Introduction to mathematical Statistics
|
|
- Gary Harrell
- 5 years ago
- Views:
Transcription
1 Itroducto to mthemtcl ttstcs Fl oluto. A grou of bbes ll of whom weghed romtely the sme t brth re rdomly dvded to two grous. The bbes smle were fed formul A; those smle were fed formul B. The weght gs tted from brth to ge s moths were recorded for ech bby. The results were s follows: mle : mle : (. Plese costruct 95% cofdece tervl for the me dffereces weght gs betwee the two formuls. d, K, (, d (b. Let N d Y,, ~ (, K Y N be two deedet smles. Furthermore, s ukow. Plese derve the geerl (-α% cofdece tervl for. *Plese clude detled dervtos d roofs for full credt. oluto: ( Iferece o two oulto mes. Two smll d deedet smles. Formul A (smle : 7.33, s.58, 9 Formul B (smle : 8.67, s.58, 9 [] Uder the ormlty ssumto, we frst test f the two oulto vrces re equl. Tht s, : >. The test sttstc s s.58 F, F8,8,.5, U s ce F < 3.44, we cot reject. Therefore t s resoble to ssume tht. [] The 95% C. I. for the me dfferece s : versus ± ( *.58 / 9 + ± t6,.5 s where s ( s + ( s.58 + Therefore 95% C.I. s [-.9,.4]. (b Iferece o two oulto mes, deedet smles: Pooled vrce t: Both smles re from orml oultos. Furthermore, we ssume the oulto vrces re ukow but equl, tht s: Prmeter of terest Pot estmtor for the rmeter of terest Y Y ~ N(, + ~ N(, + ere oe should derve ths dstrbuto usg the momet geertg fucto method, or other method. More ttstcs tutorl t
2 Y ( ~ N (, + 3 Z Z s ot votl qutty for ( sce s ukow. ere oe should derve ths dstrbuto usg the momet geertg fucto method, or other method. 4 ( W ~ χ deedet ( W ~ χ W W ~ + W χ + ere oe should derve ths dstrbuto usg the momet geertg fucto method, or other method. 5 W, W,, d Y re deedet. Thus, W d Z re deedet. By the defto of the T-dstrbuto, we hve: T where Z W + ~ t Y ( + + ( + ( + s clled the ooled-vrce. Y ( Therefore the votl qutty s T ~ t + 6 ( α % cofdece tervl for ( : + ( + + P t T t, /, / α α α P Y t +, α + Y + t +, α + α Thus the cofdece tervl s: Y ± t +, α + More ttstcs tutorl t
3 . Durg oe of the beer wrs the erly 98 s, tste test betwee chltz d Budweser ws the focus of TV commercl. eole greed to drk umrked mugs d dcte whch of the two beers they lked better. The results: ffty-four chose Bud whle the rest chose chltz. (. Plese costruct d terret the corresodg 95% cofdece tervl for - the roorto of beer drkers who refer Bud to chltz. (*Note: Plese derve the geerl formul for the (-α% cofdece tervl for oulto roorto bsed o lrge smle frst. (b. ow lrge does the smle sze eed to be order for the smle roorto ˆ to hve 95% chce of lyg wth.3 of? Plese clculte the smle sze for two sceros: ( we hve o estmte for ; (b we hve estmte for ˆ.54 (*Note: Plese frst derve the geerl formul for smle sze clculto bsed o mmum error of E d cofdece level of (-α%. oluto: ( d... Let ~ Beroull(,, L,, lese fd the (-α% CI for. Pot estmtor : ˆ (e., ˆ.6 Our gol: derve (-α% C.I. for E( By the cetrl lmt theorem (CLT, for lrge smle, we hve Z ~ & N (, Vr( E( E E(, ( Q (, ~ B ( Vr( Vr Vr( Z ~ & N(, ( ˆ Z ~ & N(, ( ˆ Z ~ & N (, (By lustky s theorem 垐 ( # ( α % (romte, or lrge smle C.I. for P( Zα/ Z Zα/ α ˆ > P( Zα/ Zα/ α 垐 ( 垐 垐 > ( ˆ P Z / Z / α α α More ttstcs tutorl t 3
4 垐 ( 垐 ( P( 垐 Z + Z α α α > / / 垐 ( 垐 ( P( 垐 + Z Z α α α > / / 垐 ( 垐 ( P( 垐 Z + Z α α α > / / ˆ( ˆ ˆ( ˆ > The ( α % lrge smle C.I. for s [ ˆ ˆ /, Zα + Zα/ ]. # CLT > lrge usully mes 3 # secl cse for the ferece o. lrge mes Let, lrge smle mes: ˆ 5 ( totl # of, d ( ˆ 5 (- totl # of F For the gve roblem, we hve, 54, d we wt 95% CI for α For 95% cofdece tervl, α.95, α.5,.5 54 ˆ.54 ; Z.5.96 ˆ( ˆ (.54( ˆ( ˆ Z The 95% cofdece tervl for s [.444,.636 ] (b Derve the geerl formul for P( ˆ E α P( E ˆ E α E 垐 E P( α d Z ~ & N(, 垐 ( 垐 ( 垐 ( 垐 ( E E Thus : P( Z α 垐 ( 垐 ( More ttstcs tutorl t 4
