EVALUATION OF PERFORMANCE MEASURES OF FMS Bottleneck Model. Part mix Mix of the various part or product styles produced by the system

Size: px
Start display at page:

Download "EVALUATION OF PERFORMANCE MEASURES OF FMS Bottleneck Model. Part mix Mix of the various part or product styles produced by the system"

Transcription

1 Natoal Ittute of Techology Calcut Deartmet of Mechacal Egeerg EVALUATION OF PERFORMANCE MEASURES OF FMS Bottleeck Model Provde tartg etmate of FMS deg arameter uch a roducto rate ad umber of worktato Bottleeck refer to the fact that the outut of the roducto ytem ha a uer lmt for a gve roduct mx The model ca be aled to ay roducto ytem that oe th bottleeck feature maually oerated maufacturg cell, job ho, etc Termology ad Symbol Part mx Mx of the varou art or roduct tyle roduced by the ytem j - The fracto of the total ytem outut that of tyle j P j= j = where P the total umber of dfferet art tyle made the FMS durg the tme erod of teret Worktato ad Server - Number of dtct worktato - Number of erver at worktato, =,, Proce routg Proce routg clude the loadg oerato at the begg of roceg o the FMS ad the uloadg oerato at the ed of roceg It aumed that every art tyle ha uque roce la t jk - Proceg tme at tato for art j for t kth oerato Work hadlg ytem Codered a (+) th worktato + - Number of carrer the FMS hadlg ytem (eg:- Number of coveyer cart, AGV, Mooral vehcle, etc) t + - Mea traort tme requred to move a art from oe worktato to the ext tato the roceg routg Oerato frequecy Exected umber of tme a gve oerato the roce routg erformed Performace meaure of FMS 7 March 0

2 Natoal Ittute of Techology Calcut Deartmet of Mechacal Egeerg f jk - Oerato frequecy for oerato k roce la j at tato FMS Oerato arameter Average workload for a gve tato the mea total tme et at the tato er art coderg all the art roceed the worktato - Average workload at tato = j k t jk f jk j Average workload of hadlg ytem the mea traort tme multled by the average umber of traort requred to comlete the roceg of a workart Average umber of traort equal to the mea umber of oerato the roce routg mu oe t - Mea umber of traort t = j f jk j k k f jk t + = t + determe average umber of oerato for art j Sytem erformace meaure Producto rate of all art Producto rate of each art tyle Utlzato of dfferet worktato Number of buy erver at worktato Above erformace meaure are calculated uder the aumto that the FMS roducg at t maxmum oble rate Producto rate cotraed by the bottleeck tato Workload er erver = Bottleeck tato = max R - Maxmum roducto rate of all art tyle roduced by the ytem Performace meaure of FMS 7 March 0

3 Natoal Ittute of Techology Calcut Deartmet of Mechacal Egeerg Bottleeck tato arameter are rereeted wth a Producto rate Producto rate the umber of ut roduced er ut tme It the roduct of the roducto rate of a erver ad umber of erver at the bottleeck tato R = R j = j ( R) = Utlzato j Mea utlzato of each worktato the roorto of tme that the erver at the tato are workg ad ot dle It aumed that the bottleeck tato ha 00% utlzato U - Utlzato of tato ( R ) U = = U - Average tato utlzato the average utlzato of all tato + U = U = + Overall FMS utlzato ca be obtaed ug a weghted average, where the weght baed o the umber of erver at each tato for the regular tato the ytem The traort ytem omtted, a roceg tato utlzato mortat FMS U - Overall FMS utlzato U = = = S U S Number of buy erver Aume that erver at bottleeck tato are buy at the maxmum roducto rate BS - Number of buy erver o average at tato Performace meaure of FMS 7 March 0

4 Natoal Ittute of Techology Calcut Deartmet of Mechacal Egeerg Number of erver buy at bottleeck tato durg the tme Number of erver buy at tato BS Note = = R The roducto rate of the ytem ca be creaed by creag the rate of the tem, whch are ot roceed the bottleeck tato If exce caacty ext other tato Exteded Bottleeck Model Some aumto of the bottleeck model are Suffcet umber of art the ytem to avod tarvg of worktato There o delay due to queue Exteded bottleeck aroach ca overcome ome of the aumto Aume a cloed queug etwork wth fxed umber of art the FMS Whe oe art comleted ad ext the FMS, a ew raw workart mmedately eter the ytem Let N be the umber of art the ytem N lay a crtcal role the oerato of the ytem If N maller tha the umber of worktato, the ome of the tato wll be dle due to tarvg eve the bottleeck tato If N large, the the ytem wll be fully loaded wth queue of art watg frot of the tato R wll rovde good etmato of the roducto caacty Log maufacturg lead tme A er Lttle law WIP = Throughut Maufacturg Lead Tme (MLT) N = R (MLT ) MLT = + + = + T w Tw - Mea watg tme exereced by a art due to queue at the tato Baed o the value of N (mall or hgh) ad Lttle law, the ytem arameter (roducto rate ad MLT) ca be calculated Performace meaure of FMS 75 March 0

5 Natoal Ittute of Techology Calcut Deartmet of Mechacal Egeerg Cae (mall N) Bottleeck tato ot fully utlzed Producto rate ot R T w of a ut theoretcally zero The MLT ca be calculated ad the roducto rate ca be calculated ug Lttle law MLT = + + = MLT - MLT for cae R = N MLT R = j j R Cae (Large N) Producto rate cotraed by bottleeck tato R = MLT ca be calculated ug Lttle law MLT = N R T w = MLT + + = The dvdg le betwee cae ad cae deedg o the crtcal value of N Let N be the crtcal value of N N = R = + + = R ( MLT ) Grahcal Rereetato of exteded bottleeck model Performace meaure of FMS 76 March 0

6 Natoal Ittute of Techology Calcut Deartmet of Mechacal Egeerg MLT R MLT R 0 N (a) N 0 N (b) N Geeral behavor of the exteded bottleeck model (a) MLT a a fucto of N ad (b) Producto rate a a fucto of N Valdty of the Model The author (Mejab) of the model comared the reult wth the reult obtaed from Queug model (Ca-Q) for everal thouad roblem A adequacy factor uggeted whch decrbe the dcreace of th model from queug model Adequacy factor (AF) N AF = U + = Atcated dcreace betwee the exteded bottleeck model ad CAN-Q model a a fucto of the adequacy factor. Adequacy Factor Atcated dcreace wth Ca-Q AF < 0.9 Dcreace < 5% are lkely 0.9 AF.5 Dcreace 5% are lkely AF >.5 Dcreace < 5% are lkely Performace meaure of FMS 77 March 0

