It is Advantageous to Make a Syllabus as Precise as Possible: Decision-Theoretic Analysis
|
|
- Nigel Tucker
- 5 years ago
- Views:
Transcription
1 Joural of Iovatve Techology ad Educato, Vol. 4, 2017, o. 1, 1-5 HIKARI Ltd, It s Advatageous to Make a Syllabus as Precse as Possble: Decso-Theoretc Aalyss Fracsco Zapata 1, Olga Kosheleva 2 ad Vladk Kreovch 1 1 Departmet of Computer Scece 2 Departmet of Teacher Educato Uversty of Texas at El Paso 500 W. Uversty El Paso, TX 79968, USA Copyrght c 2016 Fracsco Zapata, Olga Kosheleva ad Vladk Kreovch. Ths artcle s dstrbuted uder the Creatve Commos Attrbuto Lcese, whch permts urestrcted use, dstrbuto, ad reproducto ay medum, provded the orgal work s properly cted. Abstract Should a syllabus be precse? Shall we dcate exactly how may pots we should assg for each test ad for each assgmet? O the oe had, may studets lke such certaty. O the other had, structors would lke to have some flexblty: f a assgmet turs out to be more complex tha expected, we should be able to crease the umber of pots for ths assgmet, ad, vce versa, t t turs out to be smpler tha expected, we should be able to decrease the umber of pots. I ths paper, we aalyze ths problem from a decso-theoretc vewpot. Our cocluso s that whle a lttle flexblty s OK, geeral, t s beefcal to make a syllabus as precse as possble. Mathematcs Subject Classfcato: 62C05, 91B06, 97A99 Keywords: syllabus, decso makg uder ucertaty, Hurwcz optmsm-pessmsm crtero
2 2 Fracsco Zapata, Olga Kosheleva ad Vladk Kreovch 1 Should a Syllabus Be Precse? Formulato of the problem. Shall we dcate exactly how may pots we should assg for each test ad for each assgmet? O the oe had, may studets lke such certaty. O the other had, structors would lke to have some flexblty: If a assgmet turs out to be more complex tha expected, we should be able to crease ts weght. Vce versa, t t turs out to be smpler tha expected, we should be able to decrease the umber of pots. What we do ths paper. I ths paper, we aalyze ths problem from a decso-theoretc vewpot. Our cocluso s that whle a lttle flexblty s OK, geeral, t s beefcal to make a syllabus precse. 2 Decso Makg: A Bref Remder Decso makg: geeral case. Accordg to decso theory (see, e.g., [1, 4, 5, 6, 7]) decsos of a ratoal aget ca be equvaletly descrbed as maxmzg a approprate objectve fucto u(a). Ths objectve fucto s kow as the utlty fucto. Decso makg uder ucertaty. I some cases, we do ot kow the exact cosequeces of each possble acto. I ths case, for each acto a, stead of the exact value u(a) of the correspodg utlty, we oly kow the terval of possble values: [u(a), u(a)]. I such stuatos, a ratoal aget should select a acto a that maxmzes the expresso u(a) def = α u(a) + (1 α) u(a). Ths optmsm-pessmsm crtero was frst formulated by a Nobelst Leo Hurwcz [2, 3, 5, 6]: The optmsm value α = 1 meas that a perso oly takes to accout best-case cosequeces. The pessmsm value α = 0 meas that a perso oly takes to accout worst-case cosequeces. A realstc approach s to take α (0, 1). I partcular, there are reasoable argumets favor of selectg α = 0.5.
