Package diffdepprop. February 19, 2015
|
|
- Aileen Parker
- 6 years ago
- Views:
Transcription
1 Type Pakage Pakage diffdepprop Feruary 19, 2015 Title Calulates Cofidee Itervals for two Depedet Proportios Versio Date Author Maitaier Daiela Wezel The pakage iludes futios to alulate ofidee itervals for the differee of depedet proportios. There are two futios implemeted to edit the data (dihotomisig with the help of utpoits, outig aordae ad disordae of two tests or situatios). For the alulatio of the ofidee itervals etries of the fourfold tale are eeded. Depeds gee, rootsolve, PropCIs Liese GPL NeedsCompilatio o Repository CRAN Date/Puliatio :07:38 R topis doumeted: diffdepprop-pakage out.fourfold data.p diffpi exat.od exat.midp p.v p.t uod wald wilso wilso wilso.phi
2 2 diffdepprop-pakage Idex 15 diffdepprop-pakage Calulates Cofidee Itervals for two Depedet Proportios Details The pakage iludes futios to alulate ofidee itervals for the differee of depedet proportios. There are two futios implemeted to edit the data (dihotomisig with the help of utpoits, outig aordae ad disordae of two tests or situatios). For the alulatio of the ofidee itervals etries of the fourfold tale are eeded. Pakage: diffdepprop Type: Pakage Versio: Date: Liese: GPL>=2 Maitaier: Daiela Wezel Newome, R.G. (1998). Improved ofidee itervals for the differee etwee iomial proportios ased o paired data. Statistis i Mediie Clopper, C. ad Pearso, E.S. (1934). The use of ofidee or fiduial limits illustrated i the ase of the iomial. Biometrika 26, Vollset, S.E. (1993). Cofidee itervals for a iomial proportio. Statistis i Mediie Lage, K. ad Bruer, E. (2012). Sesitivity, Speifiity ad ROC-urves i multiple reader diagosti trials-a uified, oparametri approah. Statistial Methodology 9, Fleiss, Joseph L. et al. (2003). Statistial Methods for Rates ad Proportios. Wiley. See Also PropCIs
3 out.fourfold 3 # a=10, =15, =5, d=20, =50, type I error is 0.05 wilso = wilso(10,15,5,20,50,0.05) # =15, =5, =50, type I error is 0.05 exat.od = exat.od(15, 5, 50, 0.05) out.fourfold Couts the umers of disordae ad oordae of two tests I the ase two depedet tests shall e ompared a fourfold tale is mostly eeded. out.fourfold outs the umers of oordae ad disordae of oth tests. out.fourfold(data, ol.test1, ol.test2) data ol.test1 ol.test2 ame of the data umer of olum represetig the first test umer of olum represetig the seod test A vetor otaiig the four etries of the fourfold tale, row wise listed # reate a data set with zero ad oes for eah of oth tests v1=(rep(1,10),rep(0,4),rep(1,8),rep(0,8)) v2=(rep(0,10),rep(1,5),rep(0,5),rep(1,10)) =(seq(1,30,1)) ew=id(,v1,v2) # out the umer of oordae ad disordae out.fourfold(ew,1,2)
4 4 data.p data.p Creates iary data of a give data set Biary data are sometimes eeded to aalyse these data. Data of two situatio (e.g. diagosti tests) with otious outome are assumed to e give. With the help of the utpoit for eah test, these data a e dihotomise. data.p(dat, ol.test1, ol.test2, p.test1, p.test2) dat ol.test1 ol.test2 p.test1 p.test2 ame of the data set you wat to e dihotomise umer of the olum of the first test i the data set, whih shall e dihotomised umer of the olum of the seod test i the data set, whih shall e dihotomised utpoit for the first test utpoit for the seod test A matrix otaiig the two tests with iary data # reate a data set v1=(seq(1,10,0.5)) v2=(seq(2,11,0.5)) =(seq(1,19,1)) ew=id(,v1,v2) # utpoit of the first test is 1.6, of the seod test 2.3 result=data.p(ew,2,3,1.6,2.3)
5 diffpi 5 diffpi Calulates various ofidee itervals for the differee of two depedet proportios This futio gives 12 differet two-sided ofidee itervals. Data are assumed to e of a fourfold tale, whih otais the umers of oordae ad the umers of disordae of two depedet methods. The followig itervals are listed: Wald, Wald with otiuity orretio, Agresti, Tago, Exat (Clopper Pearso ad mid-p), Profile Likelihood, Wilso (without ad with otiuity orretios) ad oparametri approahes usig rak methods (with ormal ad t-approximatio). diffpi(a,,, d,, ) a d first umer of oordat paires as desried aove first umer of disordat paires as desried aove seod umer of disordat paires as desried aove seod umer of oordat paires as desried aove umer of oserved ojets Details Details are give for eah futio separately. A matrix otaiig the method, the differee estimator ad the orrespodig ofidee limits. Newome, R.G. (1998). Improved ofidee itervals for the differee etwee iomial proportios ased o paired data. Statistis i Mediie Clopper, C. ad Pearso, E.S. (1934). The use of ofidee or fiduial limits illustrated i the ase of the iomial. Biometrika 26, Vollset, S.E. (1993). Cofidee itervals for a iomial proportio. Statistis i Mediie
6 6 exat.od Lage, K. ad Bruer, E. (2012). Sesitivity, Speifiity ad ROC-urves i multiple reader diagosti trials-a uified, oparametri approah. Statistial Methodology 9, Fleiss, Joseph L. et al. (2003). Statistial Methods for Rates ad Proportios. Wiley. # a=59, =23, =3, d=37, =122, type I error is 0.05 diffpi(59,23,3,37,122,0.05) exat.od Calulates a exat oditioal ofidee iterval usig a Clopper Pearso iterval. exat.od gives a two-sided exat oditial ofidee iterval for the differee of two depedet proportios. It is uilt of a Clopper Pearso Iterval. Data are assumed to e of a fourfold tale, whih otais the umers of oordae ad the umers of disordae of two depedet methods. exat.od(,,, ) first umer of disordat pairs i a fourfold tale as desried aove seod umer of disordat pairs i a fourfold tale as desried aove umer of oserved ojets A list with lass "htest" otaiig the followig ompoets: of.it a ofidee iterval for the differee i proportios d differee i proportios Clopper, C. ad Pearso, E.S. (1934). The use of ofidee or fiduial limits illustrated i the ase of the iomial. Biometrika 26, Newome, R.G. (1998). Improved ofidee itervals for the differee etwee iomial proportios ased o paired data. Statistis i Mediie