7 Natoal Ittute of Techology Calcut Deartmet of Mechacal Egeerg Szg of FMS Determato of umber of erver requred at each worktato to acheve a ecfed roducto rate Iformato eeded Part mx, roce routg ad roceg tme S = mmum teger R ( ) S - Number of erver at tato R - Secfed roducto rate of all art to be roduced by the ytem - Workload at tato A erformace evaluato roblem: A FMS ued to roduce four art. The FMS cot of oe load/uload tato ad two automated roceg tato (rocee X ad Y). The umber of erver for each tato tye to be determed. The FMS alo clude a automated coveyor ytem wth dvdual cart to traort art betwee erver. The cart move the art from oe erver to the ext, dro them off, ad roceed to the ext delvery tak. Average tme requred er trafer.5 m. The followg table ummarze the FMS: Stato Load ad uload Stato Proce X Stato Proce Y Stato Traort ytem Number of huma erver (worker) to be determed Number of automated erver to be determed Number of automated erver to be determed Number of cart to be determed All art follow the ame routg, whch Aume that each oerato carred out oly oce for a tem. The roduct mx ad roceg tme at each tato are reeted the table below: Product Product mx Stato (m) Stato (m) Stato (m) Stato (m) A B C D Requred roducto art/hr whch dtrbuted accordg to the roduct mx dcated. Ue the bottleeck model to determe: () the mmum umber of erver at each tato ad the mmum umber of cart the traort ytem thoe are requred to atfy roducto demad ad () the utlato of each tato for the awer above. Ue arorate model to determe: () the crtcal value of work--roce vetory (v) the maufacturg lead tme f there are 8 tem the ytem? Performace meaure of FMS 78 March 0

8 Natoal Ittute of Techology Calcut Deartmet of Mechacal Egeerg Soluto For =,, (L/U tato, Proce tato X ad Y) = j k t jk Whe = (Materal hadlg ytem) t + = t + f jk j f jk t = j k = (+)0.+5x0.+5x0.+5x0. = 5 m. j = 5x0.+0x0.+0x0.+0x0. = 7.5 m. = 5x0.+0x0.+0x0.+5x0. =.5 m. For = t = [Every roduct ha ame umber of trafer] = x.5 = 0.5 m. Bottleeck tato tato (Aumg oe erver er tato) Requred Producto rate = art er hour Number of erver requred at each tato S = mmum teger R ( ) R = art er hr. = 0. art er m. For tato,,, ad R value are, 5.5,.7 ad. reectvely. Therefore, umber of erver at tato,,, ad are, 6,, ad reectvely. Utlato U = R = ( R ) Idetfcato of bottleeck tato Bottleeck tato = max for the tato are 5,.58,.5 ad.5 reectvely. S Bottleeck tato tato Performace meaure of FMS 79 March 0

9 Natoal Ittute of Techology Calcut Deartmet of Mechacal Egeerg R = 0. The utlato for tato, 0.9, 0.9 ad 0.7 reectvely. MLT = = 56.5 m N = R + + = = N = =. R ( MLT ) MLT whe 8 tem the ytem = MLT = 56.5 m A FMS Szg roblem A FMS ued to roduce four art. The FMS cot of oe load/uload tato ad two automated roceg tato (rocee X ad Y). The FMS alo clude a automated coveyor ytem wth cart to traort art betwee erver. The cart move the art from oe erver to the ext, dro them off, ad roceed to the ext delvery tak. Average tme requred er trafer.5 m. All art follow the ame routg, whch. The roduct demad ad roceg tme at each tato are reeted below a table. Aume that each oerato carred out oly oce for a tem. Product Demad Stato tme Stato tme Stato tme Stato tme (M.) (M.) (M.) (M.) A B C D Determe the workload of each tato (cludg the materal hadlg ytem) ad detfy the bottleeck tato. Requred roducto 0 art er hour dtrbuted accordg to the roduct mx dcated. Ue the bottleeck model to determe: (a) the mmum umber of erver at each tato ad the mmum umber of cart the traort ytem that are requred to atfy roducto demad ad (b) the utlato of each tato for the above awer. Soluto For =,, (L/U tato, Proce tato X ad Y) = j k t jk f jk Whe = (Materal hadlg ytem) t + = t + j Performace meaure of FMS 80 March 0

10 Natoal Ittute of Techology Calcut Deartmet of Mechacal Egeerg t = j f jk j k Product A B C D Demad P j = (+)0.+5x0.+5x0.+5x0. = 5 m. = 5x0.+0x0.+0x0.+0x0. = 7.5 m. = 5x0.+0x0.+0x0.+5x0. =.5 m. For = t = [Every roduct ha ame umber of trafer] = x.5 = 0.5 m. Bottleeck tato tato (Aumg oe erver er tato) Requred Producto rate = 0 art er hour Number of erver requred at each tato S = mmum teger R ( R = 0 art er hr. = /6 art er m. For tato,,, ad ) R value are 0.8,.58,.5 ad.75 reectvely. Therefore, umber of erver at tato,,, ad are, 5,, ad reectvely. Utlzato U = R = ( R ) Idetfcato of bottleeck tato Bottleeck tato = max Performace meaure of FMS 8 March 0

