Study on the Normal and Skewed Distribution of Isometric Grouping
|
|
- Steven Doyle
- 5 years ago
- Views:
Transcription
1 Open Journ of Sttstcs Pubshed Onne October ( Study on the orm nd Skewed Dstrbuton of Isometrc Groupng Zhensheng J Wenk J Schoo of Economcs nd Mngement Chongqng Three Gorges Unversty Chongqng Chn Students Affrs Deprtment Chongqng Three Gorges Unversty Chongqng Chn Em: kx76@sn.com eceved Juy 5 ; revsed August ; ccepted August 3 ABSTACT Becuse of thnkng ony the number of numbers but not fttng functon t woud be dequte to tke further fed when ccutng group numbers wth emprc formu. We hve proved the three theorems bsed on studyng the norm dstrbuton nd then rech the concuson tht there s better method to do the sme work. The method s smper nd more prctc thn emprc method nd so works we wth ny skewed dstrbuton. Keywords: Men Vue; Vrnce; Ft Functon; nge; One-Qurter nge. Introducton In vrous books on sttstcs when dscussng the sometrc group the emprc formu s treted s ony reference or put sde smpy. It s ttrbuted tht the css nterv s ony reevnt to the number of numbers not the shpe of ft functon n the emprc formu. Ths pper nyzes the sometrc group whe conformng to the norm dstrbuton of seres nd derves smpe nd prctc method to fnd css nterv. Furthermore the sme formu so works we wth ny skewed dstrbuton.. Hstogrm nd the Upper Bound of Css Interv.. Fnd Out Theorem ) Observe x x x nd fnd out the mnmum vue x nd mxmum vue x. And seect proper c tht sghty ess thn x nd d tht sghty greter thn x nd dvde ( cd ) nto ntervs. d c Ech nterv hs the sme ength s et d c t s esy to see. s rnge normy nd then dvde nto fve or more groups. Denote d cs d c we c s cosed rnge []. The seres s be-shped dstrbuted n symmetrc wy. Set the xs of symmetry x ts ft densty x functon s f x e ccordng to the sm- π pe numbers dvde t nto groups nd the scope of ech group s 3 nd denote t s m whch s the number of smpes n A x p A f x dx. Drwng dgrm s m m m Ths dgrm s ced frequency dstrbuton of csses of smpe numbers. Obvousy m S m m m. Copyrght Sces.
2 Z. S. JIA W. K. JIA Set terv x s bse e s heght n ech n- π mke rectnge S e nd then the hstogrm s mde π out (Fgure ). And S P A S S e S e π π π S e π s s s equs (Tbe ) S s s π e e e π H m S m( s s ) m e e π π dh d π () m e e π G e e ) Set when we get the mnmum of G. π S μ Fgure. The formton process of hstogrm. x Tbe. orm dstrbuton of dt (Exmpe ) ) When d H we hve the turnng pont d ow m e e 9 3e e 5e when the mxmum of H s nd S s the prt sum of H. Accordng to the eference [3] 6 so s the upper bound of css nterv []... Mjor Theorem Theorem. If group of numbers shows norm dstrbuton ts ft densty s e [3]. x π When we dscuss the sometrc group s the upper bound of ( s the css nterv). ) Defntons Defnton We dvde nto four equ prts Ther ponts re Q Q Q Q3 Q Q Q s the md-rnge. Q3 Q Q Q= s the qurte. Q- Q qunte of order eght. Defnton The mdpont of nterv (or the symmetrc nterv) s ced one-qurter rnge nd denoted s Defnton 3 If numbers re dstrbuted n then. ndcted the verge dstnce ech number shres. Copyrght Sces.
3 Z. S. JIA W. K. JIA 9 Defnton If there s n nterv whch mkes m then the nterv s ced smr nterv. ) Mjor theorem Lemm. Men vue theorem ntegrs. If f(x) n s consecutve nd symmetrc ccdenty functon there s pont mkes f x d x f. Theorem. There re t est two smr ntervs n normy dstrbuted seres [3]. Proof. By Lemm there s fnd m m mn. m m Let mn m then the ntervs n m re smr ntervs. By Lemm the smr ntervs re t est. Theorem 3. A normy dstrbuted seres hs smr 3 nterv n where s the css nterv [3]. 3. Work out the Wy to Fnd Css Interv by Theorem 3 ) Arrnge the dt n scendng order nd ccute the verge nd vrnce of the numbers. ) Fnd the cosed rnge nd ccute f ; to Expndng from ( to the two ponts). 3) Fnd out the mnmum smr ntervs s sn ; nd ther numbers re s s respectvey. Let t stsfy s Let s. n mn s - sn. s - be the mnmum vue thus et 3 ) Fx on the css nterv : s. 5) Groupng group of numbers nk the groups up crefuy. If t s done we t cn refect the over trend. The chn of numbers s b b bn bn where b b bn re ncuded n one group. How to nsert ths css nto the chn of dt? We set rue tht ths group shoud be ncuded n c where b b ; bn bn c 6) If t s skewed dstrbuton the bove method s so vbe but need to do twce referrng to the fowng exmpe. 3.. Exmpe By smpe survey of vng condtons of urbn househods we get the foowng numbers of per cpt monthy househod ncome (redy rrnged). The mnmum number s 6 nd the mxmum number s 3. It s mrm dstrbuton progresson. Men: c = 6 d = 6. ) Becuse t s not competey symmetrc we just consder the dt from 5 to. From 6 to there re 7 numbers nd the verge dstnce between 9 them: F ts coordnte s = 95; 5 ts coordnte s = 75; 675 ts coordnte s = ) There re ponts ncuded n tht s (75 75) the ength of ths nterv s 5. The ponts re nd When ccutng the verge dstnce of the ponts the nterv we shoud 5 consder s (Tbe ). It s suggested tht the smr nterv s coser to 3 thn to. It s esy to see tht there re sx numbers between nd whch re nd 97 nd F 33.3 whch s smr to 6 Copyrght Sces.