5 E c Zα 垐 ( ( Z 垐 α ( ( Zα E 4 E Plug Z.96,.5, E.3, we hve 68.5 ˆ Plug Z.96,.54, E.3, we hve 6.5 ˆ 3. To test whether the brth rtes of boys d grls re equl, rdom smle of fmles wth ectly three chldre wth the ge of 8 ws tke d the dstrbuto of the chldre s geder s rovded below. Plese test whether the brth rtes re equl or ot t the sgfcce level of α.5. 3 grls grls, boy grl, boys 3 boys No. of fmles oluto: Ths roblem c be doe two wys usg ether ( the test o oe oulto roorto or ( the Ch-squre goodess-of-ft test wth four ctegores. ( For the frst roch, ferece o oe roorto, lrge smle, we hve 3. Let be the totl umber of grls mog the 3 chldre, we hve 5. Let be the roorto of etrereeurs wth domestc crs, we hve 5 ˆ, d we re testg: versus 3 :. 5 :. 5. ˆ.5 The test sttstcs s: Z /3 ce Z.365 <.96 Z.5, we c ot reject the ull hyothess of equl brth rtes t the sgfcce level of.5. ( Altertvely, d equvletly, you c use the Ch-squre goodess-of-ft test. The bove tble s smly the followg four-ctegory tble: 3 grls grls, boy grl, boys 3 boys More ttstcs tutorl t 5
6 No. of fmles Let Y be the umber of grls 3-chldre fmly, uder the ull hyothess of equl brth rte (chce of gvg brth to 3 grl, t ech brth, s /, we hve (/ ( / 3 P,,,, 3 Let,,, 3 4be the roortos of fmles fll to these 4 ctegores resectvely, we re testg: : P( 3 / 8, P( 3/ 8, P( 3/ 8, P( / versus : s ot true. ece we hve e e4 */ 8, 5 e e3 *3/ The test sttstc s: W ( e.667 < χ3,.5, uer e Therefore we c ot reject the ull hyothess t the sgfcce level of.5. Of course you oly eed to show oe of the two roches bove to get full credt. 4. Let,, K, ~ N(, vrce s ukow. be rdom smle from the gve orml oulto, d furthermore, the ( Plese derve the test for : versus : t the sgfcce level of α usg the votl qutty roch. (Plese clude the dervto of the votl qutty, the roof of ts dstrbuto, d the dervto of the rejecto rego for full credt. (b Plese derve the lkelhood rto test for : versus : t the sgfcce level of α d show tht ths test s detcl to the test derved rt (. oluto: (. [] Frst we derve the votl qutty d ts dstrbuto. Pot Estmtor for : ~ N(, ; s NOT votl qutty sce s ukow. The we cosder Z ~ N(, ; Ths s lso NOT votl qutty sce s ukow. W Z T ~ t T ( Z d W re deedet. W ( By the: Theorem. mle from orml oulto Z ~ N(,, we kow Ad by the: Defto. T s votl qutty for ~ χ [] Net we derve the oe-smle t-test d ts rejecto rego. For -sded test of : versus :, the test sttstc s the votl qutty t, tht s, T. Itutvely, we would reject fvor of f T c. The roblem s how to determe c. By / the defto of the sgfcce level, we hve α P reject_ P T c P T c Thus ( ( ( / PT c d subsequetly we hve α ( c t, α / More ttstcs tutorl t 6
7 Tht s, t the sgfcce level α, we reject fvor of f T t., α / (b. For -sded test of : versus :, whe the oulto s orml d oulto vrce s ukow, we ow derve the lkelhood rto test. [] Wrte dow your rmeter sce uder {(, :, } ω f [] Wrte dow the urestrcted/orgl rmeter sce. {(, : R, } f [3] Wrte dow the lkelhood (of the dt L f(,, L, ; f( ; [4] Wrte dow your log-lkelhood. ( l l L l( π [5] Fd MLEs uder ω d lug to get m L ω ( dl + 4 d ( ˆ ω m L L(,, L, ;, ω ω ˆ ( ( ( π e ( π ( e [6] Fd MLEs uder d lug to get m L e π ( ( π e ( More ttstcs tutorl t 7
8 4 dl d dl d + ˆ ˆ ˆ ˆ m (,,, ;, L L L e π e π [7] Get the lkelhood rto m m L LR L ω [8] Derve the decso rule bsed o sgfcce level α (Reject s true α P? ( : More ttstcs tutorl t 8 PLR c ( P c : Recll t-test sttstc : ~ T t, t sgfcce level α, we reject fvor of f, T t α ( : P c ( : P c
