K. Shrvastav = z z 9 Ths s the codto eerat ucto I eq. 8, put = -z E z q N, throuh E z Q M z / G / 30 z z Where Q M/G/ be the queue leth ot clud a custoer be served the correspod M/G/ queue. Fro eq. 8 ad the dstrbuto Lttle s law.e. E z q z U QM / G / z WM / G / z E 3 By the eq. 8 wth = -z, we et E q QM G / [ z ] E z z z z z z z / 3 0 by eq. 3, we have ' We deretate eq. 3 at z = ad us the act 33 we et, 0 0 E S E S E S E[ q]

9 Perorace o a Queu Syste wth Exceptoal Servce Calculato o s As we copute 0 s or s. For deterato as well as to copute the eas o the wat te ad queue leth 0 are coputed by putt = 0 eq. 6 We et 0, 35 For o-eatve teer. Dee the operator D ad D ~ as D h h 36 ~ D h h 37! Where, D ad D ~ dstrbuto o [0, ]. be the dervatves o Laplace trasors at ; h be a Laplace trasor o a Multply both sde o eq. 6 or + stead o by yelds. j j j j j j 38 Sce, or ad k D k kk!, k 0, k 39 Apply D to both sdes o eq

10 Dr. Habeer Sh, Dr. Sajay Ja, Dr. A.K. Shrvastav! D j j j j j j! 40 j Where =j s dropped the suato. Dvd eq. 40 both sdes by! The we et ~ ~ j j j j D D a 4 j Cocluso I ths paper, we have dscussed statoary wat te eq. 7 ad sojour te, queue leth ad calculatos s equato 7, 3, 4 respectvely. The expected values o the codtoal wat te are preseted or 5 custoers, whe all the servce te are expoetally dstrbuted raph ad raph. I raph 4 ad 6 all S s are deterstc, whle raph 3 ad 5 all S s are expoetally dstrbuted. But raph 3 to 6 the expected values o the statoary wat te are preseted or the case that S s expoetally dstrbuted. I all raphs Case assues that E S, ES 0 5 =,,3,4 Whle case assues that E S, ES 0 5 =,,3,4 75

11 Perorace o a Queu Syste wth Exceptoal Servce Sce ES = so =. I raph & whe the uber o custoers creases, codtoal wat te also creases. I raph 3 & 4 whe the ubers o custoers, who et exceptoal servces are creased, statoary wat te decreases. I raph 5 & 6 as the value o teds towards oe, statoary wat te creases sharply. Graphs Graph Graph - 76

12 Dr. Habeer Sh, Dr. Sajay Ja, Dr. A.K. Shrvastav Graph - 3 Graph-4 77

13 Perorace o a Queu Syste wth Exceptoal Servce Graph - 5 Graph - 6 Graph 78

14 Dr. Habeer Sh, Dr. Sajay Ja, Dr. A.K. Shrvastav Reereces. Kella, O. ad Yechall, U. : Prortes M/G/ queues wth server vacatos, Navel Research Lostcs, Lee, T.T.: M/G//N queue wth vacato te ad lted servce dscple, Perorace Eval-I, Heker, J.: A ote o sojour tes queue etworks wth ult-server odes, J. Appl. Prob., 7, Taka H.: Aalyss o a M/G//N queue wth ultple server vacato ad ts applcatos to a poll odel, J. o Oper. Res. Soc. o Japa, 35, 3, Dosh B.T.: Queue syste wth vacato a survey, Queue systes., Iak, N. Suta, U. ad Kowada, M.: O a eeralzed M/G/ queue wth servce deradato/eorceet, Joural o Operatos Research Socety o Japa, 4, Ahahru, S.A. ad Ftzatrck, G.J.: Wat te dstrbuto o a Fo/ Lo M/D/ queue, INFOR, 37, Keaku H. ad Myazawa M.: A reeeratve cycle approach to a M/G/ queue wth exceptoal servce, Joural o the Operatos Research Socety o Japa., 43, 4, Tooyuk, K. ad Myazawa, M.: A M/G/ queue wth Markov-depedet exceptoal servce tes, O. R. Letters, 30, Basal, N.: Aalyss o the M/G/ processor-shar queue wth bulk arrvals, O.R. Letters, 3, Yechal U.: O the M X /G/ Queue wth a Wat Server ad Vacato, Sakhya: the Ida joural o stat., 66, part-, Perry D., Stadje, W.: A cotrolled M/G/ workload process wth a applcato to pershable vetory systes. Math.Methods Oper.Res. 643, Boxa, O. J., Bru, J. Fralx.B. H.: Wat tes poll systes wth varous servce dscples. Peror.Eval Des Deso & Seva Sheer: Global ad local asyptotcs or the busy perod o a M/G/ queue. Queu Syst

Analysis of System Performance IN2072 Chapter 5 Analysis of Non Markov Systems

Analysis of System Performance IN2072 Chapter 5 Analysis of Non Markov Systems Char for Network Archtectures ad Servces Prof. Carle Departmet of Computer Scece U Müche Aalyss of System Performace IN2072 Chapter 5 Aalyss of No Markov Systems Dr. Alexader Kle Prof. Dr.-Ig. Georg Carle