3 It s advatageous to make a syllabus as precse as possble 3 3 Aalyss of the Stuato I geeral, the overall grade g for the class s a weghted average of grades g o dfferet assgmets: g = w 1 g w g, wth w = 1. The grade g o each assgmet depeds o the studet s efforts g = f(e ). Let us assume that a studet has a certa overall amout of effort E dedcated to ths class; the: amog all possble combatos e wth e = E, the studet selects the oe that maxmzes hs/her utlty. 4 Case of a Precse Syllabus I a precse syllabus, the weghts w are explctly stated. I ths case, the studet maxmzes w f(e ). For equal weghts, Lagrage multpler approach leads to ( ) w f(e ) + λ e E m. Dfferetatg wth respect to e ad equatg dervatve to 0, we get w f (e ) = λ. I partcular, whe assgmets are of equal complexty ad w = cost, we get e = cost. Thus, a precse syllabus ecourages studets to lear all the topcs. Ths s exactly what we structors wat. 5 Case of a Imprecse Syllabus Imprecse syllabus: geeral case. Let us ow cosder the extreme case of a mprecse syllabus, whe o formato s provded about w. I ths case, the best-case ga s u = max g = max f(e ). Ths ga correspods to the case whe:
4 4 Fracsco Zapata, Olga Kosheleva ad Vladk Kreovch the assgmet wth the hghest grade gets weght 1, ad other assgmets get weght 0. The worst-case ga s u = m g = m f(e ). Ths ga correspods to the case whe: the assgmet wth the lowest grade gets weght 1, ad other assgmets get weght 0. Thus, a studet maxmzes the expresso u = α max f(e ) + (1 α) m f(e ). What f a studet dlgetly studes. If a studet dlgetly studes each topc, we have e = E ( ) E, ad u = f. What f a studet gambles. O the other had, f the studet gambles ad places all hs/her efforts to oe topc, the I ths case, u = α f(e). max g = f(e) ad m g = 0. So what wll a studet do? So, f α f(e) > f gamble stead of studyg each topc. No matter what α > 0 s, for suffcet large, we have ( ) E f f(0) = 0. ( E ), the studet wll Thus, for large, the above equalty wll be satsfed. So, a mprecse syllabus ecourages gamblg approach stead of a dlget thorough study. 6 Cocluso: It Is Advatageous To Make Syllab Precse A precse syllabus ecourages a studet to study all the topcs ths s what we structors would lke to see. O the other had, a mprecse syllabus ecourages gamblg approach stead of a dlget thorough study.
5 It s advatageous to make a syllabus as precse as possble 5 Thus, t s advatageous to make a syllabus as precse as possble. Ackowledgemets. Ths work was supported part by the Natoal Scece Foudato grats HRD ad HRD (Cyber-ShARE Ceter of Excellece) ad DUE , ad by a award from Prudetal Foudato. The authors are thakful to all the partcpats of the 19th UTEP/NMSU Workshop o Mathematcs, Computer Scece, ad Computatoal Scece (El Paso, Texas, November 5, 2016) for valuable dscussos. Refereces [1] P. C. Fshbur, Utlty Theory for Decso Makg, Joh Wley & Sos Ic., New York, [2] L. Hurwcz, Optmalty Crtera for Decso Makg uder Igorace, Cowles Commsso Dscusso Paper, Statstcs, o. 370, [3] V. Kreovch, Decso makg uder terval ucertaty (ad beyod), Chapter Huma-Cetrc Decso-Makg Models for Socal Sceces, P. Guo ad W. Pedrycz (eds.), Sprger Verlag, 2014, [4] D. Luce, Ivdual Choce Behavor: A Theoretcal Aalyss, Dover, New York, [5] R. D. Luce ad H. Raffa, Games ad Decsos: Itroducto ad Crtcal Survey, New York, Dover, [6] H. T. Nguye, O. Kosheleva ad V. Kreovch, Decso makg beyod Arrow s mpossblty theorem, wth the aalyss of effects of colluso ad mutual attracto, Iteratoal Joural of Itellget Systems, 24 (2009), o. 1, [7] H. Raffa, Decso Aalyss, Addso-Wesley, Readg, Massachusetts, Receved: November 22, 2016; Publshed: Jauary 3, 2017
On the Interval Zoro Symmetric Single Step. Procedure IZSS1-5D for the Simultaneous. Bounding of Real Polynomial Zeros
