HANDY REFERENCE SHEET HRP/STATS 261, Discrete Data
|
|
- Marcus Daniel
- 5 years ago
- Views:
Transcription
1 Bary prdctor Bary outcom HANDY REFERENCE SHEE HRP/SAS 6, Dscrt Data x Cotgcy abls Dsas (D No Dsas (~D Exposd (E a b Uxposd (~E c d Masurs of Assocato a /( a + b Rs Rato = c /( c + d RR * xp a /( a+ b c /( c+ d a /( a+ b c /( c+ d a c a c, RR * xp Odds Rato = bc OR * xp a b c d, OR * xp a b c d Dffrc Proportos: H 0 : pd / E pd /~ E = 0 [for cas-cotrol study: p / d p /~ d = 0 ] st Statstc: ( p d / pˆ ( p d / d / pˆ d ( p + /~ d /~ ( p ~ d /~ ~ Z ( pˆ d / pˆ d /~ ±.96 * ( pˆ d / ( pˆ d / ( pˆ + d /~ ( pˆ ~ d /~ proc frq data=yourdata; tabls yourexposur*youroutcom /masurs cl; wght couts; *f you ha groupd data;
2 Bary prdctor Bary outcom Catgorcal coarat xxk Cotgcy abls Notato: outcom=d; prdctor=; catgorcal coarat= Stps. Calculat crud OR d- (or RR d-. Calculat stratum-spcfc OR s: OR d-/=k 3. If crud OR stratum-spcfc OR s ar all smlar SOP. s ully to b a cofoudr or a ffct modfr, you may us usual mthods for x tabl (s pag, gorg. 4. If crud OR stratum-spcfc OR s dffr procd to (a or (b blow: a If stratum-spcfc OR s ar smlar to ach othr suspct cofoudg ( Apply Cochra-Matl-Haszl tst of codtoal dpdc: H 0 : d ar codtoally dpdt. st Statstc: No Dsas Dsas Exposd a b Uxposd c d [ (a = = E(a Var(a ] ( a + b ( a + c E(a = ; whr ( a + b ( a + c ( b + d ( c + d Var(a = ( = total stratum ( Calculat Matl-Haszl (MH summary OR (justd for cofoudg by. If you rjctd th ull (, th MH OR should b.0. If you dd ot rjct ull (, th MH OR should b.0. OR = d / = = a d b c For RR: RR d / = = = a ( c c ( a + d + b b If stratum-spcfc OR s dffr from ach othr suspct tracto (ffct modfcato ( Apply Brslow-Day tst of homogty of th OR s: H 0 : stratum-spcfc OR s ar qual (homogous st Statstc: complx us computr softwar If you rjct ull ffct modfcato s prst. Rport stratumspcfc OR s If you fal to rjct ull suffct dc of ffct modfcato; calculat Matl-Haszl summary OR, as abo. proc frq data=yourdata; tabls yourcoarat*yourexposur*youroutcom /cmh; wght couts; *f you ha groupd data;
3 Catgorcal Varabls that lac clar prdctor/outcom dstcto Mult-way Cotgcy abls: Notato: x, y, z ar catgorcal arabls Ch-squar tst of dpdc (RxC tabl: H 0 : x, y, z ar dpdt st Statstc: clls ( Obsrd Expctd Expctd ( rows ( colums Log-Lar Modl log( couts = + β x + β y + β z + tractos f approprat x y z Odds rato trprtato (f x tabl: log( ORd = log( = log a + log d logb logc bc log( = ( + β d + β + β d + ( ( + β d ( + β bc OR = β d* * = β d* Ch-squar tst proc frq data=yourdata; tabls yourx*youry*yourk / chsq; wght couts; *f you ha groupd data; Log-lar modls proc gmod data=yourdata; modl total = yourx youry yourk YourItractos / dst=posso l=log prd;
4 Urstrctd prdctors coarats Bary outcom Logstc Rgrsso Modl: p l( = + β x + β x p + sts of Modl Ft: Wald st... Smplst form of th logstc llhood (from x: + β a b c d l( β = ( x( x( x( + β + β Dsas No Dsas Exposd a b Uxposd c d ˆ β 0 Z = asymptotc stard rror ( ˆ β Llhood Rato st: whr full modl has paramtrs rducd modl has -p rducd l = full l( rducd [ l( full] p Itrprtato of Estmatd Coffcts Odds Probablts: OR trprtato: OR xposur = β xp OR trprtato th prsc of tracto (two bary prdctors: β + β + β OR xposur/tractg factor prst = OR xposur/tractg factor abst = β Probablty trprtato: P(D/E = + β + + β proc logstc data = aalyss; class YourPrdctor_c (rf= "YourBasl"; * automatc dummy codg to gt > OR agast th rfrc group; modl YourOutcom_c (t = "Ys" = YourPrdctor / lacft; *lacft gs Hosmr Lmshow goodss of ft ch-squar tst; output out = OutDataSt p = Prdctd; *outputs prdctd probablts to w datast;
5 Par-Matchd Data Par-Matchd Data Bary Prdctor Bary Outcom Cas Matchd-cotrol Exposd Uxposd Exposd a b Uxposd c d Odds Rato b OR = c McNmar s st H 0 : xposur dsas ar dpdt st Statstc: ( b c b + c :M Matchd Data Codtoal Logstc Rgrsso: h smplst llhood (from x: β b c l ( β = ( x( β β + + Matchd Data Urstrctd Prdctors Coarats Bary Outcom OR trprtato: OR xposur = β xp ** SAS V9 ONLY proc logstc data = yourdata ; modl YourOutcpm_c (t= "Ys" = YourPrdctor YourPrdctor YourPrdctor3...; strata MatchgVarabl MatchgVarabl / fo; output out = OutDatast p = Prdctd;