9 ( + * ( c : ( P ( P + c * ( : ( f T t, α At α, we reject ( + ( ( + ( * ( : ( P c PT ( c : ** PT ( c : ** The LR test s equvlet to the t-test. 5. uose we hve two deedet rdom smles from two orml oultos:,, K, ~ N(, Y, Y, K, Y ~ N (,, d. Furthermore, s ukow. At the sgfcce level α, lese costruct test to test whether 3 + 4or ot. (*Plese clude the dervto of the votl qutty, the roof of ts dstrbuto, d the dervto of the rejecto rego for full credt. oluto: Gve tht d thus. ere s smle outle of the dervto of the test: : 3+ 4 versus : 3 + 4, whch re equvlet to: : 3 4versus : 3 4 ( We strt wth the ot estmtor for the rmeter of terest( 3 N( 3, / + 9/ usg the mgf for (, deedece roertes of the rdom smles. From ths we hve : ( 3Y. Its dstrbuto s N whch s M ( t e( t + t / ( 3Y ( 3 Z, d the ( / + 9/ ~ N,. Ufortutely, Z c ot serve s the votl qutty becuse s ukow. (b We et look for wy to get rd of the ukow followg smlr roch the costructo of the ooledvrce t-sttstc. We foud tht W ( ( / ~ χ + + usg the mgf for χ k whch k / s M ( t, d the deedece roertes of the rdom smles. t (c The we foud, from the theorem of smlg from the orml oulto, d the deedece roertes of the rdom smles, tht Z d W re deedet, d therefore, by the defto of the t-dstrbuto, we hve 3Y 3 obted our votl qutty: T ( ( + * / ( + 9/ + ~ t +. More ttstcs tutorl t 9
10 (d The rejecto rego s derved from P ( T c α, where T ( Y + ( * / ( + 9/ + sgfcce level of α, we reject fvor of ff ~ t T t +, α /. Thus c t +, /. Therefore t the α 6. Let,, K, be deedet d detclly dstrbuted wth df ( ; ( f θ θ θ, < θ <,, (. Derve the method of momet estmtor for θ (b. Derve the mmum lkelhood estmtor for θ (c. Is there effcet estmtor for θ? Plese show the etre dervto. t: Crmér-Ro Iequlty: Let ˆ θ h(,, L, be ubsed forθ, where,, L,, smle from oulto wth df f ( ; θ stsfyg ll regulrty codtos. The ( ˆ l f ; θ l f ; θ Vr θ E E θ θ s rdom oluto: P ( f( ; θ θ ( θ ;, ; (. The oulto me s θ (becuse E( * θ *( θ θ Therefore the momet estmtor of θ s ˆ θ (b. L f( ; θ θ ( θ θ ( θ l l L l θ + ( l( θ l θ θ θ + d the smle me s.. ˆ s the MLE for θ θ (c. E( ˆ θ θ More ttstcs tutorl t
11 ˆ θ ( θ vr( θ Now we derve the C-R lower boud for ubsed estmtor of θ : P ( f( ; θ θ ( θ ;, ; l f( ; θ l θ + l( θ l f( ; θ θ θ θ l f( ; θ θ θ ( θ θ θ E θ ( θ θ ( θ θ( θ C-R lower boud ˆ θ ( θ vr( θ l f E θ The MLE of θ s ubsed d ts vrce C-R lower boud. Thus t s effcet estmtor of θ. Defto. Effcet Estmtor If ˆ θ s ubsed estmtor of θ d ts vrce C-R lower boud, the ˆ θ s effcet estmtor of θ. More ttstcs tutorl t
Stats & Summary
Stts 443.3 & 85.3 Summr The Woodbur Theorem BCD B C D B D where the verses C C D B, d est. Block Mtrces Let the m mtr m q q m be rttoed to sub-mtrces,,,, Smlrl rtto the m k mtr B B B mk m B B l kl Product
More informationChapter 7. Bounds for weighted sums of Random Variables
Chpter 7. Bouds for weghted sums of Rdom Vrbles 7. Itroducto Let d 2 be two depedet rdom vrbles hvg commo dstrbuto fucto. Htczeko (998 d Hu d L (2000 vestgted the Rylegh dstrbuto d obted some results bout
More informationChapter 3 Supplemental Text Material
S3-. The Defto of Fctor Effects Chpter 3 Supplemetl Text Mterl As oted Sectos 3- d 3-3, there re two wys to wrte the model for sglefctor expermet, the mes model d the effects model. We wll geerlly use
More informationIntroduction to mathematical Statistics
More ttistics tutoril t wwwdumblittledoctorcom Itroductio to mthemticl ttistics Chpter 7 ypothesis Testig Exmple (ull hypothesis) : the verge height is 5 8 or more ( : μ 5'8" ) (ltertive hypothesis) :
More informationSome Unbiased Classes of Estimators of Finite Population Mean
Itertol Jourl O Mtemtcs Ad ttstcs Iveto (IJMI) E-IN: 3 4767 P-IN: 3-4759 Www.Ijms.Org Volume Issue 09 etember. 04 PP-3-37 ome Ubsed lsses o Estmtors o Fte Poulto Me Prvee Kumr Msr d s Bus. Dertmet o ttstcs,
More informationSoo King Lim Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7: Figure 8: Figure 9: Figure 10: Figure 11:
Soo Kg Lm 1.0 Nested Fctorl Desg... 1.1 Two-Fctor Nested Desg... 1.1.1 Alss of Vrce... Exmple 1... 5 1.1. Stggered Nested Desg for Equlzg Degree of Freedom... 7 1.1. Three-Fctor Nested Desg... 8 1.1..1
More informationRandom variables and sampling theory
Revew Rdom vrbles d smplg theory [Note: Beg your study of ths chpter by redg the Overvew secto below. The red the correspodg chpter the textbook, vew the correspodg sldeshows o the webste, d do the strred
More informationArea and the Definite Integral. Area under Curve. The Partition. y f (x) We want to find the area under f (x) on [ a, b ]
Are d the Defte Itegrl 1 Are uder Curve We wt to fd the re uder f (x) o [, ] y f (x) x The Prtto We eg y prttog the tervl [, ] to smller su-tervls x 0 x 1 x x - x -1 x 1 The Bsc Ide We the crete rectgles
More informationDensity estimation II
CS 750 Mche Lerg Lecture 6 esty estmto II Mlos Husrecht mlos@tt.edu 539 Seott Squre t: esty estmto {.. } vector of ttrute vlues Ojectve: estmte the model of the uderlyg rolty dstruto over vrles X X usg
More informationInference on One Population Mean Hypothesis Testing
Iferece o Oe Popultio Me ypothesis Testig Scerio 1. Whe the popultio is orml, d the popultio vrice is kow i. i. d. Dt : X 1, X,, X ~ N(, ypothesis test, for istce: Exmple: : : : : : 5'7" (ull hypothesis:
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Exam: ECON430 Statstcs Date of exam: Frday, December 8, 07 Grades are gve: Jauary 4, 08 Tme for exam: 0900 am 00 oo The problem set covers 5 pages Resources allowed:
More informationPubH 7405: REGRESSION ANALYSIS REGRESSION IN MATRIX TERMS
PubH 745: REGRESSION ANALSIS REGRESSION IN MATRIX TERMS A mtr s dspl of umbers or umercl quttes ld out rectgulr rr of rows d colums. The rr, or two-w tble of umbers, could be rectgulr or squre could be
More informationSTK3100 and STK4100 Autumn 2018
SK3 ad SK4 Autum 8 Geeralzed lear models Part III Covers the followg materal from chaters 4 ad 5: Cofdece tervals by vertg tests Cosder a model wth a sgle arameter β We may obta a ( α% cofdece terval for
More informationMTH 146 Class 7 Notes
7.7- Approxmte Itegrto Motvto: MTH 46 Clss 7 Notes I secto 7.5 we lered tht some defte tegrls, lke x e dx, cot e wrtte terms of elemetry fuctos. So, good questo to sk would e: How c oe clculte somethg
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 informationCURVE FITTING LEAST SQUARES METHOD
Nuercl Alss for Egeers Ger Jord Uverst CURVE FITTING Although, the for of fucto represetg phscl sste s kow, the fucto tself ot be kow. Therefore, t s frequetl desred to ft curve to set of dt pots the ssued
More informationAn Extended Mixture Inverse Gaussian Distribution
Avlble ole t htt://wwwssstjscssructh Su Sudh Scece d Techology Jourl 016 Fculty o Scece d Techology, Su Sudh Rjbht Uversty A Eteded Mture Iverse Guss Dstrbuto Chookt Pudrommrt * Fculty o Scece d Techology,
More informationSTK3100 and STK4100 Autumn 2017
SK3 ad SK4 Autum 7 Geeralzed lear models Part III Covers the followg materal from chaters 4 ad 5: Sectos 4..5, 4.3.5, 4.3.6, 4.4., 4.4., ad 4.4.3 Sectos 5.., 5.., ad 5.5. Ørulf Borga Deartmet of Mathematcs
More informationunder the curve in the first quadrant.
NOTES 5: INTEGRALS Nme: Dte: Perod: LESSON 5. AREAS AND DISTANCES Are uder the curve Are uder f( ), ove the -s, o the dom., Prctce Prolems:. f ( ). Fd the re uder the fucto, ove the - s, etwee,.. f ( )
More informationChapter 2 Intro to Math Techniques for Quantum Mechanics
Wter 3 Chem 356: Itroductory Qutum Mechcs Chpter Itro to Mth Techques for Qutum Mechcs... Itro to dfferetl equtos... Boudry Codtos... 5 Prtl dfferetl equtos d seprto of vrbles... 5 Itroducto to Sttstcs...
