MATHEMATICAL SCIENCES

Size: px
Start display at page:

Download "MATHEMATICAL SCIENCES"

Transcription

1 SET7-Math.Sc.-II-D Roll No. 57 (Write Roll Number from left side exactly as i the Admit Card) Subject Code : 5 PAPER II Sigature of Ivigilators.. Questio Booklet Series Questio Booklet No. (Idetical with OMR Aswer Sheet Number) X MATHEMATICAL SCIENCES Time : Hour 5 Miutes Maximum Marks: 00 Istructios for the Cadidates. Write your Roll Number i the space provided o the top of this page as well as o the OMR Sheet provided.. At the commecemet of the examiatio, the questio booklet will be give to you. I the first 5 miutes, you are requested to ope the booklet ad verify it: (i) To have access to the Questio Booklet, tear off the paper seal o the edge of this cover page. (ii) Faulty booklet, if detected, should be get replaced immediately by a correct booklet from the ivigilator withi the period of 5 miutes. Afterwards, either the Questio Booklet will be replaced or ay extra time will be give. (iii) Verify whether the Questio Booklet No. is idetical with OMR Aswer Sheet No.; if ot, the full set to be replaced. (iv) After this verificatio is over, the Questio Booklet Series ad Questio Booklet Number should be etered o the OMR Sheet. 3. This paper cosists of fifty (50) multiple-choice type questios. All the questios are compulsory. Each questio carries two marks. 4. Each Questio has four alterative resposes marked: A B C D. You have to darke the circle as idicated below o the correct respose agaist each questio. Example: A B C D, where C is the correct respose. 5. Your resposes to the questios are to be idicated correctly i the OMR Sheet. If you mark your respose at ay place other tha i the circle i the OMR Sheet, it will ot be evaluated. 6. Rough work is to be doe at the ed of this booklet. 7. If you write your Name, Roll Number, Phoe Number or put ay mark o ay part of the OMR Sheet, except the space allotted for the relevat etries, which may disclose your idetity, or use abusive laguage or employ ay other ufair meas, such as chage of respose by scratchig or usig white fluid, you will reder yourself liable to disqualificatio. 8. Do ot tamper or fold the OMR Sheet i ay way. If you do so, your OMR Sheet will ot be evaluated. 9. You have to retur the Origial OMR Sheet to the ivigilator at the ed of the examiatio compulsorily ad must ot carry it with you outside the Examiatio Hall. You are, however, allowed to carry questio booklet ad duplicate copy of OMR Sheet after completio of examiatio. 0. Use oly Black Ball poit pe.. Use of ay calculator or mobile phoe etc. is strictly prohibited.. There are o egative marks for icorrect aswers. [Please Tur Over]

2

3 X 3 57 II MATHEMATICAL SCIENCES PAPER II. If f is a real valued fuctio which is cotiuous ad satisfies x + y f ( x) + f ( y) f =, x, y, f(0) = ad f (0) =, the f(5) is equal to 9 0 (D) 6. { } lim (+ )( + )... ( + ) is equal to 4e 7 7e 4 7 4e (D) 4 7e 3. Let p( z) = a0 + az az ad qz ( ) = bz+ bz bz be complex polyomials. If a0, b are o-zero complex p( z) umbers, the the residue of q( z) at 0 is equal to a0 b b a0 a b (D) a0 a 4. Let A be a real 3 4 matrix of rak. The the rak of A t A, where A t deotes the traspose of A, is exactly exactly 3 exactly 4 (D) at most but ot ecessarily 5. If A is a 5 5 real matrix with trace 5 ad if ad 3 are eigevalues of A, each with algebraic multiplicity, the the determiat of A is (D) Let V be the vector space of twice differetiable fuctios f o satisfyig f f + f = 0. Defie T : V by T( f) = ( f (0), f(0) ). The T is oe oe ad oto oe oe but ot oto oto but ot oe oe (D) either oe oe or oto 7. The probability that a teacher will give a surprize test durig ay class meetig is 3/5. If a studet is abset o two days, the the probability that he will miss at least oe test is /5 4/5 /5