Logistic Regression I. HRP 261 2/10/ am
Logstc Rgrsson I HRP 26 2/0/03 0- am Outln Introducton/rvw Th smplst logstc rgrsson from a 2x2 tabl llustrats how th math works Stp-by-stp xampls to b contnud nxt tm Dummy varabls Confoundng and ntracton
More informationBinary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit
(c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty 3 Bary Choc LPM logt logstc rgrso probt Multpl Choc Multomal Logt (c Pogsa Porchawssul,
More informationIntroduction to logistic regression
Itroducto to logstc rgrsso Gv: datast D { 2 2... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data
More informationChapter 6 Student Lecture Notes 6-1
Chaptr 6 Studnt Lctur Nots 6-1 Chaptr Goals QM353: Busnss Statstcs Chaptr 6 Goodnss-of-Ft Tsts and Contngncy Analyss Aftr compltng ths chaptr, you should b abl to: Us th ch-squar goodnss-of-ft tst to dtrmn
More informationAnalyzing Frequencies
Frquncy (# ndvduals) Frquncy (# ndvduals) /3/16 H o : No dffrnc n obsrvd sz frquncs and that prdctd by growth modl How would you analyz ths data? 15 Obsrvd Numbr 15 Expctd Numbr from growth modl 1 1 5
More informationIntroduction to logistic regression
Itroducto to logstc rgrsso Gv: datast D {... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data pots
More informationUnbalanced Panel Data Models
Ubalacd Pal Data odls Chaptr 9 from Baltag: Ecoomtrc Aalyss of Pal Data 5 by Adrás alascs 4448 troducto balacd or complt pals: a pal data st whr data/obsrvatos ar avalabl for all crosssctoal uts th tr
More informationLogistic Regression Sara Vyrostek Senior Exercise November 16, 2001
ogstc Rgrsso Sara Vrostk Sor Ercs Novmbr 6, Itroducto: I th modlg of data, aalsts dvlop rlatoshps basd upo th obsrvd valus of a st of prdctor varabls ordr to dtrm th pctd valu of th rspos varabl of trst,
More informationNote on the Computation of Sample Size for Ratio Sampling
Not o th Computato of Sampl Sz for ato Samplg alr LMa, Ph.D., PF Forst sourcs Maagmt Uvrst of B.C. acouvr, BC, CANADA Sptmbr, 999 Backgroud ato samplg s commol usd to rduc cofdc trvals for a varabl of
More informationLecture 1: Empirical economic relations
Ecoomcs 53 Lctur : Emprcal coomc rlatos What s coomtrcs? Ecoomtrcs s masurmt of coomc rlatos. W d to kow What s a coomc rlato? How do w masur such a rlato? Dfto: A coomc rlato s a rlato btw coomc varabls.
More informationNotation for Mixed Models for Finite Populations
30- otato for d odl for Ft Populato Smpl Populato Ut ad Rpo,..., Ut Labl for,..., Epctd Rpo (ovr rplcatd maurmt for,..., Rgro varabl (Luz r for,...,,,..., p Aular varabl for ut (Wu z μ for,...,,,..., p
More informationEstimating the Variance in a Simulation Study of Balanced Two Stage Predictors of Realized Random Cluster Means Ed Stanek
Etatg th Varac a Sulato Study of Balacd Two Stag Prdctor of Ralzd Rado Clutr Ma Ed Stak Itroducto W dcrb a pla to tat th varac copot a ulato tudy N ( µ µ W df th varac of th clutr paratr a ug th N ulatd
More informationST 524 NCSU - Fall 2008 One way Analysis of variance Variances not homogeneous
ST 54 NCSU - Fall 008 On way Analyss of varanc Varancs not homognous On way Analyss of varanc Exampl (Yandll, 997) A plant scntst masurd th concntraton of a partcular vrus n plant sap usng ELISA (nzym-lnkd
More informationMath Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1)
Math Trcks r! Combato - umbr o was to group r o objcts, ordr ot mportat r! r! ar 0 a r a s costat, 0 < r < k k! k 0 EX E[XX-] + EX Basc Probablt 0 or d Pr[X > ] - Pr[X ] Pr[ X ] Pr[X ] - Pr[X ] Proprts
More informationMODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f
MODEL QUESTION Statstcs (Thory) (Nw Syllabus) GROUP A d θ. ) Wrt dow th rsult of ( ) ) d OR, If M s th mod of a dscrt robablty dstrbuto wth mass fucto f th f().. at M. d d ( θ ) θ θ OR, f() mamum valu
More informationAPPENDIX: STATISTICAL TOOLS
I. Nots o radom samplig Why do you d to sampl radomly? APPENDI: STATISTICAL TOOLS I ordr to masur som valu o a populatio of orgaisms, you usually caot masur all orgaisms, so you sampl a subst of th populatio.