More informationThe z-transform. LTI System description. Prof. Siripong Potisuk
The -Trsform Prof. Srpog Potsuk LTI System descrpto Prevous bss fucto: ut smple or DT mpulse The put sequece s represeted s ler combto of shfted DT mpulses. The respose s gve by covoluto sum of the put
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 informationMATRIX AND VECTOR NORMS
Numercl lyss for Egeers Germ Jord Uversty MTRIX ND VECTOR NORMS vector orm s mesure of the mgtude of vector. Smlrly, mtr orm s mesure of the mgtude of mtr. For sgle comoet etty such s ordry umers, the
More informationAvailable online through
Avlble ole through wwwmfo FIXED POINTS FOR NON-SELF MAPPINGS ON CONEX ECTOR METRIC SPACES Susht Kumr Moht* Deprtmet of Mthemtcs West Begl Stte Uverst Brst 4 PrgsNorth) Kolt 76 West Begl Id E-ml: smwbes@yhoo
More informationSpecial Instructions / Useful Data
JAM 6 Set of all real umbers P A..d. B, p Posso Specal Istructos / Useful Data x,, :,,, x x Probablty of a evet A Idepedetly ad detcally dstrbuted Bomal dstrbuto wth parameters ad p Posso dstrbuto wth
More informationSt John s College. UPPER V Mathematics: Paper 1 Learning Outcome 1 and 2. Examiner: GE Marks: 150 Moderator: BT / SLS INSTRUCTIONS AND INFORMATION
St Joh s College UPPER V Mthemtcs: Pper Lerg Outcome d ugust 00 Tme: 3 hours Emer: GE Mrks: 50 Modertor: BT / SLS INSTRUCTIONS ND INFORMTION Red the followg structos crefull. Ths questo pper cossts of
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 informationZ = = = = X np n. n n. npq. npq pq
Stt 4, secto 4 Goodess of Ft Ctegory Probbltes Specfed otes by Tm Plchowsk Recll bck to Lectures 6c, 84 (83 the 8 th edto d 94 whe we delt wth populto proportos Vocbulry from 6c: The pot estmte for populto
More informationthis is the indefinite integral Since integration is the reverse of differentiation we can check the previous by [ ]
Atervtves The Itegrl Atervtves Ojectve: Use efte tegrl otto for tervtves. Use sc tegrto rules to f tervtves. Aother mportt questo clculus s gve ervtve f the fucto tht t cme from. Ths s the process kow
More informationChapter Linear Regression
Chpte 6.3 Le Regesso Afte edg ths chpte, ou should be ble to. defe egesso,. use sevel mmzg of esdul cte to choose the ght cteo, 3. deve the costts of le egesso model bsed o lest sques method cteo,. use
More informationDifferential Entropy 吳家麟教授
Deretl Etropy 吳家麟教授 Deto Let be rdom vrble wt cumultve dstrbuto ucto I F s cotuous te r.v. s sd to be cotuous. Let = F we te dervtve s deed. I te s clled te pd or. Te set were > 0 s clled te support set
More informationSection 7.2 Two-way ANOVA with random effect(s)
Secto 7. Two-wy ANOVA wth rdom effect(s) 1 1. Model wth Two Rdom ffects The fctors hgher-wy ANOVAs c g e cosdered fxed or rdom depedg o the cotext of the study. or ech fctor: Are the levels of tht fctor
More informationβ (cf Khan, 2006). In this model, p independent
Proc. ICCS-3, Bogor, Idoes December 8-4 Vol. Testg the Equlty of the Two Itercets for the Prllel Regresso Model Bud Prtko d Shhjh Kh Dertmet of Mthemtcs d Nturl Scece Jederl Soedrm Uversty, Purwokerto,
More informationSolutions Manual for Polymer Science and Technology Third Edition
Solutos ul for Polymer Scece d Techology Thrd Edto Joel R. Fred Uer Sddle Rver, NJ Bosto Idols S Frcsco New York Toroto otrel Lodo uch Prs drd Cetow Sydey Tokyo Sgore exco Cty Ths text s ssocted wth Fred/Polymer
More informationDr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur
Aalyss of Varace ad Desg of Exermets-I MODULE II LECTURE - GENERAL LINEAR HYPOTHESIS AND ANALYSIS OF VARIANCE Dr Shalabh Deartmet of Mathematcs ad Statstcs Ida Isttute of Techology Kaur Tukey s rocedure
More information14.2 Line Integrals. determines a partition P of the curve by points Pi ( xi, y
4. Le Itegrls I ths secto we defe tegrl tht s smlr to sgle tegrl except tht sted of tegrtg over tervl [ ] we tegrte over curve. Such tegrls re clled le tegrls lthough curve tegrls would e etter termology.
More informationClass 13,14 June 17, 19, 2015
Class 3,4 Jue 7, 9, 05 Pla for Class3,4:. Samplg dstrbuto of sample mea. The Cetral Lmt Theorem (CLT). Cofdece terval for ukow mea.. Samplg Dstrbuto for Sample mea. Methods used are based o CLT ( Cetral
More informationWu-Hausman Test: But if X and ε are independent, βˆ. ECON 324 Page 1
Wu-Hausma Test: Detectg Falure of E( ε X ) Caot drectly test ths assumpto because lack ubased estmator of ε ad the OLS resduals wll be orthogoal to X, by costructo as ca be see from the momet codto X'
More information10.2 Series. , we get. which is called an infinite series ( or just a series) and is denoted, for short, by the symbol. i i n
0. Sere I th ecto, we wll troduce ere tht wll be dcug for the ret of th chpter. Wht ere? If we dd ll term of equece, we get whch clled fte ere ( or jut ere) d deoted, for hort, by the ymbol or Doe t mke
More informationThe Mathematical Appendix
The Mathematcal Appedx Defto A: If ( Λ, Ω, where ( λ λ λ whch the probablty dstrbutos,,..., Defto A. uppose that ( Λ,,..., s a expermet type, the σ-algebra o λ λ λ are defed s deoted by ( (,,...,, σ Ω.
