FRST 531 Applied multivariate statistics
|
|
- Mercy Gallagher
- 5 years ago
- Views:
Transcription
1 FRS 5 ppled multvarate tattc a varable a tool developed Decrptve motl, rather tha feretal a clafcato of method pe of varable:. Dcrete veru cotuou. Nomal -- ame Ordal -- have order Iterval relate to oe aother but ot ecear to zero Rato relate to zero e.g. D: matr Ob. X X X X F F Data ca be: Oe matr Separated to two or more matrce pe of multvarate aal ued deped o:. he charactertc of the data. he OJEIVE of the aal Sce ma varable are volved, a udertadg of matr algebra eeded order to udertad the varou multvarate tattc tool. E:\FRS5\ote\tro_matrce.doc
2 lafcato of ultvarate ethod Oe group of varable: Prcple compoet aal, factor aal, cluter aal, ordato, multdmeoal calg wo group of varable X ad Y:. he Y group ha oe varable ad the X group cotuou, or med cotuou ad dcrete wth all cla varable repreeted a a group of dumm varable, ad the Y varable : otuou multple lear regreo Dcrete 0 ad logtc regreo Dcrete more tha categore multple dcrmat aal. he Y group ha more tha oe varable, all cotuou he X group dcrete varable repreeted a dumm varable multvarate aal of varace he X group ha a mture of cotuou ad dcrete varable repreeted a dumm varable, but there o teracto betwee cotuou ad dcrete varable multvarate aal of covarace. he Y group ha more tha oe varable, that are med cotou ad dcrete repreeted b dumm varable ad the X group ha a mture of cotuou ad dcrete varable repreeted a dumm varable caocal correlato aal, correpodece aal, redudac aal Decrpto of a Selecto of ultvarate ale ultple ear Regreo-- mple lear regreo but more tha depedet varable ued to predct the depedet varable ultple Dcrmat al D -- a techque for calbratg a procedure to claf dvdual obervato to oe of a et of group; volve dervg a lear combato of two or more depedet varable that wll dcrmate bet betwee the a pror defed group; acheved b mamzg the betwee- group varace relatve to the wth-group varace; ca alo be ued to tet the cetrod of each group for dfferece; volve dcrmat core ad weght. E:\FRS5\ote\tro_matrce.doc
3 ogt Regreo -- alo Probt regreo; clae; dea to predct the probablt of beg a partcular cla; ue amum kelhood Etmato ad traformato to obta a oluto. Prcple ompoet al P -- a procedure for retatg the formato a partcular et of obervato o oe et of varable, term of a alterate et of varable; dmeo reducg method; ue ege vector ad ege value to obta a oluto; objectve to take the orgal varable that are correlated, ad fd dce that are ucorrelated; dea to decrbe the varato the data et o that ol a few dce accout for mot of the varablt, therefore reducg the umber of varable; bet reult whe orgal varable are hghtl correlated; ca be prelmar to other aale. aocal orrelato al -- multaeou correlato betwee two group of varable; weght are foud whch mamze the betwee group correlato Factor al -- a techque for aalzg the teral tructure of a et of varable, order to decrbe the lkage amog a et of oberved varable term of uobervable uderlg cotruct, called factor; the P oluto ca be ued a the frt tage; a reduced et of prcple compoet from the P oluto the mapulated through rotato matr traformato o that the reult a mple tructure that ea to terpret luter al -- a collecto of procedure for groupg ette obervato or varable that are mlar to oe aother; group are NO KNOWN a pror ; uuall followed b D to get a profle o each defed group ultdmeoal Scalg -- procedrue for covertg put o a gle meaure of dmlart or mlart amog object to geometrc repreetato of thoe object multple dmeo; tr to mplf to or dmeo ultple al of Varace NOV -- have two or more outcome meaured o a epermet depedet varable e.g., eedlg heght, dameter, ad root/hoot legth; lke caocal correlato, but the depedet varable are dcrete cla varable factor level the epermet; ma alo clude etg of tem, ad teracto ultple al of ovarace NOV -- a NOV, but we wh to cotrol for a covarate e.g., tartg eedlg heght; depedet varable are the covarate, ad dcrete cla varable E:\FRS5\ote\tro_matrce.doc
4 Revew of atr lgebra atrce are value elemet grouped to row ad colum. I. pe of matrce:. Square -- umber of row equal the umber of colum. e.g. X matr Vector row called a row vector; colum called a colum vector e.g. colum vector X matr e.g. row vector X matr. 5. [. 5 ]. Smmetrc matr quare matr wth row ad colum the ame. e.g. X matr Null all zero or ut all oe matr e.g. ull matr e.g. ut matr Sg vector elemet are ad ol. E:\FRS5\ote\tro_matrce.doc