It. Joural of Math. Aalyss, Vol. 7, 2013, o. 59, 2947-2951 HIKARI Ltd, www.m-hkar.com http://dx.do.org/10.12988/ma.2013.310259 O the Iterval Zoro Symmetrc Sgle Step Procedure IZSS1-5D for the Smultaeous
More informationResearch 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 informationOn Modified Interval Symmetric Single-Step Procedure ISS2-5D for the Simultaneous Inclusion of Polynomial Zeros
It. Joural of Math. Aalyss, Vol. 7, 2013, o. 20, 983-988 HIKARI Ltd, www.m-hkar.com O Modfed Iterval Symmetrc Sgle-Step Procedure ISS2-5D for the Smultaeous Icluso of Polyomal Zeros 1 Nora Jamalud, 1 Masor
More informationAsymptotic Formulas Composite Numbers II
Iteratoal Matematcal Forum, Vol. 8, 203, o. 34, 65-662 HIKARI Ltd, www.m-kar.com ttp://d.do.org/0.2988/mf.203.3854 Asymptotc Formulas Composte Numbers II Rafael Jakmczuk Dvsó Matemátca, Uversdad Nacoal
More informationMulti 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 informationResearch 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 informationApplication of Generating Functions to the Theory of Success Runs
Aled Mathematcal Sceces, Vol. 10, 2016, o. 50, 2491-2495 HIKARI Ltd, www.m-hkar.com htt://dx.do.org/10.12988/ams.2016.66197 Alcato of Geeratg Fuctos to the Theory of Success Rus B.M. Bekker, O.A. Ivaov
More informationSTK4011 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 informationA new Family of Distributions Using the pdf of the. rth Order Statistic from Independent Non- Identically Distributed Random Variables
Iteratoal Joural of Cotemporary Mathematcal Sceces Vol. 07 o. 8 9-05 HIKARI Ltd www.m-hkar.com https://do.org/0.988/jcms.07.799 A ew Famly of Dstrbutos Usg the pdf of the rth Order Statstc from Idepedet
More informationAnalysis 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 informationA New Method for Decision Making Based on Soft Matrix Theory
Joural of Scetfc esearch & eports 3(5): 0-7, 04; rtcle o. JS.04.5.00 SCIENCEDOMIN teratoal www.scecedoma.org New Method for Decso Mag Based o Soft Matrx Theory Zhmg Zhag * College of Mathematcs ad Computer
More informationDr. 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 informationOn the Rational Valued Characters Table of the
Aled Mathematcal Sceces, Vol., 7, o. 9, 95-9 HIKARI Ltd, www.m-hkar.com htts://do.or/.9/ams.7.7576 O the Ratoal Valued Characters Table of the Grou (Q m C Whe m s a Eve Number Raaa Hassa Abass Deartmet
More informationSolving 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 informationFunctions 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 informationEstimation 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 informationCHAPTER VI Statistical Analysis of Experimental Data
Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca
More informationGeneralized One-Step Third Derivative Implicit Hybrid Block Method for the Direct Solution of Second Order Ordinary Differential Equation
Appled Mathematcal Sceces, Vol. 1, 16, o. 9, 417-4 HIKARI Ltd, www.m-hkar.com http://dx.do.org/1.1988/ams.16.51667 Geeralzed Oe-Step Thrd Dervatve Implct Hybrd Block Method for the Drect Soluto of Secod
More informationPROJECTION PROBLEM FOR REGULAR POLYGONS
Joural of Mathematcal Sceces: Advaces ad Applcatos Volume, Number, 008, Pages 95-50 PROJECTION PROBLEM FOR REGULAR POLYGONS College of Scece Bejg Forestry Uversty Bejg 0008 P. R. Cha e-mal: sl@bjfu.edu.c
More informationA 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 informationThe 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 informationBootstrap Method for Testing of Equality of Several Coefficients of Variation
Cloud Publcatos Iteratoal Joural of Advaced Mathematcs ad Statstcs Volume, pp. -6, Artcle ID Sc- Research Artcle Ope Access Bootstrap Method for Testg of Equalty of Several Coeffcets of Varato Dr. Navee
