Institute of Actuaries of India
|
|
- Merry Collins
- 5 years ago
- Views:
Transcription
1 Isiue of cuaries of Idia Subjec CT3-robabiliy ad Mahemaical Saisics May 008 Eamiaio INDICTIVE SOLUTION Iroducio The idicaive soluio has bee wrie by he Eamiers wih he aim of helig cadidaes. The soluios give are oly idicaive. I is realized ha here could be oher ois as valid aswers ad eamier have give credi for ay aleraive aroach or ierreaio which hey cosider o be reasoable.
2 . a. Mea ; Media 8 b. The leas wo ois are 3 ad 6. The larges wo ois are 79 ad 39. So, he rimmed mea is [40.4* ]/ c. Comme: i. Mea gives eual weigh o all he observaios so i is more likely o ge imaced by he ereme observaios. ii. Media does deed o he size of he observaios bu oly o he order. So i is leas likely o ge imaced by he ereme observaios. iii. Imac of ouliers o he rimmed mea deeds o he umber of observaios beig rimmed. I ay case i is less imaced by ereme observaios ha he simle mea. d. S ~ χ, Now 300 S > 300 S > 300 > / 3 / / 3 3 / / 3 3 / Toal [8] Toal [] B 36 4 C B C B C age of 0
3 age 3 of 0 However, 36 6 C C C B C B B B, B ad C are o muually ideede. Toal [4] 4...., ; ± ± E G [ ]... [ ]... r r r r ; < < ; < < Toal [3] 5. a. [ ] 0 ; / 3. log e f π 0 for < 0 b. E 3.7 / 3. / e e e μ σ Var σ σ μ e e e 7.4 e- c. > 8 8. Sice log ~ Nμ3., σ, 8 Ф 3. log8 Ф -., Ф deoes he cdf of sadard ormal.
4 0.34 > Toal [4] 6. a. Mos suiable disribuio for N is Biomial 90, where is he robabiliy of head. Esimae of is 50/90. Mea of N is 90*50/ Variace is 90*50/90 * -50/90 50*40/90.. b. N aroimaely follows Normal wih mea 45 ad variace.5. Usig coiuiy correcio, Bi N > 50 Nor N Nor Z where Z ~ N 0, Nor Z c. Usig he above robabiliy, he -value of he es is 0.3. We do o have sufficie evidece o rejec H o a 5% sigificace level. d. H o should be rejeced for -value < Le be he desired umber of heads. The N 0.05 N * So, H o should be rejeced for he umber of heads 53. Toal [7] 7. i ~U0, E i, Var i ; i,,,0 0 Give ha Y i ad hece Y ~ i 5 N 0, 3 Y a. [ ] Y 9. 5/ 3 5 / 3 [Z ] age 4 of 0
5 Φ Y b. [ ] 8.5 Y.7 5 / 3 5/ 3 [ -.6 Z.37] 5/ 3 Φ.37 Φ Toal [3] 8. a. Wai for e miues 0,, assegers will arrive wihi he e e miues e e e ! b. Solvig his ar deeds o he memoryless roery of oisso rocess. The waiig eriod before he arrival of e asseger is eoeially disribued wih a eeced value of hree miues. havig o wai aoher five miues obody arrives i he e five miues θ k 9. a. d θ 0 θ k θ 0 d 5 k θ [ ] 0 θ k θ * θ k For he above iegral o be, k should be. b. The likelihood fucio is θ i ; 0< i < θ i L, θ 3 e Toal [5] Differeiaig his wr θ ad euaig o 0 will o give a soluio for θ i erms of. L is a decreasig fucio of θ L is osiive oly whe θ Ma i So L is he maimum whe θ Ma i ; which is he MLE of θ. Toal [5] age 5 of 0
6 λ 0. a. For Eoeial λ, f, λ λe ad F - e λ, > 0 The likelihood fucio is 7 λ λ L λ, λ * e * e b. The log-likelihood is l, λ 7 l λ 693λ l, λ λ Euaig above o 0 gives MLE of λ 7/ c. l, λ 7 λ l, λ 7 So, E λ λ CRLB for he MLE is which is same as he l, λ 7 E asymoic variace of he MLE d. The 95% cofidece ierval for λ is ˆ λ ±.96. S. D. ˆ λ. Usig he MLE of λ ad he variace he ierval is: * i.e.0.005,0.077 e. The likelihood is 5λ 3 7 λ[ *5] L λ;, [ e ] * λ * e * e [300 3*5] λ.a. The Scaer lo Toal [] 5 4 College grade High school grade age 6 of 0