4 5 Z. S. JIA W. K. JIA F Let be the css nterv then = s the vuton of. We coud get the foowng dstrbuton seres fter further rrngement. 3.. Exmpe By smpe survey of vng condtons of urbn househods we get the foowng numbers of per cpt monthy househod ncome (redy rrnged). ) Tbe 3: Skewed dstrbuton of dt for Exmpe []. The mnmum number s nd the mxmum number s 3. And the verge s where c d the cosed rnge s 6 (Tbe ). ) Becuse t s not competey symmetrc we dvde nto two steps to fnsh t. From to 97. there re 7 numbers nd the verge dstnce between them s Tbe. orm dstrbuton of dt (Exmpe ) Tbe 3. Here choose C = 6 D = for Exmpe. Per Cpt Monthy Income (MB Yun) Househods Frequency (%) Tot 5. Tbe. Here choose C = D = for Exmpe 3 []. Per Cpt Monthy Income (MB Yun) Househods Frequency (%) Tot F The hf rnge s 697. ; 3.6 ts coordnte s =.6; 7.3 ts coord- nte s = ) There 3 re 3 ponts ncuded n tht s (97 33) the ength of ths nterv s 39. The 3 ponts re nd 3. When ccutng the verge dstnce of the 3 ponts the nterv we shoud consder s nd the verge dstnce s whch s greter thn F 5.. It suggests tht when seectng smr ntervs t s skewed to the rght. Thus we shoud deete 99 n the foowng dscusson. We hve the resut s the foowng We dd horzont ne between numbers. We fnd tht.6 s pont on the rght sde the dstnce s the verge dstnce of ponts on the eft s ; Copyrght Sces.
5 Z. S. JIA W. K. JIA 5 We coud further scertn tht there re two ponts on the rght sde of.6 t most whch re 6 nd. It s esy to fnd tht there re numbers n nterv ( ) (see the ffth ponts n the thrd prt) whch re nd. ) Snce F 5 5. F ( ) s smr nterv. And S = The Length of smr nterv s s the uton of. Mthemtcs dgrm Frequency dstrbuton of the per cpt monthy ncome vbe for vng expenses of urbn househods n certn cty. EFEECES then the nterv [] Q.. Xe nd Z. Z. Hn Prncpe of Sttstcs 6th By the concuson of ) nd ) s we coud get the foowng dstrbuton seres fter further rrngement. Edton Jnn Unversty Press Jnn 99 pp [] C. S. Wu Probbty nd Sttstcs Hgher Educton Press Bejng pp. -. [3] B. H. Qn nd L. W. Hung Sttstcs Schun Peope s Pubshng House Chengdu p. 7. [] Z. S. J A the Proof Are Pubshed n the Mthemtcs Prctces nd Theory Vo. o. pp. 3-. Copyrght Sces.
Chapter Newton-Raphson Method of Solving a Nonlinear Equation
Chpter.4 Newton-Rphson Method of Solvng Nonlner Equton After redng ths chpter, you should be ble to:. derve the Newton-Rphson method formul,. develop the lgorthm of the Newton-Rphson method,. use the Newton-Rphson
More informationMath 124B January 24, 2012
Mth 24B Jnury 24, 22 Viktor Grigoryn 5 Convergence of Fourier series Strting from the method of seprtion of vribes for the homogeneous Dirichet nd Neumnn boundry vue probems, we studied the eigenvue probem
More informationProduct Layout Optimization and Simulation Model in a Multi-level Distribution Center
Avbe onne t www.scencedrect.com Systems Engneerng Proced (0) 300 307 Product yout Optmzton nd Smuton Mode n Mut-eve Dstrbuton Center Ynru Chen,Qnn Xo, Xopng Tng Southwest otong unversty,chengdu,6003,p.r.chn
More informationDCDM BUSINESS SCHOOL NUMERICAL METHODS (COS 233-8) Solutions to Assignment 3. x f(x)
DCDM BUSINESS SCHOOL NUMEICAL METHODS (COS -8) Solutons to Assgnment Queston Consder the followng dt: 5 f() 8 7 5 () Set up dfference tble through fourth dfferences. (b) Wht s the mnmum degree tht n nterpoltng
More informationIn this appendix, we evaluate the derivative of Eq. 9 in the main text, i.e., we need to calculate
Supporting Tet Evoution of the Averge Synptic Updte Rue In this ppendi e evute the derivtive of Eq. 9 in the min tet i.e. e need to ccute Py ( ) Py ( Y ) og γ og. [] P( y Y ) P% ( y Y ) Before e strt et
More informationCoordinate Geometry. Coordinate Geometry. Curriculum Ready ACMNA: 178, 214, 294.