4 57 II X 4 8. Suppose that the variables x 0 ad x 0 satisfy the costraits x+ x 3 ad x+ x 4 ad let Z = 5x+ 7 x. Which of the followig is true? The maximum value of Z is ad it does ot have ay fiite miimum. The miimum value of Z is 7 ad it does ot have ay fiite maximum. The maximum value of Z is ad its miimum value is 7. (D) Neither has a fiite maximum or a fiite miimum. is 9. The period of the fuctio π 0π π (D) 5π 0. Cosider the power series covergece of this series is si 0 (D) a real umber greater tha x + si x 5 3 5! z. The radius of =. Which of the followig is true? ad log diverges but log ad log (D) coverges but log. The radius of covergece of the series 3. Let z is! e e (D) 0 4. The co-efficiet of z si z is z 0 (D) i the Lauret series of 5. The umber of subgroups of 48 is (D) 0 f ( Z) = Z, Z. If γ : Z i =, = 0 f( Z) dz the 0 is equal to ( Z i) γ π i(+ 0 i) 30πi πi (D) 0

5 X 5 57 II 6. Let G be a group ad a, b G such that order of a is 5 ad aba = b. The the order of b is (D) Suppose I is the group of itegers ad H = {3 x: x I} is a ormal subgroup of I. The the elemets of I H are { H, H +, H + } { H +, H +, H + 3} { H, H +, H + 3} (D) { H, H +, H + 4} 8. I the set of itegers I, defie a b= a+ b+, a b= a+ b+ ab with a, b I. The ( I,, ) is a rig such that it is commutative a itegral domai a field 9. Let S : 3 4 ad T : 4 3 be liear trasformatios such that ToS is the idetity map of 3. The SoT is idetity map of 4 SoT is oe-oe but ot oto SoT is oto but ot oe-oe (D) SoT is either oe-oe or oto 0. Let M 3 ( ) be the vector space of all 3 3 real 0 matrices. Let A = 0 0. Which oe of the followig is ot a subspace of M 3 ( )? {X M 3 ( ) : XA = AX} {X M 3 ( ) : X + A = A + X} {X M 3 ( ) : trace (AX) = 0} (D) {X M 3 ( ) : det (AX) = 0}. Let A ad B be two matrices such that BA + B = I BA 3, where I is the idetity matrix. Which oe of the followig is always true? A is o-sigular B is o-sigular A + B is o-sigular (D) BA is o-sigular. Which of the followig is a subspace of 3? {(x,y,z) 3 : xyz = 0} {(x,y,z) 3 : x y = 3} {(x,y,z) 3 : x + y + z = } (D) {(x,y,z) 3 : x + y = 0} 3. Let W be the Wroskia of two liearly idepedet solutios of the ODE: d y dy + + t y = 0, t. dt dt The for all t, a costat c such that W(t) is t ce t ce (D) t ce t ce

6 57 II X 6 u u 4. The ature of = x y elliptic parabolic hyperbolic dy 3 5. The iitial value problem 3 y, y(0) 0 dx = = i a iterval aroud x = 0 has o solutio uique solutio more tha oe solutio (D) ifiite umber of liearly idepedet solutios is 8. If X ad Y are idepedet gamma variates with parameters μ ad ν respectively, the the ratio X/(X + Y) follows Beta distributio of type I with μ ad ν parameters Beta distributio of type II with μ ad ν parameters Gamma distributio with parameters μ ad ν 9. If X ~ N (0, ), the the pdf of Y = X is y g () y = e, y 0 Y y g ( y) = / π e, y 0 Y y g ( y) = / π e, y 0 Y 6. It is ecessary to fid cumulative frequecies i order to draw a/a Histogram Frequecy polygo Ogive curve (D) Colum chart 7. If oe regressio coefficiet of the two regressio lies is greater tha uity, the other will be greater tha less tha (D) If X ad Y are idepedet Poisso variates, the the coditioal distributio of X give X + Y, is ormal biomial hypergeometric (D) Poisso 3. Let E ad F be two idepedet evets with PE ( / F) + PF ( / E) =, PE ( F) = 9 ad PF ( ) < PE ( ). The PE ( ) equals 3 3 (D) 3 4