7 b. ˆ α ; ˆ β 0.85; rˆ ; c. s he esimae of he sloe is lower ha, he eeced erformace i college is lower ha he erformace i high school. d. yˆ ˆ α ˆ β * cual college grade for a high school grade of. for observaio 0 is much higher ha he eeced. This observaio ca be reaed as a oulier while fiig he regressio. Toal [] * Y. B * YB. C * YC.. Y.. B C 8*00 0*0 9* Y a. ˆ μ Y ˆ τ Y ˆ τ Y B. B. Y.. Y ˆ τ C YC. Y SSR 075 ˆ σ k 4.. b. SS B i Y i. Y.. 8*4.50*63.49* i SS T SS B SS R c. The NOV able is: Source DF SS MS F Bewee Residual Toal F 0.05,, 4 is So here is eough evidece o rejec he ull hyohesis of eualiy of all ferilizer effecs. d. The 95% cofidece ierval for μ τ is give by ˆ σ ˆ σ Y. 0.05,4, Y. 0.05, *,00.064* ,.56 Toal [0] age 7 of 0
8 3. a.σ 378, ΣY 333, Σ 6, ΣY 347, ΣY 4340 ΣY Y r Σ ΣY Y H 0 : ρ 0 ; H : ρ 0 Tes saisic is r r The able value of disribuio for 7 df a 5% level is.895. Coclusio: Rejec H 0. b. H 0 : σ σ ; H : σ σ Usually he larger variace is ake i he umeraor 4 ; s 9. 5 ad Y 37 ; s F cal S S 4.87 ad F ab 8, Y / Y Coclusio: Rejec H 0 σ Y sy s c. 90% cofidece ierval for is σ, F α / 8,8 Fα / 8,8 s s [.46,6.757], F , The CI for σ / [.0596, ] σ Y Y d. Chages i Scores d: d Y- d 5 ; s 9.06 ad 9. d H 0 : μ d 0 Vs H : μ d >0 d 5 The Tes saisic is :. 69 s 9.06 d 3 Table value ; Coclusio: Do o rejec H 0 age 8 of 0
9 e. 95% cofidece ierval for he differece bewee he wo oulaio meas μ y - μ is { μ y μ ± α /, s d } which simlifies o -.077,.077. The cofidece ierval for μ - μ y will herefore be -.077,.077. Toal [4] 4 a. Noe ha value of k i he desiy does o affec he codiioal eecaio. E/Yy: f Y k f Y y, y d 0 y 0 d 0 k y-0 3 ; 0 < y < 30 0 Hece, f / Y / y ; 0 < < y y 0 0; oherwise y 0 E / Y y d 0 y 0 0 y 0 ; 0 < y < b. > 0 / Y 5 d 5/ Toal [7] 5. The mf of ; 0,,,... ad N ;, 3, N / N N ; 0,,,... ;, 3 age 9 of 0
10 Hece,, N / 3 6 6, N, N / 6 3, N / 6 Mea of : 5/6 Variace of : 7/36 The, 0 0, N 0, N Toal [5] *************** age 0 of 0
Exercise 3 Stochastic Models of Manufacturing Systems 4T400, 6 May
Exercise 3 Sochasic Models of Maufacurig Sysems 4T4, 6 May. Each week a very popular loery i Adorra pris 4 ickes. Each ickes has wo 4-digi umbers o i, oe visible ad he oher covered. The umbers are radomly
More informationChapter Chapter 10 Two-Sample Tests X 1 X 2. Difference Between Two Means: Different data sources Unrelated. Learning Objectives
Chaper 0 0- Learig Objecives I his chaper, you lear how o use hypohesis esig for comparig he differece bewee: Chaper 0 Two-ample Tess The meas of wo idepede populaios The meas of wo relaed populaios The
More informationMoment Generating Function
1 Mome Geeraig Fucio m h mome m m m E[ ] x f ( x) dx m h ceral mome m m m E[( ) ] ( ) ( x ) f ( x) dx Mome Geeraig Fucio For a real, M () E[ e ] e k x k e p ( x ) discree x k e f ( x) dx coiuous Example
More informationActuarial Society of India
Acuarial Sociey of Idia EXAMINAIONS Jue 5 C4 (3) Models oal Marks - 5 Idicaive Soluio Q. (i) a) Le U deoe he process described by 3 ad V deoe he process described by 4. he 5 e 5 PU [ ] PV [ ] ( e ).538!
More information10.3 Autocorrelation Function of Ergodic RP 10.4 Power Spectral Density of Ergodic RP 10.5 Normal RP (Gaussian RP)
ENGG450 Probabiliy ad Saisics for Egieers Iroducio 3 Probabiliy 4 Probabiliy disribuios 5 Probabiliy Desiies Orgaizaio ad descripio of daa 6 Samplig disribuios 7 Ifereces cocerig a mea 8 Comparig wo reames
More informationDepartment of Mathematical and Statistical Sciences University of Alberta
MATH 4 (R) Wier 008 Iermediae Calculus I Soluios o Problem Se # Due: Friday Jauary 8, 008 Deparme of Mahemaical ad Saisical Scieces Uiversiy of Albera Quesio. [Sec.., #] Fid a formula for he geeral erm
More informationIntroduction to Hypothesis Testing
Noe for Seember, Iroducio o Hyohei Teig Scieific Mehod. Sae a reearch hyohei or oe a queio.. Gaher daa or evidece (obervaioal or eerimeal) o awer he queio. 3. Summarize daa ad e he hyohei. 4. Draw a cocluio.