11 Natoal Ittute of Techology Calcut Deartmet of Mechacal Egeerg S for the tato are 5, 5.5,.5 ad 5.5 reectvely. Bottleeck tato tato R = 0.8 The utlzato for tato 0.9,, 0.8 ad 0.95 reectvely. QUESTIONS:. A FMS ued to roduce four art. The FMS cot of oe load/uload tato ad two automated roceg tato (rocee X ad Y). The FMS alo clude a automated coveyor ytem wth cart to traort art betwee erver. The cart move the art from oe erver to the ext, dro them off, ad roceed to the ext delvery tak. The umber of erver at each tato oe. Aume oly oe cart to traort. Average tme requred er trafer.5 m. The tato equece for the art to roce gve the Table. For examle, art A ue the tato the equece. The art demad ad roceg tme at each tato are gve Table. Aume that each oerato carred out oly oce for a tem. Table Stato equece requred for varou art Part Routg equece (Stato) A B C D Table Part demad ad roceg tme Part Demad Stato tme Stato tme Stato tme Stato tme (M.) (M.) (M.) (M.) A B C D Determe the workload of each tato (cludg the materal hadlg ytem) ad detfy the bottleeck tato. Alo calculate average maufacturg lead tme whe the ytem below crtcal codto.. A FMS cot of three tato lu a load/uload tato. Stato load ad uload art from the FMS ug two erver (materal hadlg worker). Stato erform horzotal mllg oerato wth two erver (two detcal CNC horzotal mllg mache). Stato erform vertcal mllg oerato wth three erver (three detcal CNC vertcal mllg mache). Stato erform drllg oerato wth two erver (two detcal drll ree). The mache are coected by a art hadlg ytem that ha two work carrer ad a mea traort tme =.5 mute. The FMS roduce four art A, B, C, ad D, whoe art mx fracto ad roce routg are reeted the table below. Aume that each oerato carred out oly oce for a Performace meaure of FMS 8 March 0

12 Natoal Ittute of Techology Calcut Deartmet of Mechacal Egeerg tem. At ay tme, there are art the ytem. Determe the average maufacturg lead-tme ad average watg tme of art. Part Part Mx Fracto Oerato Number Decrto A 0. Load H. Mll V. Mll Drll 5 Uload B 0. Load Drll H. Mll V. Mll 5 Drll 6 Uload C 0.5 D 0.5 Load H. Mll Drll Uload Load V. Mll Drll Uload Stato Proce Tme (m.) A FMS ued to roduce four art. The FMS cot of oe load/uload tato ad two automated roceg tato (rocee X ad Y). The FMS alo clude a automated coveyor ytem wth cart to traort art betwee erver. The cart move the art from oe erver to the ext, dro them off, ad roceed to the ext delvery tak. Average tme requred er trafer.5 m. All art follow the ame routg, whch. The roduct demad ad roceg tme at each tato are reeted below the table. Aume that each oerato carred out oly oce for a tem. Stato tme Stato tme Stato tme Stato tme Product Demad (M.) (M.) (M.) (M.) A B C D Determe the workload of each tato (cludg the materal hadlg ytem) ad detfy the bottleeck tato. Aume that every tato ha oe erver.. A FMS ued to roduce four art. The FMS cot of oe load/uload tato ad two automated roceg tato (rocee X ad Y). The FMS alo clude a automated coveyor ytem wth dvdual cart to traort art betwee erver. The cart move the art from oe erver to the ext, dro them off, ad roceed to the ext Performace meaure of FMS 8 March 0

13 Natoal Ittute of Techology Calcut Deartmet of Mechacal Egeerg delvery tak. Average tme requred er trafer.5 m. The followg table ummarze the cofgurato of FMS: Stato Number of erver Stato Load ad uload Stato Proce X 6 Stato Proce Y Stato Traort ytem All art have ame umber of oerato ad the art ue tato ad oly oce. The roduct mx ad roceg tme at each tato are reeted the table below: Product Product mx Stato (m) Stato (m) Stato (m) Stato (m) A B C D Determe the crtcal level of work--roce. Alo, determe the maufacturg lead tme whe tem the ytem. 5. A FMS ued to roduce four art. The FMS cot of oe load/uload tato ad two automated roceg tato (rocee X ad Y). The umber of erver for each tato tye to be determed. The FMS alo clude a automated coveyor ytem wth dvdual cart to traort art betwee erver. The cart move the art from oe erver to the ext, dro them off, ad roceed to the ext delvery tak. Average tme requred er trafer.5 m. The followg table ummarze the FMS: Stato Load ad uload Stato Proce X Stato Proce Y Stato Traort ytem Number of huma erver (worker) to be determed Number of automated erver to be determed Number of automated erver to be determed Number of cart to be determed All art follow the ame routg, whch Aume that each oerato carred out oly oce for a tem. The roduct mx ad roceg tme at each tato are reeted the table below: Product Product mx Stato (m) Stato (m) Stato (m) Stato (m) A B C D Performace meaure of FMS 8 March 0

14 Natoal Ittute of Techology Calcut Deartmet of Mechacal Egeerg Requred roducto art/hr, whch dtrbuted accordg to the roduct mx dcated. Ue the bottleeck model to determe: () the mmum umber of erver at each tato ad the mmum umber of cart the traort ytem thoe are requred to atfy roducto demad ad () the utlzato of each tato for the awer above. Ue arorate model to determe the followg: () the crtcal value of work--roce vetory (v) the maufacturg lead tme f there are 8 tem the ytem? 6. A flexble maufacturg cell cot of two machg worktato lu a load/uload tato. The load/uload tato tato. Stato erform mllg oerato ad cot of oe erver (oe CNC mllg mache). Stato ha oe erver that erform drllg (oe CNC drll re). The three tato are coected by a art hadlg ytem that ha three work carrer. The mea traort tme.5 m. The FMC roduce three art, A, B, ad C. The art mx fracto ad roce routg for the three art are rereeted the table below. The oerato frequecy =.0 for all oerato. Determe: () maxmum roducto rate of the FMC, () correodg roducto rate of each roduct, () utlzato of each mache the ytem, ad (v) umber of buy erver at each tato. Wrte dow the aumto the above tuato. Alo comute the roducto rate, maufacturg lead-tme, ad watg tme whe the ytem cota ad 6 ut. Part Part Mx Fracto Oerato Decrto Stato Proce Tme (m.) A 0. Load Mll Drll Uload 0 B 0. Load Mll Drll Uload 5 0 C 0.5 Load Drll Mll Uload 7. A FMS ued to roduce three roduct. The FMS cot of a load/uload tato, two automated roceg tato, a ecto tato, ad a automated coveyor ytem wth a dvdual cart for each roduct. The coveyor cart rema wth the art durg ther tme the ytem, ad therefore the mea traort tme clude ot oly the move tme, but alo the average total roceg tme er art. The umber of erver at each tato gve the followg table: Performace meaure of FMS 85 March 0

