New bounds on Poisson approximation to the distribution of a sum of negative binomial random variables

Size: px
Start display at page:

Download "New bounds on Poisson approximation to the distribution of a sum of negative binomial random variables"

Transcription

1 Sogklaaka J. Sc. Tchol. 4 () 4-48 Ma Ogal tcl Nw bouds o Posso aomato to th dstbuto of a sum of gatv bomal adom vaabls * Kat Taabola Datmt of Mathmatcs Faculty of Scc Buaha Uvsty Muag Chobu 3 Thalad Ct of Ecllc Mathmatcs Commsso o Hgh Educato Ratchathw Bagkok 4 Thalad Rcvd: Novmb 6; cctd: Jauay 7 bstact Th St-Ch mthod s usdto gv w bouds o-ufom bouds fo th dstacs btw th dstbuto of a sum of ddt gatv bomal adom vaabls ad a Posso dstbuto wth ma wh ad a aamts of ach gatv bomal dstbuto. Rsults of ths study a suo tha thos std Taabola (4) ad Hug ad Gag (6). Kywods: gatv bomal dstbuto Posso aomato o-ufom boud St-Ch mthod. Itoducto I obablty thoy ad statstcs th gatv bomal dstbuto wth aamts R ad () s a motat dsct dstbuto wth a log hstoy as sam as th bomal dstbuto. Wh N t s calld th Pascal dstbuto wth aamts N ad () ad wh t s calld th gomtc dstbuto wth aamt. Not that th gatv bomal dstbuto ca b cosdd as a mtu of a Posso dstbuto wth a gamma mg dstbuto (Kals & Xkalak 5). I addto som sach tocs latd to Posso aomato otd out that th Posso dstbuto wth ma *Cosodg autho Emal addss: kat@buu.ac.th o s a good aomato of th gatv bomal dstbuto wth aamts ad wh s small whch ca b foud Vvaat (969) Romaowska (977) Gb (984) Roos (3) ad Taabola (). Howv ou tst s aomatg thdstbuto of a sum of ddt gatv bomal adom vaabls by a Posso dstbuto whch s th ma cott of ths study. Lt S X wh X... X a ddt adom vaabls followg th gatv bomal dstbutos ach wth th obablty mass fucto ( ) ( ) X ( )! N {}. Lt Z dot a Posso adom vaabl wth ma ( ). Fom th cocluso mtod abov ad fo ach {... } t follows that f s small th th gatv bomal dstbuto wth aamts ad s aomatd by a Posso dstbuto wth ma

2 K. Taabola / Sogklaaka J. Sc. Tchol. 4 () o. ddtoally w kow that th dstbuto of a sum of ddt Posso adom vaabls ach wth ma s th Posso dstbuto wth ma thus t s aoat to aomat th dstbuto of S by a Posso dstbuto wth ma ES ( ) o wh all a small. I th ast fw yas som authos havto gv ufom ad o-ufom bouds o Posso aomato to th dstbuto of S wth both Posso mas as follows. ad Fo th Posso ma Vllasamy ad Uadhy (9) usd th mthod of ots to gv a ufom boud fo (.) N {} wh d S Z P( S ) P( Z ) s th dstac btw th dstbuto of S ad a Posso dstbuto wth ma.i th cas of cumulatv obablty aomato Taabola (7b) usd th St- Ch mthod to gv ufom ad o-ufom bouds fo th ato btw th cumulatv dstbuto fucto of ad th Posso cumulatv dstbuto fucto fo ad th fom (.) ( ) PS (.3) f wh ( ) P( Z ) f (. ) ad. Fo N Hug ad Gag (6) usd th St-Ch mthod to gv two o-ufom bouds th followg foms: Fo S P( S ) m P( Z ) N {} ad P S su P( Z ) N {} P( Z ) ( ) ( )! m P S P ( Z ) ( ( )) ( ) P Z P Z K d ( S Z ) m (.4) (.5) wh d S Z P( S ) P( Z ) ad ( ). I th cas of otws aomato Taabola (67a) usd th St-Ch mthod to gv a ufom boud th fom ( ) f d S (.6) Z P( Z) P( Z) ma f fo wh Fo th Posso ma Taabola (4) usd th StCh mthod ad w-fuctos to gv a ufom boud th fom (.7) fo N {}. Fo Hug ad Gag (6) usd th St-Ch mthod to gv a ufom boud as follows: (.8) ad thy also gav a o-ufom boud fo cumulatv obablty aomato th fom fo (.9). I th cas of otws aomato Taabola (5a) usd th sam tools as Taabola (4) to gv a o-ufom boud th fom fo. K N (.) W obsv that th boud (.8) s wos tha that (.7) bcaus t caot b ald to th cas ad N v though t may b sha tha that (.7). Futhmo both bouds (.7) ad (.8) do ot chag alog N {} whch may b aoat fo masug th accuacy of th aomato.notc that th boud (.9) caot b ald th cas ad N. I ths a w am to dtm w bouds o-ufom boudswth sct to th bouds (.7)-(.9). d S Z P( S ) P( Z ) N d S Z m ( ) m ( ) K N {} m N

3 44 K. Taabola / Sogklaaka J. Sc. Tchol. 4 () Mthod I 97 St toducd a ow full mthod fo th omal aomato whch s calld St s mthod. Lat Ch (975) dvlod ad ald St s mthod to th Posso aomato whch s calld th St-Ch mthod. St s uato fo Posso dstbuto wth ma fo gv h s of th fom h( ) P ( h) f ( ) f ( ) (.) wh ad f ad h a boudd al valud fuctos dfd o. Fo N {} lt f h( ) f. b dfd by (.) Followg Babou t al. (99) th soluto f of (.) ca b ssd as P ( h) h( k) k h : N {} R ( )! [ P ( ) ( hc ) P ( h ) ( )] f P hc f f (.3) wh N ad C {... }. Smlaly fo C ad N {} s of th fom ( )! [ P( hc ) P ( )] f hc (.4) fc ( ) ( )! [ P ( h ) ( )] f C P h C f. Lt f C k k! N {} f ( ) f ( ) f ( ) ad fc ( ) fc ( ) fc ( ) fo gvg th dsd sults w also d th followg lmma. Lmma. Lt N m{ } ad ma{ C } th w hav th followg: ). Fo f ad N {} f( ) m (.5) wh s tak to b wh ad fo t s gv by ad f ( ). f f (.6) ). Fo f C ad N ( ) m C ( ) f (.7) ad f (.8) C ( ). Poof. ) Th ualty (.5) follows dctly fom Taabola (5b) ad ualty (.6) follows fom Babou t al. (99). ). Fo w hav ma{ C } ad thus (.5) bcoms ( ) m C f. (.9) Taabola (7) showd that C ( ) f ( ). (.) Combg th bouds (.9) ad (.) th boud (.7) s obtad ad fally th boud (.8) ca b obtad fom th boud (.6). 3. Ma Rsults Th ma ot of ths study s to dtm w bouds o-ufom bouds fo two dstacs d S Z ad dk S Z. Th followg thom gvs o dsd sult. C Thom 3. Lt N {} ad th w hav th followg ualty. (3.) Poof. Substtutg ad h by S ad h sctvly ad w tak ctato to (.) ylds d S Z m m.