More informationSTK4080/9080 Survival and event history analysis
STK48/98 Survival ad eve hisory aalysis Marigales i discree ime Cosider a sochasic process The process M is a marigale if Lecure 3: Marigales ad oher sochasic processes i discree ime (recap) where (formally
More informationK3 p K2 p Kp 0 p 2 p 3 p
Mah 80-00 Mo Ar 0 Chaer 9 Fourier Series ad alicaios o differeial equaios (ad arial differeial equaios) 9.-9. Fourier series defiiio ad covergece. The idea of Fourier series is relaed o he liear algebra
More informationF D D D D F. smoothed value of the data including Y t the most recent data.
Module 2 Forecasig 1. Wha is forecasig? Forecasig is defied as esimaig he fuure value ha a parameer will ake. Mos scieific forecasig mehods forecas he fuure value usig pas daa. I Operaios Maageme forecasig
More informationThe Institute of Actuaries of India
The Istitute of Actuaries of Idia ubject CT3 Probabilit & Mathematical tatistics 5 th Ma 7 IDICATIVE OLUTIO Itroductio The idicative solutio has bee writte b the Examiers with the aim of helpig cadidates.
More informationOLS bias for econometric models with errors-in-variables. The Lucas-critique Supplementary note to Lecture 17
OLS bias for ecoomeric models wih errors-i-variables. The Lucas-criique Supplemeary oe o Lecure 7 RNy May 6, 03 Properies of OLS i RE models I Lecure 7 we discussed he followig example of a raioal expecaios
More informationB. Maddah INDE 504 Simulation 09/02/17
B. Maddah INDE 54 Simulaio 9/2/7 Queueig Primer Wha is a queueig sysem? A queueig sysem cosiss of servers (resources) ha provide service o cusomers (eiies). A Cusomer requesig service will sar service
More informationBEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS
BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS Opimal ear Forecasig Alhough we have o meioed hem explicily so far i he course, here are geeral saisical priciples for derivig he bes liear forecas, ad
More informationth m m m m central moment : E[( X X) ] ( X X) ( x X) f ( x)
1 Trasform Techiques h m m m m mome : E[ ] x f ( x) dx h m m m m ceral mome : E[( ) ] ( ) ( x) f ( x) dx A coveie wa of fidig he momes of a radom variable is he mome geeraig fucio (MGF). Oher rasform echiques
More informationxp (X = x) = P (X = 1) = θ. Hence, the method of moments estimator of θ is
Exercise 7 / page 356 Noe ha X i are ii from Beroulli(θ where 0 θ a Meho of momes: Sice here is oly oe parameer o be esimae we ee oly oe equaio where we equae he rs sample mome wih he rs populaio mome,
More informationAuto-correlation of Error Terms
Auo-correlaio of Error Terms Pogsa Porchaiwiseskul Faculy of Ecoomics Chulalogkor Uiversiy (c) Pogsa Porchaiwiseskul, Faculy of Ecoomics, Chulalogkor Uiversiy Geeral Auo-correlaio () YXβ + ν E(ν)0 V(ν)
More informationCalculus Limits. Limit of a function.. 1. One-Sided Limits...1. Infinite limits 2. Vertical Asymptotes...3. Calculating Limits Using the Limit Laws.
Limi of a fucio.. Oe-Sided..... Ifiie limis Verical Asympoes... Calculaig Usig he Limi Laws.5 The Squeeze Theorem.6 The Precise Defiiio of a Limi......7 Coiuiy.8 Iermediae Value Theorem..9 Refereces..
More informationComparison between Fourier and Corrected Fourier Series Methods
Malaysia Joural of Mahemaical Scieces 7(): 73-8 (13) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES Joural homepage: hp://eispem.upm.edu.my/oural Compariso bewee Fourier ad Correced Fourier Series Mehods 1
More informationECE 340 Lecture 15 and 16: Diffusion of Carriers Class Outline:
ECE 340 Lecure 5 ad 6: iffusio of Carriers Class Oulie: iffusio rocesses iffusio ad rif of Carriers Thigs you should kow whe you leave Key Quesios Why do carriers diffuse? Wha haes whe we add a elecric
More informationInference of the Second Order Autoregressive. Model with Unit Roots
Ieraioal Mahemaical Forum Vol. 6 0 o. 5 595-604 Iferece of he Secod Order Auoregressive Model wih Ui Roos Ahmed H. Youssef Professor of Applied Saisics ad Ecoomerics Isiue of Saisical Sudies ad Research
More informationCalculus BC 2015 Scoring Guidelines
AP Calculus BC 5 Scorig Guidelies 5 The College Board. College Board, Advaced Placeme Program, AP, AP Ceral, ad he acor logo are regisered rademarks of he College Board. AP Ceral is he official olie home
More informationComparisons Between RV, ARV and WRV
Comparisos Bewee RV, ARV ad WRV Cao Gag,Guo Migyua School of Maageme ad Ecoomics, Tiaji Uiversiy, Tiaji,30007 Absrac: Realized Volailiy (RV) have bee widely used sice i was pu forward by Aderso ad Bollerslev
More information11: The Analysis of Variance
: The Aalysis of Variace. I comparig 6 populaios, here are ANOVA able is show below. Source df Treames 5 Error 5 Toal 59 k degrees of freedom for reames ad ( ) = 60 = 60. The. a Refer o Eercise.. The give
More informationThe Central Limit Theorem
The Ceral Limi Theorem The ceral i heorem is oe of he mos impora heorems i probabiliy heory. While here a variey of forms of he ceral i heorem, he mos geeral form saes ha give a sufficiely large umber,
More informationAvailable online at J. Math. Comput. Sci. 2 (2012), No. 4, ISSN:
Available olie a h://scik.og J. Mah. Comu. Sci. 2 (22), No. 4, 83-835 ISSN: 927-537 UNBIASED ESTIMATION IN BURR DISTRIBUTION YASHBIR SINGH * Deame of Saisics, School of Mahemaics, Saisics ad Comuaioal
More informationStatistical Analysis on Uncertainty for Autocorrelated Measurements and its Applications to Key Comparisons
Statistical Aalysis o Ucertaity for Autocorrelated Measuremets ad its Applicatios to Key Comparisos Nie Fa Zhag Natioal Istitute of Stadards ad Techology Gaithersburg, MD 0899, USA Outlies. Itroductio.