15 Natoal Ittute of Techology Calcut Deartmet of Mechacal Egeerg Stato Stato Stato Proce X erver Stato Proce Y erver Number of erver Load ad uload worker Stato Iecto erver Traort ytem Coveyor 8 carrer All art follow ether of two routg, whch are or, the dfferece beg that ecto at tato are erformed o oly oe art four for each roduct ( f = 0.5). The roduct mx ad roce tme for the art are reeted the table below: jk Product j Part mx j Stato Stato Stato Stato Stato (m) (m) (m) (m) (m) A B C The move tme betwee tato m. Ug the bottleeck model, how that the coveyor ytem the bottleeck the reet FMS cofgurato, ad determe the overall roducto rate of the ytem. Determe how may cart are requred to elmate the coveyor ytem a the bottleeck. Performace meaure of FMS 86 March 0

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

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

2. Independence and Bernoulli Trials

2. Independence and Bernoulli Trials . Ideedece ad Beroull Trals Ideedece: Evets ad B are deedet f B B. - It s easy to show that, B deedet mles, B;, B are all deedet ars. For examle, ad so that B or B B B B B φ,.e., ad B are deedet evets.,

More information

Simple Linear Regression Analysis

Simple Linear Regression Analysis LINEAR REGREION ANALYSIS MODULE II Lecture - 5 Smple Lear Regreo Aaly Dr Shalabh Departmet of Mathematc Stattc Ida Ittute of Techology Kapur Jot cofdece rego for A jot cofdece rego for ca alo be foud Such

More information

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

f f... f 1 n n (ii) Median : It is the value of the middle-most observation(s).

f f... f 1 n n (ii) Median : It is the value of the middle-most observation(s). CHAPTER STATISTICS Pots to Remember :. Facts or fgures, collected wth a defte pupose, are called Data.. Statstcs s the area of study dealg wth the collecto, presetato, aalyss ad terpretato of data.. The

More information

European Journal of Mathematics and Computer Science Vol. 5 No. 2, 2018 ISSN

European Journal of Mathematics and Computer Science Vol. 5 No. 2, 2018 ISSN Europea Joural of Mathematc ad Computer Scece Vol. 5 o., 018 ISS 059-9951 APPLICATIO OF ASYMPTOTIC DISTRIBUTIO OF MA-HITEY STATISTIC TO DETERMIE THE DIFFERECE BETEE THE SYSTOLIC BLOOD PRESSURE OF ME AD

More information

10.2 Series. , we get. which is called an infinite series ( or just a series) and is denoted, for short, by the symbol. i i n

10.2 Series. , we get. which is called an infinite series ( or just a series) and is denoted, for short, by the symbol. i i n 0. Sere I th ecto, we wll troduce ere tht wll be dcug for the ret of th chpter. Wht ere? If we dd ll term of equece, we get whch clled fte ere ( or jut ere) d deoted, for hort, by the ymbol or Doe t mke

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

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Aalyss of Varace ad Desg of Exermets-I MODULE II LECTURE - GENERAL LINEAR HYPOTHESIS AND ANALYSIS OF VARIANCE Dr Shalabh Deartmet of Mathematcs ad Statstcs Ida Isttute of Techology Kaur Tukey s rocedure

More information

New Trade Theory (1979)

New Trade Theory (1979) Ne Trade Theory 979 Ne Trade Theory Krugma, 979: - Ecoomes of scale as reaso for trade - Elas trade betee smlar coutres Ituto of model: There s a trade-off betee ecoomes of scale the roducto of good tyes

More information

European Journal of Mathematics and Computer Science Vol. 5 No. 2, 2018 ISSN

European Journal of Mathematics and Computer Science Vol. 5 No. 2, 2018 ISSN Europea Joural of Mathematc ad Computer Scece Vol. 5 o., 018 ISS 059-9951 APPLICATIO OF ASYMPTOTIC DISTRIBUTIO OF MA-HITEY STATISTIC TO DETERMIE THE DIFFERECE BETEE THE SYSTOLIC BLOOD PRESSURE OF ME AD

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

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION I lear regreo, we coder the frequecy dtrbuto of oe varable (Y) at each of everal level of a ecod varable (X). Y kow a the depedet varable. The

More information

å 1 13 Practice Final Examination Solutions - = CS109 Dec 5, 2018

å 1 13 Practice Final Examination Solutions - = CS109 Dec 5, 2018 Chrs Pech Fal Practce CS09 Dec 5, 08 Practce Fal Examato Solutos. Aswer: 4/5 8/7. There are multle ways to obta ths aswer; here are two: The frst commo method s to sum over all ossbltes for the rak of

More information

On a Truncated Erlang Queuing System. with Bulk Arrivals, Balking and Reneging

On a Truncated Erlang Queuing System. with Bulk Arrivals, Balking and Reneging Appled Mathematcal Scece Vol. 3 9 o. 3 3-3 O a Trucated Erlag Queug Sytem wth Bul Arrval Balg ad Reegg M. S. El-aoumy ad M. M. Imal Departmet of Stattc Faculty Of ommerce Al- Azhar Uverty. Grl Brach Egypt

More information

[Houessouvo, 4(10): October, 2015] ISSN: (I2OR), Publication Impact Factor: 3.785

[Houessouvo, 4(10): October, 2015] ISSN: (I2OR), Publication Impact Factor: 3.785 IJESRT ITERATIOAL JOURAL OF EGIEERIG SCIECES & RESEARCH TECHOLOGY MODELIG UDER MATLAB OF THE FUCTIOAL AVAILABILITY OF A MEDICAL DEVICE BY A PROCESS OF MARKOV D Medeou, R C Houeouvo*, G Dega, T R Joou,

More information

CS473-Algorithms I. Lecture 12b. Dynamic Tables. CS 473 Lecture X 1

CS473-Algorithms I. Lecture 12b. Dynamic Tables. CS 473 Lecture X 1 CS473-Algorthm I Lecture b Dyamc Table CS 473 Lecture X Why Dyamc Table? I ome applcato: We do't kow how may object wll be tored a table. We may allocate pace for a table But, later we may fd out that

More information

Problem Set 3: Model Solutions

Problem Set 3: Model Solutions Ecoomc 73 Adaced Mcroecoomc Problem et 3: Model oluto. Coder a -bdder aucto wth aluato deedetly ad detcally dtrbuted accordg to F( ) o uort [,]. Let the hghet bdder ay the rce ( - k)b f + kb to the eller,

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

Introduction to Econometrics (3 rd Updated Edition, Global Edition) Solutions to Odd-Numbered End-of-Chapter Exercises: Chapter 9