More information

Some Different Perspectives on Linear Least Squares

Some Different Perspectives on Linear Least Squares Soe Dfferet Perspectves o Lear Least Squares A stadard proble statstcs s to easure a respose or depedet varable, y, at fed values of oe or ore depedet varables. Soetes there ests a deterstc odel y f (,,

More information

EP2200 Queueing theory and teletraffic systems. Queueing networks. Viktoria Fodor KTH EES/LCN KTH EES/LCN

EP2200 Queueing theory and teletraffic systems. Queueing networks. Viktoria Fodor KTH EES/LCN KTH EES/LCN EP2200 Queueg theory ad teletraffc systems Queueg etworks Vktora Fodor Ope ad closed queug etworks Queug etwork: etwork of queug systems E.g. data packets traversg the etwork from router to router Ope

More information

Lecture 8 IEEE DCF Performance

Lecture 8 IEEE DCF Performance Lecture 8 IEEE82. DCF Perforace IEEE82. DCF Basc Access Mechas A stato wth a ew packet to trast otors the chael actvty. If the chael s dle for a perod of te equal to a dstrbuted terfrae space (DIFS), the

More information

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632

More information

A New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming

A New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming ppled Matheatcal Sceces Vol 008 o 50 7-80 New Method for Solvg Fuzzy Lear Prograg by Solvg Lear Prograg S H Nasser a Departet of Matheatcs Faculty of Basc Sceces Mazadara Uversty Babolsar Ira b The Research

More information

. The set of these sums. be a partition of [ ab, ]. Consider the sum f( x) f( x 1)

. The set of these sums. be a partition of [ ab, ]. Consider the sum f( x) f( x 1) Chapter 7 Fuctos o Bouded Varato. Subject: Real Aalyss Level: M.Sc. Source: Syed Gul Shah (Charma, Departmet o Mathematcs, US Sargodha Collected & Composed by: Atq ur Rehma (atq@mathcty.org, http://www.mathcty.org

More information

Assignment 7/MATH 247/Winter, 2010 Due: Friday, March 19. Powers of a square matrix

Assignment 7/MATH 247/Winter, 2010 Due: Friday, March 19. Powers of a square matrix Assgmet 7/MATH 47/Wter, 00 Due: Frday, March 9 Powers o a square matrx Gve a square matrx A, ts powers A or large, or eve arbtrary, teger expoets ca be calculated by dagoalzg A -- that s possble (!) Namely,

More information

Multi-server preemptive priority queue with general arrivals and service times

Multi-server preemptive priority queue with general arrivals and service times Mult-server preeptve prorty queue wth geeral arrvals ad servce tes Alexadre Bradwaj Bask School of Egeerg Uversty of Calfora Sata Cruz USA alexb@soe.ucsc.edu Thoas Beg LIP UMR CNRS - ENS Lyo - UCB Lyo

More information

On Signed Product Cordial Labeling

On Signed Product Cordial Labeling Appled Mathematcs 55-53 do:.436/am..6 Publshed Ole December (http://www.scrp.or/joural/am) O Sed Product Cordal Label Abstract Jayapal Baskar Babujee Shobaa Loaatha Departmet o Mathematcs Aa Uversty Chea

More information

2/20/2013. Topics. Power Flow Part 1 Text: Power Transmission. Power Transmission. Power Transmission. Power Transmission

2/20/2013. Topics. Power Flow Part 1 Text: Power Transmission. Power Transmission. Power Transmission. Power Transmission /0/0 Topcs Power Flow Part Text: 0-0. Power Trassso Revsted Power Flow Equatos Power Flow Proble Stateet ECEGR 45 Power Systes Power Trassso Power Trassso Recall that for a short trassso le, the power

More information

ROOT-LOCUS ANALYSIS. Lecture 11: Root Locus Plot. Consider a general feedback control system with a variable gain K. Y ( s ) ( ) K

ROOT-LOCUS ANALYSIS. Lecture 11: Root Locus Plot. Consider a general feedback control system with a variable gain K. Y ( s ) ( ) K ROOT-LOCUS ANALYSIS Coder a geeral feedback cotrol yte wth a varable ga. R( Y( G( + H( Root-Locu a plot of the loc of the pole of the cloed-loop trafer fucto whe oe of the yte paraeter ( vared. Root locu

More information

Functions of Random Variables

Functions of Random Variables Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,

More information

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur

More information

A Conventional Approach for the Solution of the Fifth Order Boundary Value Problems Using Sixth Degree Spline Functions

A Conventional Approach for the Solution of the Fifth Order Boundary Value Problems Using Sixth Degree Spline Functions Appled Matheatcs, 1, 4, 8-88 http://d.do.org/1.4/a.1.448 Publshed Ole Aprl 1 (http://www.scrp.org/joural/a) A Covetoal Approach for the Soluto of the Ffth Order Boudary Value Probles Usg Sth Degree Sple