7 exat.midp 7 # =10, =20, =50, type I error is 0.05 of.it=exat.od(10,20,50,0.05) exat.midp Calulates a exat oditioal ofidee iterval usig a mid-p iterval. exat.midp gives a two-sided exat oditioal ofidee iterval for the differee of two depedet proportios. It is uilt of a mid-p Iterval. Data are assumed to e of a fourfold tale, whih otais the umers of oordae ad the umers of disordae of two depedet methods. exat.midp(,,, ) first umer of disordat pairs i a fourfold tale as desried aove seod umer of disordat pairs i a fourfold tale as desried aove umer of oserved ojets A list with lass "htest" otaiig the followig ompoets: of.it a ofidee iterval for the differee i proportios d differee i proportios Vollset, S.E. (1993). Cofidee itervals for a iomial proportio. Statistis i Mediie Newome, R.G. (1998). Improved ofidee itervals for the differee etwee iomial proportios ased o paired data. Statistis i Mediie # =10, =20, =50, type I error is 0.05 of.it=exat.midp(10,20,50,0.05)
8 8 p.v p.v Calulates a rak-ased ofidee iterval p.v gives a two-sided rak-ased ofidee iterval with ormal approximatio for the differee of two depedet proportios. Data are assumed to e of a fourfold tale, whih otais the umers of oordae ad the umers of disordae of two depedet methods. p.v(a,,, d,, ) a d first umer of oordat paires as desried aove first umer of disordat paires as desried aove seod umer of disordat paires as desried aove seod umer of oordat paires as desried aove umer of oserved ojets Details The ormal approximatio is used for the ritial value for the iterval. A list with lass "htest" otaiig the followig ompoets: of.it a ofidee iterval for the differee i proportios d differee i proportios Lage, K. ad Bruer, E. (2012). Sesitivity, Speifiity ad ROC-urves i multiple reader diagosti trials-a uified, oparametri approah. Statistial Methodology 9, # a=10, =15, =5, d=20, =50, type I error is 0.05 of.it=p.v(10,15,5,20,50,0.05)
9 p.t 9 p.t Calulates a rak-ased ofidee iterval p.t gives a two-sided rak-ased ofidee iterval with t- approximatio for the differee of two depedet proportios. Data are assumed to e of a fourfold tale, whih otais the umers of oordae ad the umers of disordae of two depedet methods. p.t(a,,, d,, ) a d first umer of oordat paires as desried aove first umer of disordat paires as desried aove seod umer of disordat paires as desried aove seod umer of oordat paires as desried aove umer of oserved ojets Details The t-approximatio is used for the ritial value for the iterval. A list with lass "htest" otaiig the followig ompoets: of.it a ofidee iterval for the differee i proportios d differee i proportios Lage, K. ad Bruer, E. (2012). Sesitivity, Speifiity ad ROC-urves i multiple reader diagosti trials-a uified, oparametri approah. Statistial Methodology 9, # a=10, =15, =5, d=20, =50, type I error is 0.05 of.it=p.t(10,15,5,20,50,0.05)
10 10 uod uod Calulates a uoditioal true profile likelihood ofidee iterval. uod gives a two-sided true profile likelihood ofidee iterval for the differee of two depedet proportios. It is uilt y the solutio of a iequality. Data are assumed to e of a fourfold tale, whih otais the umer of oordae ad the umer of disordae of two depedet methods. uod(a,,, d,, ) a d first umer of oordat paires as desried aove first umer of disordat paires as desried aove seod umer of disordat paires as desried aove seod umer of oordat paires as desried aove umer of oserved ojets Details The true profile likelihood ofidee iterval has as lower limit the miimum of the solutios for the iequality of the maximum likelihood futio ad the quatile of the ormal distriutio. The upper limit is defied as the maximum solutio of this iequality. A list with lass "htest" otaiig the followig ompoets: of.it a ofidee iterval for the differee i proportios d differee i proportios Newome, R.G. (1998). Improved ofidee itervals for the differee etwee iomial proportios ased o paired data. Statistis i Mediie
11 wald. 11 # a=10, =15, =5, d=20, =50, type I error is 0.05 of.it=uod(10,15,5,20,50,0.05) wald. Calulates a Wald ofidee iterval with otiuity orretio wald. gives a two-sided Wald ofidee iterval with otiuity orretio for the differee of two depedet proportios. The otiuity orretio fator is 1. Data are assumed to e of a fourfold tale, whih otais the umers of oordae ad the umers of disordae of two depedet methods. wald.(,,, ) first umer of disordat pairs i a fourfold tale as desried aove seod umer of disordat pairs i a fourfold tale as desried aove umer of oserved ojets A list with lass "htest" otaiig the followig ompoets: of.it a ofidee iterval for the differee i proportios d differee i proportios Fleiss, Joseph L. et al. (2003). Statistial Methods for Rates ad Proportios. Wiley. # =10, =20, =50, type I error is 0.05 of.it=wald.(10,20,50,0.05)