Introduction to Econometrics (3 rd Updated Edition, Global Edition) Solutions to Odd-Numbered End-of-Chapter Exercises: Chapter 9 Itroducto to Ecoometrcs (3 rd Udated Edto, Global Edto) by James H. Stock ad Mark W. Watso Solutos to Odd-Numbered Ed-of-Chater Exercses: Chater 9 (Ths verso August 7, 04) 05 Pearso Educato, Ltd. Stock/Watso

More information

INEQUALITIES USING CONVEX COMBINATION CENTERS AND SET BARYCENTERS

INEQUALITIES USING CONVEX COMBINATION CENTERS AND SET BARYCENTERS Joural of Mathematcal Scece: Advace ad Alcato Volume 24, 23, Page 29-46 INEQUALITIES USING CONVEX COMBINATION CENTERS AND SET BARYCENTERS ZLATKO PAVIĆ Mechacal Egeerg Faculty Slavok Brod Uverty of Ojek

More information

Trignometric Inequations and Fuzzy Information Theory

Trignometric Inequations and Fuzzy Information Theory Iteratoal Joural of Scetfc ad Iovatve Mathematcal Reearch (IJSIMR) Volume, Iue, Jauary - 0, PP 00-07 ISSN 7-07X (Prt) & ISSN 7- (Ole) www.arcjoural.org Trgometrc Iequato ad Fuzzy Iformato Theory P.K. Sharma,

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

THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Series A, OF THE ROMANIAN ACADEMY Volume 6, Number 1/2005, pp

THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Series A, OF THE ROMANIAN ACADEMY Volume 6, Number 1/2005, pp THE PUBLISHING HOUSE PROCEEDINGS OF THE ROANIAN ACADEY, Sere A, OF THE ROANIAN ACADEY Volume 6, Number /005,. 000-000 ON THE TRANSCENDENCE OF THE TRACE FUNCTION Vctor ALEXANDRU Faculty o athematc, Uverty

More information

Econometric Methods. Review of Estimation

Econometric Methods. Review of Estimation Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators

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

1. a. Houston Chronicle, Des Moines Register, Chicago Tribune, Washington Post

1. a. Houston Chronicle, Des Moines Register, Chicago Tribune, Washington Post Homework Soluto. Houto Chrocle, De Moe Regter, Chcago Trbue, Wahgto Pot b. Captal Oe, Campbell Soup, Merrll Lych, Pultzer c. Bll Japer, Kay Reke, Hele Ford, Davd Meedez d..78,.44, 3.5, 3.04 5. No, the

More information

Channel Models with Memory. Channel Models with Memory. Channel Models with Memory. Channel Models with Memory

Channel Models with Memory. Channel Models with Memory. Channel Models with Memory. Channel Models with Memory Chael Models wth Memory Chael Models wth Memory Hayder radha Electrcal ad Comuter Egeerg Mchga State Uversty I may ractcal etworkg scearos (cludg the Iteret ad wreless etworks), the uderlyg chaels are

More information

An Unbiased Class of Ratio Type Estimator for Population Mean Using an Attribute and a Variable

An Unbiased Class of Ratio Type Estimator for Population Mean Using an Attribute and a Variable Advace Comutatoal Scece ad Techology ISS 973-67 Volume, umber 7). 39-46 Reearch Ida Publcato htt://www.rublcato.com A Ubaed Cla of Rato Tye Etmator for Poulato Mea Ug a Attrbute ad a Varable Shah Bhuha,

More information

Computations with large numbers

Computations with large numbers Comutatos wth large umbers Wehu Hog, Det. of Math, Clayto State Uversty, 2 Clayto State lvd, Morrow, G 326, Mgshe Wu, Det. of Mathematcs, Statstcs, ad Comuter Scece, Uversty of Wscos-Stout, Meomoe, WI

More information

A Primer on Summation Notation George H Olson, Ph. D. Doctoral Program in Educational Leadership Appalachian State University Spring 2010

A Primer on Summation Notation George H Olson, Ph. D. Doctoral Program in Educational Leadership Appalachian State University Spring 2010 Summato Operator A Prmer o Summato otato George H Olso Ph D Doctoral Program Educatoal Leadershp Appalacha State Uversty Sprg 00 The summato operator ( ) {Greek letter captal sgma} s a structo to sum over

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

A Result of Convergence about Weighted Sum for Exchangeable Random Variable Sequence in the Errors-in-Variables Model

A Result of Convergence about Weighted Sum for Exchangeable Random Variable Sequence in the Errors-in-Variables Model AMSE JOURNALS-AMSE IIETA publcato-17-sere: Advace A; Vol. 54; N ; pp 3-33 Submtted Mar. 31, 17; Reved Ju. 11, 17, Accepted Ju. 18, 17 A Reult of Covergece about Weghted Sum for Exchageable Radom Varable

More information

( ) Thermal noise ktb (T is absolute temperature in kelvin, B is bandwidth, k is Boltzamann s constant) Shot noise

( ) Thermal noise ktb (T is absolute temperature in kelvin, B is bandwidth, k is Boltzamann s constant) Shot noise OISE Thermal oe ktb (T abolute temperature kelv, B badwdth, k Boltzama cotat) 3 k.38 0 joule / kelv ( joule /ecod watt) ( ) v ( freq) 4kTB Thermal oe refer to the ketc eergy of a body of partcle a a reult

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

Multiple Choice Test. Chapter Adequacy of Models for Regression

Multiple Choice Test. Chapter Adequacy of Models for Regression Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to

More information

Chapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements

Chapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements Aoucemets No-Parametrc Desty Estmato Techques HW assged Most of ths lecture was o the blacboard. These sldes cover the same materal as preseted DHS Bometrcs CSE 90-a Lecture 7 CSE90a Fall 06 CSE90a Fall

More information

Ideal multigrades with trigonometric coefficients

Ideal multigrades with trigonometric coefficients Ideal multgrades wth trgoometrc coeffcets Zarathustra Brady December 13, 010 1 The problem A (, k) multgrade s defed as a par of dstct sets of tegers such that (a 1,..., a ; b 1,..., b ) a j = =1 for all

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

Quantum Plain and Carry Look-Ahead Adders

Quantum Plain and Carry Look-Ahead Adders Quatum Pla ad Carry Look-Ahead Adders Ka-We Cheg u8984@cc.kfust.edu.tw Che-Cheg Tseg tcc@ccms.kfust.edu.tw Deartmet of Comuter ad Commucato Egeerg, Natoal Kaohsug Frst Uversty of Scece ad Techology, Yechao,