Coordinte Geometr Coordinte Geometr Curricuum Red ACMNA: 78, 4, 94 www.mthetics.com Coordinte COORDINATE Geometr GEOMETRY Shpes ou ve seen in geometr re put onto es nd nsed using gebr. Epect bit of both
More informationPattern Generation for Two Dimensional. cutting stock problem.
Internton Journ of Memtcs Trends nd Technoogy- Voume3 Issue- Pttern Generton for Two Dmenson Cuttng Stock Probem W N P Rodrgo, W B Dundseker nd A A I Perer 3 Deprtment of Memtcs, Fty of Scence, Unversty
More informationNeural Network Introduction. Hung-yi Lee
Neu Neto Intoducton Hung- ee Reve: Supevsed enng Mode Hpothess Functon Set f, f : : (e) Tnng: Pc the est Functon f * Best Functon f * Testng: f Tnng Dt : functon nput : functon output, ˆ,, ˆ, Neu Neto
More informationChapter Newton-Raphson Method of Solving a Nonlinear Equation
Chpter 0.04 Newton-Rphson Method o Solvng Nonlner Equton Ater redng ths chpter, you should be ble to:. derve the Newton-Rphson method ormul,. develop the lgorthm o the Newton-Rphson method,. use the Newton-Rphson
More informationApplied Statistics Qualifier Examination
Appled Sttstcs Qulfer Exmnton Qul_june_8 Fll 8 Instructons: () The exmnton contns 4 Questons. You re to nswer 3 out of 4 of them. () You my use ny books nd clss notes tht you mght fnd helpful n solvng
More informationThe Number of Rows which Equal Certain Row
Interntonl Journl of Algebr, Vol 5, 011, no 30, 1481-1488 he Number of Rows whch Equl Certn Row Ahmd Hbl Deprtment of mthemtcs Fcult of Scences Dmscus unverst Dmscus, Sr hblhmd1@gmlcom Abstrct Let be X
More information( ) ( )()4 x 10-6 C) ( ) = 3.6 N ( ) = "0.9 N. ( )ˆ i ' ( ) 2 ( ) 2. q 1 = 4 µc q 2 = -4 µc q 3 = 4 µc. q 1 q 2 q 3
3 Emple : Three chrges re fed long strght lne s shown n the fgure boe wth 4 µc, -4 µc, nd 3 4 µc. The dstnce between nd s. m nd the dstnce between nd 3 s lso. m. Fnd the net force on ech chrge due to the
More informationUNIVERSITY OF IOANNINA DEPARTMENT OF ECONOMICS. M.Sc. in Economics MICROECONOMIC THEORY I. Problem Set II
Mcroeconomc Theory I UNIVERSITY OF IOANNINA DEPARTMENT OF ECONOMICS MSc n Economcs MICROECONOMIC THEORY I Techng: A Lptns (Note: The number of ndctes exercse s dffculty level) ()True or flse? If V( y )
More informationProof that if Voting is Perfect in One Dimension, then the First. Eigenvector Extracted from the Double-Centered Transformed
Proof tht f Votng s Perfect n One Dmenson, then the Frst Egenvector Extrcted from the Doule-Centered Trnsformed Agreement Score Mtrx hs the Sme Rn Orderng s the True Dt Keth T Poole Unversty of Houston
More informationarxiv: v1 [math.co] 5 Jun 2015
First non-trivi upper bound on the circur chromtic number of the pne. Konstnty Junosz-Szniwski, Fcuty of Mthemtics nd Informtion Science, Wrsw University of Technoogy, Pond Abstrct rxiv:1506.01886v1 [mth.co]
More information4. Eccentric axial loading, cross-section core
. Eccentrc xl lodng, cross-secton core Introducton We re strtng to consder more generl cse when the xl force nd bxl bendng ct smultneousl n the cross-secton of the br. B vrtue of Snt-Vennt s prncple we
More informationINTRODUCTION TO COMPLEX NUMBERS
INTRODUCTION TO COMPLEX NUMBERS The numers -4, -3, -, -1, 0, 1,, 3, 4 represent the negtve nd postve rel numers termed ntegers. As one frst lerns n mddle school they cn e thought of s unt dstnce spced
More informationSupport vector machines for regression
S 75 Mchne ernng ecture 5 Support vector mchnes for regresson Mos Huskrecht mos@cs.ptt.edu 539 Sennott Squre S 75 Mchne ernng he decson oundr: ˆ he decson: Support vector mchnes ˆ α SV ˆ sgn αˆ SV!!: Decson
More informationTwo Coefficients of the Dyson Product
Two Coeffcents of the Dyson Product rxv:07.460v mth.co 7 Nov 007 Lun Lv, Guoce Xn, nd Yue Zhou 3,,3 Center for Combntorcs, LPMC TJKLC Nnk Unversty, Tnjn 30007, P.R. Chn lvlun@cfc.nnk.edu.cn gn@nnk.edu.cn
More informationCase Study of Markov Chains Ray-Knight Compactification
Internatonal Journal of Contemporary Mathematcal Scences Vol. 9, 24, no. 6, 753-76 HIKAI Ltd, www.m-har.com http://dx.do.org/.2988/cms.24.46 Case Study of Marov Chans ay-knght Compactfcaton HaXa Du and
More informationn f(x i ) x. i=1 In section 4.2, we defined the definite integral of f from x = a to x = b as n f(x i ) x; f(x) dx = lim i=1
The Fundmentl Theorem of Clculus As we continue to study the re problem, let s think bck to wht we know bout computing res of regions enclosed by curves. If we wnt to find the re of the region below the