12 12 wilso wilso Calulates a Wilso ofidee iterval wilso gives a two-sided Wilso ofidee iterval for the differee of two depedet proportios. There is o otiuity orretio performed. Data are assumed to e of a fourfold tale, whih otais the umers of oordae ad the umers of disordae of two depedet methods. wilso(a,,, d,, ) a d first umer of oordat paires as desried aove first umer of disordat paires as desried aove seod umer of disordat paires as desried aove seod umer of oordat paires as desried aove umer of oserved ojets A list with lass "htest" otaiig the followig ompoets: of.it a ofidee iterval for the differee i proportios d differee i proportios Newome, R.G. (1998). Improved ofidee itervals for the differee etwee iomial proportios ased o paired data. Statistis i Mediie # a=10, =15, =5, d=20, =50, type I error is 0.05 of.it=wilso(10,15,5,20,50,0.05)
13 wilso. 13 wilso. Calulates a Wilso ofidee iterval with otiuity orretio wilso. gives a two-sided Wilso ofidee iterval with otiuity orretio for the differee of two depedet proportios. The otiuity orretio is performed to the sore limits. Data are assumed to e of a fourfold tale, whih otais the umers of oordae ad the umers of disordae of two depedet methods. wilso.(a,,, d,, ) a d first umer of oordat paires as desried aove first umer of disordat paires as desried aove seod umer of disordat paires as desried aove seod umer of oordat paires as desried aove umer of oserved ojets A list with lass "htest" otaiig the followig ompoets: of.it a ofidee iterval for the differee i proportios d differee i proportios Newome, R.G. (1998). Improved ofidee itervals for the differee etwee iomial proportios ased o paired data. Statistis i Mediie # a=10, =15, =5, d=20, =50, type I error is 0.05 of.it=wilso.(10,15,5,20,50,0.05)
14 14 wilso.phi wilso.phi Calulates a Wilso ofidee iterval with otiuity orretio wilso.phi gives a two-sided Wilso ofidee iterval with otiuity orretio for the differee of two depedet proportios. Data are assumed to e of a fourfold tale, whih otais the umers of oordae ad the umers of disordae of two depedet methods. The otiuity orretio is performed to the d phi whih is alulated y the etries of the fourfold tale. wilso.phi(a,,, d,, ) a d first umer of oordat paires as desried aove first umer of disordat paires as desried aove seod umer of disordat paires as desried aove seod umer of oordat paires as desried aove umer of oserved ojets A list with lass "htest" otaiig the followig ompoets: of.it a ofidee iterval for the differee i proportios d differee i proportios Newome, R.G. (1998). Improved ofidee itervals for the differee etwee iomial proportios ased o paired data. Statistis i Mediie # a=10, =15, =5, d=20, =50, type I error is 0.05 of.it=wilso.phi(10,15,5,20,50,0.05)
15 Idex out.fourfold, 3 data.p, 4 diffdepprop (diffdepprop-pakage), 2 diffdepprop-pakage, 2 diffpi, 5 exat.od, 6 exat.midp, 7 p.v, 8 p.t, 9 uod, 10 wald., 11 wilso, 12 wilso., 13 wilso.phi, 14 15
20.2 Normal and Critical Slopes
Hdraulis Prof. B.. Thadaveswara Rao. Normal ad Critial lopes Whe disharge ad roughess are give, the Maig formula a e used for determiig the slope of the prismati hael i whih the flow is uiform at a give
More informationProbability & Statistics Chapter 8
I. Estimatig with Large Samples Probability & Statistis Poit Estimate of a parameter is a estimate of a populatio parameter give by a sigle umber. Use x (the sample mea) as a poit estimate for (the populatio
More informationChapter 5: Take Home Test
Chapter : Take Home Test AB Calulus - Hardtke Name Date: Tuesday, / MAY USE YOUR CALCULATOR FOR THIS PAGE. Roud aswers to three plaes. Sore: / Show diagrams ad work to justify eah aswer.. Approimate the
More informationOne way Analysis of Variance (ANOVA)
Oe way Aalysis of Variae (ANOVA) ANOVA Geeral ANOVA Settig"Slide 43-45) Ivestigator otrols oe or more fators of iterest Eah fator otais two or more levels Levels a be umerial or ategorial ifferet levels
More informationME260W Mid-Term Exam Instructor: Xinyu Huang Date: Mar
ME60W Mid-Term Exam Istrutor: Xiyu Huag Date: Mar-03-005 Name: Grade: /00 Problem. A atilever beam is to be used as a sale. The bedig momet M at the gage loatio is P*L ad the strais o the top ad the bottom
More informationDigital Signal Processing. Homework 2 Solution. Due Monday 4 October Following the method on page 38, the difference equation
Digital Sigal Proessig Homework Solutio Due Moda 4 Otober 00. Problem.4 Followig the method o page, the differee equatio [] (/4[-] + (/[-] x[-] has oeffiiets a0, a -/4, a /, ad b. For these oeffiiets A(z
More informationChapter 8 Hypothesis Testing
Chapter 8 for BST 695: Speial Topis i Statistial Theory Kui Zhag, Chapter 8 Hypothesis Testig Setio 8 Itrodutio Defiitio 8 A hypothesis is a statemet about a populatio parameter Defiitio 8 The two omplemetary
More information(Dependent or paired samples) Step (1): State the null and alternate hypotheses: Case1: One-tailed test (Right)
(epedet or paired samples) Step (1): State the ull ad alterate hypotheses: Case1: Oe-tailed test (Right) Upper tail ritial (where u1> u or u1 -u> 0) H0: 0 H1: > 0 Case: Oe-tailed test (Left) Lower tail
More informationCalculus 2 TAYLOR SERIES CONVERGENCE AND TAYLOR REMAINDER
Calulus TAYLO SEIES CONVEGENCE AND TAYLO EMAINDE Let the differee betwee f () ad its Taylor polyomial approimatio of order be (). f ( ) P ( ) + ( ) Cosider to be the remaider with the eat value ad the
More informationSupplement S1: RNA secondary structure. structure + sequence format