5 . Idett matrce quare matr wth all off-dagoal elemet are zero; all dagoal elemet are. E.g. I : Scalar matr ha a gle elemet.. atrce are equal f all elemet are equal 9. Sgular matrce occur whe a row [or colum] of a matr ca be obtaed b a lear combato of the elemet other row [or colum] of the matr Eample: h alo called lear depedece. NOE: he determat wll be zero ad the matr wll ot be of full rak ee later eplaato for th. II. atr operato. ddto of matrce matrce mut be the ame ze order to add them ame umber of colum ad of row. ddto mpl reult from addg correpodg elemet. 7. ultplcato b a calar multpl each elemet b the calar E:\FRS5\ote\tro_matrce.doc
6 . Subtracto matrce mut be the ame ze. ultpl each elemet the ecod matr b, the add the lke elemet of the two matrce.. rapoe a matr wtch the colum ad the row. he trapoe of matr labelled a or a ultplcato the two matrce to be multpled mut have the ame teror dmeo 5 0 [ X ] [ X ] [ X ] e.g. for the elemet row ad colum of the ew matr, multpl row oe wth colum : X X X 5. Determat of the matr Relate to the ze of the matr atr mut be quare gular matr ha a zero determat For [ X ] matrce, the determat eal calculated a: a b d e a e b d Where deote the determat of. 7. Dvo Qute dffcult wth matrce mut be a quare matr E:\FRS5\ote\tro_matrce.doc
7 called atr Ivero proce mlar to that for gle value. o dvde b a value, ou ca mpl multpl b the vere of that value e.g. to dvde 5 b, we ca ue 5 X /. Note that tme the vere of equal. For matrce, to dvde b, we ue X -, where - the vere of. What -? ut atf: I X - [ X ] [ X ] [ X ] ethod ued: ofactor Pvotal approach a other he determat of the matr eeded, order to ue ma of thee method. If the determat zero, matr vero ot poble. Ug the cofactor approach: of For the cofactor approach, the cofactor matr [X] ad the determat calar are eeded. he elemet of the cofactor matr are foud b: cj j j where j the determat of a ubmatr for the th row ad jth colum. lo, oce the cofactor matr foud, the determat of the matr ca be calculated a: a c j j j h ca be ummed over the elemet of a row or a colum. E:\FRS5\ote\tro_matrce.doc
8 Eample: Ug Row of matr : ce the determat of a X matr ca be calculated a ad bc: What the cofactor of? h alo called the adjot of. of of he vere of the foud b: E:\FRS5\ote\tro_matrce.doc
9 It ca be verfed that X - I What about a larger matr? 7 Epadg over Row : 7 herefore, the determat of each X ubmatr mut frt be determed.. Rak the order of the larget quare ubmatr wth determat ot equal to zero. Full Rak occur whe the rak equal to the dmeo of the matr, meag that the determat of the full matr ot equal to zero. atrce that are ot full rak have lear depedece ad the matr caot be verted. 9. race of the matr the um of all of the dagoal elemet. E:\FRS5\ote\tro_matrce.doc
10 III. Rule for atr Operato λ λ λ IV. Properte of Smmetrc atrce:. f mmetrc.. If - X, the kew mmetrc.. ad wll reult mmetrc matrce f mmetrc.. If quare, the alo mmetrc. 5. If mmetrc, the a calar tme wll alo reult a mmetrc matr.. he um of a umber of mmetrc matrce mmetrc. 7. he product of two mmetrc matrce IS NO ecear mmetrc. E:\FRS5\ote\tro_matrce.doc
11 ommo Data atrce Y q q q q O Y X X q for a gle depedet varable for q depedet varable p p p p O X X p for p depedet varable Sum of quare ad cro product for X a pxp matr produced b: SSX X X. lo called the ucorrected um of quare matr. SSX p p p p p p p O he um of quare ad cro product for Y matr a q X q matr produced b: SSYY Y, ad the ame a the SSX matr ecept that the elemet are baed o um for Y. E:\FRS5\ote\tro_matrce.doc
12 he corrected um of quare ad cro product for X matr S; called the SSP matr SS are produced b ubtractg average value from each value pror to multplg. For eample, the elemet the frt row ad frt colum of S : where the mea of the frt colum of value frt varable of the X matr. h repeated ug all of the X varable, reultg um of quare for each of the dagoal elemet of S. he off-dagoal elemet are the corrected cro product. For eample, for row ad colum the ame value occur for row ad colum ce the matr mmetrc: ug matr otato, S ca be calculated a S X X X X ce X reult um of each X varable. lteratvel, ever elemet the X matr ca be adjuted b ubtractg the mea for that varable. For varable, th would be: d he ew matr would the be the X value adjuted for the mea matr Xd, ad S ca be calculated a: S Xd Xd o obta the covarace matr for the X varable baed o the ample data ; called OV SS, all of the elemet of S would be dvded b -. he matrce S ad would be calculated the ame maer. he reultg covarace matrce would have varace o the dagoal ad covarace o the off-dagoal. E:\FRS5\ote\tro_matrce.doc