More informationF.Y. Diploma : Sem. II [AE/CH/FG/ME/PT/PG] Applied Mathematics
F.Y. Diploma : Sem. II [AE/CH/FG/ME/PT/PG] Applied Mahemaics Prelim Quesio Paper Soluio Q. Aemp ay FIVE of he followig : [0] Q.(a) Defie Eve ad odd fucios. [] As.: A fucio f() is said o be eve fucio if
More informationA Note on Prediction with Misspecified Models
ITB J. Sci., Vol. 44 A, No. 3,, 7-9 7 A Noe o Predicio wih Misspecified Models Khresha Syuhada Saisics Research Divisio, Faculy of Mahemaics ad Naural Scieces, Isiu Tekologi Badug, Jala Gaesa Badug, Jawa
More informationDirection: This test is worth 150 points. You are required to complete this test within 55 minutes.
Term Test 3 (Part A) November 1, 004 Name Math 6 Studet Number Directio: This test is worth 10 poits. You are required to complete this test withi miutes. I order to receive full credit, aswer each problem
More informationMath 2414 Homework Set 7 Solutions 10 Points
Mah Homework Se 7 Soluios 0 Pois #. ( ps) Firs verify ha we ca use he iegral es. The erms are clearly posiive (he epoeial is always posiive ad + is posiive if >, which i is i his case). For decreasig we
More informationindependence of the random sample measurements, we have U = Z i ~ χ 2 (n) with σ / n 1. Now let W = σ 2. We then have σ 2 (x i µ + µ x ) 2 i =1 ( )
MATH 48 Chi-Square Aalysis of a Normal Stadard Deviatio Dr Neal, WKU We ow shall use the chi-square distriutios to aalyze the stadard deviatio of a measuremet that is kow to e ormally distriuted The proof
More informationMath 6710, Fall 2016 Final Exam Solutions
Mah 67, Fall 6 Fial Exam Soluios. Firs, a sude poied ou a suble hig: if P (X i p >, he X + + X (X + + X / ( evaluaes o / wih probabiliy p >. This is roublesome because a radom variable is supposed o be
More informationFOR 496 / 796 Introduction to Dendrochronology. Lab exercise #4: Tree-ring Reconstruction of Precipitation
FOR 496 Iroducio o Dedrochroology Fall 004 FOR 496 / 796 Iroducio o Dedrochroology Lab exercise #4: Tree-rig Recosrucio of Precipiaio Adaped from a exercise developed by M.K. Cleavelad ad David W. Sahle,
More information11: The Analysis of Variance
: The alysis of Variace. I comparig 6 populaios, here are k degrees of freedom for reames ad NOV able is show below. Source df Treames 5 Error 5 Toal 59 = 60 = 60. The. a Refer o Eercise.. The give sums
More informationECE 340 Lecture 19 : Steady State Carrier Injection Class Outline:
ECE 340 ecure 19 : Seady Sae Carrier Ijecio Class Oulie: iffusio ad Recombiaio Seady Sae Carrier Ijecio Thigs you should kow whe you leave Key Quesios Wha are he major mechaisms of recombiaio? How do we
More informationLecture 15: Three-tank Mixing and Lead Poisoning
Lecure 15: Three-ak Miig ad Lead Poisoig Eigevalues ad eigevecors will be used o fid he soluio of a sysem for ukow fucios ha saisfy differeial equaios The ukow fucios will be wrie as a 1 colum vecor [
More informationO & M Cost O & M Cost
5/5/008 Turbie Reliabiliy, Maieace ad Faul Deecio Zhe Sog, Adrew Kusiak 39 Seamas Ceer Iowa Ciy, Iowa 54-57 adrew-kusiak@uiowa.edu Tel: 39-335-5934 Fax: 39-335-5669 hp://www.icae.uiowa.edu/~akusiak Oulie
More informationConvergence theorems. Chapter Sampling
Chaper Covergece heorems We ve already discussed he difficuly i defiig he probabiliy measure i erms of a experimeal frequecy measureme. The hear of he problem lies i he defiiio of he limi, ad his was se
More informationChapter 3 Moments of a Distribution
Chaper 3 Moes of a Disribuio Epecaio We develop he epecaio operaor i ers of he Lebesgue iegral. Recall ha he Lebesgue easure λ(a) for soe se A gives he legh/area/volue of he se A. If A = (3; 7), he λ(a)
More informationTMA4245 Statistics. Corrected 30 May and 4 June Norwegian University of Science and Technology Department of Mathematical Sciences.