Supplemet S1: RN seodary struture RN struture is ofte expressed shematially y its ase pairig: the Watso-rik (W) ase pairs (deie) with (rail), ad G (Guaie) with (ytosie) ad also the o-watso-rik (o-w) ase
More informationNonparametric Goodness-of-Fit Tests for Discrete, Grouped or Censored Data 1
Noparametri Goodess-of-Fit Tests for Disrete, Grouped or Cesored Data Boris Yu. Lemeshko, Ekateria V. Chimitova ad Stepa S. Kolesikov Novosibirsk State Tehial Uiversity Departmet of Applied Mathematis
More informationThe beta density, Bayes, Laplace, and Pólya
The beta desity, Bayes, Laplae, ad Pólya Saad Meimeh The beta desity as a ojugate form Suppose that is a biomial radom variable with idex ad parameter p, i.e. ( ) P ( p) p ( p) Applyig Bayes s rule, we
More informationBernoulli Numbers. n(n+1) = n(n+1)(2n+1) = n(n 1) 2
Beroulli Numbers Beroulli umbers are amed after the great Swiss mathematiia Jaob Beroulli5-705 who used these umbers i the power-sum problem. The power-sum problem is to fid a formula for the sum of the
More informationSTK4011 and STK9011 Autumn 2016
STK4 ad STK9 Autum 6 ypothesis testig Covers (most of) the followig material from hapter 8: Setio 8. Setios 8.. ad 8..3 Setio 8.3. Setio 8.3. (util defiitio 8.3.6) Ørulf Borga Departmet of Mathematis Uiversity
More informationBinary Data in Epidemiology Epidemiologic Measures of Association with Time to Event Data
Leture 3: Measures of Assoiatio tober 3, 23 Biost 536 / Epi 536 Categorial Data Aalysis i Epidemiology Sott S. Emerso, M.D., Ph.D. Professor of Biostatistis Uiversity of Washigto Leture utlie Measures
More informationProduct Moments of Sample Variances and Correlation for Variables with Bivariate Normal Distribution
Joural of Matheatis ad Statistis Origial Researh Paper Produt Moets of Saple Variaes ad Correlatio for Variales with Bivariate Noral Distriutio Jua Roero-Padilla LNPP-Coayt, Ceter for Researh ad Teahig
More informationMath Third Midterm Exam November 17, 2010
Math 37 1. Treibergs σιι Third Midterm Exam Name: November 17, 1 1. Suppose that the mahie fillig mii-boxes of Fruitlad Raisis fills boxes so that the weight of the boxes has a populatio mea µ x = 14.1
More information1 Models for Matched Pairs
1 Models for Matched Pairs Matched pairs occur whe we aalyse samples such that for each measuremet i oe of the samples there is a measuremet i the other sample that directly relates to the measuremet i
More informationSome Properties of the Exact and Score Methods for Binomial Proportion and Sample Size Calculation
Some Properties of the Exact ad Score Methods for Biomial Proportio ad Sample Size Calculatio K. KRISHNAMOORTHY AND JIE PENG Departmet of Mathematics, Uiversity of Louisiaa at Lafayette Lafayette, LA 70504-1010,
More informationOn generalized Simes critical constants
Biometrial Joural 56 04 6, 035 054 DOI: 0.00/bimj.030058 035 O geeralized Simes ritial ostats Jiagtao Gou ad Ajit C. Tamhae, Departmet of Statistis, Northwester Uiversity, 006 Sherida Road, Evasto, IL
More informationThe binom Package. February 13, 2007
The biom Package February 13, 2007 Title Biomial Cofidece Itervals For Several Parameterizatios Versio 1.0-1 Author Sudar Dorai-Raj Costructs cofidece itervals o the probability
More informationANOTHER PROOF FOR FERMAT S LAST THEOREM 1. INTRODUCTION
ANOTHER PROOF FOR FERMAT S LAST THEOREM Mugur B. RĂUŢ Correspodig author: Mugur B. RĂUŢ, E-mail: m_b_raut@yahoo.om Abstrat I this paper we propose aother proof for Fermat s Last Theorem (FLT). We foud
More informationAbstract. Introduction
Derrick, B., White, P., ad Toher, D. A Iverse Normal Trasformatio Solutio for the compariso of two samples that cotai oth paired oservatios ad idepedet oservatios. Astract Iverse ormal trasformatios applied
More informationAfter the completion of this section the student. V.4.2. Power Series Solution. V.4.3. The Method of Frobenius. V.4.4. Taylor Series Solution
Chapter V ODE V.4 Power Series Solutio Otober, 8 385 V.4 Power Series Solutio Objetives: After the ompletio of this setio the studet - should reall the power series solutio of a liear ODE with variable
More informationSociété de Calcul Mathématique SA Mathematical Modelling Company, Corp.
oiété de Calul Mathéatique A Matheatial Modellig Copay, Corp. Deisio-aig tools, sie 995 iple Rado Wals Part V Khihi's Law of the Iterated Logarith: Quatitative versios by Berard Beauzay August 8 I this
More informationPackage PredictionR. October 6, Index 7. Best fitting of a distribution to a data
Package PredictioR October 6, 2018 Title Predictio for Future Data from ay Cotiuous Distributio Versio 1.0-11 Author H. M. Barakat [aut], O. M. Khaled [aut], Hadeer A. Ghoem [aut, cre] Maitaier Hadeer
More informationName Date PRECALCULUS SUMMER PACKET
Name Date PRECALCULUS SUMMER PACKET This packet covers some of the cocepts that you eed to e familiar with i order to e successful i Precalculus. This summer packet is due o the first day of school! Make
More informationHOUSEHOLDER S APPROXIMANTS AND CONTINUED FRACTION EXPANSION OF QUADRATIC IRRATIONALS. Vinko Petričević University of Zagreb, Croatia
HOUSEHOLDER S APPROXIMANTS AND CONTINUED FRACTION EXPANSION OF QUADRATIC IRRATIONALS Viko Petričević Uiversity of Zagre, Croatia Astract There are umerous methods for ratioal approximatio of real umers
More informationε > 0 N N n N a n < ε. Now notice that a n = a n.