4 E f ( S ) S f ( S ) wh K. Taabola / Sogklaaka J. Sc. Tchol. 4 () E f ( S ) X f ( S) E f ( S ) X f ( S) (3.) s dfd (.3). Fo... lt S S X th w obta f f E f ( S ) X f ( S) E f ( S X ) X f ( S X ) E E f ( S X ) X f ( S X ) X E f ( S X ) X f ( S X ) X X ( ) E ( ) () ( ) ( ) () f S X E f S f S X E ( 3) ( ) () ( 4) 3 ( 3) (3) f S f S X E f S f S X E f ( S ) E f ( S ) E f ( S ) 3 ( ) ( 3) ( ) ( ) 4 3 ( )( ) ( )( ) ( 3! 4) E f S E f ( S 3) 3 ( ) ( ) ( ) 4 ( ) 3 ( 3) ( ) ( 3) 5 4 ( )( ) ( )( ) 3! ( 4) ( 4) 3 E f ( S ) E f ( S ) E f ( S ) 4 3 ( ) 3 E f ( S ) E f ( S 3) ( ) E f ( S 3) ( )( ) ( )( ) ( ) ( 3) ( 3! 4) E f S E f S 5 ( )( ) E f ( S 4) E f ( S 4) E f ( S ) E f ( S ) 3 3 ( ) E f ( S ) ( ) E f ( S 3) 4 4 ( )( ) ( )( ) (by ) ( 3) ( 4) 5 5 ( )( )( 3) ( )( )( 3) 3! 3! ( 4) ( 5) 3 E f ( S ) f ( S ) ( ) E f ( S ) f ( S 3) 4 5 ( )( ) ( )( )( 3) ( 3) ( 4) f S 3! ( ) ( ) ( )! f S ( )! f S E f ( S 4) f ( S 5) ( ) ( ) ( ) ( 3)

5 46 K. Taabola / Sogklaaka J. Sc. Tchol. 4 () ( 3) 3 ( 4) 4 ( )3! f S ( )4! 3 ( 3) ( 4) 4 E f ( S 4) f ( S 5) X ( ) E ( ) ( ) f S f S Puttg th sult (3.3) to (3.) w hav that X Bcaus by (.5). (3.3) ( ) E f ( S ) f ( S ) X ( ) E f ( S ) f ( S ) X ( ) E f ( S ) X j. ( ) f ( j ) P( S j) X j ( ) f ( j ) P( S j) X j ( ) m P ( S j ) EX m ( ) (3.4) m (3.5) Ths gvs th Thom 3.. Fo cumulatv obablty aomato t s otd that th cas w ca comut th act o- bablty of that s P( S ). So ths cas a w o-ufom boud fo d S Z wh N s as follows. Thom 3. Lt ad th th followg ualty holds: ( ) K d S Z m m. (3.8) Poof. Usg th sam agumts dtald as th oof of Thom 3. togth wth Lmma.() th sult (3.8) s obtad. Rmak ) By comag th bouds (.7) (.8) ad (3.) t s s that S m m N m m ad K ad by (.6) X j ( ) f ( j ) P( S j) X ( ) ( ) j P S j j X ( ) ( ) P S j j X ( ) (3.6) thus fom (3.5) ad (3.6) w obta X j ( ) f ( j ) P( S j) m m. (3.7) Substtutg th boud (3.7) to (3.4) t follows that m m m ad th boud (3.) ca b ald fo all cass of whch s wd tha th boud (.8). Thfo th sult (3.) s btt tha thos std (.7) ad (.8). Smlaly th sult (3.8) s also btt tha that std (.9). ) If w comb th sults (3.) ad (.) th a w o-ufom boud fo wh N s of th fom It s a slghtly movmt of (.). (3.9) Fo aomatg th dstbuto of a gatv bomal adom vaabl X wth aamts R ad by d S Z m m.

6 () K. Taabola / Sogklaaka J. Sc. Tchol. 4 () a Posso dstbuto wth ma w ca aly th sults Thoms 3. ad 3. ad (3.9) to gv w sults as follows. Coollay 3. Fo ) Fo ) Fo ad th w hav th followg. (3.) (3.). (3.) Poof. Bcaus all sults (3.)-(3.) ca b obtad by usg smla mthod t suffcs to show th sult (3.). lyg (3.) w hav d X Z m m m. (3.3) Bcaus by Taylo s aso [( ) ] [( ) ]!! hav w ad whch mls that. Thfo th ualty (3.3) duc to d X Z m. Fom whch th sult (3.) s ovd. If th ad th sults Thoms 3. ad 3. ad (3.9) bcom to b th sults th Posso aomato fo a sum of ddt gomtc adom vaabls whch st th followg coollay. N {} Coollay 3. If ad th w hav th followg. ) Fo N {} m d X Z N m m K d X Z d X Z d S Z m m. (3.4) ) Fo ad N m m ( ) K d S Z m m. 4. Coclusos (3.5) (3.6) Th w bouds o-ufom bouds ths study w obtad by usg th St-Ch mthod. Each boud ca b usd to aomat th o of th dstac btw th dstbuto of a sum of ddt gatv bomal dstbuto ad a Posso dstbuto wth ma ES ( ) as wll wh all a small. Futhmo by comag th sults ths study ad th sults Taabola (4) ad Hug ad Gag (6) t ca b cocludd that th sults ths studyasuotha thos std Taabola (4) ad Hug ad Gag (6). Rfcs Babou. D. Holst L. & Jaso S. (99). Posso aomato (Ofod studs obablty ). Ofod Eglad: Clado Pss. Ch L. H. Y. (975). Posso aomato fo ddt tals. als of Pobablty Gb H. U. (984). Eo bouds fo th comoud Posso aomato. Isuac Mathmatcs Ecoomcs Hug T. L. & Gag L. T. (6). O bouds Posso aomato fo dstbutos of ddt gatv bomal dstbutd adom vaabls. SgPlus 5 -. Kals D. & Xkalak E. (5). Md Posso Dstbutos. Itatoal Statstcal Rvw Romaowska M. (977). ot o th u boud fo th dstac total vaato btw th bomal ad th Posso dstbutos. Statstca Nladca