13 p p p p p p p O p X p Smple correlato value rage from to ad the varable mut be at leat terval cale amog varable ca be calculated a how for ad : var var, cov r For the X varable, the correlato matr called ORR SS calculated b D D R p X p where D a dagoal matr ad the elemet alog the dagoal are the vere of the tadard devato for the X varable. he dagoal elemet of the correlato matr are all equal to ce the varable perfectl ad potvel related to telf. h mea that the trace of the correlato matr wll equal the umber of varable. lteratvel, ever elemet the X matr ca be ormalzed b ubtractg the mea for that varable, ad dvdg b the tadard devato. For varable, th would be: d E:\FRS5\ote\tro_matrce.doc
14 he ew matr would the be the X value adjuted for the mea ad caled ug the tadard devato matr X, ad R ca be calculated a: R X X h mea that the correlato of the orgal data the ame a the covarace of the tadardzed data. he correlato matr for the Y varable ca be calculated a mlar maer. E:\FRS5\ote\tro_matrce.doc
Linear Regression. Can height information be used to predict weight of an individual? How long should you wait till next eruption?
Iter-erupto Tme Weght Correlato & Regreo 1 1 Lear Regreo 0 80 70 80 Heght 1 Ca heght formato be ued to predct weght of a dvdual? How log hould ou wat tll et erupto? Weght: Repoe varable (Outcome, Depedet)
More informationLecture Notes 2. The ability to manipulate matrices is critical in economics.
Lecture Notes. Revew of Matrces he ablt to mapulate matrces s crtcal ecoomcs.. Matr a rectagular arra of umbers, parameters, or varables placed rows ad colums. Matrces are assocated wth lear equatos. lemets
More informationSimple Linear Regression Analysis
LINEAR REGREION ANALYSIS MODULE II Lecture - 5 Smple Lear Regreo Aaly Dr Shalabh Departmet of Mathematc Stattc Ida Ittute of Techology Kapur Jot cofdece rego for A jot cofdece rego for ca alo be foud Such
More informationSummarizing Bivariate Data. Correlation. Scatter Plot. Pearson s Sample Correlation. Summarizing Bivariate Data SBD - 1
Summarzg Bvarate Data Summarzg Bvarate Data - Eamg relato betwee two quattatve varable I there relato betwee umber of hadgu regtered the area ad umber of people klled? Ct NGR ) Nkll ) 447 3 4 3 48 4 4
More informationReaction Time VS. Drug Percentage Subject Amount of Drug Times % Reaction Time in Seconds 1 Mary John Carl Sara William 5 4
CHAPTER Smple Lear Regreo EXAMPLE A expermet volvg fve ubject coducted to determe the relatohp betwee the percetage of a certa drug the bloodtream ad the legth of tme t take the ubject to react to a tmulu.
More informationr y Simple Linear Regression How To Study Relation Between Two Quantitative Variables? Scatter Plot Pearson s Sample Correlation Correlation
Maatee Klled Correlato & Regreo How To Study Relato Betwee Two Quattatve Varable? Smple Lear Regreo 6.11 A Smple Regreo Problem 1 I there relato betwee umber of power boat the area ad umber of maatee klled?
More informationREVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION
REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION I lear regreo, we coder the frequecy dtrbuto of oe varable (Y) at each of everal level of a ecod varable (X). Y kow a the depedet varable. The
More informationSimple Linear Regression. How To Study Relation Between Two Quantitative Variables? Scatter Plot. Pearson s Sample Correlation.
Correlato & Regreo How To Study Relato Betwee Two Quattatve Varable? Smple Lear Regreo 6. A Smple Regreo Problem I there relato betwee umber of power boat the area ad umber of maatee klled? Year NPB( )
More informationQuiz 1- Linear Regression Analysis (Based on Lectures 1-14)
Quz - Lear Regreo Aaly (Baed o Lecture -4). I the mple lear regreo model y = β + βx + ε, wth Tme: Hour Ε ε = Ε ε = ( ) 3, ( ), =,,...,, the ubaed drect leat quare etmator ˆβ ad ˆβ of β ad β repectvely,
More informationε. Therefore, the estimate
Suggested Aswers, Problem Set 3 ECON 333 Da Hugerma. Ths s ot a very good dea. We kow from the secod FOC problem b) that ( ) SSE / = y x x = ( ) Whch ca be reduced to read y x x = ε x = ( ) The OLS model
More informationPredicting the eruption time after observed an eruption of 4 minutes in duration.