4 Sequees.5. Null sequees..5.. Defiitio. A ull sequee is a sequee (a ) N that overges to 0. Hee, by defiitio of (a ) N overges to 0, a sequee (a ) N is a ull sequee if ad oly if ( ) ε > 0 N N N a < ε..5..
More informationLet us give one more example of MLE. Example 3. The uniform distribution U[0, θ] on the interval [0, θ] has p.d.f.
Lecture 5 Let us give oe more example of MLE. Example 3. The uiform distributio U[0, ] o the iterval [0, ] has p.d.f. { 1 f(x =, 0 x, 0, otherwise The likelihood fuctio ϕ( = f(x i = 1 I(X 1,..., X [0,
More informationSupplementary Material for: Classical Testing in Functional Linear Models
To appear i the Joural of Noparametri Statistis Vol. 00, No. 00, Moth 20XX, 1 16 Supplemetary Material for: Classial Testig i utioal Liear Models Deha Kog a Aa-Maria Staiu b ad Arab Maity b a Departmet
More informationSummation Method for Some Special Series Exactly
The Iteratioal Joural of Mathematis, Siee, Tehology ad Maagemet (ISSN : 39-85) Vol. Issue Summatio Method for Some Speial Series Eatly D.A.Gismalla Deptt. Of Mathematis & omputer Studies Faulty of Siee
More informationOne-Sample Test for Proportion
Oe-Sample Test for Proportio Approximated Oe-Sample Z Test for Proportio CF Jeff Li, MD., PhD. November 1, 2005 c Jeff Li, MD., PhD. c Jeff Li, MD., PhD. Oe Sample Test for Proportio, 1 I DM-TKR Data,
More informationObserver Design with Reduced Measurement Information
Observer Desig with Redued Measuremet Iformatio I pratie all the states aot be measured so that SVF aot be used Istead oly a redued set of measuremets give by y = x + Du p is available where y( R We assume
More informationSx [ ] = x must yield a
Math -b Leture #5 Notes This wee we start with a remider about oordiates of a vetor relative to a basis for a subspae ad the importat speial ase where the subspae is all of R. This freedom to desribe vetors
More informationAN OPTIMIZATION APPROACH TO UNCERTAINTY PROPAGATION IN BOUNDARY LOAD FLOW
AN OPIMIZAION APPROACH O UNCERAINY PROPAGAION IN BOUNDARY LOAD FLOW Adrija. Sarić Northeaster Uiversity Bosto, Massahusetts asari@tf.kg.a.yu Brako Glišović Northeaster Uiversity Bosto, Massahusetts glisovi@ee.eu.edu
More informationAddendum. Addendum. Vector Review. Department of Computer Science and Engineering 1-1
Addedum Addedum Vetor Review Deprtmet of Computer Siee d Egieerig - Coordite Systems Right hded oordite system Addedum y z Deprtmet of Computer Siee d Egieerig - -3 Deprtmet of Computer Siee d Egieerig
More informationMATH/STAT 352: Lecture 15
MATH/STAT 352: Lecture 15 Sectios 5.2 ad 5.3. Large sample CI for a proportio ad small sample CI for a mea. 1 5.2: Cofidece Iterval for a Proportio Estimatig proportio of successes i a biomial experimet
More informationSolutions 3.2-Page 215
Solutios.-Page Problem Fid the geeral solutios i powers of of the differetial equatios. State the reurree relatios ad the guarateed radius of overgee i eah ase. ) Substitutig,, ad ito the differetial equatio
More informationGRAPHING LINEAR EQUATIONS. Linear Equations ( 3,1 ) _x-axis. Origin ( 0, 0 ) Slope = change in y change in x. Equation for l 1.
GRAPHING LINEAR EQUATIONS Quadrat II Quadrat I ORDERED PAIR: The first umer i the ordered pair is the -coordiate ad the secod umer i the ordered pair is the y-coordiate. (,1 ) Origi ( 0, 0 ) _-ais Liear
More informationWRITTEN ASSIGNMENT 1 ANSWER KEY
CISC 65 Itrodutio Desig ad Aalysis of Algorithms WRITTEN ASSIGNMENT ANSWER KEY. Problem -) I geeral, this problem requires f() = some time period be solve for a value. This a be doe for all ase expet lg
More informationExplicit and closed formed solution of a differential equation. Closed form: since finite algebraic combination of. converges for x x0
Chapter 4 Series Solutios Epliit ad losed formed solutio of a differetial equatio y' y ; y() 3 ( ) ( 5 e ) y Closed form: sie fiite algebrai ombiatio of elemetary futios Series solutio: givig y ( ) as
More informationSYNTHESIS OF SIGNAL USING THE EXPONENTIAL FOURIER SERIES
SYNTHESIS OF SIGNAL USING THE EXPONENTIAL FOURIER SERIES Sadro Adriao Fasolo ad Luiao Leoel Medes Abstrat I 748, i Itrodutio i Aalysi Ifiitorum, Leohard Euler (707-783) stated the formula exp( jω = os(
More informationAdvanced Sensitivity Analysis of the Semi-Assignment Problem
Proeedigs of the 202 Iteratioal Coferee o Idustrial Egieerig ad Operatios Maagemet Istabul, Turkey, July 3 6, 202 Advaed Sesitivity Aalysis of the Semi-Assigmet Problem Shih-Tig Zeg ad Ue-Pyg We Departmet
More informationBayesian Estimation and Prediction for. a Mixture of Exponentiated Kumaraswamy. Distributions
Iteratioal Joural of Cotemporary Mathematial Siees Vol. 11, 2016, o. 11, 497-508 HIKARI Ltd, www.m-hikari.om https://doi.org/10.12988/ijms.2016.61165 Bayesia Estimatio ad Preditio for a Mixture of Expoetiated
More informationSOME NOTES ON INEQUALITIES
SOME NOTES ON INEQUALITIES Rihard Hoshio Here are four theorems that might really be useful whe you re workig o a Olympiad problem that ivolves iequalities There are a buh of obsure oes Chebyheff, Holder,
More informationOptimal Management of the Spare Parts Stock at Their Regular Distribution