7 48 K. Taabola / Sogklaaka J. Sc. Tchol. 4 () Roos B. (3). Imovmts th Posso aomato of md Posso dstbutos. Joual of Statstcal Plag ad Ifc St C. M. (97). boud fo th o omal aomato to th dstbuto of a sum of ddtadom vaabls. Pocdgs of th Sth Bkly Symosum o Mathmatcal Statstcs ad Pobablty Taabola K. (). Th last u boud o th Posso-gatv bomal latv o.commucatos Statstcs-Thoy ad Mthods Taabola K. (4). Posso aomato fo ddt gatv bomal adom vaabls.itatoal Joual of Pu ad ld Mathmatcs Taabola K. (5a). Potws Posso aomato fo ddt gatv bomal adom vaabls.global Joual of Pu ad ld Mathmatcs Taabola K. (5b). Nw o-ufom bouds o Posso aomato fo ddt Boull Tals. Bullt of th Malaysa Mathmatcal Sccs Socty Taabola K. (7a). o-ufom boud o Posso aomato fo a sum of gatv bomal adom vaabls. Sogklaaka Joual of Scc ad Tchology 39(3) Taabola K. (7b). Posso aomato fo a sum of gatv bomal adom vaabls. Bullt of th Malaysa Mathmatcal Scc Socty 4() Vllasamy P. & Uadhy N. S. (9). Comoud gatv bomal aomatos fo sums of adom vaabls. Pobablty ad Mathmatcal Statstcs Vvaat W. (969). U boud fo dstac total vaato btw th bomal o gatv bomal ad th Posso dstbuto. Statstca Nladca

International Journal of Advanced Scientific Research and Management, Volume 3 Issue 11, Nov

International Journal of Advanced Scientific Research and Management, Volume 3 Issue 11, Nov 199 Algothm ad Matlab Pogam fo Softwa Rlablty Gowth Modl Basd o Wbull Od Statstcs Dstbuto Akladswa Svasa Vswaatha 1 ad Saavth Rama 2 1 Mathmatcs, Saaatha Collg of Egg, Tchy, Taml Nadu, Ida Abstact I ths

More information

SIMULTANEOUS METHODS FOR FINDING ALL ZEROS OF A POLYNOMIAL

SIMULTANEOUS METHODS FOR FINDING ALL ZEROS OF A POLYNOMIAL Joual of athmatcal Sccs: Advacs ad Applcatos Volum, 05, ags 5-8 SIULTANEUS ETHDS FR FINDING ALL ZERS F A LYNIAL JUN-SE SNG ollg of dc Yos Uvsty Soul Rpublc of Koa -mal: usopsog@yos.ac. Abstact Th pupos

More information

Reliability of time dependent stress-strength system for various distributions

Reliability 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 information

Homework 1: Solutions

Homework 1: Solutions Howo : Solutos No-a Fals supposto tst but passs scal tst lthouh -f th ta as slowss [S /V] vs t th appaac of laty alty th path alo whch slowss s to b tat to obta tavl ts ps o th ol paat S o V as a cosquc

More information

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis Dpartmt of Mathmatcs ad Statstcs Ida Isttut of Tchology Kapur MSOA/MSO Assgmt 3 Solutos Itroducto To omplx Aalyss Th problms markd (T) d a xplct dscusso th tutoral class. Othr problms ar for hacd practc..

More information

The Odd Generalized Exponential Modified. Weibull Distribution

The Odd Generalized Exponential Modified. Weibull Distribution Itatoal Mathmatcal oum Vol. 6 o. 9 943-959 HIKARI td www.m-ha.com http://d.do.og/.988/m.6.6793 Th Odd Galzd Epotal Modd Wbull Dstbuto Yassm Y. Abdlall Dpatmt o Mathmatcal Statstcs Isttut o Statstcal Studs

More information

Professor Wei Zhu. 1. Sampling from the Normal Population

Professor Wei Zhu. 1. Sampling from the Normal Population AMS570 Pofesso We Zhu. Samplg fom the Nomal Populato *Example: We wsh to estmate the dstbuto of heghts of adult US male. It s beleved that the heght of adult US male follows a omal dstbuto N(, ) Def. Smple

More information

Order Statistics from Exponentiated Gamma. Distribution and Associated Inference

Order Statistics from Exponentiated Gamma. Distribution and Associated Inference It J otm Mth Scc Vo 4 9 o 7-9 Od Stttc fom Eottd Gmm Dtto d Aoctd Ifc A I Shw * d R A Bo G og of Edcto PO Bo 369 Jddh 438 Sd A G og of Edcto Dtmt of mthmtc PO Bo 469 Jddh 49 Sd A Atct Od tttc fom ottd

More information

In the name of Allah Proton Electromagnetic Form Factors

In the name of Allah Proton Electromagnetic Form Factors I th a of Allah Poto Elctoagtc o actos By : Maj Hazav Pof A.A.Rajab Shahoo Uvsty of Tchology Atoc o acto: W cos th tactos of lcto bas wth atos assu to b th gou stats. Th ct lcto ay gt scatt lastcally wth

More information

Today s topics. How did we solve the H atom problem? CMF Office Hours

Today s topics. How did we solve the H atom problem? CMF Office Hours CMF Offc ous Wd. Nov. 4 oo-p Mo. Nov. 9 oo-p Mo. Nov. 6-3p Wd. Nov. 8 :30-3:30 p Wd. Dc. 5 oo-p F. Dc. 7 4:30-5:30 Mo. Dc. 0 oo-p Wd. Dc. 4:30-5:30 p ouly xa o Th. Dc. 3 Today s topcs Bf vw of slctd sults

More information

Anouncements. Conjugate Gradients. Steepest Descent. Outline. Steepest Descent. Steepest Descent

Anouncements. Conjugate Gradients. Steepest Descent. Outline. Steepest Descent. Steepest Descent oucms Couga Gas Mchal Kazha (6.657) Ifomao abou h Sma (6.757) hav b pos ol: hp://www.cs.hu.u/~msha Tch Spcs: o M o Tusay afoo. o Two paps scuss ach w. o Vos fo w s caa paps u by Thusay vg. Oul Rvw of Sps

More information

School of Aerospace Engineering Origins of Quantum Theory. Measurements of emission of light (EM radiation) from (H) atoms found discrete lines

School of Aerospace Engineering Origins of Quantum Theory. Measurements of emission of light (EM radiation) from (H) atoms found discrete lines Ogs of Quatu Thoy Masuts of sso of lght (EM adato) fo (H) atos foud dsct ls 5 4 Abl to ft to followg ss psso ν R λ c λwavlgth, νfqucy, cspd lght RRydbg Costat (~09,7677.58c - ),,, +, +,..g.,,.6, 0.6, (Lya

More information

Chapter 5 Special Discrete Distributions. Wen-Guey Tzeng Computer Science Department National Chiao University

Chapter 5 Special Discrete Distributions. Wen-Guey Tzeng Computer Science Department National Chiao University Chatr 5 Scal Dscrt Dstrbutos W-Guy Tzg Comutr Scc Dartmt Natoal Chao Uvrsty Why study scal radom varabls Thy aar frqutly thory, alcatos, statstcs, scc, grg, fac, tc. For aml, Th umbr of customrs a rod

More information

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES DEFINITION OF A COMPLEX NUMBER: A umbr of th form, whr = (, ad & ar ral umbrs s calld a compl umbr Th ral umbr, s calld ral part of whl s calld

More information

CBSE , ˆj. cos CBSE_2015_SET-1. SECTION A 1. Given that a 2iˆ ˆj. We need to find. 3. Consider the vector equation of the plane.