7 X 7 57 II 3. Let the radom variables X ad Y have the joit pmf p(x, y), the its distributio fuctio is 35. The test statistic for testig the sigificace of a observed partial correlatio coefficiet is i : x j : y i x j y F ( xy, ) px (, y) = i= j= i j r r (D) i : x j : y i > x j y F ( xy, ) px (, y) = i= j= i : x j : y i x j y F ( xy, ) px (, y) = i= j= i : x j : y i x j y F ( xy, ) px (, y) = i= j= i i i j j j r ( k+ ) ( r ( k+ ) ) r ( k+ ) ( r ( k+ ) ) r 34...( k+ ) (D) ( r ( k+ ) ) k k k 33. The stadard chi-squared test for a by cotigecy table is valid oly if all the expected frequecies are greater tha five both variables are cotiuous at least oe variable is from a ormal distributio 34. Let X be a radom variable such that E X <. The E X c is miimized if we choose c is equal to the mea of the distributio. the media of the distributio. the quartile deviatio of the distributio. (D) the stadard deviatio of the distributio. 36. A experimet is coducted uder the followig circumstaces: (a) whe there are pairs of observatios o two thigs beig compared. (b) for ay pair, each of the two observatios is made uder similar extraeous coditios. (c) differet pairs are observed uder differet coditios. I such a situatio which test ca be used? Paired t-test Sig test Media test (D) Idepedet t-test 37. Homogeeity of several variaces ca be tested by Bartlett s test Fisher s exact test F-test (D) t-test

8 57 II X Let X,..., X be a radom sample of size draw from R(0, θ). Defie T = X( ), T = X() + X( ). The which oe is correct? T is cosistet but ot T. T is cosistet but ot T. Both are cosistet. 39. Asymptotic distributio of U statistic is m m( m + ) N, (D) m m( m + + N, m m( m ) N, m m( m + ) N, 40. The statistic t for testig the hypothesis ρ = 0 based o a sample of size from a bivariate populatio has degrees of freedom (D) 3 4. If i a liear programmig problem the umber of variables i primal is, ad the umber of costraits i its dual is m, the m = m m (D) m Game is a situatio where players have same objectives. players have coflictig objectives. players have o objectives. 43. The demad for a item is determiistic ad costat over the time ad it is equal to 400 uit per year. The per uit cost of the item is ` 50 while the cost of placig a order is ` 5. The ivetory carryig cost is 0% of the cost of ivetory per aum ad the cost of shortage is ` per uit per year. Whe the stock outs are permitted the optimal orderig quatity is 33 uits 66 uits 8 uits (D) 88 uits 44. Whe the equipmet starts deterioratig with respect to time, its maiteace cost gradually starts/remais decreasig costat icreasig (D) zero 45. Uder the proportioal allocatio, the size of the sample from each stratum depeds o total sample size size of the stratum populatio size (D) All of the above 46. A populatio is perfectly homogeeous i respect of a characteristic. What size of sample would you prefer? A large sample A small sample A sigle sample (D) No item

9 X 9 57 II 47. Sowball samplig is a Radom samplig Cluster samplig No-radom samplig 48. I the ANOVA, treatmet refers to experimetal uits differet levels of a factor a factor 49. The Stadard Error (S.E.) of ay treatmet mea is give by for RBD S.E. ( ti) = se, i=,,..., t t = s r i= t S.E. ( i) E /,,,..., S.E. ( ) 3/ t = s / r, i =,,..., t i E (D) S.E. ( ) = /, =,,..., ti se r i t where s E stads for sum of squares due to error ad r the o. of replicatio 50. For a factorial experimet with three factors N, P ad K, each at two levels, the key block of a replicate is give below: () pk k p The cofouded effect is pk p k (D) pk

10 57 II X 0 ROUGH WORK

11 X 57 II ROUGH WORK

12 57 II X ROUGH WORK

Mathematical Statistics - MS

Mathematical Statistics - MS Paper Specific Istructios. The examiatio is of hours duratio. There are a total of 60 questios carryig 00 marks. The etire paper is divided ito three sectios, A, B ad C. All sectios are compulsory. Questios

More information

MATHEMATICAL SCIENCE

MATHEMATICAL SCIENCE Test Booklet No. F Sigature ad Name of Ivigilator 1. (Sigature)... (Name).... (Sigature)... (Name)... AUG - 3015 MATHEMATICAL SCIENCE Seat No. (I figures as i Admit Card) Seat No.... (I words) OMR Sheet

More information

PAPER : IIT-JAM 2010

PAPER : IIT-JAM 2010 MATHEMATICS-MA (CODE A) Q.-Q.5: Oly oe optio is correct for each questio. Each questio carries (+6) marks for correct aswer ad ( ) marks for icorrect aswer.. Which of the followig coditios does NOT esure

More information

MATHEMATICS Code No. 13 INSTRUCTIONS

MATHEMATICS Code No. 13 INSTRUCTIONS DO NOT OPEN THIS TEST BOOKLET UNTIL YOU ARE ASKED TO DO SO COMBINED COMPETITIVE (PRELIMINARY) EXAMINATION, 0 Serial No. MATHEMATICS Code No. A Time Allowed : Two Hours Maimum Marks : 00 INSTRUCTIONS. IMMEDIATELY

More information

A) is empty. B) is a finite set. C) can be a countably infinite set. D) can be an uncountable set.