More informationResearch Article Some Strong Limit Theorems for Weighted Product Sums of ρ-mixing Sequences of Random Variables
Hdaw Publshg Corporato Joural of Iequaltes ad Applcatos Volume 2009, Artcle ID 174768, 10 pages do:10.1155/2009/174768 Research Artcle Some Strog Lmt Theorems for Weghted Product Sums of ρ-mxg Sequeces
More informationIntroduction to local (nonparametric) density estimation. methods
Itroducto to local (oparametrc) desty estmato methods A slecture by Yu Lu for ECE 66 Sprg 014 1. Itroducto Ths slecture troduces two local desty estmato methods whch are Parze desty estmato ad k-earest
More informationMultiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades
STAT 101 Dr. Kar Lock Morga 11/20/12 Exam 2 Grades Multple Regresso SECTIONS 9.2, 10.1, 10.2 Multple explaatory varables (10.1) Parttog varablty R 2, ANOVA (9.2) Codtos resdual plot (10.2) Trasformatos
More information1. 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 informationENGI 4421 Propagation of Error Page 8-01
ENGI 441 Propagato of Error Page 8-01 Propagato of Error [Navd Chapter 3; ot Devore] Ay realstc measuremet procedure cotas error. Ay calculatos based o that measuremet wll therefore also cota a error.
More informationArithmetic Mean and Geometric Mean
Acta Mathematca Ntresa Vol, No, p 43 48 ISSN 453-6083 Arthmetc Mea ad Geometrc Mea Mare Varga a * Peter Mchalča b a Departmet of Mathematcs, Faculty of Natural Sceces, Costate the Phlosopher Uversty Ntra,
More informationA Remark on the Uniform Convergence of Some Sequences of Functions
Advaces Pure Mathematcs 05 5 57-533 Publshed Ole July 05 ScRes. http://www.scrp.org/joural/apm http://dx.do.org/0.436/apm.05.59048 A Remark o the Uform Covergece of Some Sequeces of Fuctos Guy Degla Isttut
More informationLecture 9: Tolerant Testing
Lecture 9: Tolerat Testg Dael Kae Scrbe: Sakeerth Rao Aprl 4, 07 Abstract I ths lecture we prove a quas lear lower boud o the umber of samples eeded to do tolerat testg for L dstace. Tolerat Testg We have
More informationNP!= P. By Liu Ran. Table of Contents. The P vs. NP problem is a major unsolved problem in computer
NP!= P By Lu Ra Table of Cotets. Itroduce 2. Strategy 3. Prelmary theorem 4. Proof 5. Expla 6. Cocluso. Itroduce The P vs. NP problem s a major usolved problem computer scece. Iformally, t asks whether
More informationChapter 11 The Analysis of Variance
Chapter The Aalyss of Varace. Oe Factor Aalyss of Varace. Radomzed Bloc Desgs (ot for ths course) NIPRL . Oe Factor Aalyss of Varace.. Oe Factor Layouts (/4) Suppose that a expermeter s terested populatos
More informationOn quaternions with generalized Fibonacci and Lucas number components
Polatl Kesm Advaces Dfferece Equatos (205) 205:69 DOI 0.86/s3662-05-05-x R E S E A R C H Ope Access O quateros wth geeralzed Fboacc Lucas umber compoets Emrah Polatl * Seyhu Kesm * Correspodece: emrah.polatl@beu.edu.tr
More informationNP!= P. By Liu Ran. Table of Contents. The P versus NP problem is a major unsolved problem in computer
NP!= P By Lu Ra Table of Cotets. Itroduce 2. Prelmary theorem 3. Proof 4. Expla 5. Cocluso. Itroduce The P versus NP problem s a major usolved problem computer scece. Iformally, t asks whether a computer
More informationANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK
Far East Joural of Appled Mathematcs Volume, Number, 2008, Pages Ths paper s avalable ole at http://www.pphm.com 2008 Pushpa Publshg House ANALYSIS ON THE NATURE OF THE ASI EQUATIONS IN SYNERGETI INTER-REPRESENTATION
More informationOn the convergence of derivatives of Bernstein approximation
O the covergece of dervatves of Berste approxmato Mchael S. Floater Abstract: By dfferetatg a remader formula of Stacu, we derve both a error boud ad a asymptotc formula for the dervatves of Berste approxmato.