Lear Regreo ad Correlato 00 Predctg the erupto tme after oberved a erupto of 4 mute durato. 90 80 70 Iter-erupto Tme.5.0.5 3.0 3.5 4.0 4.5 5.0 5.5 Durato A ample of tererupto tme wa take durg Augut -8,
More informationLecture 3 Probability review (cont d)
STATS 00: Itroducto to Statstcal Iferece Autum 06 Lecture 3 Probablty revew (cot d) 3. Jot dstrbutos If radom varables X,..., X k are depedet, the ther dstrbuto may be specfed by specfyg the dvdual dstrbuto
More informationChapter Two. An Introduction to Regression ( )
ubject: A Itroducto to Regresso Frst tage Chapter Two A Itroducto to Regresso (018-019) 1 pg. ubject: A Itroducto to Regresso Frst tage A Itroducto to Regresso Regresso aalss s a statstcal tool for the
More informationCollapsing to Sample and Remainder Means. Ed Stanek. In order to collapse the expanded random variables to weighted sample and remainder
Collapg to Saple ad Reader Mea Ed Staek Collapg to Saple ad Reader Average order to collape the expaded rado varable to weghted aple ad reader average, we pre-ultpled by ( M C C ( ( M C ( M M M ( M M M,
More informationEconometric Methods. Review of Estimation
Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators
More informationObjectives of Multiple Regression
Obectves of Multple Regresso Establsh the lear equato that best predcts values of a depedet varable Y usg more tha oe eplaator varable from a large set of potetal predctors {,,... k }. Fd that subset of
More informationSummary of the lecture in Biostatistics
Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the
More informationIntroduction to Matrices and Matrix Approach to Simple Linear Regression
Itroducto to Matrces ad Matrx Approach to Smple Lear Regresso Matrces Defto: A matrx s a rectagular array of umbers or symbolc elemets I may applcatos, the rows of a matrx wll represet dvduals cases (people,
More information( ) = ( ) ( ) Chapter 13 Asymptotic Theory and Stochastic Regressors. Stochastic regressors model
Chapter 3 Asmptotc Theor ad Stochastc Regressors The ature of eplaator varable s assumed to be o-stochastc or fed repeated samples a regresso aalss Such a assumpto s approprate for those epermets whch
More informationMEASURES OF DISPERSION
MEASURES OF DISPERSION Measure of Cetral Tedecy: Measures of Cetral Tedecy ad Dsperso ) Mathematcal Average: a) Arthmetc mea (A.M.) b) Geometrc mea (G.M.) c) Harmoc mea (H.M.) ) Averages of Posto: a) Meda
More information8 The independence problem
Noparam Stat 46/55 Jame Kwo 8 The depedece problem 8.. Example (Tua qualty) ## Hollader & Wolfe (973), p. 87f. ## Aemet of tua qualty. We compare the Huter L meaure of ## lghte to the average of coumer
More informationChapter 11 Systematic Sampling
Chapter stematc amplg The sstematc samplg techue s operatoall more coveet tha the smple radom samplg. It also esures at the same tme that each ut has eual probablt of cluso the sample. I ths method of
More informationSimple Linear Regression
Statstcal Methods I (EST 75) Page 139 Smple Lear Regresso Smple regresso applcatos are used to ft a model descrbg a lear relatoshp betwee two varables. The aspects of least squares regresso ad correlato
More information1. a. Houston Chronicle, Des Moines Register, Chicago Tribune, Washington Post
Homework Soluto. Houto Chrocle, De Moe Regter, Chcago Trbue, Wahgto Pot b. Captal Oe, Campbell Soup, Merrll Lych, Pultzer c. Bll Japer, Kay Reke, Hele Ford, Davd Meedez d..78,.44, 3.5, 3.04 5. No, the
More informationMean is only appropriate for interval or ratio scales, not ordinal or nominal.