CBSE , ˆj. cos CBSE_2015_SET-1. SECTION A 1. Given that a 2iˆ ˆj. We need to find. 3. Consider the vector equation of the plane. CBSE CBSE SET- SECTION. Gv tht d W d to fd 7 7 Hc, 7 7 7. Lt,. W ow tht.. Thus,. Cosd th vcto quto of th pl.. z. - + z = - + z = Thus th Cts quto of th pl s - + z = Lt d th dstc tw th pot,, - to th pl.

More information

Existence of Nonoscillatory Solutions for a Class of N-order Neutral Differential Systems

Existence of Nonoscillatory Solutions for a Class of N-order Neutral Differential Systems Vo 3 No Mod Appd Scc Exsc of Nooscaoy Souos fo a Cass of N-od Nua Dffa Sysms Zhb Ch & Apg Zhag Dpam of Ifomao Egg Hua Uvsy of Tchoogy Hua 4 Cha E-ma: chzhbb@63com Th sach s facd by Hua Povc aua sccs fud

More information

Edge Product Cordial Labeling of Some Cycle Related Graphs

Edge Product Cordial Labeling of Some Cycle Related Graphs Op Joua o Dsct Mathmatcs, 6, 6, 68-78 http://.scp.o/joua/ojdm ISSN O: 6-7643 ISSN Pt: 6-7635 Ed Poduct Coda Lab o Som Cyc Ratd Gaphs Udaya M. Pajapat, Ntta B. Pat St. Xav s Co, Ahmdabad, Ida Shaksh Vaha

More information

Independent Domination in Line Graphs

Independent Domination in Line Graphs Itratoal Joural of Sctfc & Egrg Rsarch Volum 3 Issu 6 Ju-1 1 ISSN 9-5518 Iddt Domato L Grahs M H Muddbhal ad D Basavarajaa Abstract - For ay grah G th l grah L G H s th trscto grah Thus th vrtcs of LG

More information

Introduction to logistic regression

Introduction 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 information

MODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f

MODEL 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 information

ADDITIVE INTEGRAL FUNCTIONS IN VALUED FIELDS. Ghiocel Groza*, S. M. Ali Khan** 1. Introduction

ADDITIVE INTEGRAL FUNCTIONS IN VALUED FIELDS. Ghiocel Groza*, S. M. Ali Khan** 1. Introduction ADDITIVE INTEGRAL FUNCTIONS IN VALUED FIELDS Ghiocl Goza*, S. M. Ali Khan** Abstact Th additiv intgal functions with th cofficints in a comlt non-achimdan algbaically closd fild of chaactistic 0 a studid.

More information

CBSE SAMPLE PAPER SOLUTIONS CLASS-XII MATHS SET-2 CBSE , ˆj. cos. SECTION A 1. Given that a 2iˆ ˆj. We need to find

CBSE SAMPLE PAPER SOLUTIONS CLASS-XII MATHS SET-2 CBSE , ˆj. cos. SECTION A 1. Given that a 2iˆ ˆj. We need to find BSE SMLE ER SOLUTONS LSS-X MTHS SET- BSE SETON Gv tht d W d to fd 7 7 Hc, 7 7 7 Lt, W ow tht Thus, osd th vcto quto of th pl z - + z = - + z = Thus th ts quto of th pl s - + z = Lt d th dstc tw th pot,,

More information

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES

LECTURE 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 information

Numerical Method: Finite difference scheme

Numerical 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 information

Chapter 6. pn-junction diode: I-V characteristics

Chapter 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 information

The Linear Probability Density Function of Continuous Random Variables in the Real Number Field and Its Existence Proof

The Linear Probability Density Function of Continuous Random Variables in the Real Number Field and Its Existence Proof MATEC Web of Cofeeces ICIEA 06 600 (06) DOI: 0.05/mateccof/0668600 The ea Pobablty Desty Fucto of Cotuous Radom Vaables the Real Numbe Feld ad Its Estece Poof Yya Che ad Ye Collee of Softwae, Taj Uvesty,

More information

Course 10 Shading. 1. Basic Concepts: Radiance: the light energy. Light Source:

Course 10 Shading. 1. Basic Concepts: Radiance: the light energy. Light Source: Cour 0 Shadg Cour 0 Shadg. Bac Coct: Lght Sourc: adac: th lght rg radatd from a ut ara of lght ourc or urfac a ut old agl. Sold agl: $ # r f lght ourc a ot ourc th ut ara omttd abov dfto. llumato: lght

More information

Control Systems. Lecture 8 Root Locus. Root Locus. Plant. Controller. Sensor

Control Systems. Lecture 8 Root Locus. Root Locus. Plant. Controller. Sensor Cotol Syt ctu 8 Root ocu Clacal Cotol Pof. Eugo Schut hgh Uvty Root ocu Cotoll Plat R E C U Y - H C D So Y C C R C H Wtg th loo ga a w a ttd tackg th clod-loo ol a ga va Clacal Cotol Pof. Eugo Schut hgh

More information

3.4 Properties of the Stress Tensor

3.4 Properties of the Stress Tensor cto.4.4 Proprts of th trss sor.4. trss rasformato Lt th compots of th Cauchy strss tsor a coordat systm wth bas vctors b. h compots a scod coordat systm wth bas vctors j,, ar gv by th tsor trasformato

More information

Lecture 1: Empirical economic relations

Lecture 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 information

On Jackson's Theorem

On Jackson's Theorem It. J. Cotm. Math. Scics, Vol. 7, 0, o. 4, 49 54 O Jackso's Thom Ema Sami Bhaya Datmt o Mathmatics, Collg o Educatio Babylo Uivsity, Babil, Iaq mabhaya@yahoo.com Abstact W ov that o a uctio W [, ], 0

More information

The Exponentiated Lomax Distribution: Different Estimation Methods

The Exponentiated Lomax Distribution: Different Estimation Methods Ameca Joual of Appled Mathematcs ad Statstcs 4 Vol. No. 6 364-368 Avalable ole at http://pubs.scepub.com/ajams//6/ Scece ad Educato Publshg DOI:.69/ajams--6- The Expoetated Lomax Dstbuto: Dffeet Estmato

More information

Unbalanced Panel Data Models

Unbalanced 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 information

A study on Ricci soliton in S -manifolds.