More information3. REVIEW OF PROPERTIES OF EIGENVALUES AND EIGENVECTORS
. REVIEW OF PROPERTIES OF EIGENVLUES ND EIGENVECTORS. EIGENVLUES ND EIGENVECTORS We hll ow revew ome bc fct from mtr theory. Let be mtr. clr clled egevlue of f there et ozero vector uch tht Emle: Let 9
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 informationå 1 13 Practice Final Examination Solutions - = CS109 Dec 5, 2018
Chrs Pech Fal Practce CS09 Dec 5, 08 Practce Fal Examato Solutos. Aswer: 4/5 8/7. There are multle ways to obta ths aswer; here are two: The frst commo method s to sum over all ossbltes for the rak of
More informationDATA FITTING. Intensive Computation 2013/2014. Annalisa Massini
DATA FITTING Itesve Computto 3/4 Als Mss Dt fttg Dt fttg cocers the problem of fttg dscrete dt to obt termedte estmtes. There re two geerl pproches two curve fttg: Iterpolto Dt s ver precse. The strteg
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 informationPatterns of Continued Fractions with a Positive Integer as a Gap
IOSR Jourl of Mthemtcs (IOSR-JM) e-issn: 78-578, -ISSN: 39-765X Volume, Issue 3 Ver III (My - Ju 6), PP -5 wwwosrjourlsorg Ptters of Cotued Frctos wth Postve Iteger s G A Gm, S Krth (Mthemtcs, Govermet
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 informationChapter Unary Matrix Operations
Chpter 04.04 Ury trx Opertos After redg ths chpter, you should be ble to:. kow wht ury opertos mes,. fd the trspose of squre mtrx d t s reltoshp to symmetrc mtrces,. fd the trce of mtrx, d 4. fd the ermt
More informationCHAPTER 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 informationProbability and Statistics. What is probability? What is statistics?
robablt ad Statstcs What s robablt? What s statstcs? robablt ad Statstcs robablt Formall defed usg a set of aoms Seeks to determe the lkelhood that a gve evet or observato or measuremet wll or has haeed
More informationA Technique for Constructing Odd-order Magic Squares Using Basic Latin Squares
Itertol Jourl of Scetfc d Reserch Publctos, Volume, Issue, My 0 ISSN 0- A Techque for Costructg Odd-order Mgc Squres Usg Bsc Lt Squres Tomb I. Deprtmet of Mthemtcs, Mpur Uversty, Imphl, Mpur (INDIA) tombrom@gml.com
More informationME 501A Seminar in Engineering Analysis Page 1
Mtr Trsformtos usg Egevectors September 8, Mtr Trsformtos Usg Egevectors Lrry Cretto Mechcl Egeerg A Semr Egeerg Alyss September 8, Outle Revew lst lecture Trsformtos wth mtr of egevectors: = - A ermt
More informationRegression. By Jugal Kalita Based on Chapter 17 of Chapra and Canale, Numerical Methods for Engineers
Regresso By Jugl Klt Bsed o Chpter 7 of Chpr d Cle, Numercl Methods for Egeers Regresso Descrbes techques to ft curves (curve fttg) to dscrete dt to obt termedte estmtes. There re two geerl pproches two
More information: : 8.2. Test About a Population Mean. STT 351 Hypotheses Testing Case I: A Normal Population with Known. - null hypothesis states 0
8.2. Test About Popultio Me. Cse I: A Norml Popultio with Kow. H - ull hypothesis sttes. X1, X 2,..., X - rdom smple of size from the orml popultio. The the smple me X N, / X X Whe H is true. X 8.2.1.
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 informationITERATIVE METHODS FOR SOLVING SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS
Numercl Alyss for Egeers Germ Jord Uversty ITERATIVE METHODS FOR SOLVING SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS Numercl soluto of lrge systems of ler lgerc equtos usg drect methods such s Mtr Iverse, Guss
More informationPROGRESSIONS AND SERIES
PROGRESSIONS AND SERIES A sequece is lso clled progressio. We ow study three importt types of sequeces: () The Arithmetic Progressio, () The Geometric Progressio, () The Hrmoic Progressio. Arithmetic Progressio.
More informationSTATISTICAL INFERENCE
(STATISTICS) STATISTICAL INFERENCE COMPLEMENTARY COURSE B.Sc. MATHEMATICS III SEMESTER ( Admsso) UNIVERSITY OF CALICUT SCHOOL OF DISTANCE EDUCATION CALICUT UNIVERSITY P.O., MALAPPURAM, KERALA, INDIA -
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 informationDISCRETE TIME MODELS OF FORWARD CONTRACTS INSURANCE
G Tstsshvl DSCRETE TME MODELS OF FORWARD CONTRACTS NSURANCE (Vol) 008 September DSCRETE TME MODELS OF FORWARD CONTRACTS NSURANCE GSh Tstsshvl e-ml: gurm@mdvoru 69004 Vldvosto Rdo str 7 sttute for Appled
More informationPoint Estimation: definition of estimators
Pot Estmato: defto of estmators Pot estmator: ay fucto W (X,..., X ) of a data sample. The exercse of pot estmato s to use partcular fuctos of the data order to estmate certa ukow populato parameters.