More informationSolution of Assignment #2
olution of Assignmnt #2 Instructor: Alirza imchi Qustion #: For simplicity, assum that th distribution function of T is continuous. Th distribution function of R is: F R ( r = P( R r = P( log ( T r = P(log
More informationThe number of observed cases The number of parameters. ith case of the dichotomous dependent variable. the ith case of the jth parameter
LOGISTIC REGRESSION Notato Model Logstc regresso regresses a dchotomous depedet varable o a set of depedet varables. Several methods are mplemeted for selectg the depedet varables. The followg otato s
More informationA COMPARISON OF SEVERAL TESTS FOR EQUALITY OF COEFFICIENTS IN QUADRATIC REGRESSION MODELS UNDER HETEROSCEDASTICITY
Colloquum Bomtrcum 44 04 09 7 COMPISON OF SEVEL ESS FO EQULIY OF COEFFICIENS IN QUDIC EGESSION MODELS UNDE HEEOSCEDSICIY Małgorzata Szczpa Dorota Domagała Dpartmt of ppld Mathmatcs ad Computr Scc Uvrsty
More informationCorrelation in tree The (ferromagnetic) Ising model
5/3/00 :\ jh\slf\nots.oc\7 Chaptr 7 Blf propagato corrlato tr Corrlato tr Th (frromagtc) Isg mol Th Isg mol s a graphcal mol or par ws raom Markov fl cosstg of a urct graph wth varabls assocat wth th vrtcs.
More informationReview Statistics review 14: Logistic regression Viv Bewick 1, Liz Cheek 1 and Jonathan Ball 2
Critical Car Fbruary 2005 Vol 9 No 1 Bwick t al. Rviw Statistics rviw 14: Logistic rgrssion Viv Bwick 1, Liz Chk 1 and Jonathan Ball 2 1 Snior Lcturr, School of Computing, Mathmatical and Information Scincs,
More informationOn Selection of Best Sensitive Logistic Estimator in the Presence of Collinearity
Amrcan Journal of Appld Mathmatcs and Statstcs, 05, Vol. 3, No., 7- Avalabl onln at http://pubs.scpub.com/ajams/3// Scnc and Educaton Publshng DOI:0.69/ajams-3-- On Slcton of Bst Snstv Logstc Estmator
More informationFrequency Measurement in Noise
Frqucy Masurmt i ois Porat Sctio 6.5 /4 Frqucy Mas. i ois Problm Wat to o look at th ct o ois o usig th DFT to masur th rqucy o a siusoid. Cosidr sigl complx siusoid cas: j y +, ssum Complx Whit ois Gaussia,
More informationToday s logistic regression topics. Lecture 15: Effect modification, and confounding in logistic regression. Variables. Example
Today s stc rgrsson tocs Lctur 15: Effct modfcaton, and confoundng n stc rgrsson Sandy Eckl sckl@jhsh.du 16 May 28 Includng catgorcal rdctor crat dummy/ndcator varabls just lk for lnar rgrsson Comarng
More informationEstimation Theory. Chapter 4
Estmato ory aptr 4 LIEAR MOELS W - I matrx form Estmat slop B ad trcpt A,,.. - WG W B A l fttg Rcall W W W B A W ~ calld vctor I gral, ormal or Gaussa ata obsrvato paramtr Ma, ovarac KOW p matrx to b stmatd,
More informationOutlier-tolerant parameter estimation
Outlr-tolrant paramtr stmaton Baysan thods n physcs statstcs machn larnng and sgnal procssng (SS 003 Frdrch Fraundorfr fraunfr@cg.tu-graz.ac.at Computr Graphcs and Vson Graz Unvrsty of Tchnology Outln
More informationThe real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k.