A study on Ricci soliton in S -manifolds. IO Joual of Mathmatc IO-JM -IN: 78-578 p-in: 9-765 olum Iu I Ja - Fb 07 PP - wwwojoualo K dyavath ad Bawad Dpatmt of Mathmatc Kuvmpu vtyhaaahatta - 577 5 hmoa Kaataa Ida Abtact: I th pap w tudy m ymmtc

More information

Math 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 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 information

IFYFM002 Further Maths Appendix C Formula Booklet

IFYFM002 Further Maths Appendix C Formula Booklet Ittol Foudto Y (IFY) IFYFM00 Futh Mths Appd C Fomul Booklt Rltd Documts: IFY Futh Mthmtcs Syllbus 07/8 Cotts Mthmtcs Fomul L Equtos d Mtcs... Qudtc Equtos d Rmd Thom... Boml Epsos, Squcs d Ss... Idcs,

More information

RECAPITULATION & CONDITIONAL PROBABILITY. Number of favourable events n E Total number of elementary events n S

RECAPITULATION & CONDITIONAL PROBABILITY. Number of favourable events n E Total number of elementary events n S Fomulae Fo u Pobablty By OP Gupta [Ida Awad We, +91-9650 350 480] Impotat Tems, Deftos & Fomulae 01 Bascs Of Pobablty: Let S ad E be the sample space ad a evet a expemet espectvely Numbe of favouable evets

More information

2.1.1 The Art of Estimation Examples of Estimators Properties of Estimators Deriving Estimators Interval Estimators

2.1.1 The Art of Estimation Examples of Estimators Properties of Estimators Deriving Estimators Interval Estimators . ploatoy Statstcs. Itoducto to stmato.. The At of stmato.. amples of stmatos..3 Popetes of stmatos..4 Devg stmatos..5 Iteval stmatos . Itoducto to stmato Samplg - The samplg eecse ca be epeseted by a

More information

Lecture 7 Diffusion. Our fluid equations that we developed before are: v t v mn t

Lecture 7 Diffusion. Our fluid equations that we developed before are: v t v mn t Cla ot fo EE6318/Phy 6383 Spg 001 Th doumt fo tutoal u oly ad may ot b opd o dtbutd outd of EE6318/Phy 6383 tu 7 Dffuo Ou flud quato that w dvlopd bfo a: f ( )+ v v m + v v M m v f P+ q E+ v B 13 1 4 34

More information

Chapter 2 Reciprocal Lattice. An important concept for analyzing periodic structures

Chapter 2 Reciprocal Lattice. An important concept for analyzing periodic structures Chpt Rcpocl Lttc A mpott cocpt o lyzg podc stuctus Rsos o toducg cpocl lttc Thoy o cystl dcto o x-ys, utos, d lctos. Wh th dcto mxmum? Wht s th tsty? Abstct study o uctos wth th podcty o Bvs lttc Fou tsomto.

More information

L-MOMENTS EVALUATION FOR IDENTICALLY AND NONIDENTICALLY WEIBULL DISTRIBUTED RANDOM VARIABLES

L-MOMENTS EVALUATION FOR IDENTICALLY AND NONIDENTICALLY WEIBULL DISTRIBUTED RANDOM VARIABLES THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Sees A, OF THE ROMANIAN ACADEMY Volume 8, Numbe 3/27,. - L-MOMENTS EVALUATION FOR IDENTICALLY AND NONIDENTICALLY WEIBULL DISTRIBUTED RANDOM VARIABLES

More information

k of the incident wave) will be greater t is too small to satisfy the required kinematics boundary condition, (19)

k of the incident wave) will be greater t is too small to satisfy the required kinematics boundary condition, (19) TOTAL INTRNAL RFLTION Kmacs pops Sc h vcos a coplaa, l s cosd h cd pla cocds wh h X pla; hc 0. y y y osd h cas whch h lgh s cd fom h mdum of hgh dx of faco >. Fo cd agls ga ha h ccal agl s 1 ( /, h hooal

More information

such that for 1 From the definition of the k-fibonacci numbers, the firsts of them are presented in Table 1. Table 1: First k-fibonacci numbers F 1

such that for 1 From the definition of the k-fibonacci numbers, the firsts of them are presented in Table 1. Table 1: First k-fibonacci numbers F 1 Scholas Joual of Egeeg ad Techology (SJET) Sch. J. Eg. Tech. 0; (C):669-67 Scholas Academc ad Scetfc Publshe (A Iteatoal Publshe fo Academc ad Scetfc Resouces) www.saspublshe.com ISSN -X (Ole) ISSN 7-9

More information

Statics. Consider the free body diagram of link i, which is connected to link i-1 and link i+1 by joint i and joint i-1, respectively. = r r r.

Statics. Consider the free body diagram of link i, which is connected to link i-1 and link i+1 by joint i and joint i-1, respectively. = r r r. Statcs Th cotact btw a mapulato ad ts vomt sults tactv ocs ad momts at th mapulato/vomt tac. Statcs ams at aalyzg th latoshp btw th actuato dv tous ad th sultat oc ad momt appld at th mapulato dpot wh

More information

Handout 7. Properties of Bloch States and Electron Statistics in Energy Bands

Handout 7. Properties of Bloch States and Electron Statistics in Energy Bands Hdout 7 Popts of Bloch Stts d Elcto Sttstcs Eg Bds I ths lctu ou wll l: Popts of Bloch fuctos Podc boud codtos fo Bloch fuctos Dst of stts -spc Elcto occupto sttstcs g bds ECE 407 Spg 009 Fh R Coll Uvst

More information

Fairing of Parametric Quintic Splines

Fairing of Parametric Quintic Splines ISSN 46-69 Eglad UK Joual of Ifomato ad omputg Scece Vol No 6 pp -8 Fag of Paametc Qutc Sples Yuau Wag Shagha Isttute of Spots Shagha 48 ha School of Mathematcal Scece Fuda Uvesty Shagha 4 ha { P t )}

More information

Estimation of Population Mean under Non-Response using Various Imputation Methods for Stratified Population

Estimation of Population Mean under Non-Response using Various Imputation Methods for Stratified Population olumba tatoal Publshg Joual of Advacd omutg (5 Vol. o.. - do:.776/ac.5.8 sach Atcl stmato of Poulato a ud o-sos usg Vaous mutato ods fo tatfd Poulato Paa gh *, At Kuma gh, ad V.K. gh cvd: tmb 5 Acctd:

More information

CHAPTER-4 A BROAD CLASS OF ADDITIVE ERROR CODING, CHANNELS AND LOWER BOUND ON THE PROBABILITY OF ERROR FOR BLOCK CODES USING SK- METRIC