Joural of Evirometal Siee ad Egieerig 7 (018) 55-60 doi:10.1765/16-598/018.06.005 D DVID PUBLISHING Optimal Maagemet of the Spare Parts Stok at Their Regular Distributio Svetozar Madzhov Forest Researh
More informationFluids Lecture 2 Notes
Fluids Leture Notes. Airfoil orte Sheet Models. Thi-Airfoil Aalysis Problem Readig: Aderso.,.7 Airfoil orte Sheet Models Surfae orte Sheet Model A aurate meas of represetig the flow about a airfoil i a
More informationPass-Fail Testing: Statistical Requirements and Interpretations
Joural of Researh of the Natioal Istitute of Stadards ad Tehology [J. Res. Natl. Ist. Stad. Tehol. 4, 95-99 (2009)] Pass-Fail Testig: Statistial Requiremets ad Iterpretatios Volume 4 Number 3 May-Jue 2009
More informationf(x i ; ) L(x; p) = i=1 To estimate the value of that maximizes L or equivalently ln L we will set =0, for i =1, 2,...,m p x i (1 p) 1 x i i=1
Parameter Estimatio Samples from a probability distributio F () are: [,,..., ] T.Theprobabilitydistributio has a parameter vector [,,..., m ] T. Estimator: Statistic used to estimate ukow. Estimate: Observed
More informationThe picture in figure 1.1 helps us to see that the area represents the distance traveled. Figure 1: Area represents distance travelled
1 Lecture : Area Area ad distace traveled Approximatig area by rectagles Summatio The area uder a parabola 1.1 Area ad distace Suppose we have the followig iformatio about the velocity of a particle, how
More informationEfficient GMM LECTURE 12 GMM II
DECEMBER 1 010 LECTURE 1 II Efficiet The estimator depeds o the choice of the weight matrix A. The efficiet estimator is the oe that has the smallest asymptotic variace amog all estimators defied by differet
More informationBasic Probability/Statistical Theory I
Basi Probability/Statistial Theory I Epetatio The epetatio or epeted values of a disrete radom variable X is the arithmeti mea of the radom variable s distributio. E[ X ] p( X ) all Epetatio by oditioig
More informationCOMP26120: Introducing Complexity Analysis (2018/19) Lucas Cordeiro
COMP60: Itroduig Complexity Aalysis (08/9) Luas Cordeiro luas.ordeiro@mahester.a.uk Itroduig Complexity Aalysis Textbook: Algorithm Desig ad Appliatios, Goodrih, Mihael T. ad Roberto Tamassia (hapter )
More informationProduction Test of Rotary Compressors Using Wavelet Analysis
Purdue Uiversity Purdue e-pubs Iteratioal Compressor Egieerig Coferee Shool of Mehaial Egieerig 2006 Produtio Test of Rotary Compressors Usig Wavelet Aalysis Haishui Ji Shaghai Hitahi Eletrial Appliatio
More information(8) 1f = f. can be viewed as a real vector space where addition is defined by ( a1+ bi
Geeral Liear Spaes (Vetor Spaes) ad Solutios o ODEs Deiitio: A vetor spae V is a set, with additio ad salig o elemet deied or all elemets o the set, that is losed uder additio ad salig, otais a zero elemet
More informationIntroduction to Matrix Algebra
Itrodutio to Mtri Alger George H Olso, Ph D Dotorl Progrm i Edutiol Ledership Applhi Stte Uiversit Septemer Wht is mtri? Dimesios d order of mtri A p q dimesioed mtri is p (rows) q (olums) rr of umers,
More informationDepartment of Biostatistics University of North Carolina at Chapel Bill. Institute of Statistics- Mimeo Series No
ON SEQUENTIAL NONPARAMETRIC ESTIMATION OF MULTIVARIATE LQCATION by Praab Kumar Se Departmet of Biostatistis Uiversity of North Carolia at Chapel Bill Istitute of Statistis- Mimeo Series No. 1452 Otober
More informationTHE MEASUREMENT OF THE SPEED OF THE LIGHT
THE MEASUREMENT OF THE SPEED OF THE LIGHT Nyamjav, Dorjderem Abstrat The oe of the physis fudametal issues is a ature of the light. I this experimet we measured the speed of the light usig MihelsoÕs lassial
More informationThe program calculates the required thickness of doubler plates using the following algorithms. The shear force in the panel zone is given by: V p =
COMPUTERS AND STRUCTURES, INC., BERKELEY, CALIFORNIA DECEMBER 2001 STEEL FRAME DESIGN AISC-ASD89 Tehial Note Oe aspet of the esig of a steel framig system is a evaluatio of the shear fores that exist i
More informationWhat is a Hypothesis? Hypothesis is a statement about a population parameter developed for the purpose of testing.
What is a ypothesis? ypothesis is a statemet about a populatio parameter developed for the purpose of testig. What is ypothesis Testig? ypothesis testig is a proedure, based o sample evidee ad probability
More informationHOUSEHOLDER S APPROXIMANTS AND CONTINUED FRACTION EXPANSION OF QUADRATIC IRRATIONALS. Vinko Petričević University of Zagreb, Croatia
HOUSEHOLDER S APPROXIMANTS AND CONTINUED FRACTION EXPANSION OF QUADRATIC IRRATIONALS Viko Petričević Uiversity of Zagre, Croatia Astract There are umerous methods for ratioal approximatio of real umers
More informationN A N A ( ) We re-arrange and collapse the random variables into a set corresponding to the weighted
7- trodutio Note that rom 08bx0v3.do (p6) while rom page 7, ad Title Bo Xu Y = μ T B E N N ( Y ) = ( ) UV N μ Commet: Bo, Write a brie itrodutio explaiig what this doumet will do. You a opy parts rom the
More informationConfidence intervals summary Conservative and approximate confidence intervals for a binomial p Examples. MATH1005 Statistics. Lecture 24. M.