Mea Same as ordary average Sum all the data values ad dvde by the sample sze. x = ( x + x +... + x Usg summato otato, we wrte ths as x = x = x = = ) x Mea s oly approprate for terval or rato scales, ot
More informationThird handout: On the Gini Index
Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The
More informationX X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then
Secto 5 Vectors of Radom Varables Whe workg wth several radom varables,,..., to arrage them vector form x, t s ofte coveet We ca the make use of matrx algebra to help us orgaze ad mapulate large umbers
More informationLecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model
Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The
More informationKR20 & Coefficient Alpha Their equivalence for binary scored items
KR0 & Coeffcet Alpha Ther equvalece for bary cored tem Jue, 007 http://www.pbarrett.et/techpaper/r0.pdf f of 7 Iteral Cotecy Relablty for Dchotomou Item KR 0 & Alpha There apparet cofuo wth ome dvdual
More informationMultiple Choice Test. Chapter Adequacy of Models for Regression
Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to
More informationRegression. Chapter 11 Part 4. More than you ever wanted to know about how to interpret the computer printout
Regreo Chapter Part 4 More tha you ever wated to kow about how to terpret the computer prtout February 7, 009 Let go back to the etrol/brthweght problem. We are ug the varable bwt00 for brthweght o brthweght
More informationLinear Approximating to Integer Addition
Lear Approxmatg to Iteger Addto L A-Pg Bejg 00085, P.R. Cha apl000@a.com Abtract The teger addto ofte appled cpher a a cryptographc mea. I th paper we wll preet ome reult about the lear approxmatg for
More informationChapter 4 Multiple Random Variables
Revew for the prevous lecture: Theorems ad Examples: How to obta the pmf (pdf) of U = g (, Y) ad V = g (, Y) Chapter 4 Multple Radom Varables Chapter 44 Herarchcal Models ad Mxture Dstrbutos Examples:
More informationENGI 4421 Joint Probability Distributions Page Joint Probability Distributions [Navidi sections 2.5 and 2.6; Devore sections
ENGI 441 Jot Probablty Dstrbutos Page 7-01 Jot Probablty Dstrbutos [Navd sectos.5 ad.6; Devore sectos 5.1-5.] The jot probablty mass fucto of two dscrete radom quattes, s, P ad p x y x y The margal probablty
More informationLecture Notes Forecasting the process of estimating or predicting unknown situations
Lecture Notes. Ecoomc Forecastg. Forecastg the process of estmatg or predctg ukow stuatos Eample usuall ecoomsts predct future ecoomc varables Forecastg apples to a varet of data () tme seres data predctg
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 informationBasic Structures: Sets, Functions, Sequences, and Sums
ac Structure: Set, Fucto, Sequece, ad Sum CSC-9 Dcrete Structure Kotat uch - LSU Set et a uordered collecto o object Eglh alphabet vowel: V { a, e,, o, u} a V b V Odd potve teger le tha : elemet o et member
More informationStatistics. Correlational. Dr. Ayman Eldeib. Simple Linear Regression and Correlation. SBE 304: Linear Regression & Correlation 1/3/2018
/3/08 Sstems & Bomedcal Egeerg Departmet SBE 304: Bo-Statstcs Smple Lear Regresso ad Correlato Dr. Ama Eldeb Fall 07 Descrptve Orgasg, summarsg & descrbg data Statstcs Correlatoal Relatoshps Iferetal Geeralsg
More informationCHAPTER 4 RADICAL EXPRESSIONS
6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube
More informationCorrelation: Examine Quantitative Bivariate Data
Correlato ad Regreo Correlato: Eame Quattatve Bvarate Data The correlato, ρ, betwee two radom varable, X ad Y, defed a, ( X µ ρ average σx X ) ( Y µ Y σ Y ) product of the tadard devate of X ad Y, quatfe
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 information9.1 Introduction to the probit and logit models
EC3000 Ecoometrcs Lecture 9 Probt & Logt Aalss 9. Itroducto to the probt ad logt models 9. The logt model 9.3 The probt model Appedx 9. Itroducto to the probt ad logt models These models are used regressos
More informationCorrelation. Pearson s Sample Correlation. Correlation and Linear Regression. Scatter Plot
Correlato ad Lear Regreo Dr. Thoma Smotzer Eame Relato Betwee Two Quattatve Varable I there a relato betwee the umber of hadgu regtered ad the umber of people klled b gu? ear #reg #kll 77 447 78 4 79 48
More information12.2 Estimating Model parameters Assumptions: ox and y are related according to the simple linear regression model
1. Estmatg Model parameters Assumptos: ox ad y are related accordg to the smple lear regresso model (The lear regresso model s the model that says that x ad y are related a lear fasho, but the observed
More informationρ < 1 be five real numbers. The
Lecture o BST 63: Statstcal Theory I Ku Zhag, /0/006 Revew for the prevous lecture Deftos: covarace, correlato Examples: How to calculate covarace ad correlato Theorems: propertes of correlato ad covarace
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 informationFormulas and Tables from Beginning Statistics
Fmula ad Table from Begg Stattc Chater Cla Mdot Relatve Frequecy Chater 3 Samle Mea Poulato Mea Weghted Mea Rage Lower Lmt Uer Lmt Cla Frequecy Samle Se µ ( w) w f Mamum Data Value - Mmum Data Value Poulato
More informationENGI 4421 Propagation of Error Page 8-01
ENGI 441 Propagato of Error Page 8-01 Propagato of Error [Navd Chapter 3; ot Devore] Ay realstc measuremet procedure cotas error. Ay calculatos based o that measuremet wll therefore also cota a error.