CHAPTER-4 A BROAD CLASS OF ADDITIVE ERROR CODING, CHANNELS AND LOWER BOUND ON THE PROBABILITY OF ERROR FOR BLOCK CODES USING SK- METRIC CHATER-4 A ROAD CLASS OF ADDITIVE ERROR CODING CHANNELS AND LOWER OUND ON THE ROAILITY OF ERROR FOR LOCK CODES USING SK- METRIC Th ctts f ths Chat a basd m fllwg ublshd a: Gau A Shama D A ad Class f Addtv

More information

A COMPARISON OF SEVERAL TESTS FOR EQUALITY OF COEFFICIENTS IN QUADRATIC REGRESSION MODELS UNDER HETEROSCEDASTICITY

A 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 information

SUNWAY UNIVERSITY BUSINESS SCHOOL SAMPLE FINAL EXAMINATION FOR FIN 3024 INVESTMENT MANAGEMENT

SUNWAY UNIVERSITY BUSINESS SCHOOL SAMPLE FINAL EXAMINATION FOR FIN 3024 INVESTMENT MANAGEMENT UNWA UNIVRIT BUIN HOOL AMPL FINAL AMINATION FOR FIN 34 INVTMNT MANAGMNT TION A A ALL qto th cto. Qto tha kg facg fo a ca. Th local bak ha ag to gv hm a loa fo 9% of th cot of th ca h ll pay th t cah a

More information

Repeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space.

Repeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space. Rpatd Trals: As w hav lood at t, th thory of probablty dals wth outcoms of sgl xprmts. I th applcatos o s usually trstd two or mor xprmts or rpatd prformac or th sam xprmt. I ordr to aalyz such problms

More information

The probability of Riemann's hypothesis being true is. equal to 1. Yuyang Zhu 1

The probability of Riemann's hypothesis being true is. equal to 1. Yuyang Zhu 1 Th robablty of Ra's hyothss bg tru s ual to Yuyag Zhu Abstract Lt P b th st of all r ubrs P b th -th ( ) lt of P ascdg ordr of sz b ostv tgrs ad s a rutato of wth Th followg rsults ar gv ths ar: () Th

More information

Lecture 3.2: Cosets. Matthew Macauley. Department of Mathematical Sciences Clemson University

Lecture 3.2: Cosets. Matthew Macauley. Department of Mathematical Sciences Clemson University Lctu 3.2: Costs Matthw Macauly Dpatmnt o Mathmatical Scincs Clmson Univsity http://www.math.clmson.du/~macaul/ Math 4120, Modn Algba M. Macauly (Clmson) Lctu 3.2: Costs Math 4120, Modn Algba 1 / 11 Ovviw

More information

ERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca**

ERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca** ERDO-MARANDACHE NUMBER b Tbrc* Tt Tbrc** *Trslv Uvrsty of Brsov, Computr cc Dprtmt **Uvrsty of Mchstr, Computr cc Dprtmt Th strtg pot of ths rtcl s rprstd by rct work of Fch []. Bsd o two symptotc rsults

More information

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are Itratoal Joural Of Computatoal Egrg Rsarch (crol.com) Vol. Issu. 5 Total Prm Graph M.Rav (a) Ramasubramaa 1, R.Kala 1 Dpt.of Mathmatcs, Sr Shakth Isttut of Egrg & Tchology, Combator 641 06. Dpt. of Mathmatcs,

More information

GTAP Eleventh Annual Conference, 2008 "Future of Global Economy" Helsinki

GTAP Eleventh Annual Conference, 2008 Future of Global Economy Helsinki GTAP Elvth Aual Cof, 28 "Futu of Global Eooy" lsk SAM laboato as a ultobtv austt pobl Casao Maqu Laa Pñat Dpto Aálss Eoóo Aplao Uvsa Las Palas Ga Caaa (Spa aqu@aa.ulpg.s Dolos R. Satos-Pñat Dpto Métoos

More information

Continuous Distributions

Continuous Distributions 7//3 Cotuous Dstrbutos Radom Varables of the Cotuous Type Desty Curve Percet Desty fucto, f (x) A smooth curve that ft the dstrbuto 3 4 5 6 7 8 9 Test scores Desty Curve Percet Probablty Desty Fucto, f

More information

Correlation in tree The (ferromagnetic) Ising model

Correlation 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 information

Aotomorphic Functions And Fermat s Last Theorem(4)

Aotomorphic 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 information

Estimation of Population Mean with. a New Imputation Method

Estimation of Population Mean with. a New Imputation Method Aled Mathematal Sees, Vol. 9, 015, o. 34, 1663-167 HIKARI Ltd, www.m-hka.om htt://d.do.og/10.1988/ams.015.593 Estmato of Poulato Mea wth a New Imutato Method Abdeltawab A. Ga Isttute of Statstal Studes

More information

(Reference: sections in Silberberg 5 th ed.)

(Reference: sections in Silberberg 5 th ed.) ALE. Atomic Structur Nam HEM K. Marr Tam No. Sctio What is a atom? What is th structur of a atom? Th Modl th structur of a atom (Rfrc: sctios.4 -. i Silbrbrg 5 th d.) Th subatomic articls that chmists

More information

ENGG 1203 Tutorial. Difference Equations. Find the Pole(s) Finding Equations and Poles

ENGG 1203 Tutorial. Difference Equations. Find the Pole(s) Finding Equations and Poles ENGG 03 Tutoial Systms ad Cotol 9 Apil Laig Obctivs Z tasfom Complx pols Fdbac cotol systms Ac: MIT OCW 60, 6003 Diffc Equatios Cosid th systm pstd by th followig diffc quatio y[ ] x[ ] (5y[ ] 3y[ ]) wh

More information

Complex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP)

Complex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP) th Topc Compl Nmbrs Hyprbolc fctos ad Ivrs hyprbolc fctos, Rlato btw hyprbolc ad crclar fctos, Formla of hyprbolc fctos, Ivrs hyprbolc fctos Prpard by: Prof Sl Dpartmt of Mathmatcs NIT Hamrpr (HP) Hyprbolc

More information

International Journal of Pure and Applied Sciences and Technology

International Journal of Pure and Applied Sciences and Technology I J Pu Appl Sc Tchol 8 pp 59-7 Iaoal Joual o Pu ad Appld Sccs ad Tchology ISSN 9-67 Avalabl ol a wwwopaasa Rsach Pap Tasmud Quas Ldly Dsbuo: A Galzao o h Quas Ldly Dsbuo I Elbaal ad M Elgahy * Isu o Sascal

More information

Reliability analysis of time - dependent stress - strength system when the number of cycles follows binomial distribution

Reliability analysis of time - dependent stress - strength system when the number of cycles follows binomial distribution raoal Joural of Sascs ad Ssms SSN 97-675 Volum, Numbr 7,. 575-58 sarch da Publcaos h://www.rublcao.com labl aalss of m - dd srss - srgh ssm wh h umbr of ccls follows bomal dsrbuo T.Sumah Umamahswar, N.Swah,

More information

D. Bertsekas and R. Gallager, "Data networks." Q: What are the labels for the x-axis and y-axis of Fig. 4.2?

D. Bertsekas and R. Gallager, Data networks. Q: What are the labels for the x-axis and y-axis of Fig. 4.2? pd by J. Succ ECE 543 Octob 22 2002 Outl Slottd Aloh Dft Stblzd Slottd Aloh Uslottd Aloh Splttg Algoths Rfc D. Btsks d R. llg "Dt twoks." Rvw (Slottd Aloh): : Wht th lbls fo th x-xs d y-xs of Fg. 4.2?