More informationBayes Interval Estimation for binomial proportion and difference of two binomial proportions with Simulation Study
IJIEST Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue 5, July 04. Bayes Iterval Estmato for bomal proporto ad dfferece of two bomal proportos wth Smulato Study Masoud Gaj, Solmaz hlmad
More informationLecture 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 informationA 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 informationUNIVERSITY 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 informationSummary 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 informationEntropy ISSN by MDPI
Etropy 2003, 5, 233-238 Etropy ISSN 1099-4300 2003 by MDPI www.mdp.org/etropy O the Measure Etropy of Addtve Cellular Automata Hasa Aı Arts ad Sceces Faculty, Departmet of Mathematcs, Harra Uversty; 63100,
More informationContinuous Distributions
7//3 Cotuous Dstrbutos Radom Varables of the Cotuous Type Desty Curve Percet Desty fucto, f (x) A smooth curve that ft the dstrbuto 3 4 5 6 7 8 9 Test scores Desty Curve Percet Probablty Desty Fucto, f
More informationThe k-nacci triangle and applications
Kuhapataakul & Aataktpasal, Coget Mathematcs 7, : 9 https://doorg/8/879 PURE MATHEMATICS RESEARCH ARTICLE The k-acc tragle ad applcatos Katapho Kuhapataakul * ad Porpawee Aataktpasal Receved: March 7 Accepted:
More informationEVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM
EVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM Jose Javer Garca Moreta Ph. D research studet at the UPV/EHU (Uversty of Basque coutry) Departmet of Theoretcal
More informationDepartment of Agricultural Economics. PhD Qualifier Examination. August 2011
Departmet of Agrcultural Ecoomcs PhD Qualfer Examato August 0 Istructos: The exam cossts of sx questos You must aswer all questos If you eed a assumpto to complete a questo, state the assumpto clearly
More informationPoint Estimation: definition of estimators
Pot Estmato: defto of estmators Pot estmator: ay fucto W (X,..., X ) of a data sample. The exercse of pot estmato s to use partcular fuctos of the data order to estmate certa ukow populato parameters.
More informationBeam Warming Second-Order Upwind Method
Beam Warmg Secod-Order Upwd Method Petr Valeta Jauary 6, 015 Ths documet s a part of the assessmet work for the subject 1DRP Dfferetal Equatos o Computer lectured o FNSPE CTU Prague. Abstract Ths documet
More informationResearch Article Multidimensional Hilbert-Type Inequalities with a Homogeneous Kernel
Hdaw Publshg Corporato Joural of Iequaltes ad Applcatos Volume 29, Artcle ID 3958, 2 pages do:.55/29/3958 Research Artcle Multdmesoal Hlbert-Type Iequaltes wth a Homogeeous Kerel Predrag Vuovć Faculty
More informationAnalysis 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 informationComparing 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 informationOn the Link Between the Concepts of Kurtosis and Bipolarization. Abstract
O the Lk etwee the Cocepts of Kurtoss ad polarzato Jacques SILE ar-ila Uversty Joseph Deutsch ar-ila Uversty Metal Haoka ar-ila Uversty h.d. studet) Abstract I a paper o the measuremet of the flatess of
More informationJournal 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 informationPermutation Tests for More Than Two Samples
Permutato Tests for ore Tha Two Samples Ferry Butar Butar, Ph.D. Abstract A F statstc s a classcal test for the aalyss of varace where the uderlyg dstrbuto s a ormal. For uspecfed dstrbutos, the permutato
More informationCIS 800/002 The Algorithmic Foundations of Data Privacy October 13, Lecture 9. Database Update Algorithms: Multiplicative Weights
CIS 800/002 The Algorthmc Foudatos of Data Prvacy October 13, 2011 Lecturer: Aaro Roth Lecture 9 Scrbe: Aaro Roth Database Update Algorthms: Multplcatve Weghts We ll recall aga) some deftos from last tme:
More informationResearch Article A New Derivation and Recursive Algorithm Based on Wronskian Matrix for Vandermonde Inverse Matrix
Mathematcal Problems Egeerg Volume 05 Artcle ID 94757 7 pages http://ddoorg/055/05/94757 Research Artcle A New Dervato ad Recursve Algorthm Based o Wroska Matr for Vadermode Iverse Matr Qu Zhou Xja Zhag
More informationA 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 informationDiscrete 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 informationThe equation is sometimes presented in form Y = a + b x. This is reasonable, but it s not the notation we use.