More information{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:
Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed
More informationCS473-Algorithms I. Lecture 12b. Dynamic Tables. CS 473 Lecture X 1
CS473-Algorthm I Lecture b Dyamc Table CS 473 Lecture X Why Dyamc Table? I ome applcato: We do't kow how may object wll be tored a table. We may allocate pace for a table But, later we may fd out that
More information2SLS Estimates ECON In this case, begin with the assumption that E[ i
SLS Estmates ECON 3033 Bll Evas Fall 05 Two-Stage Least Squares (SLS Cosder a stadard lear bvarate regresso model y 0 x. I ths case, beg wth the assumto that E[ x] 0 whch meas that OLS estmates of wll
More informationCHAPTER VI Statistical Analysis of Experimental Data
Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca
More informationCHAPTER 3 POSTERIOR DISTRIBUTIONS
CHAPTER 3 POSTERIOR DISTRIBUTIONS If scece caot measure the degree of probablt volved, so much the worse for scece. The practcal ma wll stck to hs apprecatve methods utl t does, or wll accept the results
More informationUnsupervised Learning and Other Neural Networks
CSE 53 Soft Computg NOT PART OF THE FINAL Usupervsed Learg ad Other Neural Networs Itroducto Mture Destes ad Idetfablty ML Estmates Applcato to Normal Mtures Other Neural Networs Itroducto Prevously, all
More informationx z Increasing the size of the sample increases the power (reduces the probability of a Type II error) when the significance level remains fixed.
] z-tet for the mea, μ If the P-value i a mall or maller tha a pecified value, the data are tatitically igificat at igificace level. Sigificace tet for the hypothei H 0: = 0 cocerig the ukow mea of a populatio
More informationSimple Linear Regression and Correlation.
Smple Lear Regresso ad Correlato. Correspods to Chapter 0 Tamhae ad Dulop Sldes prepared b Elzabeth Newto (MIT) wth some sldes b Jacquele Telford (Johs Hopks Uverst) Smple lear regresso aalss estmates
More informationECONOMETRIC THEORY. MODULE VIII Lecture - 26 Heteroskedasticity
ECONOMETRIC THEORY MODULE VIII Lecture - 6 Heteroskedastcty Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur . Breusch Paga test Ths test ca be appled whe the replcated data
More informationSTA 105-M BASIC STATISTICS (This is a multiple choice paper.)
DCDM BUSINESS SCHOOL September Mock Eamatos STA 0-M BASIC STATISTICS (Ths s a multple choce paper.) Tme: hours 0 mutes INSTRUCTIONS TO CANDIDATES Do ot ope ths questo paper utl you have bee told to do
More informationQR Factorization and Singular Value Decomposition COS 323
QR Factorzato ad Sgular Value Decomposto COS 33 Why Yet Aother Method? How do we solve least-squares wthout currg codto-squarg effect of ormal equatos (A T A A T b) whe A s sgular, fat, or otherwse poorly-specfed?
More informationMultiple Linear Regression Analysis
LINEA EGESSION ANALYSIS MODULE III Lecture - 4 Multple Lear egresso Aalyss Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur Cofdece terval estmato The cofdece tervals multple
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 informationFunctions of Random Variables
Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,
More informationSTATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1
STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ
More informationUNIT 7 RANK CORRELATION
UNIT 7 RANK CORRELATION Rak Correlato Structure 7. Itroucto Objectves 7. Cocept of Rak Correlato 7.3 Dervato of Rak Correlato Coeffcet Formula 7.4 Te or Repeate Raks 7.5 Cocurret Devato 7.6 Summar 7.7
More informationMATH 247/Winter Notes on the adjoint and on normal operators.
MATH 47/Wter 00 Notes o the adjot ad o ormal operators I these otes, V s a fte dmesoal er product space over, wth gve er * product uv, T, S, T, are lear operators o V U, W are subspaces of V Whe we say
More informationbest estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best
Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg
More informationCorrelation and Regression Analysis
Chapter V Correlato ad Regresso Aalss R. 5.. So far we have cosdered ol uvarate dstrbutos. Ma a tme, however, we come across problems whch volve two or more varables. Ths wll be the subject matter of the
More informationCentroids & Moments of Inertia of Beam Sections
RCH 614 Note Set 8 S017ab Cetrods & Momets of erta of Beam Sectos Notato: b C d d d Fz h c Jo L O Q Q = ame for area = ame for a (base) wdth = desgato for chael secto = ame for cetrod = calculus smbol
More informationConvergence of the Desroziers scheme and its relation to the lag innovation diagnostic
Covergece of the Desrozers scheme ad ts relato to the lag ovato dagostc chard Méard Evromet Caada, Ar Qualty esearch Dvso World Weather Ope Scece Coferece Motreal, August 9, 04 o t t O x x x y x y Oservato
More informationCan we take the Mysticism Out of the Pearson Coefficient of Linear Correlation?