More informationTorsion, Thermal Effects and Indeterminacy
ENDS Note Set 7 F007bn orson, herml Effects nd Indetermncy Deformton n orsonlly Loded Members Ax-symmetrc cross sectons subjected to xl moment or torque wll remn plne nd undstorted. At secton, nternl torque
More informationEnergy Balance of Solar Collector
Renewbe Energy Grou Gret Ides Grow Better Beow Zero! Wecome! Energy Bnce of Sor Coector Mohmd Khrseh E-mi:m.Khrseh@gmi.com Renewbe Energy Grou Gret Ides Grow Better Beow Zero! Liuid Ft Pte Coectors. Het
More informationON AUTOMATIC CONTINUITY OF DERIVATIONS FOR BANACH ALGEBRAS WITH INVOLUTION
European Journa of Mathematcs and Computer Scence Vo. No. 1, 2017 ON AUTOMATC CONTNUTY OF DERVATONS FOR BANACH ALGEBRAS WTH NVOLUTON Mohamed BELAM & Youssef T DL MATC Laboratory Hassan Unversty MORO CCO
More information8.1 Arc Length. What is the length of a curve? How can we approximate it? We could do it following the pattern we ve used before
.1 Arc Length hat s the length of a curve? How can we approxmate t? e could do t followng the pattern we ve used before Use a sequence of ncreasngly short segments to approxmate the curve: As the segments
More informationIntroduction to statically indeterminate structures
Sttics of Buiding Structures I., EASUS Introduction to stticy indeterminte structures Deprtment of Structur echnics Fcuty of Civi Engineering, VŠB-Technic University of Ostrv Outine of Lecture Stticy indeterminte
More information2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification
E395 - Pattern Recognton Solutons to Introducton to Pattern Recognton, Chapter : Bayesan pattern classfcaton Preface Ths document s a soluton manual for selected exercses from Introducton to Pattern Recognton
More informationLinear Momentum. Center of Mass.
Lecture 6 Chapter 9 Physcs I 03.3.04 Lnear omentum. Center of ass. Course webste: http://faculty.uml.edu/ndry_danylov/teachng/physcsi Lecture Capture: http://echo360.uml.edu/danylov03/physcssprng.html
More informationFoundations of Arithmetic
Foundatons of Arthmetc Notaton We shall denote the sum and product of numbers n the usual notaton as a 2 + a 2 + a 3 + + a = a, a 1 a 2 a 3 a = a The notaton a b means a dvdes b,.e. ac = b where c s an
More informationIntroduction to Numerical Integration Part II
Introducton to umercl Integrton Prt II CS 75/Mth 75 Brn T. Smth, UM, CS Dept. Sprng, 998 4/9/998 qud_ Intro to Gussn Qudrture s eore, the generl tretment chnges the ntegrton prolem to ndng the ntegrl w
More informationStatistics and Probability Letters
Sttstcs nd Probblty Letters 79 (2009) 105 111 Contents lsts vlble t ScenceDrect Sttstcs nd Probblty Letters journl homepge: www.elsever.com/locte/stpro Lmtng behvour of movng verge processes under ϕ-mxng
More informationCHOVER-TYPE LAWS OF THE ITERATED LOGARITHM FOR WEIGHTED SUMS OF ρ -MIXING SEQUENCES
CHOVER-TYPE LAWS OF THE ITERATED LOGARITHM FOR WEIGHTED SUMS OF ρ -MIXING SEQUENCES GUANG-HUI CAI Receved 24 September 2004; Revsed 3 My 2005; Accepted 3 My 2005 To derve Bum-Ktz-type result, we estblsh
More informationFluid Flow through a Tube
. Theory through Tube In this experiment we wi determine how we physic retionship (so ced w ), nmey Poiseue s eqution, ppies. In the suppementry reding mteri this eqution ws derived s p Q 8 where Q is
More informationA Family of Multivariate Abel Series Distributions. of Order k
Appled Mthemtcl Scences, Vol. 2, 2008, no. 45, 2239-2246 A Fmly of Multvrte Abel Seres Dstrbutons of Order k Rupk Gupt & Kshore K. Ds 2 Fculty of Scence & Technology, The Icf Unversty, Agrtl, Trpur, Ind
More informationResearch on Complex Networks Control Based on Fuzzy Integral Sliding Theory
Advanced Scence and Technoogy Letters Vo.83 (ISA 205), pp.60-65 http://dx.do.org/0.4257/ast.205.83.2 Research on Compex etworks Contro Based on Fuzzy Integra Sdng Theory Dongsheng Yang, Bngqng L, 2, He
More informationPartially Observable Systems. 1 Partially Observable Markov Decision Process (POMDP) Formalism
CS294-40 Lernng for Rootcs nd Control Lecture 10-9/30/2008 Lecturer: Peter Aeel Prtlly Oservle Systems Scre: Dvd Nchum Lecture outlne POMDP formlsm Pont-sed vlue terton Glol methods: polytree, enumerton,
More informationChapter 6. Rotations and Tensors
Vector Spaces n Physcs 8/6/5 Chapter 6. Rotatons and ensors here s a speca knd of near transformaton whch s used to transforms coordnates from one set of axes to another set of axes (wth the same orgn).