INTRODUCTORY NOTE ON LINEAR REGREION We have data of the form (x y ) (x y ) (x y ) These wll most ofte be preseted to us as two colum of a spreadsheet As the topc develops we wll see both upper case ad
More informationGOALS The Samples Why Sample the Population? What is a Probability Sample? Four Most Commonly Used Probability Sampling Methods
GOLS. Epla why a sample s the oly feasble way to lear about a populato.. Descrbe methods to select a sample. 3. Defe ad costruct a samplg dstrbuto of the sample mea. 4. Epla the cetral lmt theorem. 5.
More informationComparison of Parameters of Lognormal Distribution Based On the Classical and Posterior Estimates
Joural of Moder Appled Statstcal Methods Volume Issue Artcle 8 --03 Comparso of Parameters of Logormal Dstrbuto Based O the Classcal ad Posteror Estmates Raja Sulta Uversty of Kashmr, Sragar, Ida, hamzasulta8@yahoo.com
More informationLINEAR REGRESSION ANALYSIS
LINEAR REGRESSION ANALYSIS MODULE V Lecture - Correctg Model Iadequaces Through Trasformato ad Weghtg Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur Aalytcal methods for
More informationOn Monotone Eigenvectors of a Max-T Fuzzy Matrix
Joural of Appled Mathematcs ad hyscs, 08, 6, 076-085 http://wwwscrporg/joural/jamp ISSN Ole: 37-4379 ISSN rt: 37-435 O Mootoe Egevectors of a Max-T Fuzzy Matrx Qg Wag, Na Q, Zxua Yag, Lfe Su, Lagju eg,
More informationBayes (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 informationResearch on SVM Prediction Model Based on Chaos Theory
Advaced Scece ad Techology Letters Vol.3 (SoftTech 06, pp.59-63 http://dx.do.org/0.457/astl.06.3.3 Research o SVM Predcto Model Based o Chaos Theory Sog Lagog, Wu Hux, Zhag Zezhog 3, College of Iformato
More informationA Combination of Adaptive and Line Intercept Sampling Applicable in Agricultural and Environmental Studies
ISSN 1684-8403 Joural of Statstcs Volume 15, 008, pp. 44-53 Abstract A Combato of Adaptve ad Le Itercept Samplg Applcable Agrcultural ad Evrometal Studes Azmer Kha 1 A adaptve procedure s descrbed for
More informationComplete 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 informationOrdinary Least Squares Regression. Simple Regression. Algebra and Assumptions.