More information

MA 524 Homework 6 Solutions

MA 524 Homework 6 Solutions MA 524 Homework 6 Solutos. Sce S(, s the umber of ways to partto [] to k oempty blocks, ad c(, s the umber of ways to partto to k oempty blocks ad also the arrage each block to a cycle, we must have S(,

More information

Mid Year Examination F.4 Mathematics Module 1 (Calculus & Statistics) Suggested Solutions

Mid Year Examination F.4 Mathematics Module 1 (Calculus & Statistics) Suggested Solutions Mid Ya Eamination 3 F. Matmatics Modul (Calculus & Statistics) Suggstd Solutions Ma pp-: 3 maks - Ma pp- fo ac qustion: mak. - Sam typ of pp- would not b countd twic fom wol pap. - In any cas, no pp maks

More information

i 2 σ ) i = 1,2,...,n , and = 3.01 = 4.01

i 2 σ ) i = 1,2,...,n , and = 3.01 = 4.01 ECO 745, Homework 6 Le Cabrera. Assume that the followg data come from the lear model: ε ε ~ N, σ,,..., -6. -.5 7. 6.9 -. -. -.9. -..6.4.. -.6 -.7.7 Fd the mamum lkelhood estmates of,, ad σ ε s.6. 4. ε

More information

Counting the compositions of a positive integer n using Generating Functions Start with, 1. x = 3 ), the number of compositions of 4.

Counting the compositions of a positive integer n using Generating Functions Start with, 1. x = 3 ), the number of compositions of 4. Coutg th compostos of a postv tgr usg Gratg Fuctos Start wth,... - Whr, for ampl, th co-ff of s, for o summad composto of aml,. To obta umbr of compostos of, w d th co-ff of (...) ( ) ( ) Hr for stac w

More information

Option 3. b) xe dx = and therefore the series is convergent. 12 a) Divergent b) Convergent Proof 15 For. p = 1 1so the series diverges.

Option 3. b) xe dx = and therefore the series is convergent. 12 a) Divergent b) Convergent Proof 15 For. p = 1 1so the series diverges. Optio Chaptr Ercis. Covrgs to Covrgs to Covrgs to Divrgs Covrgs to Covrgs to Divrgs 8 Divrgs Covrgs to Covrgs to Divrgs Covrgs to Covrgs to Covrgs to Covrgs to 8 Proof Covrgs to π l 8 l a b Divrgt π Divrgt

More information

Randomly Weighted Averages on Order Statistics

Randomly Weighted Averages on Order Statistics Apple Mathematcs 3 4 34-346 http://oog/436/am3498 Publshe Ole Septembe 3 (http://wwwscpog/joual/am Raomly Weghte Aveages o Oe Statstcs Home Haj Hasaaeh Lela Ma Ghasem Depatmet of Statstcs aculty of Mathematcal

More information

The 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.

The 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 information

Introduction to logistic regression

Introduction 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 information

Nuclear Chemistry -- ANSWERS

Nuclear Chemistry -- ANSWERS Hoor Chstry Mr. Motro 5-6 Probl St Nuclar Chstry -- ANSWERS Clarly wrt aswrs o sparat shts. Show all work ad uts.. Wrt all th uclar quatos or th radoactv dcay srs o Urau-38 all th way to Lad-6. Th dcay

More information

GREEN S FUNCTION FOR HEAT CONDUCTION PROBLEMS IN A MULTI-LAYERED HOLLOW CYLINDER

GREEN S FUNCTION FOR HEAT CONDUCTION PROBLEMS IN A MULTI-LAYERED HOLLOW CYLINDER Joual of ppled Mathematcs ad Computatoal Mechacs 4, 3(3), 5- GREE S FUCTIO FOR HET CODUCTIO PROBLEMS I MULTI-LYERED HOLLOW CYLIDER Stasław Kukla, Uszula Sedlecka Isttute of Mathematcs, Czestochowa Uvesty

More information

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit

Binary 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 information

Estimating the Variance in a Simulation Study of Balanced Two Stage Predictors of Realized Random Cluster Means Ed Stanek

Estimating 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 information

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations HP 30S Statstcs Averages ad Stadard Devatos Average ad Stadard Devato Practce Fdg Averages ad Stadard Devatos HP 30S Statstcs Averages ad Stadard Devatos Average ad stadard devato The HP 30S provdes several

More information

Today s topic 2 = Setting up the Hydrogen Atom problem. Schematic of Hydrogen Atom

Today s topic 2 = Setting up the Hydrogen Atom problem. Schematic of Hydrogen Atom Today s topic Sttig up th Hydog Ato pobl Hydog ato pobl & Agula Motu Objctiv: to solv Schödig quatio. st Stp: to dfi th pottial fuctio Schatic of Hydog Ato Coulob s aw - Z 4ε 4ε fo H ato Nuclus Z What

More information

On interval-valued optimization problems with generalized invex functions

On interval-valued optimization problems with generalized invex functions Ahmad t al. Jounal of Inqualitis and Alications 203, 203:33 htt://www.jounalofinqualitisandalications.com/contnt/203//33 R E S E A R C H On Accss On intval-valud otimization oblms with gnalizd inv functions

More information

Convolution of Generated Random Variable from. Exponential Distribution with Stabilizer Constant

Convolution of Generated Random Variable from. Exponential Distribution with Stabilizer Constant Appld Mamacal Scc Vol 9 5 o 9 78-789 HIKARI Ld wwwm-acom p://dxdoog/988/am5559 Covoluo of Gad Radom Vaabl fom Expoal Dbuo w Sablz Coa Dod Dvao Maa Lufaa Oaa ad Maa Aa Dpam of Mamac Facul of Mamac ad Naual

More information

( ) ( ) ( ) ( ) ( ) 1 ( ) ( ) ( ) ( ) ( p ) ( ) Review. Markov String/Process: ln2. d df. dx dx f x. 1 p