Ca we tae the Mstcsm Out of the Pearso Coeffcet of Lear Correlato? Itroducto As the ttle of ths tutoral dcates, our purpose s to egeder a clear uderstadg of the Pearso coeffcet of lear correlato studets
More informationThe expected value of a sum of random variables,, is the sum of the expected values:
Sums of Radom Varables xpected Values ad Varaces of Sums ad Averages of Radom Varables The expected value of a sum of radom varables, say S, s the sum of the expected values: ( ) ( ) S Ths s always true
More informationSampling Theory MODULE V LECTURE - 14 RATIO AND PRODUCT METHODS OF ESTIMATION
Samplg Theor MODULE V LECTUE - 4 ATIO AND PODUCT METHODS OF ESTIMATION D. SHALABH DEPATMENT OF MATHEMATICS AND STATISTICS INDIAN INSTITUTE OF TECHNOLOG KANPU A mportat objectve a statstcal estmato procedure
More informationA note on testing the covariance matrix for large dimension
A ote o tetg the covarace matrx for large dmeo Melae Brke Ruhr-Uvertät Bochum Fakultät für Mathematk 44780 Bochum, Germay e-mal: melae.brke@ruhr-u-bochum.de Holger ette Ruhr-Uvertät Bochum Fakultät für
More informationChapter 5 Properties of a Random Sample
Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample
More informationOrdinary Least Squares Regression. Simple Regression. Algebra and Assumptions.
Ordary Least Squares egresso. Smple egresso. Algebra ad Assumptos. I ths part of the course we are gog to study a techque for aalysg the lear relatoshp betwee two varables Y ad X. We have pars of observatos
More 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 informationROOT-LOCUS ANALYSIS. Lecture 11: Root Locus Plot. Consider a general feedback control system with a variable gain K. Y ( s ) ( ) K
ROOT-LOCUS ANALYSIS Coder a geeral feedback cotrol yte wth a varable ga. R( Y( G( + H( Root-Locu a plot of the loc of the pole of the cloed-loop trafer fucto whe oe of the yte paraeter ( vared. Root locu
More informationMultiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades
STAT 101 Dr. Kar Lock Morga 11/20/12 Exam 2 Grades Multple Regresso SECTIONS 9.2, 10.1, 10.2 Multple explaatory varables (10.1) Parttog varablty R 2, ANOVA (9.2) Codtos resdual plot (10.2) Trasformatos
More informationECON 482 / WH Hong The Simple Regression Model 1. Definition of the Simple Regression Model
ECON 48 / WH Hog The Smple Regresso Model. Defto of the Smple Regresso Model Smple Regresso Model Expla varable y terms of varable x y = β + β x+ u y : depedet varable, explaed varable, respose varable,
More informationSTATISTICS 13. Lecture 5 Apr 7, 2010
STATISTICS 13 Leture 5 Apr 7, 010 Revew Shape of the data -Bell shaped -Skewed -Bmodal Measures of eter Arthmet Mea Meda Mode Effets of outlers ad skewess Measures of Varablt A quattatve measure that desrbes
More informationSTA302/1001-Fall 2008 Midterm Test October 21, 2008
STA3/-Fall 8 Mdterm Test October, 8 Last Name: Frst Name: Studet Number: Erolled (Crcle oe) STA3 STA INSTRUCTIONS Tme allowed: hour 45 mutes Ads allowed: A o-programmable calculator A table of values from
More informationSection 2 Notes. Elizabeth Stone and Charles Wang. January 15, Expectation and Conditional Expectation of a Random Variable.
Secto Notes Elzabeth Stoe ad Charles Wag Jauar 5, 9 Jot, Margal, ad Codtoal Probablt Useful Rules/Propertes. P ( x) P P ( x; ) or R f (x; ) d. P ( xj ) P (x; ) P ( ) 3. P ( x; ) P ( xj ) P ( ) 4. Baes
More informationTESTS BASED ON MAXIMUM LIKELIHOOD
ESE 5 Toy E. Smth. The Basc Example. TESTS BASED ON MAXIMUM LIKELIHOOD To llustrate the propertes of maxmum lkelhood estmates ad tests, we cosder the smplest possble case of estmatg the mea of the ormal
More informationRegression. Linear Regression. A Simple Data Display. A Batch of Data. The Mean is 220. A Value of 474. STAT Handout Module 15 1 st of June 2009
STAT Hadout Module 5 st of Jue 9 Lear Regresso Regresso Joh D. Sork, M.D. Ph.D. Baltmore VA Medcal Ceter GRCC ad Uversty of Marylad School of Medce Claude D. Pepper Older Amercas Idepedece Ceter Reducg
More information4. Standard Regression Model and Spatial Dependence Tests
4. Stadard Regresso Model ad Spatal Depedece Tests Stadard regresso aalss fals the presece of spatal effects. I case of spatal depedeces ad/or spatal heterogeet a stadard regresso model wll be msspecfed.