Norwegia Uiversity of Sciece ad Techology Departmet of Mathematical Scieces Corrected 3 May ad 4 Jue Solutios TMA445 Statistics Saturday 6 May 9: 3: Problem Sow desity a The probability is.9.5 6x x dx
More informationMAHALAKSHMI ENGINEERING COLLEGE TIRUCHIRAPALLI
MAHALAKSHMI EGIEERIG COLLEGE TIRUCHIRAALLI 6 QUESTIO BAK - ASWERS -SEMESTER: V MA 6 - ROBABILITY AD QUEUEIG THEORY UIT IV:QUEUEIG THEORY ART-A Quesio : AUC M / J Wha are he haraerisis of a queueig heory?
More informationt = s D Overview of Tests Two-Sample t-test: Independent Samples Independent Samples t-test Difference between Means in a Two-sample Experiment
Overview of Te Two-Sample -Te: Idepede Sample Chaper 4 z-te Oe Sample -Te Relaed Sample -Te Idepede Sample -Te Compare oe ample o a populaio Compare wo ample Differece bewee Mea i a Two-ample Experime
More informationProcedia - Social and Behavioral Sciences 230 ( 2016 ) Joint Probability Distribution and the Minimum of a Set of Normalized Random Variables
Available olie a wwwsciecedireccom ScieceDirec Procedia - Social ad Behavioral Scieces 30 ( 016 ) 35 39 3 rd Ieraioal Coferece o New Challeges i Maageme ad Orgaizaio: Orgaizaio ad Leadership, May 016,
More informationStatistics 20: Final Exam Solutions Summer Session 2007
1. 20 poits Testig for Diabetes. Statistics 20: Fial Exam Solutios Summer Sessio 2007 (a) 3 poits Give estimates for the sesitivity of Test I ad of Test II. Solutio: 156 patiets out of total 223 patiets
More informationStationarity and Error Correction
Saioariy ad Error Correcio. Saioariy a. If a ie series of a rado variable Y has a fiie σ Y ad σ Y,Y-s or deeds oly o he lag legh s (s > ), bu o o, he series is saioary, or iegraed of order - I(). The rocess
More informationSUMMATION OF INFINITE SERIES REVISITED
SUMMATION OF INFINITE SERIES REVISITED I several aricles over he las decade o his web page we have show how o sum cerai iiie series icludig he geomeric series. We wa here o eed his discussio o he geeral
More informationBig O Notation for Time Complexity of Algorithms
BRONX COMMUNITY COLLEGE of he Ciy Uiversiy of New York DEPARTMENT OF MATHEMATICS AND COMPUTER SCIENCE CSI 33 Secio E01 Hadou 1 Fall 2014 Sepember 3, 2014 Big O Noaio for Time Complexiy of Algorihms Time
More informationAn interesting result about subset sums. Nitu Kitchloo. Lior Pachter. November 27, Abstract
A ieresig resul abou subse sums Niu Kichloo Lior Pacher November 27, 1993 Absrac We cosider he problem of deermiig he umber of subses B f1; 2; : : :; g such ha P b2b b k mod, where k is a residue class
More information2 f(x) dx = 1, 0. 2f(x 1) dx d) 1 4t t6 t. t 2 dt i)
Mah PracTes Be sure o review Lab (ad all labs) There are los of good quesios o i a) Sae he Mea Value Theorem ad draw a graph ha illusraes b) Name a impora heorem where he Mea Value Theorem was used i he
More informationTopic 9: Sampling Distributions of Estimators
Topic 9: Samplig Distributios of Estimators Course 003, 2016 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be
More informationLecture 8 April 18, 2018
Sas 300C: Theory of Saisics Sprig 2018 Lecure 8 April 18, 2018 Prof Emmauel Cades Scribe: Emmauel Cades Oulie Ageda: Muliple Tesig Problems 1 Empirical Process Viewpoi of BHq 2 Empirical Process Viewpoi
More informationReview - Week 10. There are two types of errors one can make when performing significance tests:
Review - Week Read: Chaper -3 Review: There are wo ype of error oe ca make whe performig igificace e: Type I error The ull hypohei i rue, bu we miakely rejec i (Fale poiive) Type II error The ull hypohei
More informationAffine term structure models
/5/07 Affie erm srucure models A. Iro o Gaussia affie erm srucure models B. Esimaio by miimum chi square (Hamilo ad Wu) C. Esimaio by OLS (Adria, Moech, ad Crump) D. Dyamic Nelso-Siegel model (Chrisese,
More informationFIXED FUZZY POINT THEOREMS IN FUZZY METRIC SPACE
Mohia & Samaa, Vol. 1, No. II, December, 016, pp 34-49. ORIGINAL RESEARCH ARTICLE OPEN ACCESS FIED FUZZY POINT THEOREMS IN FUZZY METRIC SPACE 1 Mohia S. *, Samaa T. K. 1 Deparme of Mahemaics, Sudhir Memorial
More informationSamuel Sindayigaya 1, Nyongesa L. Kennedy 2, Adu A.M. Wasike 3
Ieraioal Joural of Saisics ad Aalysis. ISSN 48-9959 Volume 6, Number (6, pp. -8 Research Idia Publicaios hp://www.ripublicaio.com The Populaio Mea ad is Variace i he Presece of Geocide for a Simple Birh-Deah-
More informationPure Math 30: Explained!