More information

DTS5322-SC01: SC01: Control Systems

DTS5322-SC01: SC01: Control Systems DTS53-SC0: SC0: Cotrol Sytem Be M. Che Profeor Deartmet of Electrcal & Comuter Egeerg Natoal Uverty of Sgaore Phoe: 656-89 Offce: E4-06 06-0808 Emal: bmche@u.edu.g ~ Webte: htt://www.bmche.et Lat Udated:

More information

ON BIVARIATE GEOMETRIC DISTRIBUTION. K. Jayakumar, D.A. Mundassery 1. INTRODUCTION

ON BIVARIATE GEOMETRIC DISTRIBUTION. K. Jayakumar, D.A. Mundassery 1. INTRODUCTION STATISTICA, ao LXVII, 4, 007 O BIVARIATE GEOMETRIC DISTRIBUTIO ITRODUCTIO Probablty dstrbutos of radom sums of deedetly ad detcally dstrbuted radom varables are maly aled modelg ractcal roblems that deal

More information

Theory study about quarter-wave-stack dielectric mirrors

Theory study about quarter-wave-stack dielectric mirrors Theor tud about quarter-wave-tack delectrc rror Stratfed edu tratted reflected reflected Stratfed edu tratted cdet cdet T T Frt, coder a wave roagato a tratfed edu. A we kow, a arbtrarl olared lae wave

More information

Parallel Programming: Speedups and Amdahl s law. Definition of Speedup

Parallel Programming: Speedups and Amdahl s law. Definition of Speedup Programmg: Seedus ad Amdahl s law Mke Baley mjb@cs.oregostate.edu Orego State Uversty Orego State Uversty Comuter Grahcs seedus.ad.amdahls.law.tx Defto of Seedu 2 If you are usg rocessors, your Seedu s:

More information

Lecture 25 Highlights Phys 402

Lecture 25 Highlights Phys 402 Lecture 5 Hhlht Phy 40 e are ow o to coder the tattcal mechac of quatum ytem. I partcular we hall tudy the macrocopc properte of a collecto of may (N ~ 0 detcal ad dtuhable Fermo ad Boo wth overlapp wavefucto.

More information

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits Block-Based Compact hermal Modelg of Semcoductor Itegrated Crcuts Master s hess Defese Caddate: Jg Ba Commttee Members: Dr. Mg-Cheg Cheg Dr. Daqg Hou Dr. Robert Schllg July 27, 2009 Outle Itroducto Backgroud

More information

Introducing Sieve of Eratosthenes as a Theorem

Introducing Sieve of Eratosthenes as a Theorem ISSN(Ole 9-8 ISSN (Prt - Iteratoal Joural of Iovatve Research Scece Egeerg ad echolog (A Hgh Imact Factor & UGC Aroved Joural Webste wwwrsetcom Vol Issue 9 Setember Itroducg Seve of Eratosthees as a heorem

More information

Linear Approximating to Integer Addition

Linear Approximating to Integer Addition Lear Approxmatg to Iteger Addto L A-Pg Bejg 00085, P.R. Cha apl000@a.com Abtract The teger addto ofte appled cpher a a cryptographc mea. I th paper we wll preet ome reult about the lear approxmatg for

More information

Lecture 1. (Part II) The number of ways of partitioning n distinct objects into k distinct groups containing n 1,

Lecture 1. (Part II) The number of ways of partitioning n distinct objects into k distinct groups containing n 1, Lecture (Part II) Materals Covered Ths Lecture: Chapter 2 (2.6 --- 2.0) The umber of ways of parttog dstct obects to dstct groups cotag, 2,, obects, respectvely, where each obect appears exactly oe group

More information

Factorization of Finite Abelian Groups

Factorization of Finite Abelian Groups Iteratoal Joural of Algebra, Vol 6, 0, o 3, 0-07 Factorzato of Fte Abela Grous Khald Am Uversty of Bahra Deartmet of Mathematcs PO Box 3038 Sakhr, Bahra kamee@uobedubh Abstract If G s a fte abela grou

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

Third handout: On the Gini Index

Third handout: On the Gini Index Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The

More information

Queueing Theory (Part 3)

Queueing Theory (Part 3) Queueig Theory art 3 M/M/ Queueig Sytem with Variatio M/M/, M/M///K, M/M//// Queueig Theory- M/M/ Queueig Sytem We defie λ mea arrival rate mea ervice rate umber of erver > ρ λ / utilizatio ratio We require

More information

Test Paper-II. 1. If sin θ + cos θ = m and sec θ + cosec θ = n, then (a) 2n = m (n 2 1) (b) 2m = n (m 2 1) (c) 2n = m (m 2 1) (d) none of these

Test Paper-II. 1. If sin θ + cos θ = m and sec θ + cosec θ = n, then (a) 2n = m (n 2 1) (b) 2m = n (m 2 1) (c) 2n = m (m 2 1) (d) none of these Test Paer-II. If s θ + cos θ = m ad sec θ + cosec θ =, the = m ( ) m = (m ) = m (m ). If a ABC, cos A = s B, the t s C a osceles tragle a eulateral tragle a rght agled tragle. If cos B = cos ( A+ C), the

More information

PTAS for Bin-Packing

PTAS for Bin-Packing CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,

More information

Some distances and sequences in a weighted graph

Some distances and sequences in a weighted graph IOSR Joural of Mathematc (IOSR-JM) e-issn: 78-578 p-issn: 19 765X PP 7-15 wwworjouralorg Some dtace ad equece a weghted graph Jll K Mathew 1, Sul Mathew Departmet of Mathematc Federal Ittute of Scece ad

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

CHAPTER 6. d. With success = observation greater than 10, x = # of successes = 4, and

CHAPTER 6. d. With success = observation greater than 10, x = # of successes = 4, and CHAPTR 6 Secto 6.. a. We use the samle mea, to estmate the oulato mea µ. Σ 9.80 µ 8.407 7 ~ 7. b. We use the samle meda, 7 (the mddle observato whe arraged ascedg order. c. We use the samle stadard devato,

More information

On the characteristics of partial differential equations

On the characteristics of partial differential equations Sur les caractérstques des équatos au dérvées artelles Bull Soc Math Frace 5 (897) 8- O the characterstcs of artal dfferetal equatos By JULES BEUDON Traslated by D H Delhech I a ote that was reseted to

More information

Reaction Time VS. Drug Percentage Subject Amount of Drug Times % Reaction Time in Seconds 1 Mary John Carl Sara William 5 4

Reaction Time VS. Drug Percentage Subject Amount of Drug Times % Reaction Time in Seconds 1 Mary John Carl Sara William 5 4 CHAPTER Smple Lear Regreo EXAMPLE A expermet volvg fve ubject coducted to determe the relatohp betwee the percetage of a certa drug the bloodtream ad the legth of tme t take the ubject to react to a tmulu.