Modr Smcoductor Dvcs for Itgratd rcuts haptr. lctros ad Hols Smcoductors or a bad ctrd at k=0, th -k rlatoshp ar th mmum s usually parabolc: m = k * m* d / dk d / dk gatv gatv ffctv mass Wdr small d /
More informationEstimators for Finite Population Variance Using Mean and Variance of Auxiliary Variable
Itratoal Jal o Probablt a tattc 5 : - DOI:.59/j.jp.5. tmat Ft Poplato Varac U Ma a Varac o Alar Varabl Ph Mra * R. Kara h Dpartmt o tattc Lcow Urt Lcow Ia Abtract F tmat t poplato arac mato o l alar arabl
More informationReview - Probabilistic Classification
Mmoral Unvrsty of wfoundland Pattrn Rcognton Lctur 8 May 5, 6 http://www.ngr.mun.ca/~charlsr Offc Hours: Tusdays Thursdays 8:3-9:3 PM E- (untl furthr notc) Gvn lablld sampls { ɛc,,,..., } {. Estmat Rvw
More informationThe R Package PK for Basic Pharmacokinetics
Wolfsggr, h R Pacag PK St 6 h R Pacag PK for Basc Pharmacotcs Mart J. Wolfsggr Dpartmt of Bostatstcs, Baxtr AG, Va, Austra Addrss of th author: Mart J. Wolfsggr Dpartmt of Bostatstcs Baxtr AG Wagramr Straß
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 informationLECTURE 6 TRANSFORMATION OF RANDOM VARIABLES
LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES TRANSFORMATION OF FUNCTION OF A RANDOM VARIABLE UNIVARIATE TRANSFORMATIONS TRANSFORMATION OF RANDOM VARIABLES If s a rv wth cdf F th Y=g s also a rv. If w wrt
More informationWhat are those βs anyway? Understanding Design Matrix & Odds ratios
Ral paramtr stimat WILD 750 - Wildlif Population Analysis of 6 What ar thos βs anyway? Undrsting Dsign Matrix & Odds ratios Rfrncs Hosmr D.W.. Lmshow. 000. Applid logistic rgrssion. John Wily & ons Inc.
More informationElectromagnetics Research Group A THEORETICAL MODEL OF A LOSSY DIELECTRIC SLAB FOR THE CHARACTERIZATION OF RADAR SYSTEM PERFORMANCE SPECIFICATIONS
Elctromagntics Rsarch Group THEORETICL MODEL OF LOSSY DIELECTRIC SLB FOR THE CHRCTERIZTION OF RDR SYSTEM PERFORMNCE SPECIFICTIONS G.L. Charvat, Prof. Edward J. Rothwll Michigan Stat Univrsit 1 Ovrviw of
More informationReliability of time dependent stress-strength system for various distributions
IOS Joural of Mathmatcs (IOS-JM ISSN: 78-578. Volum 3, Issu 6 (Sp-Oct., PP -7 www.osrjourals.org lablty of tm dpdt strss-strgth systm for varous dstrbutos N.Swath, T.S.Uma Mahswar,, Dpartmt of Mathmatcs,
More informationEEC 686/785 Modeling & Performance Evaluation of Computer Systems. Lecture 12
EEC 686/785 Modlng & Prformanc Evaluaton of Computr Systms Lctur Dpartmnt of Elctrcal and Computr Engnrng Clvland Stat Unvrsty wnbng@.org (basd on Dr. Ra Jan s lctur nots) Outln Rvw of lctur k r Factoral
More information10/7/14. Mixture Models. Comp 135 Introduction to Machine Learning and Data Mining. Maximum likelihood estimation. Mixture of Normals in 1D
Comp 35 Introducton to Machn Larnng and Data Mnng Fall 204 rofssor: Ron Khardon Mxtur Modls Motvatd by soft k-mans w dvlopd a gnratv modl for clustrng. Assum thr ar k clustrs Clustrs ar not rqurd to hav
More informationNumerical Method: Finite difference scheme
Numrcal Mthod: Ft dffrc schm Taylor s srs f(x 3 f(x f '(x f ''(x f '''(x...(1! 3! f(x 3 f(x f '(x f ''(x f '''(x...(! 3! whr > 0 from (1, f(x f(x f '(x R Droppg R, f(x f(x f '(x Forward dffrcg O ( x from
More informationMore Statistics tutorial at 1. Introduction to mathematical Statistics
Mor Sttstcs tutorl t wwwdumblttldoctorcom Itroducto to mthmtcl Sttstcs Fl Soluto A Gllup survy portrys US trprurs s " th mvrcks, drmrs, d lors whos rough dgs d ucompromsg d to do t thr ow wy st thm shrp
More information2. Grundlegende Verfahren zur Übertragung digitaler Signale (Zusammenfassung) Informationstechnik Universität Ulm
. Grundlgnd Vrfahrn zur Übrtragung dgtalr Sgnal (Zusammnfassung) wt Dc. 5 Transmsson of Dgtal Sourc Sgnals Sourc COD SC COD MOD MOD CC dg RF s rado transmsson mdum Snk DC SC DC CC DM dg DM RF g physcal
More informationLinear Non-Gaussian Structural Equation Models
IMPS 8, Durham, NH Linar Non-Gaussian Structural Equation Modls Shohi Shimizu, Patrik Hoyr and Aapo Hyvarinn Osaka Univrsity, Japan Univrsity of Hlsinki, Finland Abstract Linar Structural Equation Modling
More informationEntropy Equation for a Control Volume
Fudamtals of Thrmodyamcs Chaptr 7 Etropy Equato for a Cotrol Volum Prof. Syoug Jog Thrmodyamcs I MEE2022-02 Thrmal Egrg Lab. 2 Q ds Srr T Q S2 S1 1 Q S S2 S1 Srr T t t T t S S s m 1 2 t S S s m tt S S
More informationConsistency of the Maximum Likelihood Estimator in Logistic Regression Model: A Different Approach
ISSN 168-8 Joural of Statstcs Volum 16, 9,. 1-11 Cosstcy of th Mamum Lklhood Estmator Logstc Rgrsso Modl: A Dffrt Aroach Abstract Mamuur Rashd 1 ad Nama Shfa hs artcl vstgats th cosstcy of mamum lklhood
More informationBayesian Shrinkage Estimator for the Scale Parameter of Exponential Distribution under Improper Prior Distribution
Itratoal Joural of Statstcs ad Applcatos, (3): 35-3 DOI:.593/j.statstcs.3. Baysa Shrkag Estmator for th Scal Paramtr of Expotal Dstrbuto udr Impropr Pror Dstrbuto Abbas Najm Salma *, Rada Al Sharf Dpartmt
More informationARIMA Methods of Detecting Outliers in Time Series Periodic Processes
Articl Intrnational Journal of Modrn Mathmatical Scincs 014 11(1): 40-48 Intrnational Journal of Modrn Mathmatical Scincs Journal hompag:www.modrnscintificprss.com/journals/ijmms.aspx ISSN:166-86X Florida
More informationYou already learned about dummies as independent variables. But. what do you do if the dependent variable is a dummy?