More informationLecture 4: Piecewise Cubic Interpolation
Lecture notes on Vrtonl nd Approxmte Methods n Appled Mthemtcs - A Perce UBC Lecture 4: Pecewse Cubc Interpolton Compled 6 August 7 In ths lecture we consder pecewse cubc nterpolton n whch cubc polynoml
More informationCOMPLEX NUMBER & QUADRATIC EQUATION
MCQ COMPLEX NUMBER & QUADRATIC EQUATION Syllus : Comple numers s ordered prs of rels, Representton of comple numers n the form + nd ther representton n plne, Argnd dgrm, lger of comple numers, modulus
More informationProblem Set 9. Figure 1: Diagram. This picture is a rough sketch of the 4 parabolas that give us the area that we need to find. The equations are:
(x + y ) = y + (x + y ) = x + Problem Set 9 Discussion: Nov., Nov. 8, Nov. (on probbility nd binomil coefficients) The nme fter the problem is the designted writer of the solution of tht problem. (No one
More information7.2 Volume. A cross section is the shape we get when cutting straight through an object.
7. Volume Let s revew the volume of smple sold, cylnder frst. Cylnder s volume=se re heght. As llustrted n Fgure (). Fgure ( nd (c) re specl cylnders. Fgure () s rght crculr cylnder. Fgure (c) s ox. A
More informationAssociative Memories
Assocatve Memores We consder now modes for unsupervsed earnng probems, caed auto-assocaton probems. Assocaton s the task of mappng patterns to patterns. In an assocatve memory the stmuus of an ncompete
More informationMath1110 (Spring 2009) Prelim 3 - Solutions
Math 1110 (Sprng 2009) Solutons to Prelm 3 (04/21/2009) 1 Queston 1. (16 ponts) Short answer. Math1110 (Sprng 2009) Prelm 3 - Solutons x a 1 (a) (4 ponts) Please evaluate lm, where a and b are postve numbers.
More informationRank One Update And the Google Matrix by Al Bernstein Signal Science, LLC
Introducton Rnk One Updte And the Google Mtrx y Al Bernsten Sgnl Scence, LLC www.sgnlscence.net here re two dfferent wys to perform mtrx multplctons. he frst uses dot product formulton nd the second uses
More informationImproved Frame Synchronization and Frequency Offset Estimation in OFDM System and its Application to WMAN. and
Iprove Fre Synchronzton n Frequency Offset Estton n OFD Syste n ts Appcton to WAN Ch. Nn Kshore Heosoft In Pvt. Lt., Hyerb, In n V. Upth Rey Internton Insttute of Inforton Technoogy Gchbow, Hyerb, In Ths
More informationMATH FIELD DAY Contestants Insructions Team Essay. 1. Your team has forty minutes to answer this set of questions.
MATH FIELD DAY 2012 Contestnts Insructions Tem Essy 1. Your tem hs forty minutes to nswer this set of questions. 2. All nswers must be justified with complete explntions. Your nswers should be cler, grmmticlly
More informationQuiz: Experimental Physics Lab-I
Mxmum Mrks: 18 Totl tme llowed: 35 mn Quz: Expermentl Physcs Lb-I Nme: Roll no: Attempt ll questons. 1. In n experment, bll of mss 100 g s dropped from heght of 65 cm nto the snd contner, the mpct s clled
More informationReview of linear algebra. Nuno Vasconcelos UCSD
Revew of lner lgebr Nuno Vsconcelos UCSD Vector spces Defnton: vector spce s set H where ddton nd sclr multplcton re defned nd stsf: ) +( + ) (+ )+ 5) λ H 2) + + H 6) 3) H, + 7) λ(λ ) (λλ ) 4) H, - + 8)
More informationEquations and Inequalities
Equtions nd Inequlities Equtions nd Inequlities Curriculum Redy ACMNA: 4, 5, 6, 7, 40 www.mthletics.com Equtions EQUATIONS & Inequlities & INEQUALITIES Sometimes just writing vribles or pronumerls in
More informationStatistical Inference. 2.3 Summary Statistics Measures of Center and Spread. parameters ( population characteristics )
Ismor Fscher, 8//008 Stat 54 / -8.3 Summary Statstcs Measures of Center and Spread Dstrbuton of dscrete contnuous POPULATION Random Varable, numercal True center =??? True spread =???? parameters ( populaton
More informationComplete Description of the Thelen2003Muscle Model
Compete Description o the he23usce ode Chnd John One o the stndrd musce modes used in OpenSim is the he23usce ctutor Unortuntey, to my knowedge, no other pper or document, incuding the he, 23 pper describing
More informationSVMs for regression Non-parametric/instance based classification method
S 75 Mchne ernng ecture Mos Huskrecht mos@cs.ptt.edu 539 Sennott Squre SVMs for regresson Non-prmetrc/nstnce sed cssfcton method S 75 Mchne ernng Soft-mrgn SVM Aos some fet on crossng the seprtng hperpne
More informationSVMs for regression Multilayer neural networks