ure Mah : Explaied! www.puremah.com 6 Logarihms Lesso ar Basic Expoeial Applicaios Expoeial Growh & Decay: Siuaios followig his ype of chage ca be modeled usig he formula: (b) A = Fuure Amou A o = iial
More informationResearch Design - - Topic 2 Inferential Statistics: The t-test 2010 R.C. Gardner, Ph.D. Independent t-test
Research Desig - - Topic Ifereial aisics: The -es 00 R.C. Garer, Ph.D. Geeral Raioale Uerlyig he -es (Garer & Tremblay, 007, Ch. ) The Iepee -es The Correlae (paire) -es Effec ize a Power (Kirk, 995, pp
More informationSampling Example. ( ) δ ( f 1) (1/2)cos(12πt), T 0 = 1
Samplig Example Le x = cos( 4π)cos( π). The fudameal frequecy of cos 4π fudameal frequecy of cos π is Hz. The ( f ) = ( / ) δ ( f 7) + δ ( f + 7) / δ ( f ) + δ ( f + ). ( f ) = ( / 4) δ ( f 8) + δ ( f
More informationPage 1. Before-After Control-Impact (BACI) Power Analysis For Several Related Populations. Richard A. Hinrichsen. March 3, 2010
Page Before-Afer Corol-Impac BACI Power Aalysis For Several Relaed Populaios Richard A. Hirichse March 3, Cavea: This eperimeal desig ool is for a idealized power aalysis buil upo several simplifyig assumpios
More informationExtremal graph theory II: K t and K t,t
Exremal graph heory II: K ad K, Lecure Graph Theory 06 EPFL Frak de Zeeuw I his lecure, we geeralize he wo mai heorems from he las lecure, from riagles K 3 o complee graphs K, ad from squares K, o complee
More informationComparing Means: t-tests for One Sample & Two Related Samples
Comparing Means: -Tess for One Sample & Two Relaed Samples Using he z-tes: Assumpions -Tess for One Sample & Two Relaed Samples The z-es (of a sample mean agains a populaion mean) is based on he assumpion
More informationThe Eigen Function of Linear Systems
1/25/211 The Eige Fucio of Liear Sysems.doc 1/7 The Eige Fucio of Liear Sysems Recall ha ha we ca express (expad) a ime-limied sigal wih a weighed summaio of basis fucios: v ( ) a ψ ( ) = where v ( ) =
More informationN! AND THE GAMMA FUNCTION
N! AND THE GAMMA FUNCTION Cosider he produc of he firs posiive iegers- 3 4 5 6 (-) =! Oe calls his produc he facorial ad has ha produc of he firs five iegers equals 5!=0. Direcly relaed o he discree! fucio
More informationA Note on Random k-sat for Moderately Growing k
A Noe o Radom k-sat for Moderaely Growig k Ju Liu LMIB ad School of Mahemaics ad Sysems Sciece, Beihag Uiversiy, Beijig, 100191, P.R. Chia juliu@smss.buaa.edu.c Zogsheg Gao LMIB ad School of Mahemaics
More informationCOS 522: Complexity Theory : Boaz Barak Handout 10: Parallel Repetition Lemma
COS 522: Complexiy Theory : Boaz Barak Hadou 0: Parallel Repeiio Lemma Readig: () A Parallel Repeiio Theorem / Ra Raz (available o his websie) (2) Parallel Repeiio: Simplificaios ad he No-Sigallig Case
More informationMA238 Assignment 4 Solutions (part a)
(i) Sigle sample tests. Questio. MA38 Assigmet 4 Solutios (part a) (a) (b) (c) H 0 : = 50 sq. ft H A : < 50 sq. ft H 0 : = 3 mpg H A : > 3 mpg H 0 : = 5 mm H A : 5mm Questio. (i) What are the ull ad alterative
More informationReview Answers for E&CE 700T02
Review Aswers for E&CE 700T0 . Deermie he curre soluio, all possible direcios, ad sepsizes wheher improvig or o for he simple able below: 4 b ma c 0 0 0-4 6 0 - B N B N ^0 0 0 curre sol =, = Ch for - -
More information12 th Mathematics Objective Test Solutions
Maemaics Objecive Tes Soluios Differeiaio & H.O.D A oes idividual is saisfied wi imself as muc as oer are saisfied wi im. Name: Roll. No. Bac [Moda/Tuesda] Maimum Time: 90 Miues [Eac rig aswer carries
More informationReview Exercises for Chapter 9
0_090R.qd //0 : PM Page 88 88 CHAPTER 9 Ifiie Series I Eercises ad, wrie a epressio for he h erm of he sequece..,., 5, 0,,,, 0,... 7,... I Eercises, mach he sequece wih is graph. [The graphs are labeled
More informationAdditional Tables of Simulation Results
Saisica Siica: Suppleme REGULARIZING LASSO: A CONSISTENT VARIABLE SELECTION METHOD Quefeg Li ad Ju Shao Uiversiy of Wiscosi, Madiso, Eas Chia Normal Uiversiy ad Uiversiy of Wiscosi, Madiso Supplemeary
More informationLIMITS OF FUNCTIONS (I)
LIMITS OF FUNCTIO (I ELEMENTARY FUNCTIO: (Elemeary fucios are NOT piecewise fucios Cosa Fucios: f(x k, where k R Polyomials: f(x a + a x + a x + a x + + a x, where a, a,..., a R Raioal Fucios: f(x P (x,
More informationFresnel Dragging Explained