More information

D KL (P Q) := p i ln p i q i

D KL (P Q) := p i ln p i q i Cheroff-Bouds 1 The Geeral Boud Let P 1,, m ) ad Q q 1,, q m ) be two dstrbutos o m elemets, e,, q 0, for 1,, m, ad m 1 m 1 q 1 The Kullback-Lebler dvergece or relatve etroy of P ad Q s defed as m D KL

More information

Wu-Hausman Test: But if X and ε are independent, βˆ. ECON 324 Page 1

Wu-Hausman Test: But if X and ε are independent, βˆ. ECON 324 Page 1 Wu-Hausma Test: Detectg Falure of E( ε X ) Caot drectly test ths assumpto because lack ubased estmator of ε ad the OLS resduals wll be orthogoal to X, by costructo as ca be see from the momet codto X'

More information

MATH 247/Winter Notes on the adjoint and on normal operators.

MATH 247/Winter Notes on the adjoint and on normal operators. MATH 47/Wter 00 Notes o the adjot ad o ormal operators I these otes, V s a fte dmesoal er product space over, wth gve er * product uv, T, S, T, are lear operators o V U, W are subspaces of V Whe we say

More information

Bayes (Naïve or not) Classifiers: Generative Approach

Bayes (Naïve or not) Classifiers: Generative Approach Logstc regresso Bayes (Naïve or ot) Classfers: Geeratve Approach What do we mea by Geeratve approach: Lear p(y), p(x y) ad the apply bayes rule to compute p(y x) for makg predctos Ths s essetally makg

More information

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class)

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class) Assgmet 5/MATH 7/Wter 00 Due: Frday, February 9 class (!) (aswers wll be posted rght after class) As usual, there are peces of text, before the questos [], [], themselves. Recall: For the quadratc form

More information

Mu Sequences/Series Solutions National Convention 2014

Mu Sequences/Series Solutions National Convention 2014 Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed

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

T-DOF PID Controller Design using Characteristic Ratio Assignment Method for Quadruple Tank Process

T-DOF PID Controller Design using Characteristic Ratio Assignment Method for Quadruple Tank Process World Academy of Scece, Egeerg ad Techology Iteratoal Joural of Electrcal ad Iformato Egeerg Vol:, No:, 7 T-DOF PID Cotroller Deg ug Charactertc Rato Agmet Method for Quadruple Tak Proce Tacha Sukr, U-tha

More information

Mean is only appropriate for interval or ratio scales, not ordinal or nominal.

Mean is only appropriate for interval or ratio scales, not ordinal or nominal. Mea Same as ordary average Sum all the data values ad dvde by the sample sze. x = ( x + x +... + x Usg summato otato, we wrte ths as x = x = x = = ) x Mea s oly approprate for terval or rato scales, ot

More information

L5 Polynomial / Spline Curves

L5 Polynomial / Spline Curves L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a

More information

Two Fuzzy Probability Measures

Two Fuzzy Probability Measures Two Fuzzy robablty Measures Zdeěk Karíšek Isttute of Mathematcs Faculty of Mechacal Egeerg Bro Uversty of Techology Techcká 2 66 69 Bro Czech Reublc e-mal: karsek@umfmevutbrcz Karel Slavíček System dmstrato

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

A Method for Damping Estimation Based On Least Square Fit

A Method for Damping Estimation Based On Least Square Fit Amerca Joural of Egeerg Research (AJER) 5 Amerca Joural of Egeerg Research (AJER) e-issn: 3-847 p-issn : 3-936 Volume-4, Issue-7, pp-5-9 www.ajer.org Research Paper Ope Access A Method for Dampg Estmato

More information

The Performance of Feedback Control Systems

The Performance of Feedback Control Systems The Performace of Feedbac Cotrol Sytem Objective:. Secify the meaure of erformace time-domai the firt te i the deig roce Percet overhoot / Settlig time T / Time to rie / Steady-tate error e. ut igal uch

More information

The Selection Problem - Variable Size Decrease/Conquer (Practice with algorithm analysis)

The Selection Problem - Variable Size Decrease/Conquer (Practice with algorithm analysis) We have covered: Selecto, Iserto, Mergesort, Bubblesort, Heapsort Next: Selecto the Qucksort The Selecto Problem - Varable Sze Decrease/Coquer (Practce wth algorthm aalyss) Cosder the problem of fdg the

More information

SPECIAL CONSIDERATIONS FOR VOLUMETRIC Z-TEST FOR PROPORTIONS

SPECIAL CONSIDERATIONS FOR VOLUMETRIC Z-TEST FOR PROPORTIONS SPECIAL CONSIDERAIONS FOR VOLUMERIC Z-ES FOR PROPORIONS Oe s stctve reacto to the questo of whether two percetages are sgfcatly dfferet from each other s to treat them as f they were proportos whch the

More information

means the first term, a2 means the term, etc. Infinite Sequences: follow the same pattern forever.

means the first term, a2 means the term, etc. Infinite Sequences: follow the same pattern forever. 9.4 Sequeces ad Seres Pre Calculus 9.4 SEQUENCES AND SERIES Learg Targets:. Wrte the terms of a explctly defed sequece.. Wrte the terms of a recursvely defed sequece. 3. Determe whether a sequece s arthmetc,

More information

The internal structure of natural numbers, one method for the definition of large prime numbers, and a factorization test

The internal structure of natural numbers, one method for the definition of large prime numbers, and a factorization test Fal verso The teral structure of atural umbers oe method for the defto of large prme umbers ad a factorzato test Emmaul Maousos APM Isttute for the Advacemet of Physcs ad Mathematcs 3 Poulou str. 53 Athes