Lecture SVMs for regresson Muter neur netors Mos Husrecht mos@cs.ptt.edu 539 Sennott Squre Support vector mchne SVM SVM mmze the mrgn round the seprtng hperpne. he decson functon s fu specfed suset of
More informationHaddow s Experiment:
schemtc drwng of Hddow's expermentl set-up movng pston non-contctng moton sensor bems of sprng steel poston vres to djust frequences blocks of sold steel shker Hddow s Experment: terr frm Theoretcl nd
More information5.2 Exponent Properties Involving Quotients
5. Eponent Properties Involving Quotients Lerning Objectives Use the quotient of powers property. Use the power of quotient property. Simplify epressions involving quotient properties of eponents. Use
More informationragsdale (zdr82) HW6 ditmire (58335) 1 the direction of the current in the figure. Using the lower circuit in the figure, we get
rgsdle (zdr8) HW6 dtmre (58335) Ths prnt-out should hve 5 questons Multple-choce questons my contnue on the next column or pge fnd ll choces efore nswerng 00 (prt of ) 00 ponts The currents re flowng n
More informationRiemann Sums and Riemann Integrals
Riemnn Sums nd Riemnn Integrls Jmes K. Peterson Deprtment of Biologicl Sciences nd Deprtment of Mthemticl Sciences Clemson University August 26, 2013 Outline 1 Riemnn Sums 2 Riemnn Integrls 3 Properties
More informationPhysics 121 Sample Common Exam 2 Rev2 NOTE: ANSWERS ARE ON PAGE 7. Instructions:
Physcs 121 Smple Common Exm 2 Rev2 NOTE: ANSWERS ARE ON PAGE 7 Nme (Prnt): 4 Dgt ID: Secton: Instructons: Answer ll 27 multple choce questons. You my need to do some clculton. Answer ech queston on the
More information0.1 Properties of regulated functions and their Integrals.
MA244 Anlysis III Solutions. Sheet 2. NB. THESE ARE SKELETON SOLUTIONS, USE WISELY!. Properties of regulted functions nd their Integrls.. (Q.) Pick ny ɛ >. As f, g re regulted, there exist φ, ψ S[, b]:
More information1. The vibrating string problem revisited.
Weeks 7 8: S eprtion of Vribes In the pst few weeks we hve expored the possibiity of soving first nd second order PDEs by trnsforming them into simper forms ( method of chrcteristics. Unfortuntey, this
More informationEach term is formed by adding a constant to the previous term. Geometric progression
Chpter 4 Mthemticl Progressions PROGRESSION AND SEQUENCE Sequence A sequence is succession of numbers ech of which is formed ccording to definite lw tht is the sme throughout the sequence. Arithmetic Progression
More informationSection 5.1 #7, 10, 16, 21, 25; Section 5.2 #8, 9, 15, 20, 27, 30; Section 5.3 #4, 6, 9, 13, 16, 28, 31; Section 5.4 #7, 18, 21, 23, 25, 29, 40
Mth B Prof. Audrey Terrs HW # Solutions by Alex Eustis Due Tuesdy, Oct. 9 Section 5. #7,, 6,, 5; Section 5. #8, 9, 5,, 7, 3; Section 5.3 #4, 6, 9, 3, 6, 8, 3; Section 5.4 #7, 8,, 3, 5, 9, 4 5..7 Since
More informationUsing T.O.M to Estimate Parameter of distributions that have not Single Exponential Family
IOSR Journal of Mathematcs IOSR-JM) ISSN: 2278-5728. Volume 3, Issue 3 Sep-Oct. 202), PP 44-48 www.osrjournals.org Usng T.O.M to Estmate Parameter of dstrbutons that have not Sngle Exponental Famly Jubran
More informationPresentation Problems 5
Presenttion Problems 5 21-355 A For these problems, ssume ll sets re subsets of R unless otherwise specified. 1. Let P nd Q be prtitions of [, b] such tht P Q. Then U(f, P ) U(f, Q) nd L(f, P ) L(f, Q).
More informationSUMMER KNOWHOW STUDY AND LEARNING CENTRE
SUMMER KNOWHOW STUDY AND LEARNING CENTRE Indices & Logrithms 2 Contents Indices.2 Frctionl Indices.4 Logrithms 6 Exponentil equtions. Simplifying Surds 13 Opertions on Surds..16 Scientific Nottion..18
More informationJens Siebel (University of Applied Sciences Kaiserslautern) An Interactive Introduction to Complex Numbers
Jens Sebel (Unversty of Appled Scences Kserslutern) An Interctve Introducton to Complex Numbers 1. Introducton We know tht some polynoml equtons do not hve ny solutons on R/. Exmple 1.1: Solve x + 1= for
More informationSolution of Tutorial 5 Drive dynamics & control
ELEC463 Unversty of New South Wles School of Electrcl Engneerng & elecommunctons ELEC463 Electrc Drve Systems Queston Motor Soluton of utorl 5 Drve dynmcs & control 500 rev/mn = 5.3 rd/s 750 rted 4.3 Nm
More informationapproaches as n becomes larger and larger. Since e > 1, the graph of the natural exponential function is as below
. Eponentil nd rithmic functions.1 Eponentil Functions A function of the form f() =, > 0, 1 is clled n eponentil function. Its domin is the set of ll rel f ( 1) numbers. For n eponentil function f we hve.