( ) ( ) ( ) ( ) ( ) 1 ( ) ( ) ( ) ( ) ( p ) ( ) Review. Markov String/Process: ln2. d df. dx dx f x. 1 p Rvw d x l x = l x + = l x dx d x log x = log x + = log x dx l2 d x x log x dx = l2 ( log h( = log ( log( dh ( d = log h( f( x ( x f ( x d df = log dx dx f x ( h 2 = h 2 = b = b b h logb log( b Pobablty

More information

SOME IMPUTATION METHODS IN DOUBLE SAMPLING SCHEME FOR ESTIMATION OF POPULATION MEAN

SOME IMPUTATION METHODS IN DOUBLE SAMPLING SCHEME FOR ESTIMATION OF POPULATION MEAN aoal Joual of Mod Egg Rsach (JMER) www.jm.com ol. ssu. Ja-F 0 pp-00-07 N: 9- OME MPUTATON METHOD N DOUBLE AMPLNG HEME FOR ETMATON OF POPULATON MEAN ABTRAT Nada gh Thaku Kalpaa adav fo Mahmacal ccs (M)

More information

In 1991 Fermat s Last Theorem Has Been Proved

In 1991 Fermat s Last Theorem Has Been Proved I 99 Frmat s Last Thorm Has B Provd Chu-Xua Jag P.O.Box 94Bg 00854Cha Jcxua00@s.com;cxxxx@6.com bstract I 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral

More information

Load Equations. So let s look at a single machine connected to an infinite bus, as illustrated in Fig. 1 below.

Load Equations. So let s look at a single machine connected to an infinite bus, as illustrated in Fig. 1 below. oa Euatons Thoughout all of chapt 4, ou focus s on th machn tslf, thfo w wll only pfom a y smpl tatmnt of th ntwok n o to s a complt mol. W o that h, but alz that w wll tun to ths ssu n Chapt 9. So lt

More information

Channel Capacity Course - Information Theory - Tetsuo Asano and Tad matsumoto {t-asano,

Channel Capacity Course - Information Theory - Tetsuo Asano and Tad matsumoto   {t-asano, School of Iformato Scc Chal Capacty 009 - Cours - Iformato Thory - Ttsuo Asao ad Tad matsumoto Emal: {t-asao matumoto}@jast.ac.jp Japa Advacd Isttut of Scc ad Tchology Asahda - Nom Ishkawa 93-9 Japa http://www.jast.ac.jp

More information

NEW ATTACKS ON TAKAGI CRYPTOSYSTEM

NEW ATTACKS ON TAKAGI CRYPTOSYSTEM Jounal of Algba umb Thoy: Advancs and Alcatons Volum 8 umb - 7 Pags 5-59 Avalabl at htt://scntfcadvancscon DOI: htt://dxdoog/86/antaa_785 EW ATTACKS O TAKAGI CRYPTOSYSTEM MUHAMMAD REAL KAMEL ARIFFI SADIQ

More information

No-Bend Orthogonal Drawings of Subdivisions of Planar Triconnected Cubic Graphs

No-Bend Orthogonal Drawings of Subdivisions of Planar Triconnected Cubic Graphs N-B Oh Dw f Sv f P Tcc Cc Gh (Ex Ac) M. S Rh, N E, T Nhz G Sch f If Scc, Th Uvy, A-y 05, S 980-8579, J. {,}@hz.c.h.c. h@c.h.c. Ac. A h h wh fx. I - h w f h, ch vx w ch w hz vc. A h hv - h w f f h - h w.

More information

APPENDIX: STATISTICAL TOOLS

APPENDIX: 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 information

Series of New Information Divergences, Properties and Corresponding Series of Metric Spaces

Series of New Information Divergences, Properties and Corresponding Series of Metric Spaces Srs of Nw Iforao Dvrgcs, Proprs ad Corrspodg Srs of Mrc Spacs K.C.Ja, Praphull Chhabra Profssor, Dpar of Mahacs, Malavya Naoal Isu of Tchology, Japur (Rajasha), Ida Ph.d Scholar, Dpar of Mahacs, Malavya

More information

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

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

More information

STK4011 and STK9011 Autumn 2016

STK4011 and STK9011 Autumn 2016 STK4 ad STK9 Autum 6 Pot estmato Covers (most of the followg materal from chapter 7: Secto 7.: pages 3-3 Secto 7..: pages 3-33 Secto 7..: pages 35-3 Secto 7..3: pages 34-35 Secto 7.3.: pages 33-33 Secto

More information

Non-degenerate Perturbation Theory

Non-degenerate Perturbation Theory No-degeerate Perturbato Theory Proble : H E ca't solve exactly. But wth H H H' H" L H E Uperturbed egevalue proble. Ca solve exactly. E Therefore, kow ad. H ' H" called perturbatos Copyrght Mchael D. Fayer,

More information

FUZZY MULTINOMIAL CONTROL CHART WITH VARIABLE SAMPLE SIZE

FUZZY MULTINOMIAL CONTROL CHART WITH VARIABLE SAMPLE SIZE A. Paduaga et al. / Iteatoal Joual of Egeeg Scece ad Techology (IJEST) FUZZY MUTINOMIA CONTRO CHART WITH VARIABE SAMPE SIZE A. PANDURANGAN Pofesso ad Head Depatmet of Compute Applcatos Vallamma Egeeg College,

More information

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy Bouds o the expected etropy ad KL-dvergece of sampled multomal dstrbutos Brado C. Roy bcroy@meda.mt.edu Orgal: May 18, 2011 Revsed: Jue 6, 2011 Abstract Iformato theoretc quattes calculated from a sampled

More information

Mathematical Statistics. Chapter VIII Sampling Distributions and the Central Limit Theorem

Mathematical 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 information

Phys Nov. 3, 2017 Today s Topics. Continue Chapter 2: Electromagnetic Theory, Photons, and Light Reading for Next Time

Phys Nov. 3, 2017 Today s Topics. Continue Chapter 2: Electromagnetic Theory, Photons, and Light Reading for Next Time Phys 31. No. 3, 17 Today s Topcs Cou Chap : lcomagc Thoy, Phoos, ad Lgh Radg fo Nx Tm 1 By Wdsday: Radg hs Wk Fsh Fowls Ch. (.3.11 Polazao Thoy, Jos Macs, Fsl uaos ad Bws s Agl Homwok hs Wk Chap Homwok

More information

On Estimation of Unknown Parameters of Exponential- Logarithmic Distribution by Censored Data

On 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 information

Chapter 7 Varying Probability Sampling

Chapter 7 Varying Probability Sampling Chapte 7 Vayg Pobablty Samplg The smple adom samplg scheme povdes a adom sample whee evey ut the populato has equal pobablty of selecto. Ude ceta ccumstaces, moe effcet estmatos ae obtaed by assgg uequal

More information