More information

Stationary states of atoms and molecules

Stationary states of atoms and molecules Statoary states of atos ad olecules I followg wees the geeral aspects of the eergy level structure of atos ad olecules that are essetal for the terpretato ad the aalyss of spectral postos the rotatoal

More information

A New Family of Transformations for Lifetime Data

A New Family of Transformations for Lifetime Data Proceedgs of the World Cogress o Egeerg 4 Vol I, WCE 4, July - 4, 4, Lodo, U.K. A New Famly of Trasformatos for Lfetme Data Lakhaa Watthaacheewakul Abstract A famly of trasformatos s the oe of several

More information

Queueing Networks. γ 3

Queueing Networks. γ 3 Queueg Networks Systes odeled by queueg etworks ca roughly be grouped to four categores. Ope etworks Custoers arrve fro outsde the syste are served ad the depart. Exaple: acket swtched data etwork. γ µ

More information

Complete Convergence and Some Maximal Inequalities for Weighted Sums of Random Variables

Complete Convergence and Some Maximal Inequalities for Weighted Sums of Random Variables Joural of Sceces, Islamc Republc of Ira 8(4): -6 (007) Uversty of Tehra, ISSN 06-04 http://sceces.ut.ac.r Complete Covergece ad Some Maxmal Iequaltes for Weghted Sums of Radom Varables M. Am,,* H.R. Nl

More information

Study of Impact of Negative Arrivals in Single. Server Fixed Batch Service Queueing System. with Multiple Vacations

Study of Impact of Negative Arrivals in Single. Server Fixed Batch Service Queueing System. with Multiple Vacations Appled Mathematcal Sceces, Vol. 7, 23, o. 4, 6967-6976 HIKARI Ltd, www.m-hkar.com http://dx.do.org/.2988/ams.23.354 Study of Impact of Negatve Arrvals Sgle Server Fxed Batch Servce Queueg System wth Multple

More information

Basics of Information Theory: Markku Juntti. Basic concepts and tools 1 Introduction 2 Entropy, relative entropy and mutual information

Basics of Information Theory: Markku Juntti. Basic concepts and tools 1 Introduction 2 Entropy, relative entropy and mutual information : Markku Jutt Overvew Te basc cocepts o ormato teory lke etropy mutual ormato ad EP are eeralzed or cotuous-valued radom varables by troduc deretal etropy ource Te materal s maly based o Capter 9 o te

More information

THE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5

THE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5 THE ROYAL STATISTICAL SOCIETY 06 EAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5 The Socety s provdg these solutos to assst cadtes preparg for the examatos 07. The solutos are teded as learg ads ad should

More information

On Face Bimagic Labeling of Graphs

On Face Bimagic Labeling of Graphs IOSR Joural of Mathematcs (IOSR-JM) e-issn: 78-578, p-issn: 319-765X Volume 1, Issue 6 Ver VI (Nov - Dec016), PP 01-07 wwwosrouralsor O Face Bmac Label of Graphs Mohammed Al Ahmed 1,, J Baskar Babuee 1

More information

Non-degenerate Perturbation Theory

Non-degenerate Perturbation Theory No-degeerate Perturbato Theory Proble : H E ca't solve exactly. But wth H H H' H" L H E Uperturbed egevalue proble. Ca solve exactly. E Therefore, kow ad. H ' H" called perturbatos Copyrght Mchael D. Fayer,

More information

Numerical Experiments with the Lagrange Multiplier and Conjugate Gradient Methods (ILMCGM)

Numerical Experiments with the Lagrange Multiplier and Conjugate Gradient Methods (ILMCGM) Aerca Joural of Appled Matheatcs 4; (6: -6 Publshed ole Jauary 5, 5 (http://wwwscecepublshroupco//aa do: 648/aa465 ISSN: 33-43 (Prt; ISSN: 33-6X (Ole Nuercal Eperets wth the Larae Multpler ad Couate Gradet

More information

Journal Of Inequalities And Applications, 2008, v. 2008, p

Journal Of Inequalities And Applications, 2008, v. 2008, p Ttle O verse Hlbert-tye equaltes Authors Chagja, Z; Cheug, WS Ctato Joural Of Iequaltes Ad Alcatos, 2008, v. 2008,. 693248 Issued Date 2008 URL htt://hdl.hadle.et/0722/56208 Rghts Ths work s lcesed uder

More information

Simulation Output Analysis

Simulation Output Analysis Smulato Output Aalyss Summary Examples Parameter Estmato Sample Mea ad Varace Pot ad Iterval Estmato ermatg ad o-ermatg Smulato Mea Square Errors Example: Sgle Server Queueg System x(t) S 4 S 4 S 3 S 5

More information

Queueing Theory II. Summary. M/M/1 Output process Networks of Queue Method of Stages. General Distributions

Queueing Theory II. Summary. M/M/1 Output process Networks of Queue Method of Stages. General Distributions Queueig Theory II Suary M/M/1 Output process Networks of Queue Method of Stages Erlag Distributio Hyperexpoetial Distributio Geeral Distributios Ebedded Markov Chais 1 M/M/1 Output Process Burke s Theore:

More information

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b CS 70 Dscrete Mathematcs ad Probablty Theory Fall 206 Sesha ad Walrad DIS 0b. Wll I Get My Package? Seaky delvery guy of some compay s out delverg packages to customers. Not oly does he had a radom package

More information

Steady-state Behavior of a Multi-phase M/M/1 Queue in Random Evolution subject to Catastrophe failure

Steady-state Behavior of a Multi-phase M/M/1 Queue in Random Evolution subject to Catastrophe failure Advaces Theoretcal ad Appled Mathematcs ISSN 973-4554 Volume, Number 3 (26), pp. 23-22 Research Ida Publcatos http://www.rpublcato.com Steady-state Behavor of a Mult-phase M/M/ Queue Radom Evoluto subect

More information

IS 709/809: Computational Methods in IS Research. Simple Markovian Queueing Model

IS 709/809: Computational Methods in IS Research. Simple Markovian Queueing Model IS 79/89: Comutatoal Methods IS Research Smle Marova Queueg Model Nrmalya Roy Deartmet of Iformato Systems Uversty of Marylad Baltmore Couty www.umbc.edu Queueg Theory Software QtsPlus software The software

More information

3.1 Introduction to Multinomial Logit and Probit

3.1 Introduction to Multinomial Logit and Probit ES3008 Ecooetrcs Lecture 3 robt ad Logt - Multoal 3. Itroducto to Multoal Logt ad robt 3. Estato of β 3. Itroducto to Multoal Logt ad robt The ultoal Logt odel s used whe there are several optos (ad therefore

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

Study of Correlation using Bayes Approach under bivariate Distributions

Study of Correlation using Bayes Approach under bivariate Distributions Iteratoal Joural of Scece Egeerg ad Techolog Research IJSETR Volume Issue Februar 4 Stud of Correlato usg Baes Approach uder bvarate Dstrbutos N.S.Padharkar* ad. M.N.Deshpade** *Govt.Vdarbha Isttute of

More information

Optimal Strategy Analysis of an N-policy M/E k /1 Queueing System with Server Breakdowns and Multiple Vacations

Optimal Strategy Analysis of an N-policy M/E k /1 Queueing System with Server Breakdowns and Multiple Vacations Iteratoal Joural of Scetfc ad Research ublcatos, Volume 3, Issue, ovember 3 ISS 5-353 Optmal Strategy Aalyss of a -polcy M/E / Queueg System wth Server Breadows ad Multple Vacatos.Jayachtra*, Dr.A.James

More information

Chapter 5 Properties of a Random Sample

Chapter 5 Properties of a Random Sample Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample

More information

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Exam: ECON430 Statstcs Date of exam: Frday, December 8, 07 Grades are gve: Jauary 4, 08 Tme for exam: 0900 am 00 oo The problem set covers 5 pages Resources allowed:

More information

Analysis of Variance with Weibull Data

Analysis of Variance with Weibull Data Aalyss of Varace wth Webull Data Lahaa Watthaacheewaul Abstract I statstcal data aalyss by aalyss of varace, the usual basc assumptos are that the model s addtve ad the errors are radomly, depedetly, ad

More information

Sebastián Martín Ruiz. Applications of Smarandache Function, and Prime and Coprime Functions

Sebastián Martín Ruiz. Applications of Smarandache Function, and Prime and Coprime Functions Sebastá Martí Ruz Alcatos of Saradache Fucto ad Pre ad Core Fuctos 0 C L f L otherwse are core ubers Aerca Research Press Rehoboth 00 Sebastá Martí Ruz Avda. De Regla 43 Choa 550 Cadz Sa Sarada@telele.es

More information

PRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION

PRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION PRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION Bars Erkus, 4 March 007 Itroducto Ths docuet provdes a revew of fudaetal cocepts structural dyacs ad soe applcatos hua-duced vbrato aalyss ad tgato of

More information

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy Bouds o the expected etropy ad KL-dvergece of sampled multomal dstrbutos Brado C. Roy bcroy@meda.mt.edu Orgal: May 18, 2011 Revsed: Jue 6, 2011 Abstract Iformato theoretc quattes calculated from a sampled

More information

Basic Concepts in Numerical Analysis November 6, 2017

Basic Concepts in Numerical Analysis November 6, 2017 Basc Cocepts Nuercal Aalyss Noveber 6, 7 Basc Cocepts Nuercal Aalyss Larry Caretto Mecacal Egeerg 5AB Sear Egeerg Aalyss Noveber 6, 7 Outle Revew last class Mdter Exa Noveber 5 covers ateral o deretal

More information

A Penalty Function Algorithm with Objective Parameters and Constraint Penalty Parameter for Multi-Objective Programming