CHATER 5: DUMMY DEENDENT VARIABLES AND NON-LINEAR REGRESSION. Th roblm of Dummy Dpndnt Varabls You alrady larnd about dumms as ndpndnt varabls. But what do you do f th dpndnt varabl s a dummy? On answr
More informationErrata. Items with asterisks will still be in the Second Printing
Errata Itms with astrisks will still b in th Scond Printing Author wbsit URL: http://chs.unl.du/edpsych/rjsit/hom. P7. Th squar root of rfrrd to σ E (i.., σ E is rfrrd to not Th squar root of σ E (i..,
More informationME 501A Seminar in Engineering Analysis Page 1
St Ssts o Ordar Drtal Equatos Novbr 7 St Ssts o Ordar Drtal Equatos Larr Cartto Mcacal Er 5A Sar Er Aalss Novbr 7 Outl Mr Rsults Rvw last class Stablt o urcal solutos Stp sz varato or rror cotrol Multstp
More informationLecture notes on epidemiology
Bostatstcs 2002, ÅS Lecture otes o epdemology The ams of epdemology: Epdemology s the study of the dstrbuto ad determats of health related states or evets specfed populatos ad the applcato of ths study
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 informationNote: Torque is prop. to current Stationary voltage is prop. to speed
DC Mach Cotrol Mathmatcal modl. Armatr ad orq f m m a m m r a a a a a dt d ψ ψ ψ ω Not: orq prop. to crrt Statoary voltag prop. to pd Mathmatcal modl. Fld magtato f f f f d f dt a f ψ m m f f m fλ h torq
More informationHigher-Order Discrete Calculus Methods
Highr-Ordr Discrt Calculus Mthods J. Blair Prot V. Subramanian Ralistic Practical, Cost-ctiv, Physically Accurat Paralll, Moving Msh, Complx Gomtry, Slid 1 Contxt Discrt Calculus Mthods Finit Dirnc Mimtic
More informationLogistic regression (continued)
STAT562 page 138 Logstc regresso (cotued) Suppose we ow cosder more complex models to descrbe the relatoshp betwee a categorcal respose varable (Y) that takes o two (2) possble outcomes ad a set of p explaatory
More informationLecture 5. Estimation of Variance Components
Lctur 5 Etmato of Varac Compot Gulhrm J. M. Roa Uvrt of Wco-Mado Mxd Modl Quattatv Gtc SISG Sattl 8 0 Sptmbr 08 Etmato of Varac Compot ANOVA Etmato Codr th data t blow rlatd to obrvato of half-b faml of
More informationNARAYANA I I T / P M T A C A D E M Y. C o m m o n P r a c t i c e T e s t 1 6 XII STD BATCHES [CF] Date: PHYSIS HEMISTRY MTHEMTIS
. (D). (A). (D). (D) 5. (B) 6. (A) 7. (A) 8. (A) 9. (B). (A). (D). (B). (B). (C) 5. (D) NARAYANA I I T / P M T A C A D E M Y C o m m o n P r a c t i c T s t 6 XII STD BATCHES [CF] Dat: 8.8.6 ANSWER PHYSIS
More informationMCB137: Physical Biology of the Cell Spring 2017 Homework 6: Ligand binding and the MWC model of allostery (Due 3/23/17)
MCB37: Physical Biology of th Cll Spring 207 Homwork 6: Ligand binding and th MWC modl of allostry (Du 3/23/7) Hrnan G. Garcia March 2, 207 Simpl rprssion In class, w drivd a mathmatical modl of how simpl
More informationA Stochastic Approximation Iterative Least Squares Estimation Procedure
Joural of Al Azhar Uvrst-Gaza Natural Sccs, 00, : 35-54 A Stochastc Appromato Itratv Last Squars Estmato Procdur Shahaz Ezald Abu- Qamar Dpartmt of Appld Statstcs Facult of Ecoomcs ad Admstrato Sccs Al-Azhar
More informationOrdinary Least Squares at advanced level
Ordary Last Squars at advacd lvl. Rvw of th two-varat cas wth algbra OLS s th fudamtal tchqu for lar rgrssos. You should by ow b awar of th two-varat cas ad th usual drvatos. I ths txt w ar gog to rvw
More informationMachine Learning. Principle Component Analysis. Prof. Dr. Volker Sperschneider
Mach Larg Prcpl Compot Aalyss Prof. Dr. Volkr Sprschdr AG Maschlls Lr ud Natürlchsprachlch Systm Isttut für Iformatk chsch Fakultät Albrt-Ludgs-Uvrstät Frburg sprschdr@formatk.u-frburg.d I. Archtctur II.