Ordary Least Squares egresso. Smple egresso. Algebra ad Assumptos. I ths part of the course we are gog to study a techque for aalysg the lear relatoshp betwee two varables Y ad X. We have pars of observatos
More informationA NEW LOG-NORMAL DISTRIBUTION
Joural of Statstcs: Advaces Theory ad Applcatos Volume 6, Number, 06, Pages 93-04 Avalable at http://scetfcadvaces.co. DOI: http://dx.do.org/0.864/jsata_700705 A NEW LOG-NORMAL DISTRIBUTION Departmet of
More informationSufficiency in Blackwell s theorem
Mathematcal Socal Sceces 46 (23) 21 25 www.elsever.com/locate/ecobase Suffcecy Blacwell s theorem Agesza Belsa-Kwapsz* Departmet of Agrcultural Ecoomcs ad Ecoomcs, Motaa State Uversty, Bozema, MT 59717,
More informationFurther Results on Pair Sum Labeling of Trees
Appled Mathematcs 0 70-7 do:046/am0077 Publshed Ole October 0 (http://wwwscrporg/joural/am) Further Results o Par Sum Labelg of Trees Abstract Raja Poraj Jeyaraj Vjaya Xaver Parthpa Departmet of Mathematcs
More informationOptimality Conditions for Distributive Justice
Optmalty Codtos for Dstrbutve Justce Joh Hooker Carege Mello Uversty Aprl 2008 1 Just Dstrbuto The problem: How to dstrbute resources Tax breaks Medcal care Salares Educato Govermet beefts 2 Justce ad
More informationLecture 8: Linear Regression
Lecture 8: Lear egresso May 4, GENOME 56, Sprg Goals Develop basc cocepts of lear regresso from a probablstc framework Estmatg parameters ad hypothess testg wth lear models Lear regresso Su I Lee, CSE
More informationTESTS BASED ON MAXIMUM LIKELIHOOD
ESE 5 Toy E. Smth. The Basc Example. TESTS BASED ON MAXIMUM LIKELIHOOD To llustrate the propertes of maxmum lkelhood estmates ad tests, we cosder the smplest possble case of estmatg the mea of the ormal
More informationComparison of Dual to Ratio-Cum-Product Estimators of Population Mean
Research Joural of Mathematcal ad Statstcal Sceces ISS 30 6047 Vol. 1(), 5-1, ovember (013) Res. J. Mathematcal ad Statstcal Sc. Comparso of Dual to Rato-Cum-Product Estmators of Populato Mea Abstract
More informationESS 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 information3. 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(Monte Carlo) Resampling Technique in Validity Testing and Reliability Testing
Iteratoal Joural of Computer Applcatos (0975 8887) (Mote Carlo) Resamplg Techque Valdty Testg ad Relablty Testg Ad Setawa Departmet of Mathematcs, Faculty of Scece ad Mathematcs, Satya Wacaa Chrsta Uversty
More informationChapter 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 informationInternational Journal of Mathematical Archive-5(8), 2014, Available online through ISSN
Iteratoal Joural of Mathematcal Archve-5(8) 204 25-29 Avalable ole through www.jma.fo ISSN 2229 5046 COMMON FIXED POINT OF GENERALIZED CONTRACTION MAPPING IN FUZZY METRIC SPACES Hamd Mottagh Golsha* ad
More informationBAYESIAN INFERENCES FOR TWO PARAMETER WEIBULL DISTRIBUTION
Iteratoal Joural of Mathematcs ad Statstcs Studes Vol.4, No.3, pp.5-39, Jue 06 Publshed by Europea Cetre for Research Trag ad Developmet UK (www.eajourals.org BAYESIAN INFERENCES FOR TWO PARAMETER WEIBULL
More informationThe 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 informationAnalyzing Fuzzy System Reliability Using Vague Set Theory
Iteratoal Joural of Appled Scece ad Egeerg 2003., : 82-88 Aalyzg Fuzzy System Relablty sg Vague Set Theory Shy-Mg Che Departmet of Computer Scece ad Iformato Egeerg, Natoal Tawa versty of Scece ad Techology,
More informationChapter 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 informationε. Therefore, the estimate
Suggested Aswers, Problem Set 3 ECON 333 Da Hugerma. Ths s ot a very good dea. We kow from the secod FOC problem b) that ( ) SSE / = y x x = ( ) Whch ca be reduced to read y x x = ε x = ( ) The OLS model