A Penalty Function Algorithm with Objective Parameters and Constraint Penalty Parameter for Multi-Objective Programming Aerca Joural of Operatos Research, 4, 4, 33-339 Publshed Ole Noveber 4 ScRes http://wwwscrporg/oural/aor http://ddoorg/436/aor4463 A Pealty Fucto Algorth wth Obectve Paraeters ad Costrat Pealty Paraeter

More information

Chapter Business Statistics: A First Course Fifth Edition. Learning Objectives. Correlation vs. Regression. In this chapter, you learn:

Chapter Business Statistics: A First Course Fifth Edition. Learning Objectives. Correlation vs. Regression. In this chapter, you learn: Chapter 3 3- Busess Statstcs: A Frst Course Ffth Edto Chapter 2 Correlato ad Smple Lear Regresso Busess Statstcs: A Frst Course, 5e 29 Pretce-Hall, Ic. Chap 2- Learg Objectves I ths chapter, you lear:

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

On Eccentricity Sum Eigenvalue and Eccentricity Sum Energy of a Graph

On Eccentricity Sum Eigenvalue and Eccentricity Sum Energy of a Graph Aals of Pure ad Appled Mathematcs Vol. 3, No., 7, -3 ISSN: 79-87X (P, 79-888(ole Publshed o 3 March 7 www.researchmathsc.org DOI: http://dx.do.org/.7/apam.3a Aals of O Eccetrcty Sum Egealue ad Eccetrcty

More information

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971)) art 4b Asymptotc Results for MRR usg RESS Recall that the RESS statstc s a specal type of cross valdato procedure (see Alle (97)) partcular to the regresso problem ad volves fdg Y $,, the estmate at the

More information

Standard Deviation for PDG Mass Data

Standard Deviation for PDG Mass Data 4 Dec 06 Stadard Devato for PDG Mass Data M. J. Gerusa Retred, 47 Clfde Road, Worghall, HP8 9JR, UK. gerusa@aol.co, phoe: +(44) 844 339754 Abstract Ths paper aalyses the data for the asses of eleetary

More information

Summary of the lecture in Biostatistics

Summary of the lecture in Biostatistics Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the

More information

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The

More information

Waiting Time Distribution of Demand Requiring Multiple Items under a Base Stock Policy

Waiting Time Distribution of Demand Requiring Multiple Items under a Base Stock Policy Joural of Servce Scece ad Maagemet 23 6 266-272 http://d.do.org/.4236/jssm.23.643 Publshed Ole October 23 (http://www.scrp.org/joural/jssm) Watg Tme Dstrbuto of Demad Requrg Multple Items uder a Base Stoc

More information

7.0 Equality Contraints: Lagrange Multipliers

7.0 Equality Contraints: Lagrange Multipliers Systes Optzato 7.0 Equalty Cotrats: Lagrage Multplers Cosder the zato of a o-lear fucto subject to equalty costrats: g f() R ( ) 0 ( ) (7.) where the g ( ) are possbly also olear fuctos, ad < otherwse

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

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

A Bivariate Distribution with Conditional Gamma and its Multivariate Form

A Bivariate Distribution with Conditional Gamma and its Multivariate Form Joural of Moder Appled Statstcal Methods Volue 3 Issue Artcle 9-4 A Bvarate Dstrbuto wth Codtoal Gaa ad ts Multvarate For Sue Se Old Doo Uversty, sxse@odu.edu Raja Lachhae Texas A&M Uversty, raja.lachhae@tauk.edu

More information

Multi Objective Fuzzy Inventory Model with. Demand Dependent Unit Cost and Lead Time. Constraints A Karush Kuhn Tucker Conditions.

Multi Objective Fuzzy Inventory Model with. Demand Dependent Unit Cost and Lead Time. Constraints A Karush Kuhn Tucker Conditions. It. Joural of Math. Aalyss, Vol. 8, 204, o. 4, 87-93 HIKARI Ltd, www.m-hkar.com http://dx.do.org/0.2988/jma.204.30252 Mult Objectve Fuzzy Ivetory Model wth Demad Depedet Ut Cost ad Lead Tme Costrats A

More information

Parallelized methods for solving polynomial equations

Parallelized methods for solving polynomial equations IOSR Joural of Matheatcs (IOSR-JM) e-issn: 2278-5728, p-issn: 239-765X. Volue 2, Issue 4 Ver. II (Jul. - Aug.206), PP 75-79 www.osrourals.org Paralleled ethods for solvg polyoal equatos Rela Kapçu, Fatr

More information

CHAPTER 4 RADICAL EXPRESSIONS

CHAPTER 4 RADICAL EXPRESSIONS 6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube

More information

9 U-STATISTICS. Eh =(m!) 1 Eh(X (1),..., X (m ) ) i.i.d

9 U-STATISTICS. Eh =(m!) 1 Eh(X (1),..., X (m ) ) i.i.d 9 U-STATISTICS Suppose,,..., are P P..d. wth CDF F. Our goal s to estmate the expectato t (P)=Eh(,,..., m ). Note that ths expectato requres more tha oe cotrast to E, E, or Eh( ). Oe example s E or P((,