Fresel Draggig Explaied 07/05/008 Decla Traill Decla@espace.e.au The Fresel Draggig Coefficie required o explai he resul of he Fizeau experime ca be easily explaied by usig he priciples of Eergy Field
More informationKurskod: TAMS11 Provkod: TENB 21 March 2015, 14:00-18:00. English Version (no Swedish Version)
Kurskod: TAMS Provkod: TENB 2 March 205, 4:00-8:00 Examier: Xiagfeg Yag (Tel: 070 2234765). Please aswer i ENGLISH if you ca. a. You are allowed to use: a calculator; formel -och tabellsamlig i matematisk
More informationLinear System Theory
Naioal Tsig Hua Uiversiy Dearme of Power Mechaical Egieerig Mid-Term Eamiaio 3 November 11.5 Hours Liear Sysem Theory (Secio B o Secio E) [11PME 51] This aer coais eigh quesios You may aswer he quesios
More informationTime Series, Part 1 Content Literature
Time Series, Par Coe - Saioariy, auocorrelaio, parial auocorrelaio, removal of osaioary compoes, idepedece es for ime series - Liear Sochasic Processes: auoregressive (AR), movig average (MA), auoregressive
More informationLecture 15 First Properties of the Brownian Motion
Lecure 15: Firs Properies 1 of 8 Course: Theory of Probabiliy II Term: Sprig 2015 Isrucor: Gorda Zikovic Lecure 15 Firs Properies of he Browia Moio This lecure deals wih some of he more immediae properies
More informationOn the Validity of the Pairs Bootstrap for Lasso Estimators
O he Validiy of he Pairs Boosrap for Lasso Esimaors Lorezo Campoovo Uiversiy of S.Galle Ocober 2014 Absrac We sudy he validiy of he pairs boosrap for Lasso esimaors i liear regressio models wih radom covariaes
More information( θ. sup θ Θ f X (x θ) = L. sup Pr (Λ (X) < c) = α. x : Λ (x) = sup θ H 0. sup θ Θ f X (x θ) = ) < c. NH : θ 1 = θ 2 against AH : θ 1 θ 2
82 CHAPTER 4. MAXIMUM IKEIHOOD ESTIMATION Defiitio: et X be a radom sample with joit p.m/d.f. f X x θ. The geeralised likelihood ratio test g.l.r.t. of the NH : θ H 0 agaist the alterative AH : θ H 1,
More informationCommon Large/Small Sample Tests 1/55
Commo Large/Small Sample Tests 1/55 Test of Hypothesis for the Mea (σ Kow) Covert sample result ( x) to a z value Hypothesis Tests for µ Cosider the test H :μ = μ H 1 :μ > μ σ Kow (Assume the populatio
More informationSTAT431 Review. X = n. n )
STAT43 Review I. Results related to ormal distributio Expected value ad variace. (a) E(aXbY) = aex bey, Var(aXbY) = a VarX b VarY provided X ad Y are idepedet. Normal distributios: (a) Z N(, ) (b) X N(µ,
More informationDirection: This test is worth 250 points. You are required to complete this test within 50 minutes.
Term Test October 3, 003 Name Math 56 Studet Number Directio: This test is worth 50 poits. You are required to complete this test withi 50 miutes. I order to receive full credit, aswer each problem completely
More informationIdeal Amplifier/Attenuator. Memoryless. where k is some real constant. Integrator. System with memory
Liear Time-Ivaria Sysems (LTI Sysems) Oulie Basic Sysem Properies Memoryless ad sysems wih memory (saic or dyamic) Causal ad o-causal sysems (Causaliy) Liear ad o-liear sysems (Lieariy) Sable ad o-sable
More informationOverview. p 2. Chapter 9. Pooled Estimate of. q = 1 p. Notation for Two Proportions. Inferences about Two Proportions. Assumptions
Chapter 9 Slide Ifereces from Two Samples 9- Overview 9- Ifereces about Two Proportios 9- Ifereces about Two Meas: Idepedet Samples 9-4 Ifereces about Matched Pairs 9-5 Comparig Variatio i Two Samples
More informationMath 152. Rumbos Fall Solutions to Review Problems for Exam #2. Number of Heads Frequency
Math 152. Rumbos Fall 2009 1 Solutios to Review Problems for Exam #2 1. I the book Experimetatio ad Measuremet, by W. J. Youde ad published by the by the Natioal Sciece Teachers Associatio i 1962, the
More informationODEs II, Supplement to Lectures 6 & 7: The Jordan Normal Form: Solving Autonomous, Homogeneous Linear Systems. April 2, 2003
ODEs II, Suppleme o Lecures 6 & 7: The Jorda Normal Form: Solvig Auoomous, Homogeeous Liear Sysems April 2, 23 I his oe, we describe he Jorda ormal form of a marix ad use i o solve a geeral homogeeous
More informationTopic 9: Sampling Distributions of Estimators
Topic 9: Samplig Distributios of Estimators Course 003, 2018 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be
More informationECE-314 Fall 2012 Review Questions
ECE-34 Fall 0 Review Quesios. A liear ime-ivaria sysem has he ipu-oupu characerisics show i he firs row of he diagram below. Deermie he oupu for he ipu show o he secod row of he diagram. Jusify your aswer.