MATH1005 Statistics Lecture 24 M. Stewart School of Mathematics ad Statistics Uiversity of Sydey Outlie Cofidece itervals summary Coservative ad approximate cofidece itervals for a biomial p The aïve iterval
More informationRecommendation T/N (Edinburgh 1988)
o T/N -0 E B Page Reommedatio T/N -0 (Ediburgh ) TESTNG THE COMPLANCE OF AN EQUPMENT WTH TS RELABLTY, MANTANABLTY AND AVALABLTY SPECFCATONS Reommedatio proposed by Workig Group T/WG Network Aspets (NA)
More information4. Optical Resonators
S. Blair September 3, 2003 47 4. Optial Resoators Optial resoators are used to build up large itesities with moderate iput. Iput Iteral Resoators are typially haraterized by their quality fator: Q w stored
More informationPrincipal Component Analysis. Nuno Vasconcelos ECE Department, UCSD
Priipal Compoet Aalysis Nuo Vasoelos ECE Departmet, UCSD Curse of dimesioality typial observatio i Bayes deisio theory: error ireases whe umber of features is large problem: eve for simple models (e.g.
More informationComputer Science 188 Artificial Intelligence. Introduction to Probability. Probability Ryan Waliany
Computer Siee 88 Artifiial Itelligee Rya Waliay Note: this is meat to be a simple referee sheet ad help studets uder the derivatios. If there s aythig that seems shaky or iorret do t hesitate to email
More informationExploring randomness
Explorig radomess swers Skills hek Write i asedig order:,, 9, 9, 9, 9, 9,,,,, a Media is etwee 9 ad 9 9. kg Mode kg (most frequet) Mea total umer of oservatios 9. kg d Rage 9 kg e f Lower quartile th oservatio
More informationPackage labelrank. November 22, 2015
Tpe Package Title Predictig Rakigs of Labels Versio 0.1 Date 2015-11-20 Depeds R (>= 2.10) Imports pdist Package labelrak November 22, 2015 A implemetatio of distace-based rakig algorithms to predict rakigs
More informationRENEWAL AND AVAILABILITY FUNCTIONS
Advaes ad Appliatios i Statistis 14 Pushpa Publishig House, Allahabad, Idia Available olie at http://pphmj.om/jourals/adas.htm Volume 43, Number 1, 14, Pages 65-89 RENEWAL AND AVAILABILITY FUNCTIONS Dilu
More informationPrincipal Component Analysis
Priipal Compoet Aalysis Nuo Vasoelos (Ke Kreutz-Delgado) UCSD Curse of dimesioality Typial observatio i Bayes deisio theory: Error ireases whe umber of features is large Eve for simple models (e.g. Gaussia)
More informationThe Poisson Distribution
MATH 382 The Poisso Distributio Dr. Neal, WKU Oe of the importat distributios i probabilistic modelig is the Poisso Process X t that couts the umber of occurreces over a period of t uits of time. This
More informationAbstract Vector Spaces. Abstract Vector Spaces
Astract Vector Spaces The process of astractio is critical i egieerig! Physical Device Data Storage Vector Space MRI machie Optical receiver 0 0 1 0 1 0 0 1 Icreasig astractio 6.1 Astract Vector Spaces
More informationTo investigate the relationship between the work done to accelerate a trolley and the energy stored in the moving trolley.
SP2h.1 Aelerating trolleys Your teaher may wath to see if you an follow instrutions safely take areful measurements. Introdution The work done y a fore is a measure of the energy transferred when a fore
More informationThe effect of Soret parameter on the onset of double diffusive convection in a Darcy porous medium saturated with couple stress fluid
Availale olie at www.pelagiaresearhlirary.om Advaes i Applied iee Researh, 1, 3 (3):146-1434 IN: 976-861 CODEN (UA): AARFC he effet of oret parameter o the oset of doule diffusive ovetio i a Dary porous
More informationStatistical Inference Based on Extremum Estimators
T. Rotheberg Fall, 2007 Statistical Iferece Based o Extremum Estimators Itroductio Suppose 0, the true value of a p-dimesioal parameter, is kow to lie i some subset S R p : Ofte we choose to estimate 0
More informationNCSS Statistical Software. Tolerance Intervals
Chapter 585 Itroductio This procedure calculates oe-, ad two-, sided tolerace itervals based o either a distributio-free (oparametric) method or a method based o a ormality assumptio (parametric). A two-sided
More informationAnother face of DIRECT
Aother ae o DIEC Lakhdar Chiter Departmet o Mathematis, Seti Uiversity, Seti 19000, Algeria E-mail address: hiterl@yahoo.r Abstrat It is show that, otrary to a laim o [D. E. Fikel, C.. Kelley, Additive
More informationMULTILEVEL ANALYSIS OF DELAMINATION INITIATED NEAR THE EDGES OF COMPOSITE STRUCTURES
MULTILEVEL ANALYSIS OF DELAMINATION INITIATED NEAR THE EDGES OF COMPOSITE STRUCTURES N. Carrere 1, T. Vadellos 1, E. Marti 1 ONERA, 9 av. de la Divisio Leler, 930 Châtillo, Frae LCTS, 3 Allée de la Boétie,
More informationPOWER SERIES METHODS CHAPTER 8 SECTION 8.1 INTRODUCTION AND REVIEW OF POWER SERIES
CHAPTER 8 POWER SERIES METHODS SECTION 8. INTRODUCTION AND REVIEW OF POWER SERIES The power series method osists of substitutig a series y = ito a give differetial equatio i order to determie what the
More informationSolving the ZF Receiver Equation for MIMO Systems Under Variable Channel Conditions Using the Block Fourier Algorithm
006 IEEE ith Iteratioal Symposium o Spread Spetrum Tehiques ad Appliatios Solvig the ZF Reeiver Equatio for MIMO Systems Uder Variable hael oditios Usig the Blok Fourier Algorithm João arlos Silva, Rui
More informationLecture Note 8 Point Estimators and Point Estimation Methods. MIT Spring 2006 Herman Bennett
Lecture Note 8 Poit Estimators ad Poit Estimatio Methods MIT 14.30 Sprig 2006 Herma Beett Give a parameter with ukow value, the goal of poit estimatio is to use a sample to compute a umber that represets
More informationNumber Of Real Zeros Of Random Trigonometric Polynomial
Iteratioal Joral of Comtatioal iee ad Mathematis. IN 97-389 Volme 7, Nmer (5),. 9- Iteratioal Researh Pliatio Hose htt://www.irhose.om Nmer Of Real Zeros Of Radom Trigoometri Polyomial Dr.P.K.Mishra, DR.A.K.Mahaatra,
More information\,. Si2:nal Detection and. Optical AmpUfler or Signal Regenentor ""' Fiber La~er Laser Coupler Driver Diode,-~ [> I I ~ : Modulator. Splice.