More informationThe calculation method for SNE
Elctroic Supplmtary Matrial (ESI) for RSC Advacs. This joural is Th Royal Socity of Chmistry 015 Th ulatio mthod for SNE (1) Slct o isothrm ad o rror fuctio (for xampl, th ERRSQ rror fuctio) ad gt th solvr
More informationEstimation of Population Variance Using a Generalized Double Sampling Estimator
r Laka Joural o Appl tatstcs Vol 5-3 stmato o Populato Varac Us a Gralz Doubl ampl stmator Push Msra * a R. Kara h Dpartmt o tatstcs D.A.V.P.G. Coll Dhrau- 8 Uttarakha Ia. Dpartmt o tatstcs Luckow Uvrst
More informationText: WMM, Chapter 5. Sections , ,
Lcturs 6 - Continuous Probabilit Distributions Tt: WMM, Chaptr 5. Sctions 6.-6.4, 6.6-6.8, 7.-7. In th prvious sction, w introduc som of th common probabilit distribution functions (PDFs) for discrt sampl
More informationUsing Markov Chain Monte Carlo for Modeling Correct Enumeration and Match Rate Variability
Usng Marov Chan Mont Carlo for Modlng Corrct Enumraton and Match Rat Varablty Andrw Kllr U.S. Cnsus urau Washngton, DC 033/andrw.d.llr@cnsus.gov Ths rport s rlasd to nform ntrstd parts of ongong rsarch
More informationMathematical Statistics. Chapter VIII Sampling Distributions and the Central Limit Theorem
Mahmacal ascs 8 Chapr VIII amplg Dsrbos ad h Cral Lm Thorm Fcos of radom arabls ar sall of rs sascal applcao Cosdr a s of obsrabl radom arabls L For ampl sppos h arabls ar a radom sampl of s from a poplao
More informationWhat does the data look like? Logistic Regression. How can we apply linear model to categorical data like this? Linear Probability Model
Logistic Rgrssion Almost sam to rgrssion ct that w hav binary rsons variabl (ys or no, succss or failur) W assum that th qualitativ rsons variabl has a discrt distribution lik binomial. What dos th data
More informationECE602 Exam 1 April 5, You must show ALL of your work for full credit.
ECE62 Exam April 5, 27 Nam: Solution Scor: / This xam is closd-book. You must show ALL of your work for full crdit. Plas rad th qustions carfully. Plas chck your answrs carfully. Calculators may NOT b
More information22/ Breakdown of the Born-Oppenheimer approximation. Selection rules for rotational-vibrational transitions. P, R branches.
Subjct Chmistry Papr No and Titl Modul No and Titl Modul Tag 8/ Physical Spctroscopy / Brakdown of th Born-Oppnhimr approximation. Slction ruls for rotational-vibrational transitions. P, R branchs. CHE_P8_M
More informationEstimation of odds ratios in Logistic Regression models under different parameterizations and Design matrices
Advancs in Computational Intllignc, Man-Machin Systms and Cybrntics Estimation of odds ratios in Logistic Rgrssion modls undr diffrnt paramtrizations and Dsign matrics SURENDRA PRASAD SINHA*, LUIS NAVA
More informationComparisons of the Variance of Predictors with PPS sampling (update of c04ed26.doc) Ed Stanek
Coparo o th Varac o Prdctor wth PPS aplg (updat o c04d6doc Ed Sta troducto W copar prdctor o a PSU a or total bad o PPS aplg Th tratgy to ollow that o Sta ad Sgr (JASA, 004 whr w xpr th prdctor a a lar
More informationOn Estimation of Unknown Parameters of Exponential- Logarithmic Distribution by Censored Data
saqartvlos mcrbata rovul akadms moamb, t 9, #2, 2015 BULLETIN OF THE GEORGIAN NATIONAL ACADEMY OF SCIENCES, vol 9, o 2, 2015 Mathmatcs O Estmato of Ukow Paramtrs of Epotal- Logarthmc Dstrbuto by Csord
More informationSER/BER in a Fading Channel
SER/BER in a Fading Channl Major points for a fading channl: * SNR is a R.V. or R.P. * SER(BER) dpnds on th SNR conditional SER(BER). * Two prformanc masurs: outag probability and avrag SER(BER). * Ovrall,
More informationAotomorphic Functions And Fermat s Last Theorem(4)
otomorphc Fuctos d Frmat s Last Thorm(4) Chu-Xua Jag P. O. Box 94 Bg 00854 P. R. Cha agchuxua@sohu.com bsract 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral
More informationWorksheet: Taylor Series, Lagrange Error Bound ilearnmath.net
Taylor s Thorm & Lagrag Error Bouds Actual Error This is th ral amout o rror, ot th rror boud (worst cas scario). It is th dirc btw th actual () ad th polyomial. Stps:. Plug -valu ito () to gt a valu.