More informationPART 1 MULTIPLE CHOICE Circle the appropriate response to each of the questions below. Each question has a value of 1 point.
PART MULTIPLE CHOICE Circle the pproprite response to ech of the questions below. Ech question hs vlue of point.. If in sequence the second level difference is constnt, thn the sequence is:. rithmetic
More informationWe partition C into n small arcs by forming a partition of [a, b] by picking s i as follows: a = s 0 < s 1 < < s n = b.
Mth 255 - Vector lculus II Notes 4.2 Pth nd Line Integrls We begin with discussion of pth integrls (the book clls them sclr line integrls). We will do this for function of two vribles, but these ides cn
More informationRiemann Sums and Riemann Integrals
Riemnn Sums nd Riemnn Integrls Jmes K. Peterson Deprtment of Biologicl Sciences nd Deprtment of Mthemticl Sciences Clemson University August 26, 203 Outline Riemnn Sums Riemnn Integrls Properties Abstrct
More informationLecture 12: Discrete Laplacian
Lecture 12: Dscrete Laplacan Scrbe: Tanye Lu Our goal s to come up wth a dscrete verson of Laplacan operator for trangulated surfaces, so that we can use t n practce to solve related problems We are mostly
More informationCALCULUS CLASSROOM CAPSULES
CALCULUS CLASSROOM CAPSULES SESSION S86 Dr. Sham Alfred Rartan Valley Communty College salfred@rartanval.edu 38th AMATYC Annual Conference Jacksonvlle, Florda November 8-, 202 2 Calculus Classroom Capsules
More information523 P a g e. is measured through p. should be slower for lesser values of p and faster for greater values of p. If we set p*
R. Smpth Kumr, R. Kruthk, R. Rdhkrshnn / Interntonl Journl of Engneerng Reserch nd Applctons (IJERA) ISSN: 48-96 www.jer.com Vol., Issue 4, July-August 0, pp.5-58 Constructon Of Mxed Smplng Plns Indexed
More informationSOLUTIONS FOR ADMISSIONS TEST IN MATHEMATICS, COMPUTER SCIENCE AND JOINT SCHOOLS WEDNESDAY 5 NOVEMBER 2014
SOLUTIONS FOR ADMISSIONS TEST IN MATHEMATICS, COMPUTER SCIENCE AND JOINT SCHOOLS WEDNESDAY 5 NOVEMBER 014 Mrk Scheme: Ech prt of Question 1 is worth four mrks which re wrded solely for the correct nswer.
More informationChapter 12. Ordinary Differential Equation Boundary Value (BV) Problems
Chapter. Ordnar Dfferental Equaton Boundar Value (BV) Problems In ths chapter we wll learn how to solve ODE boundar value problem. BV ODE s usuall gven wth x beng the ndependent space varable. p( x) q(
More informationIMPROVISED CHANNEL ASSIGNMENT TECHNIQUE FOR WIRELESS NETWORK USING GENETIC ALGORITHM
Avbe Onne t www.jcsmc.com Internton Journ of Computer Scence nd Mobe Computng A Monthy Journ of Computer Scence nd Informton Technoogy IJCSMC, Vo. 3, Issue. 10, October 2014, pg.932 943 RESEARCH ARTICLE
More informationLECTURE 21 Mohr s Method for Calculation of General Displacements. 1 The Reciprocal Theorem
V. DEMENKO MECHANICS OF MATERIALS 05 LECTURE Mohr s Method for Cacuaton of Genera Dspacements The Recproca Theorem The recproca theorem s one of the genera theorems of strength of materas. It foows drect
More informationBEAM DIAGRAMS AND FORMULAS. Nomenclature
BEA DIAGAS AND FOULAS Nomencture E = moduus of esticity of stee t 9,000 ksi I = moment of inerti of em (in. 4 ) L = tot ength of em etween rection points (ft) m = mimum moment (kip-in.) = mimum moment
More informationA B= ( ) because from A to B is 3 right, 2 down.
8. Vectors nd vector nottion Questions re trgeted t the grdes indicted Remember: mgnitude mens size. The vector ( ) mens move left nd up. On Resource sheet 8. drw ccurtely nd lbel the following vectors.
More informationTrigonometry. Trigonometry. Solutions. Curriculum Ready ACMMG: 223, 224, 245.