More information1. Linear second-order circuits
ear eco-orer crcut Sere R crcut Dyamc crcut cotag two capactor or two uctor or oe uctor a oe capactor are calle the eco orer crcut At frt we coer a pecal cla of the eco-orer crcut, amely a ere coecto of
More informationSTA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1
STA 08 Appled Lear Models: Regresso Aalyss Sprg 0 Soluto for Homework #. Let Y the dollar cost per year, X the umber of vsts per year. The the mathematcal relato betwee X ad Y s: Y 300 + X. Ths s a fuctoal
More informationGenerative classification models
CS 75 Mache Learg Lecture Geeratve classfcato models Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square Data: D { d, d,.., d} d, Classfcato represets a dscrete class value Goal: lear f : X Y Bar classfcato
More informationb. There appears to be a positive relationship between X and Y; that is, as X increases, so does Y.
.46. a. The frst varable (X) s the frst umber the par ad s plotted o the horzotal axs, whle the secod varable (Y) s the secod umber the par ad s plotted o the vertcal axs. The scatterplot s show the fgure
More informationMidterm Exam 1, section 2 (Solution) Thursday, February hour, 15 minutes
coometrcs, CON Sa Fracsco State Uverst Mchael Bar Sprg 5 Mdterm xam, secto Soluto Thursda, Februar 6 hour, 5 mutes Name: Istructos. Ths s closed book, closed otes exam.. No calculators of a kd are allowed..
More informationLecture 3. Sampling, sampling distributions, and parameter estimation
Lecture 3 Samplg, samplg dstrbutos, ad parameter estmato Samplg Defto Populato s defed as the collecto of all the possble observatos of terest. The collecto of observatos we take from the populato s called
More informationMultivariate Transformation of Variables and Maximum Likelihood Estimation
Marquette Uversty Multvarate Trasformato of Varables ad Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Assocate Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Copyrght 03 by Marquette Uversty
More informationThe equation is sometimes presented in form Y = a + b x. This is reasonable, but it s not the notation we use.
INTRODUCTORY NOTE ON LINEAR REGREION We have data of the form (x y ) (x y ) (x y ) These wll most ofte be preseted to us as two colum of a spreadsheet As the topc develops we wll see both upper case ad
More informationFundamentals of Regression Analysis
Fdametals of Regresso Aalyss Regresso aalyss s cocered wth the stdy of the depedece of oe varable, the depedet varable, o oe or more other varables, the explaatory varables, wth a vew of estmatg ad/or
More informationf f... f 1 n n (ii) Median : It is the value of the middle-most observation(s).
CHAPTER STATISTICS Pots to Remember :. Facts or fgures, collected wth a defte pupose, are called Data.. Statstcs s the area of study dealg wth the collecto, presetato, aalyss ad terpretato of data.. The
More informationStatistical Inference Procedures
Statitical Iferece Procedure Cofidece Iterval Hypothei Tet Statitical iferece produce awer to pecific quetio about the populatio of iteret baed o the iformatio i a ample. Iferece procedure mut iclude a
More informationLecture Note to Rice Chapter 8
ECON 430 HG revsed Nov 06 Lecture Note to Rce Chapter 8 Radom matrces Let Y, =,,, m, =,,, be radom varables (r.v. s). The matrx Y Y Y Y Y Y Y Y Y Y = m m m s called a radom matrx ( wth a ot m-dmesoal dstrbuto,
More informationSimulation Output Analysis
Smulato Output Aalyss Summary Examples Parameter Estmato Sample Mea ad Varace Pot ad Iterval Estmato ermatg ad o-ermatg Smulato Mea Square Errors Example: Sgle Server Queueg System x(t) S 4 S 4 S 3 S 5
More informationSolving Constrained Flow-Shop Scheduling. Problems with Three Machines
It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632
More informationMidterm Exam 1, section 1 (Solution) Thursday, February hour, 15 minutes
coometrcs, CON Sa Fracsco State Uversty Mchael Bar Sprg 5 Mdterm am, secto Soluto Thursday, February 6 hour, 5 mutes Name: Istructos. Ths s closed book, closed otes eam.. No calculators of ay kd are allowed..
More information