More informationCh. 9 Common Emitter Amplifier
Ch. 9 Common mttr mplfr Common mttr mplfr nput and put oltags ar 180 o -of-phas, whl th nput and put currnts ar n-phas wth th nput oltag. Output oltag ( V ) V V V C CC C C C C and V C ar -of-phas 10 μ
More informationENGI 3423 Simple Linear Regression Page 12-01
ENGI 343 mple Lear Regresso Page - mple Lear Regresso ometmes a expermet s set up where the expermeter has cotrol over the values of oe or more varables X ad measures the resultg values of aother varable
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted
More informationChapter 6. pn-junction diode: I-V characteristics
Chatr 6. -jucto dod: -V charactrstcs Tocs: stady stat rsos of th jucto dod udr ald d.c. voltag. ucto udr bas qualtatv dscusso dal dod quato Dvatos from th dal dod Charg-cotrol aroach Prof. Yo-S M Elctroc
More informationDeepak Rajput
Q Prov: (a than an infinit point lattic is only capabl of showing,, 4, or 6-fold typ rotational symmtry; (b th Wiss zon law, i.. if [uvw] is a zon axis and (hkl is a fac in th zon, thn hu + kv + lw ; (c
More informationSoft k-means Clustering. Comp 135 Machine Learning Computer Science Tufts University. Mixture Models. Mixture of Normals in 1D
Comp 35 Machn Larnng Computr Scnc Tufts Unvrsty Fall 207 Ron Khardon Th EM Algorthm Mxtur Modls Sm-Suprvsd Larnng Soft k-mans Clustrng ck k clustr cntrs : Assocat xampls wth cntrs p,j ~~ smlarty b/w cntr
More informationExam 1. It is important that you clearly show your work and mark the final answer clearly, closed book, closed notes, no calculator.
Exam N a m : _ S O L U T I O N P U I D : I n s t r u c t i o n s : It is important that you clarly show your work and mark th final answr clarly, closd book, closd nots, no calculator. T i m : h o u r
More informationcycle that does not cross any edges (including its own), then it has at least
W prov th following thorm: Thorm If a K n is drawn in th plan in such a way that it has a hamiltonian cycl that dos not cross any dgs (including its own, thn it has at last n ( 4 48 π + O(n crossings Th
More informationProbability and. Lecture 13: and Correlation
933 Probablty ad Statstcs for Software ad Kowledge Egeers Lecture 3: Smple Lear Regresso ad Correlato Mocha Soptkamo, Ph.D. Outle The Smple Lear Regresso Model (.) Fttg the Regresso Le (.) The Aalyss of
More informationMor Tutorial at www.dumblittldoctor.com Work th problms without a calculator, but us a calculator to chck rsults. And try diffrntiating your answrs in part III as a usful chck. I. Applications of Intgration
More informationSampling Theory MODULE X LECTURE - 35 TWO STAGE SAMPLING (SUB SAMPLING)
Samplg Theory ODULE X LECTURE - 35 TWO STAGE SAPLIG (SUB SAPLIG) DR SHALABH DEPARTET OF ATHEATICS AD STATISTICS IDIA ISTITUTE OF TECHOLOG KAPUR Two stage samplg wth uequal frst stage uts: Cosder two stage
More informationEcon107 Applied Econometrics Topic 10: Dummy Dependent Variable (Studenmund, Chapter 13)
Pag- Econ7 Appld Economtrcs Topc : Dummy Dpndnt Varabl (Studnmund, Chaptr 3) I. Th Lnar Probablty Modl Suppos w hav a cross scton of 8-24 yar-olds. W spcfy a smpl 2-varabl rgrsson modl. Th probablty of
More informationCOMPUTATIONAL NUCLEAR THERMAL HYDRAULICS
COMPUTTIONL NUCLER THERML HYDRULICS Cho, Hyoung Kyu Dpartmnt of Nuclar Enginring Soul National Univrsity CHPTER4. THE FINITE VOLUME METHOD FOR DIFFUSION PROBLEMS 2 Tabl of Contnts Chaptr 1 Chaptr 2 Chaptr
More informationOn the Possible Coding Principles of DNA & I Ching
Sctfc GOD Joural May 015 Volum 6 Issu 4 pp. 161-166 Hu, H. & Wu, M., O th Possbl Codg Prcpls of DNA & I Chg 161 O th Possbl Codg Prcpls of DNA & I Chg Hupg Hu * & Maox Wu Rvw Artcl ABSTRACT I ths rvw artcl,
More informationA primary objective of a phase II trial is to screen for antitumor activity; agents which are found to have substantial antitumor activity and an
SURVIVAL ANALYSIS A prmary objctv of a phas II tral s to scrn for anttumor actvty; agnts whch ar found to hav substantal anttumor actvty and an approprat spctrum of toxcty ar lkly ncorporatd nto combnatons
More informationThree-Dimensional Theory of Nonlinear-Elastic. Bodies Stability under Finite Deformations
Appld Mathmatcal Sccs ol. 9 5 o. 43 75-73 HKAR Ltd www.m-hkar.com http://dx.do.org/.988/ams.5.567 Thr-Dmsoal Thory of Nolar-Elastc Bods Stablty udr Ft Dformatos Yu.. Dmtrko Computatoal Mathmatcs ad Mathmatcal
More informationNaresuan University Journal: Science and Technology 2018; (26)1
Narsuan Unvrsty Journal: Scnc and Tchnology 018; (6)1 Th Dvlopmnt o a Corrcton Mthod or Ensurng a Contnuty Valu o Th Ch-squar Tst wth a Small Expctd Cll Frquncy Kajta Matchma 1 *, Jumlong Vongprasrt and
More informationMAXIMA-MINIMA EXERCISE - 01 CHECK YOUR GRASP
EXERCISE - MAXIMA-MINIMA CHECK YOUR GRASP. f() 5 () 75 f'() 5. () 75 75.() 7. 5 + 5. () 7 {} 5 () 7 ( ) 5. f() 9a + a +, a > f'() 6 8a + a 6( a + a ) 6( a) ( a) p a, q a a a + + a a a (rjctd) or a a 6.