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 informationA Brief Introduction to Olympiad Inequalities
Ev Che Aprl 0, 04 The gol of ths documet s to provde eser troducto to olympd equltes th the stdrd exposto Olympd Iequltes, by Thoms Mldorf I ws motvted to wrte t by feelg gulty for gettg free 7 s o problems
More informationSTK4011 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 informationBASIC PRINCIPLES OF STATISTICS
BASIC PRINCIPLES OF STATISTICS PROBABILITY DENSITY DISTRIBUTIONS DISCRETE VARIABLES BINOMIAL DISTRIBUTION ~ B 0 0 umber of successes trals Pr E [ ] Var[ ] ; BINOMIAL DISTRIBUTION B7 0. B30 0.3 B50 0.5
More information: At least two means differ SST
Formula Card for Eam 3 STA33 ANOVA F-Test: Completely Radomzed Desg ( total umber of observatos, k = Number of treatmets,& T = total for treatmet ) Step : Epress the Clam Step : The ypotheses: :... 0 A
More informationPredicting Survival Outcomes Based on Compound Covariate Method under Cox Proportional Hazard Models with Microarrays
Predctg Survvl Outcomes Bsed o Compoud Covrte Method uder Cox Proportol Hzrd Models wth Mcrorrys PLoS ONE 7(10). do:10.1371/ourl.poe.0047627. http://dx.plos.org/10.1371/ourl.poe.0047627 Tkesh Emur Grdute
More informationInterval Estimation. Consider a random variable X with a mean of X. Let X be distributed as X X
ECON 37: Ecoomercs Hypohess Tesg Iervl Esmo Wh we hve doe so fr s o udersd how we c ob esmors of ecoomcs reloshp we wsh o sudy. The queso s how comforble re we wh our esmors? We frs exme how o produce
More informationIn Calculus I you learned an approximation method using a Riemann sum. Recall that the Riemann sum is
Mth Sprg 08 L Approxmtg Dete Itegrls I Itroducto We hve studed severl methods tht llow us to d the exct vlues o dete tegrls However, there re some cses whch t s ot possle to evlute dete tegrl exctly I
More informationChapter Gauss-Seidel Method
Chpter 04.08 Guss-Sedel Method After redg ths hpter, you should be ble to:. solve set of equtos usg the Guss-Sedel method,. reogze the dvtges d ptflls of the Guss-Sedel method, d. determe uder wht odtos
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 informationOptimality of Strategies for Collapsing Expanded Random Variables In a Simple Random Sample Ed Stanek
Optmlt of Strteges for Collpsg Expe Rom Vrles Smple Rom Smple E Stek troucto We revew the propertes of prectors of ler comtos of rom vrles se o rom vrles su-spce of the orgl rom vrles prtculr, we ttempt
More information6.6 Moments and Centers of Mass
th 8 www.tetodre.co 6.6 oets d Ceters of ss Our ojectve here s to fd the pot P o whch th plte of gve shpe lces horzotll. Ths pot s clled the ceter of ss ( or ceter of grvt ) of the plte.. We frst cosder
More informationX ε ) = 0, or equivalently, lim
Revew for the prevous lecture Cocepts: order statstcs Theorems: Dstrbutos of order statstcs Examples: How to get the dstrbuto of order statstcs Chapter 5 Propertes of a Radom Sample Secto 55 Covergece
More information19 22 Evaluate the integral by interpreting it in terms of areas. (1 x ) dx. y Write the given sum or difference as a single integral in
SECTION. THE DEFINITE INTEGRAL. THE DEFINITE INTEGRAL A Clck here for swers. S Clck here for solutos. Use the Mdpot Rule wth the gve vlue of to pproxmte the tegrl. Roud the swer to four decml plces. 9
More information20 23 Evaluate the integral by interpreting it in terms of areas. (1 x ) dx. y 2 2
SECTION 5. THE DEFINITE INTEGRAL 5. THE DEFINITE INTEGRAL A Clck here for swers. S Clck here for solutos. 7 Use the Mdpot Rule wth the gve vlue of to pproxmte the tegrl. Roud the swer to four decml plces.
More informationChapter 5 Properties of a Random Sample
Lecture 3 o BST 63: Statstcal Theory I Ku Zhag, /6/006 Revew for the revous lecture Cocets: radom samle, samle mea, samle varace Theorems: roertes of a radom samle, samle mea, samle varace Examles: how
More informationChapter 2 Intro to Math Techniques for Quantum Mechanics
Fll 4 Chem 356: Itroductory Qutum Mechcs Chpter Itro to Mth Techques for Qutum Mechcs... Itro to dfferetl equtos... Boudry Codtos... 5 Prtl dfferetl equtos d seprto of vrbles... 5 Itroducto to Sttstcs...
More informationmeans the first term, a2 means the term, etc. Infinite Sequences: follow the same pattern forever.
9.4 Sequeces ad Seres Pre Calculus 9.4 SEQUENCES AND SERIES Learg Targets:. Wrte the terms of a explctly defed sequece.. Wrte the terms of a recursvely defed sequece. 3. Determe whether a sequece s arthmetc,
More informationEmpirical likelihood ratio tests with power one
Ercl lelhood rto tests wth ower oe Albert Vexler * L Zou Dertet of Bosttstcs The Stte Uversty of New Yor t Bufflo NY 46 USA ABSTRACT I the 97s ofessor Robbs d hs couthors exteded the Vle d Wld equlty order
More informationProbability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers Roy D. Yates and David J.