More informationSolutions to Problems 3, Level 4
Soluios o Problems 3, Level 4 23 Improve he resul of Quesio 3 whe l. i Use log log o prove ha for real >, log ( {}log + 2 d log+ P ( + P ( d 2. Here P ( is defied i Quesio, ad parial iegraio has bee used.
More informationSupplement for SADAGRAD: Strongly Adaptive Stochastic Gradient Methods"
Suppleme for SADAGRAD: Srogly Adapive Sochasic Gradie Mehods" Zaiyi Che * 1 Yi Xu * Ehog Che 1 iabao Yag 1. Proof of Proposiio 1 Proposiio 1. Le ɛ > 0 be fixed, H 0 γi, γ g, EF (w 1 ) F (w ) ɛ 0 ad ieraio
More informationC(p, ) 13 N. Nuclear reactions generate energy create new isotopes and elements. Notation for stellar rates: p 12
Iroducio o sellar reacio raes Nuclear reacios geerae eergy creae ew isoopes ad elemes Noaio for sellar raes: p C 3 N C(p,) 3 N The heavier arge ucleus (Lab: arge) he ligher icomig projecile (Lab: beam)
More informationINVESTMENT PROJECT EFFICIENCY EVALUATION
368 Miljeko Crjac Domiika Crjac INVESTMENT PROJECT EFFICIENCY EVALUATION Miljeko Crjac Professor Faculy of Ecoomics Drsc Domiika Crjac Faculy of Elecrical Egieerig Osijek Summary Fiacial efficiecy of ivesme
More informationStatistical Estimation
Learig Objecives Cofidece Levels, Iervals ad T-es Kow he differece bewee poi ad ierval esimaio. Esimae a populaio mea from a sample mea f large sample sizes. Esimae a populaio mea from a sample mea f small
More information3/3/2014. CDS M Phil Econometrics. Types of Relationships. Types of Relationships. Types of Relationships. Vijayamohanan Pillai N.
3/3/04 CDS M Phil Old Least Squares (OLS) Vijayamohaa Pillai N CDS M Phil Vijayamoha CDS M Phil Vijayamoha Types of Relatioships Oly oe idepedet variable, Relatioship betwee ad is Liear relatioships Curviliear
More informationLecture 6 - Testing Restrictions on the Disturbance Process (References Sections 2.7 and 2.10, Hayashi)
Lecure 6 - esing Resricions on he Disurbance Process (References Secions 2.7 an 2.0, Hayashi) We have eveloe sufficien coniions for he consisency an asymoic normaliy of he OLS esimaor ha allow for coniionally
More informationData Analysis and Statistical Methods Statistics 651
Data Aalysis ad Statistical Methods Statistics 651 http://www.stat.tamu.edu/~suhasii/teachig.html Suhasii Subba Rao Review of testig: Example The admistrator of a ursig home wats to do a time ad motio
More informationOutline. simplest HMM (1) simple HMMs? simplest HMM (2) Parameter estimation for discrete hidden Markov models
Oulie Parameer esimaio for discree idde Markov models Juko Murakami () ad Tomas Taylor (2). Vicoria Uiversiy of Welligo 2. Arizoa Sae Uiversiy Descripio of simple idde Markov models Maximum likeliood esimae
More informationMath 140 Introductory Statistics
8.2 Testig a Proportio Math 1 Itroductory Statistics Professor B. Abrego Lecture 15 Sectios 8.2 People ofte make decisios with data by comparig the results from a sample to some predetermied stadard. These
More information12 Getting Started With Fourier Analysis
Commuicaios Egieerig MSc - Prelimiary Readig Geig Sared Wih Fourier Aalysis Fourier aalysis is cocered wih he represeaio of sigals i erms of he sums of sie, cosie or complex oscillaio waveforms. We ll
More informationFermat Numbers in Multinomial Coefficients
1 3 47 6 3 11 Joural of Ieger Sequeces, Vol. 17 (014, Aricle 14.3. Ferma Numbers i Muliomial Coefficies Shae Cher Deparme of Mahemaics Zhejiag Uiversiy Hagzhou, 31007 Chia chexiaohag9@gmail.com Absrac
More information