Trgonometry Trgonometry Solutons Currulum Redy CMMG:, 4, 4 www.mthlets.om Trgonometry Solutons Bss Pge questons. Identfy f the followng trngles re rght ngled or not. Trngles,, d, e re rght ngled ndted
More informationLine Drawing and Clipping Week 1, Lecture 2
CS 43 Computer Graphcs I Lne Drawng and Clppng Week, Lecture 2 Davd Breen, Wllam Regl and Maxm Peysakhov Geometrc and Intellgent Computng Laboratory Department of Computer Scence Drexel Unversty http://gcl.mcs.drexel.edu
More informationTheoretical foundations of Gaussian quadrature
Theoreticl foundtions of Gussin qudrture 1 Inner product vector spce Definition 1. A vector spce (or liner spce) is set V = {u, v, w,...} in which the following two opertions re defined: (A) Addition of
More informationResearch Article On the Upper Bounds of Eigenvalues for a Class of Systems of Ordinary Differential Equations with Higher Order
Hndw Publshng Corporton Interntonl Journl of Dfferentl Equtons Volume 0, Artcle ID 7703, pges do:055/0/7703 Reserch Artcle On the Upper Bounds of Egenvlues for Clss of Systems of Ordnry Dfferentl Equtons
More informationThe Wave Equation I. MA 436 Kurt Bryan
1 Introduction The Wve Eqution I MA 436 Kurt Bryn Consider string stretching long the x xis, of indeterminte (or even infinite!) length. We wnt to derive n eqution which models the motion of the string
More informationStanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011
Stanford Unversty CS359G: Graph Parttonng and Expanders Handout 4 Luca Trevsan January 3, 0 Lecture 4 In whch we prove the dffcult drecton of Cheeger s nequalty. As n the past lectures, consder an undrected
More informationThe Geometry of Logit and Probit
The Geometry of Logt and Probt Ths short note s meant as a supplement to Chapters and 3 of Spatal Models of Parlamentary Votng and the notaton and reference to fgures n the text below s to those two chapters.
More informationChapter 3 Describing Data Using Numerical Measures
Chapter 3 Student Lecture Notes 3-1 Chapter 3 Descrbng Data Usng Numercal Measures Fall 2006 Fundamentals of Busness Statstcs 1 Chapter Goals To establsh the usefulness of summary measures of data. The
More informationNUMERICAL INTEGRATION. The inverse process to differentiation in calculus is integration. Mathematically, integration is represented by.
NUMERICAL INTEGRATION 1 Introduction The inverse process to differentition in clculus is integrtion. Mthemticlly, integrtion is represented by f(x) dx which stnds for the integrl of the function f(x) with
More informationMath Solutions to homework 1
Mth 75 - Solutions to homework Cédric De Groote October 5, 07 Problem, prt : This problem explores the reltionship between norms nd inner products Let X be rel vector spce ) Suppose tht is norm on X tht
More information4.4 Areas, Integrals and Antiderivatives
. res, integrls nd ntiderivtives 333. Ares, Integrls nd Antiderivtives This section explores properties of functions defined s res nd exmines some connections mong res, integrls nd ntiderivtives. In order
More informationLecture 3 ( ) (translated and slightly adapted from lecture notes by Martin Klazar)
Lecture 3 (5.3.2018) (trnslted nd slightly dpted from lecture notes by Mrtin Klzr) Riemnn integrl Now we define precisely the concept of the re, in prticulr, the re of figure U(, b, f) under the grph of
More informationIs there an easy way to find examples of such triples? Why yes! Just look at an ordinary multiplication table to find them!
PUSHING PYTHAGORAS 009 Jmes Tnton A triple of integers ( bc,, ) is clled Pythgoren triple if exmple, some clssic triples re ( 3,4,5 ), ( 5,1,13 ), ( ) fond of ( 0,1,9 ) nd ( 119,10,169 ). + b = c. For
More informationSolution of Linear System of Equations and Matrix Inversion Gauss Seidel Iteration Method
Soluton of Lnear System of Equatons and Matr Inverson Gauss Sedel Iteraton Method It s another well-known teratve method for solvng a system of lnear equatons of the form a + a22 + + ann = b a2 + a222
More informationChapter Runge-Kutta 2nd Order Method for Ordinary Differential Equations
Cter. Runge-Kutt nd Order Metod or Ordnr Derentl Eutons Ater redng ts cter ou sould be ble to:. understnd te Runge-Kutt nd order metod or ordnr derentl eutons nd ow to use t to solve roblems. Wt s te Runge-Kutt
More informationPhysics 9 Fall 2011 Homework 2 - Solutions Friday September 2, 2011
Physics 9 Fll 0 Homework - s Fridy September, 0 Mke sure your nme is on your homework, nd plese box your finl nswer. Becuse we will be giving prtil credit, be sure to ttempt ll the problems, even if you
More informationDefinition. Measures of Dispersion. Measures of Dispersion. Definition. The Range. Measures of Dispersion 3/24/2014
Measures of Dsperson Defenton Range Interquartle Range Varance and Standard Devaton Defnton Measures of dsperson are descrptve statstcs that descrbe how smlar a set of scores are to each other The more
More informationMath 61CM - Solutions to homework 9
Mth 61CM - Solutions to homework 9 Cédric De Groote November 30 th, 2018 Problem 1: Recll tht the left limit of function f t point c is defined s follows: lim f(x) = l x c if for ny > 0 there exists δ
More informationModel Fitting and Robust Regression Methods
Dertment o Comuter Engneerng Unverst o Clorn t Snt Cruz Model Fttng nd Robust Regresson Methods CMPE 64: Imge Anlss nd Comuter Vson H o Fttng lnes nd ellses to mge dt Dertment o Comuter Engneerng Unverst
More informationComparison of the Population Variance Estimators. of 2-Parameter Exponential Distribution Based on. Multiple Criteria Decision Making Method
Appled Mathematcal Scences, Vol. 7, 0, no. 47, 07-0 HIARI Ltd, www.m-hkar.com Comparson of the Populaton Varance Estmators of -Parameter Exponental Dstrbuton Based on Multple Crtera Decson Makng Method
More information