More information

A Characterization of Jacobson Radical in Γ-Banach Algebras

A Characterization of Jacobson Radical in Γ-Banach Algebras Advaces Pure Matheatcs 43-48 http://dxdoorg/436/ap66 Publshed Ole Noveber (http://wwwscrporg/joural/ap) A Characterzato of Jacobso Radcal Γ-Baach Algebras Nlash Goswa Departet of Matheatcs Gauhat Uversty

More information

Comparing Different Estimators of three Parameters for Transmuted Weibull Distribution

Comparing Different Estimators of three Parameters for Transmuted Weibull Distribution Global Joural of Pure ad Appled Mathematcs. ISSN 0973-768 Volume 3, Number 9 (207), pp. 55-528 Research Ida Publcatos http://www.rpublcato.com Comparg Dfferet Estmators of three Parameters for Trasmuted

More information

Multivariate Transformation of Variables and Maximum Likelihood Estimation

Multivariate Transformation of Variables and Maximum Likelihood Estimation Marquette Uversty Multvarate Trasformato of Varables ad Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Assocate Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Copyrght 03 by Marquette Uversty

More information

Chapter 14 Logistic Regression Models

Chapter 14 Logistic Regression Models Chapter 4 Logstc Regresso Models I the lear regresso model X β + ε, there are two types of varables explaatory varables X, X,, X k ad study varable y These varables ca be measured o a cotuous scale as

More information

Class 13,14 June 17, 19, 2015

Class 13,14 June 17, 19, 2015 Class 3,4 Jue 7, 9, 05 Pla for Class3,4:. Samplg dstrbuto of sample mea. The Cetral Lmt Theorem (CLT). Cofdece terval for ukow mea.. Samplg Dstrbuto for Sample mea. Methods used are based o CLT ( Cetral

More information

Analysis of a Repairable (n-1)-out-of-n: G System with Failure and Repair Times Arbitrarily Distributed

Analysis of a Repairable (n-1)-out-of-n: G System with Failure and Repair Times Arbitrarily Distributed Amerca Joural of Mathematcs ad Statstcs. ; (: -8 DOI:.593/j.ajms.. Aalyss of a Reparable (--out-of-: G System wth Falure ad Repar Tmes Arbtrarly Dstrbuted M. Gherda, M. Boushaba, Departmet of Mathematcs,

More information

Random Variables and Probability Distributions

Random Variables and Probability Distributions Radom Varables ad Probablty Dstrbutos * If X : S R s a dscrete radom varable wth rage {x, x, x 3,. } the r = P (X = xr ) = * Let X : S R be a dscrete radom varable wth rage {x, x, x 3,.}.If x r P(X = x

More information

STK4011 and STK9011 Autumn 2016

STK4011 and STK9011 Autumn 2016 STK4 ad STK9 Autum 6 Pot estmato Covers (most of the followg materal from chapter 7: Secto 7.: pages 3-3 Secto 7..: pages 3-33 Secto 7..: pages 35-3 Secto 7..3: pages 34-35 Secto 7.3.: pages 33-33 Secto

More information

Strong Laws of Large Numbers for Fuzzy Set-Valued Random Variables in Gα Space

Strong Laws of Large Numbers for Fuzzy Set-Valued Random Variables in Gα Space Advaces Pure Matheatcs 26 6 583-592 Publshed Ole August 26 ScRes http://wwwscrporg/oural/ap http://dxdoorg/4236/ap266947 Strog Laws of Large Nubers for uzzy Set-Valued Rado Varables G Space Lae She L Gua

More information

THE TRUNCATED RANDIĆ-TYPE INDICES

THE TRUNCATED RANDIĆ-TYPE INDICES Kragujeac J Sc 3 (00 47-5 UDC 547:54 THE TUNCATED ANDIĆ-TYPE INDICES odjtaba horba, a ohaad Al Hossezadeh, b Ia uta c a Departet of atheatcs, Faculty of Scece, Shahd ajae Teacher Trag Uersty, Tehra, 785-3,

More information

Outline. Basic Components of a Queue. Queueing Notation. EEC 686/785 Modeling & Performance Evaluation of Computer Systems.

Outline. Basic Components of a Queue. Queueing Notation. EEC 686/785 Modeling & Performance Evaluation of Computer Systems. EEC 686/785 Modelg & Performace Evaluato of Computer Systems Lecture 5 Departmet of Electrcal ad Computer Egeerg Clevelad State Uversty webg@eee.org (based o Dr. Raj Ja s lecture otes) Outle Homework #5

More information

Hájek-Rényi Type Inequalities and Strong Law of Large Numbers for NOD Sequences

Hájek-Rényi Type Inequalities and Strong Law of Large Numbers for NOD Sequences Appl Math If Sc 7, No 6, 59-53 03 59 Appled Matheatcs & Iforato Sceces A Iteratoal Joural http://dxdoorg/0785/as/070647 Háje-Réy Type Iequaltes ad Strog Law of Large Nuers for NOD Sequeces Ma Sogl Departet