More informationTHE BALANCED CREDIBILITY ESTIMATORS WITH MULTITUDE CONTRACTS OBTAINED UNDER LINEX LOSS FUNCTION
Joural of Stattc: Advac Thory ad Applcato Volum 4 Numbr 5 Pag - Avalabl at http://ctfcadvac.co. DOI: http://dx.do.org/.864/jata_746 THE BALANCED CREDIBILITY ESTIMATORS WITH MULTITUDE CONTRACTS OBTAINED
More informationApplication of Local Influence Diagnostics to the Linear Logistic Regression Models
Dhaka Unv. J. Sc., 5(): 6978 003(July) Applcaton of Local Influnc Dagnostcs to th Lnar Logstc Rgrsson Modls Monzur Hossan * and M. Ataharul Islam Dpartmnt of Statstcs, Unvrsty of Dhaka Rcvd on 5.0.00.
More informationPage 1. Before-After Control-Impact (BACI) Power Analysis For Several Related Populations (Variance Known) Richard A. Hinrichsen. September 24, 2010
Pag for-aftr Cotrol-Impact (ACI) Powr Aalysis For Svral Rlatd Populatios (Variac Kow) Richard A. Hirichs Sptmbr 4, Cavat: This primtal dsig tool is a idalizd powr aalysis built upo svral simplifyig assumptios
More informationare given in the table below. t (hours)
CALCULUS WORKSHEET ON INTEGRATION WITH DATA Work th following on notbook papr. Giv dcimal answrs corrct to thr dcimal placs.. A tank contains gallons of oil at tim t = hours. Oil is bing pumpd into th
More informationLecture Outline. Biost 517 Applied Biostatistics I. Comparing Independent Proportions. Summary Measures. Comparing Independent Proportions
Bost 57 Aled Bostatstcs I Scott S. Emerso, M.D., Ph.D. Professor of Bostatstcs Uversty of Washgto Lecture 4: Two Samle Iferece About Ideedet Proortos Lecture Outle Comarg Ideedet Proortos Large Samles
More informationβ-spline Estimation in a Semiparametric Regression Model with Nonlinear Time Series Errors
Amrca Joural of Appld Sccs, (9): 343-349, 005 ISSN 546-939 005 Scc Publcatos β-spl Estmato a Smparamtrc Rgrsso Modl wth Nolar Tm Srs Errors Jhog You, ma Ch ad 3 Xa Zhou Dpartmt of ostatstcs, Uvrsty of
More informationLecture Outline Biost 517 Applied Biostatistics I. Summary Measures Comparing Independent Proportions
Aled Bostatstcs I, AUT November 6, Lecture Outle Bost 57 Aled Bostatstcs I Scott S. Emerso, M.D., Ph.D. Professor of Bostatstcs Uversty of Washgto Comarg Ideedet Proortos Large Samles (Ucesored) Ch Squared
More informationPetru P. Blaga-Reducing of variance by a combined scheme based on Bernstein polynomials
Ptru P Blaa-Rdu o vara by a obd sh basd o Brst olyoals REUCG OF VARACE BY A COMBE SCHEME BASE O BERSTE POYOMAS by Ptru P Blaa Abstrat A obd sh o th otrol varats ad whtd uor sal thods or rdu o vara s vstatd
More informationAn Overview of Markov Random Field and Application to Texture Segmentation
An Ovrvw o Markov Random Fld and Applcaton to Txtur Sgmntaton Song-Wook Joo Octobr 003. What s MRF? MRF s an xtnson o Markov Procss MP (D squnc o r.v. s unlatral (causal: p(x t x,
More information