More informationDimensionality Reduction and Learning
CMSC 35900 (Sprg 009) Large Scale Learg Lecture: 3 Dmesoalty Reducto ad Learg Istructors: Sham Kakade ad Greg Shakharovch L Supervsed Methods ad Dmesoalty Reducto The theme of these two lectures s that
More informationAssignment 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 informationOn Several Inequalities Deduced Using a Power Series Approach
It J Cotemp Mth Sceces, Vol 8, 203, o 8, 855-864 HIKARI Ltd, wwwm-hrcom http://dxdoorg/02988/jcms2033896 O Severl Iequltes Deduced Usg Power Seres Approch Lored Curdru Deprtmet of Mthemtcs Poltehc Uversty
More informationON THE LOGARITHMIC INTEGRAL
Hacettepe Joural of Mathematcs ad Statstcs Volume 39(3) (21), 393 41 ON THE LOGARITHMIC INTEGRAL Bra Fsher ad Bljaa Jolevska-Tueska Receved 29:9 :29 : Accepted 2 :3 :21 Abstract The logarthmc tegral l(x)
More informationWaiting 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 informationSome Notes on the Probability Space of Statistical Surveys
Metodološk zvezk, Vol. 7, No., 200, 7-2 ome Notes o the Probablty pace of tatstcal urveys George Petrakos Abstract Ths paper troduces a formal presetato of samplg process usg prcples ad cocepts from Probablty
More informationTHE ROYAL STATISTICAL SOCIETY 2009 EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA MODULAR FORMAT MODULE 2 STATISTICAL INFERENCE
THE ROYAL STATISTICAL SOCIETY 009 EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA MODULAR FORMAT MODULE STATISTICAL INFERENCE The Socety provdes these solutos to assst caddates preparg for the examatos future
More informationBayesian Inferences for Two Parameter Weibull Distribution Kipkoech W. Cheruiyot 1, Abel Ouko 2, Emily Kirimi 3
IOSR Joural of Mathematcs IOSR-JM e-issn: 78-578, p-issn: 9-765X. Volume, Issue Ver. II Ja - Feb. 05, PP 4- www.osrjourals.org Bayesa Ifereces for Two Parameter Webull Dstrbuto Kpkoech W. Cheruyot, Abel
More informationLecture Notes Types of economic variables
Lecture Notes 3 1. Types of ecoomc varables () Cotuous varable takes o a cotuum the sample space, such as all pots o a le or all real umbers Example: GDP, Polluto cocetrato, etc. () Dscrete varables fte
More informationSource-Channel Prediction in Error Resilient Video Coding
Source-Chael Predcto Error Reslet Vdeo Codg Hua Yag ad Keeth Rose Sgal Compresso Laboratory ECE Departmet Uversty of Calfora, Sata Barbara Outle Itroducto Source-chael predcto Smulato results Coclusos
More informationCENTRE FOR ECONOMIC RESEARCH WORKING PAPER SERIES
CENTRE FOR ECONOMIC RESEARCH WORKING PAPER SERIES 2001 Mmum Wages for Roald McDoald Moopsoes: A Theory of Moopsostc Competto by V Bhaskar ad Ted To A Commet Frak Walsh, Uversty College Dubl WP01/18 September
More informationChapter 8. Inferences about More Than Two Population Central Values
Chapter 8. Ifereces about More Tha Two Populato Cetral Values Case tudy: Effect of Tmg of the Treatmet of Port-We tas wth Lasers ) To vestgate whether treatmet at a youg age would yeld better results tha
More informationComparison of Four Methods for Estimating. the Weibull Distribution Parameters
Appled Mathematal Sees, Vol. 8, 14, o. 83, 4137-4149 HIKARI Ltd, www.m-hkar.om http://dx.do.org/1.1988/ams.14.45389 Comparso of Four Methods for Estmatg the Webull Dstrbuto Parameters Ivaa Pobočíková ad
More informationOn generalized fuzzy mean code word lengths. Department of Mathematics, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India
merca Joural of ppled Mathematcs 04; (4): 7-34 Publshed ole ugust 30, 04 (http://www.scecepublshggroup.com//aam) do: 0.648/.aam.04004.3 ISSN: 330-0043 (Prt); ISSN: 330-006X (Ole) O geeralzed fuzzy mea
More informationEconometric 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