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

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

Unsupervised Learning and Other Neural Networks

Unsupervised Learning and Other Neural Networks CSE 53 Soft Computg NOT PART OF THE FINAL Usupervsed Learg ad Other Neural Networs Itroducto Mture Destes ad Idetfablty ML Estmates Applcato to Normal Mtures Other Neural Networs Itroducto Prevously, all

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

Lecture 9. Some Useful Discrete Distributions. Some Useful Discrete Distributions. The observations generated by different experiments have

Lecture 9. Some Useful Discrete Distributions. Some Useful Discrete Distributions. The observations generated by different experiments have NM 7 Lecture 9 Some Useful Dscrete Dstrbutos Some Useful Dscrete Dstrbutos The observatos geerated by dfferet eermets have the same geeral tye of behavor. Cosequetly, radom varables assocated wth these

More information

Median as a Weighted Arithmetic Mean of All Sample Observations

Median as a Weighted Arithmetic Mean of All Sample Observations Meda as a Weghted Arthmetc Mea of All Sample Observatos SK Mshra Dept. of Ecoomcs NEHU, Shllog (Ida). Itroducto: Iumerably may textbooks Statstcs explctly meto that oe of the weakesses (or propertes) of

More information

Formulas and Tables from Beginning Statistics

Formulas and Tables from Beginning Statistics Fmula ad Table from Begg Stattc Chater Cla Mdot Relatve Frequecy Chater 3 Samle Mea Poulato Mea Weghted Mea Rage Lower Lmt Uer Lmt Cla Frequecy Samle Se µ ( w) w f Mamum Data Value - Mmum Data Value Poulato

More information

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set.

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set. Addtoal Decrease ad Coquer Algorthms For combatoral problems we mght eed to geerate all permutatos, combatos, or subsets of a set. Geeratg Permutatos If we have a set f elemets: { a 1, a 2, a 3, a } the

More information

Module 7: Probability and Statistics

Module 7: Probability and Statistics Lecture 4: Goodess of ft tests. Itroducto Module 7: Probablty ad Statstcs I the prevous two lectures, the cocepts, steps ad applcatos of Hypotheses testg were dscussed. Hypotheses testg may be used to

More information

Section l h l Stem=Tens. 8l Leaf=Ones. 8h l 03. 9h 58

Section l h l Stem=Tens. 8l Leaf=Ones. 8h l 03. 9h 58 Secto.. 6l 34 6h 667899 7l 44 7h Stem=Tes 8l 344 Leaf=Oes 8h 5557899 9l 3 9h 58 Ths dsplay brgs out the gap the data: There are o scores the hgh 7's. 6. a. beams cylders 9 5 8 88533 6 6 98877643 7 488

More information

MATH 371 Homework assignment 1 August 29, 2013

MATH 371 Homework assignment 1 August 29, 2013 MATH 371 Homework assgmet 1 August 29, 2013 1. Prove that f a subset S Z has a smallest elemet the t s uque ( other words, f x s a smallest elemet of S ad y s also a smallest elemet of S the x y). We kow

More information

F. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties

F. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties 進佳數學團隊 Dr. Herbert Lam 林康榮博士 HKAL Pure Mathematcs F. Ieualtes. Basc propertes Theorem Let a, b, c be real umbers. () If a b ad b c, the a c. () If a b ad c 0, the ac bc, but f a b ad c 0, the ac bc. Theorem

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

Evaluating Polynomials

Evaluating Polynomials Uverst of Nebraska - Lcol DgtalCommos@Uverst of Nebraska - Lcol MAT Exam Expostor Papers Math the Mddle Isttute Partershp 7-7 Evaluatg Polomals Thomas J. Harrgto Uverst of Nebraska-Lcol Follow ths ad addtoal

More information

ESS Line Fitting

ESS Line Fitting ESS 5 014 17. Le Fttg A very commo problem data aalyss s lookg for relatoshpetwee dfferet parameters ad fttg les or surfaces to data. The smplest example s fttg a straght le ad we wll dscuss that here

More information

We have already referred to a certain reaction, which takes place at high temperature after rich combustion.

We have already referred to a certain reaction, which takes place at high temperature after rich combustion. ME 41 Day 13 Topcs Chemcal Equlbrum - Theory Chemcal Equlbrum Example #1 Equlbrum Costats Chemcal Equlbrum Example #2 Chemcal Equlbrum of Hot Bured Gas 1. Chemcal Equlbrum We have already referred to a

More information

Quiz 1- Linear Regression Analysis (Based on Lectures 1-14)

Quiz 1- Linear Regression Analysis (Based on Lectures 1-14) Quz - Lear Regreo Aaly (Baed o Lecture -4). I the mple lear regreo model y = β + βx + ε, wth Tme: Hour Ε ε = Ε ε = ( ) 3, ( ), =,,...,, the ubaed drect leat quare etmator ˆβ ad ˆβ of β ad β repectvely,

More information

Scheduling Jobs with a Common Due Date via Cooperative Game Theory

Scheduling Jobs with a Common Due Date via Cooperative Game Theory Amerca Joural of Operato Reearch, 203, 3, 439-443 http://dx.do.org/0.4236/ajor.203.35042 Publhed Ole eptember 203 (http://www.crp.org/joural/ajor) chedulg Job wth a Commo Due Date va Cooperatve Game Theory

More information

2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen.

2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen. .5 x 54.5 a. x 7. 786 7 b. The raked observatos are: 7.4, 7.5, 7.7, 7.8, 7.9, 8.0, 8.. Sce the sample sze 7 s odd, the meda s the (+)/ 4 th raked observato, or meda 7.8 c. The cosumer would more lkely

More information

Lifetime Performance of an Energy Efficient Clustering Algorithm for Cluster-Based Wireless Sensor Networks

Lifetime Performance of an Energy Efficient Clustering Algorithm for Cluster-Based Wireless Sensor Networks Lfetme Performace of a Eerg Effcet Cluterg Algorthm for Cluter-Baed Wrele Seor Networ Yug-Fa Huag, Wu-He Luo, Joh Sum, L-Huag Chag 3, Chh-We Chag, ad Rug-Chg Che 4 Graduate Ittute of Networg ad Commucato

More information

Laboratory I.10 It All Adds Up

Laboratory I.10 It All Adds Up Laboratory I. It All Adds Up Goals The studet wll work wth Rema sums ad evaluate them usg Derve. The studet wll see applcatos of tegrals as accumulatos of chages. The studet wll revew curve fttg sklls.

More information