More information

Coherent Potential Approximation

Coherent Potential Approximation Coheret Potetal Approxato Noveber 29, 2009 Gree-fucto atrces the TB forals I the tght bdg TB pcture the atrx of a Haltoa H s the for H = { H j}, where H j = δ j ε + γ j. 2 Sgle ad double uderles deote

More information

An Innovative Algorithmic Approach for Solving Profit Maximization Problems

An Innovative Algorithmic Approach for Solving Profit Maximization Problems Matheatcs Letters 208; 4(: -5 http://www.scecepublshggroup.co/j/l do: 0.648/j.l.208040. ISSN: 2575-503X (Prt; ISSN: 2575-5056 (Ole A Iovatve Algorthc Approach for Solvg Proft Maxzato Probles Abul Kala

More information

The Mathematics of Portfolio Theory

The Mathematics of Portfolio Theory The Matheatcs of Portfolo Theory The rates of retur of stocks, ad are as follows Market odtos state / scearo) earsh Neutral ullsh Probablty 0. 0.5 0.3 % 5% 9% -3% 3% % 5% % -% Notato: R The retur of stock

More information

Analysis of Lagrange Interpolation Formula

Analysis of Lagrange Interpolation Formula P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal

More information

Some Applications of the Resampling Methods in Computational Physics

Some Applications of the Resampling Methods in Computational Physics Iteratoal Joural of Mathematcs Treds ad Techoloy Volume 6 February 04 Some Applcatos of the Resampl Methods Computatoal Physcs Sotraq Marko #, Lorec Ekoom * # Physcs Departmet, Uversty of Korca, Albaa,

More information

MEASURES OF DISPERSION

MEASURES OF DISPERSION MEASURES OF DISPERSION Measure of Cetral Tedecy: Measures of Cetral Tedecy ad Dsperso ) Mathematcal Average: a) Arthmetc mea (A.M.) b) Geometrc mea (G.M.) c) Harmoc mea (H.M.) ) Averages of Posto: a) Meda

More information

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ  1 STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ

More information

Likewise, properties of the optimal policy for equipment replacement & maintenance problems can be used to reduce the computation.

Likewise, properties of the optimal policy for equipment replacement & maintenance problems can be used to reduce the computation. Whe solvg a vetory repleshmet problem usg a MDP model, kowg that the optmal polcy s of the form (s,s) ca reduce the computatoal burde. That s, f t s optmal to replesh the vetory whe the vetory level s,

More information

Relations to Other Statistical Methods Statistical Data Analysis with Positive Definite Kernels

Relations to Other Statistical Methods Statistical Data Analysis with Positive Definite Kernels Relatos to Other Statstcal Methods Statstcal Data Aalyss wth Postve Defte Kerels Kej Fukuzu Isttute of Statstcal Matheatcs, ROIS Departet of Statstcal Scece, Graduate Uversty for Advaced Studes October

More information

X X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then

X X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then Secto 5 Vectors of Radom Varables Whe workg wth several radom varables,,..., to arrage them vector form x, t s ofte coveet We ca the make use of matrx algebra to help us orgaze ad mapulate large umbers

More information

Q-analogue of a Linear Transformation Preserving Log-concavity

Q-analogue of a Linear Transformation Preserving Log-concavity Iteratoal Joural of Algebra, Vol. 1, 2007, o. 2, 87-94 Q-aalogue of a Lear Trasformato Preservg Log-cocavty Daozhog Luo Departmet of Mathematcs, Huaqao Uversty Quazhou, Fua 362021, P. R. Cha ldzblue@163.com

More information

Research Article A New Iterative Method for Common Fixed Points of a Finite Family of Nonexpansive Mappings

Research Article A New Iterative Method for Common Fixed Points of a Finite Family of Nonexpansive Mappings Hdaw Publshg Corporato Iteratoal Joural of Mathematcs ad Mathematcal Sceces Volume 009, Artcle ID 391839, 9 pages do:10.1155/009/391839 Research Artcle A New Iteratve Method for Commo Fxed Pots of a Fte

More information

A tighter lower bound on the circuit size of the hardest Boolean functions

A tighter lower bound on the circuit size of the hardest Boolean functions Electroc Colloquum o Computatoal Complexty, Report No. 86 2011) A tghter lower boud o the crcut sze of the hardest Boolea fuctos Masak Yamamoto Abstract I [IPL2005], Fradse ad Mlterse mproved bouds o the

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

1. The weight of six Golden Retrievers is 66, 61, 70, 67, 92 and 66 pounds. The weight of six Labrador Retrievers is 54, 60, 72, 78, 84 and 67.