Probbility d Stochstic Processes: A Friedly Itroductio for Electricl d Computer Egieers Roy D. Ytes d Dvid J. Goodm Problem Solutios : Ytes d Goodm,4..4 4..4 4..7 4.4. 4.4. 4..6 4.6.8 4.6.9 4.7.4 4.7.
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 informationD KL (P Q) := p i ln p i q i
Cheroff-Bouds 1 The Geeral Boud Let P 1,, m ) ad Q q 1,, q m ) be two dstrbutos o m elemets, e,, q 0, for 1,, m, ad m 1 m 1 q 1 The Kullback-Lebler dvergece or relatve etroy of P ad Q s defed as m D KL
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 information2. Independence and Bernoulli Trials
. Ideedece ad Beroull Trals Ideedece: Evets ad B are deedet f B B. - It s easy to show that, B deedet mles, B;, B are all deedet ars. For examle, ad so that B or B B B B B φ,.e., ad B are deedet evets.,
More informationSequences and summations
Lecture 0 Sequeces d summtos Istructor: Kgl Km CSE) E-ml: kkm0@kokuk.c.kr Tel. : 0-0-9 Room : New Mleum Bldg. 0 Lb : New Egeerg Bldg. 0 All sldes re bsed o CS Dscrete Mthemtcs for Computer Scece course
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 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 information1. The weight of six Golden Retrievers is 66, 61, 70, 67, 92 and 66 pounds. The weight of six Labrador Retrievers is 54, 60, 72, 78, 84 and 67.
Ecoomcs 3 Itroducto to Ecoometrcs Sprg 004 Professor Dobk Name Studet ID Frst Mdterm Exam You must aswer all the questos. The exam s closed book ad closed otes. You may use your calculators but please
More informationL(θ X) s 0 (1 θ 0) m s. (s/m) s (1 s/m) m s
Hw 4 (due March ) 83 The LRT statstcs s λ(x) sup θ θ 0 L(θ X) The lkelhood s L(θ) θ P x ( sup Θ L(θ X) θ) m P x ad ad the log-lkelhood s (θ) x log θ +(m x ) log( θ) Let S X Note that the ucostraed MLE
More informationApplied Statistics Qualifier Examination
Appled Sttstcs Qulfer Exmnton Qul_june_8 Fll 8 Instructons: () The exmnton contns 4 Questons. You re to nswer 3 out of 4 of them. () You my use ny books nd clss notes tht you mght fnd helpful n solvng
More informationSTA261H1.doc. i 1 X n be a random sample. The sample mean is defined by i= 1 X 1 + ( ) X has a N ( σ ) 1 n. N distribution. Then n. distribution.
TA6Hdoc tttcl Iferece RADOM AMPLE Defto: Rdom mple clled rdom mple from dtruto wth pdf f ( (or pf ( depedet d hve detcl dtruto wth pdf f (or pf P (depedet-detcll-dtruted P f re It ofte deoted d ote Let
More informationChapter 8: Statistical Analysis of Simulated Data
Marquette Uversty MSCS600 Chapter 8: Statstcal Aalyss of Smulated Data Dael B. Rowe, Ph.D. Departmet of Mathematcs, Statstcs, ad Computer Scece Copyrght 08 by Marquette Uversty MSCS600 Ageda 8. The Sample
More informationCS473-Algorithms I. Lecture 3. Solving Recurrences. Cevdet Aykanat - Bilkent University Computer Engineering Department
CS473-Algorthms I Lecture 3 Solvg Recurreces Cevdet Aykt - Blket Uversty Computer Egeerg Deprtmet Solvg Recurreces The lyss of merge sort Lecture requred us to solve recurrece. Recurreces re lke solvg
More informationICS141: Discrete Mathematics for Computer Science I
Uversty o Hw ICS: Dscrete Mthemtcs or Computer Scece I Dept. Iormto & Computer Sc., Uversty o Hw J Stelovsy bsed o sldes by Dr. Be d Dr. Stll Orgls by Dr. M. P. Fr d Dr. J.L. Gross Provded by McGrw-Hll
More informationDepartment of Statistics, Dibrugarh University, Dibrugarh, Assam, India. Department of Statistics, G. C. College, Silchar, Assam, India.
A Dscrete Power Dstruto Surt Chkrort * d Dhrujot Chkrvrt Dertet of Sttstcs Drugrh Uverst Drugrh Ass Id. Dertet of Sttstcs G. C. College Slchr Ass Id. *el: surt_r@hoo.co. Astrct A ew dscrete dstruto hs
More informationTHE ROYAL STATISTICAL SOCIETY HIGHER CERTIFICATE
THE ROYAL STATISTICAL SOCIETY 00 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE PAPER I STATISTICAL THEORY The Socety provdes these solutos to assst caddates preparg for the examatos future years ad for the
More informationPTAS for Bin-Packing
CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,
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 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 information