Sieal Geeratio!! Ietroi Fiber Laer Laser Coupler Driver Diode, [> I I : Modulator Iterfae I I: t A Trasissio Mediu Splie ptial ApUfler or Sigal Regeetor ""' I N C y Coditioig ' Eletrois I Eletroil Detetor
More informationLesson 23: The Defining Equation of a Line
Student Outomes Students know that two equations in the form of ax + y = and a x + y = graph as the same line when a = = and at least one of a or is nonzero. a Students know that the graph of a linear
More informationAnna Janicka Mathematical Statistics 2018/2019 Lecture 1, Parts 1 & 2
Aa Jaicka Mathematical Statistics 18/19 Lecture 1, Parts 1 & 1. Descriptive Statistics By the term descriptive statistics we will mea the tools used for quatitative descriptio of the properties of a sample
More informationCanimals. borrowed, with thanks, from Malaspina University College/Kwantlen University College
Canimals borrowed, with thanks, from Malaspina University College/Kwantlen University College http://ommons.wikimedia.org/wiki/file:ursus_maritimus_steve_amstrup.jpg Purpose Investigate the rate of heat
More informationRandom Matrices with Blocks of Intermediate Scale Strongly Correlated Band Matrices
Radom Matrices with Blocks of Itermediate Scale Strogly Correlated Bad Matrices Jiayi Tog Advisor: Dr. Todd Kemp May 30, 07 Departmet of Mathematics Uiversity of Califoria, Sa Diego Cotets Itroductio Notatio
More informationUniversity of Arizona ECE/OPTI 500C: Photonic Communications Engineering I C Fall Forward Error Correction (FEC) By Ivan B.
Uiversity of Arizoa ECE/OPTI 500C: Photoi Commuiatios Egieerig I C Fall 00 Forward Error Corretio (FEC) By Iva B. Djordjevi j Codig for Optial Chaels Importat lasses of odes: Liear lok odes Cyli odes Covolutioal
More informationβ COMPACT SPACES IN FUZZIFYING TOPOLOGY *
Iraia Joural of Siee & Tehology, Trasatio A, Vol 30, No A3 Prited i The Islami Republi of Ira, 2006 Shiraz Uiversity FUZZ IRRESOLUTE FUNCTIONS AND FUZZ COMPACT SPACES IN FUZZIFING TOPOLOG * O R SAED **
More informationMaximum Likelihood Estimation
Chapter 9 Maximum Likelihood Estimatio 9.1 The Likelihood Fuctio The maximum likelihood estimator is the most widely used estimatio method. This chapter discusses the most importat cocepts behid maximum
More informationMath 151 Introduction to Eigenvectors
Math 151 Introdution to Eigenvetors The motivating example we used to desrie matrixes was landsape hange and vegetation suession. We hose the simple example of Bare Soil (B), eing replaed y Grasses (G)
More informationLesson 8 Refraction of Light
Physis 30 Lesso 8 Refratio of Light Refer to Pearso pages 666 to 674. I. Refletio ad Refratio of Light At ay iterfae betwee two differet mediums, some light will be refleted ad some will be refrated, exept
More informationSolution of heat equation with variable coefficient using derive
Buffespoort TIME8 Peer-reviewed Coferee Proeedigs, 6 Septeber 8 Soutio of heat equatio with variabe oeffiiet ug derive RS Lebeo α, I Fedotov ad M Shataov β Departet of Matheatis ad Statistis Tshwae Uiversity
More informationCompression Members Local Buckling and Section Classification
Compression Memers Loal Bukling and Setion Classifiation Summary: Strutural setions may e onsidered as an assemly of individual plate elements. Plate elements may e internal (e.g. the wes of open eams
More informationPhysics 30 Lesson 8 Refraction of Light
Physis 30 Lesso 8 Refratio of Light Refer to Pearso pages 666 to 674. I. Refletio ad refratio of light At ay iterfae betwee two differet mediums, some light will be refleted ad some will be refrated, exept
More informationLesson 4. Thermomechanical Measurements for Energy Systems (MENR) Measurements for Mechanical Systems and Production (MMER)
Lesso 4 Thermomehaial Measuremets for Eergy Systems (MENR) Measuremets for Mehaial Systems ad Produtio (MMER) A.Y. 15-16 Zaaria (Rio ) Del Prete RAPIDITY (Dyami Respose) So far the measurad (the physial
More informationPhysics 3 (PHYF144) Chap 8: The Nature of Light and the Laws of Geometric Optics - 1
Physis 3 (PHYF44) Chap 8: The Nature of Light ad the Laws of Geometri Optis - 8. The ature of light Before 0 th etury, there were two theories light was osidered to be a stream of partiles emitted by a
More informationRecursive Computations for Discrete Random Variables
Recursive Computatios for Discrete Radom Variables Ofte times, oe sees a problem stated like this: A machie is shut dow for repairs is a radom sample of 100 items selected from the dail output of the machie
More information