1. The weight of six Golden Retrievers is 66, 61, 70, 67, 92 and 66 pounds. The weight of six Labrador Retrievers is 54, 60, 72, 78, 84 and 67. Ecoomcs 3 Itroducto to Ecoometrcs Sprg 004 Professor Dobk Name Studet ID Frst Mdterm Exam You must aswer all the questos. The exam s closed book ad closed otes. You may use your calculators but please

More information

Research Article Gauss-Lobatto Formulae and Extremal Problems

Research Article Gauss-Lobatto Formulae and Extremal Problems Hdaw Publshg Corporato Joural of Iequaltes ad Applcatos Volume 2008 Artcle ID 624989 0 pages do:055/2008/624989 Research Artcle Gauss-Lobatto Formulae ad Extremal Problems wth Polyomals Aa Mara Acu ad

More information

Marcinkiewicz strong laws for linear statistics of ρ -mixing sequences of random variables

Marcinkiewicz strong laws for linear statistics of ρ -mixing sequences of random variables Aas da Academa Braslera de Cêcas 2006 784: 65-62 Aals of the Brazla Academy of Sceces ISSN 000-3765 www.scelo.br/aabc Marckewcz strog laws for lear statstcs of ρ -mxg sequeces of radom varables GUANG-HUI

More information

Algorithms behind the Correlation Setting Window

Algorithms behind the Correlation Setting Window Algorths behd the Correlato Settg Wdow Itroducto I ths report detaled forato about the correlato settg pop up wdow s gve. See Fgure. Ths wdow s obtaed b clckg o the rado butto labelled Kow dep the a scree

More information

Non-uniform Turán-type problems

Non-uniform Turán-type problems Joural of Combatoral Theory, Seres A 111 2005 106 110 wwwelsevercomlocatecta No-uform Turá-type problems DhruvMubay 1, Y Zhao 2 Departmet of Mathematcs, Statstcs, ad Computer Scece, Uversty of Illos at

More information

THE ROYAL STATISTICAL SOCIETY HIGHER CERTIFICATE

THE ROYAL STATISTICAL SOCIETY HIGHER CERTIFICATE THE ROYAL STATISTICAL SOCIETY 00 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE PAPER I STATISTICAL THEORY The Socety provdes these solutos to assst caddates preparg for the examatos future years ad for the

More information

System in Weibull Distribution

System in Weibull Distribution Internatonal Matheatcal Foru 4 9 no. 9 94-95 Relablty Equvalence Factors of a Seres-Parallel Syste n Webull Dstrbuton M. A. El-Dacese Matheatcs Departent Faculty of Scence Tanta Unversty Tanta Egypt eldacese@yahoo.co

More information

3. Basic Concepts: Consequences and Properties

3. Basic Concepts: Consequences and Properties : 3. Basc Cocepts: Cosequeces ad Propertes Markku Jutt Overvew More advaced cosequeces ad propertes of the basc cocepts troduced the prevous lecture are derved. Source The materal s maly based o Sectos.6.8

More information

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations HP 30S Statstcs Averages ad Stadard Devatos Average ad Stadard Devato Practce Fdg Averages ad Stadard Devatos HP 30S Statstcs Averages ad Stadard Devatos Average ad stadard devato The HP 30S provdes several

More information

Midterm Exam 1, section 1 (Solution) Thursday, February hour, 15 minutes

Midterm Exam 1, section 1 (Solution) Thursday, February hour, 15 minutes coometrcs, CON Sa Fracsco State Uversty Mchael Bar Sprg 5 Mdterm am, secto Soluto Thursday, February 6 hour, 5 mutes Name: Istructos. Ths s closed book, closed otes eam.. No calculators of ay kd are allowed..

More information

IFYMB002 Mathematics Business Appendix C Formula Booklet

IFYMB002 Mathematics Business Appendix C Formula Booklet Iteratoal Foudato Year (IFY IFYMB00 Mathematcs Busess Apped C Formula Booklet Related Documet: IFY Mathematcs Busess Syllabus 07/8 IFYMB00 Maths Busess Apped C Formula Booklet Cotets lease ote that the

More information

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted

More information

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information Malaysa Joural of Mathematcal Sceces (): 97- (9) Bayes Estmator for Expoetal Dstrbuto wth Exteso of Jeffery Pror Iformato Hadeel Salm Al-Kutub ad Noor Akma Ibrahm Isttute for Mathematcal Research, Uverst

More information

Chapter 13, Part A Analysis of Variance and Experimental Design. Introduction to Analysis of Variance. Introduction to Analysis of Variance

Chapter 13, Part A Analysis of Variance and Experimental Design. Introduction to Analysis of Variance. Introduction to Analysis of Variance Chapter, Part A Aalyss of Varace ad Epermetal Desg Itroducto to Aalyss of Varace Aalyss of Varace: Testg for the Equalty of Populato Meas Multple Comparso Procedures Itroducto to Aalyss of Varace Aalyss

More information

D. L. Bricker, 2002 Dept of Mechanical & Industrial Engineering The University of Iowa. CPL/XD 12/10/2003 page 1

D. L. Bricker, 2002 Dept of Mechanical & Industrial Engineering The University of Iowa. CPL/XD 12/10/2003 page 1 D. L. Brcker, 2002 Dept of Mechacal & Idustral Egeerg The Uversty of Iowa CPL/XD 2/0/2003 page Capactated Plat Locato Proble: Mze FY + C X subject to = = j= where Y = j= X D, j =, j X SY, =,... X 0, =,

More information