A) is empty. B) is a finite set. C) can be a countably infinite set. D) can be an uncountable set. M.A./M.Sc. (Mathematics) Etrace Examiatio 016-17 Max Time: hours Max Marks: 150 Istructios: There are 50 questios. Every questio has four choices of which exactly oe is correct. For correct aswer, 3 marks

More information

MATHEMATICAL SCIENCES PAPER-II

MATHEMATICAL SCIENCES PAPER-II MATHEMATICAL SCIENCES PAPER-II. Let {x } ad {y } be two sequeces of real umbers. Prove or disprove each of the statemets :. If {x y } coverges, ad if {y } is coverget, the {x } is coverget.. {x + y } coverges

More information

NANYANG TECHNOLOGICAL UNIVERSITY SYLLABUS FOR ENTRANCE EXAMINATION FOR INTERNATIONAL STUDENTS AO-LEVEL MATHEMATICS

NANYANG TECHNOLOGICAL UNIVERSITY SYLLABUS FOR ENTRANCE EXAMINATION FOR INTERNATIONAL STUDENTS AO-LEVEL MATHEMATICS NANYANG TECHNOLOGICAL UNIVERSITY SYLLABUS FOR ENTRANCE EXAMINATION FOR INTERNATIONAL STUDENTS AO-LEVEL MATHEMATICS STRUCTURE OF EXAMINATION PAPER. There will be oe 2-hour paper cosistig of 4 questios.

More information

NBHM QUESTION 2007 Section 1 : Algebra Q1. Let G be a group of order n. Which of the following conditions imply that G is abelian?

NBHM QUESTION 2007 Section 1 : Algebra Q1. Let G be a group of order n. Which of the following conditions imply that G is abelian? NBHM QUESTION 7 NBHM QUESTION 7 NBHM QUESTION 7 Sectio : Algebra Q Let G be a group of order Which of the followig coditios imply that G is abelia? 5 36 Q Which of the followig subgroups are ecesarily

More information

CEE 522 Autumn Uncertainty Concepts for Geotechnical Engineering

CEE 522 Autumn Uncertainty Concepts for Geotechnical Engineering CEE 5 Autum 005 Ucertaity Cocepts for Geotechical Egieerig Basic Termiology Set A set is a collectio of (mutually exclusive) objects or evets. The sample space is the (collectively exhaustive) collectio

More information

Chapter 6 Principles of Data Reduction

Chapter 6 Principles of Data Reduction Chapter 6 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 0 Chapter 6 Priciples of Data Reductio Sectio 6. Itroductio Goal: To summarize or reduce the data X, X,, X to get iformatio about a

More information

PRELIM PROBLEM SOLUTIONS

PRELIM PROBLEM SOLUTIONS PRELIM PROBLEM SOLUTIONS THE GRAD STUDENTS + KEN Cotets. Complex Aalysis Practice Problems 2. 2. Real Aalysis Practice Problems 2. 4 3. Algebra Practice Problems 2. 8. Complex Aalysis Practice Problems

More information

TABLES AND FORMULAS FOR MOORE Basic Practice of Statistics

TABLES AND FORMULAS FOR MOORE Basic Practice of Statistics TABLES AND FORMULAS FOR MOORE Basic Practice of Statistics Explorig Data: Distributios Look for overall patter (shape, ceter, spread) ad deviatios (outliers). Mea (use a calculator): x = x 1 + x 2 + +

More information

IIT JAM Mathematical Statistics (MS) 2006 SECTION A

IIT JAM Mathematical Statistics (MS) 2006 SECTION A IIT JAM Mathematical Statistics (MS) 6 SECTION A. If a > for ad lim a / L >, the which of the followig series is ot coverget? (a) (b) (c) (d) (d) = = a = a = a a + / a lim a a / + = lim a / a / + = lim

More information

SCORE. Exam 2. MA 114 Exam 2 Fall 2016

SCORE. Exam 2. MA 114 Exam 2 Fall 2016 MA 4 Exam Fall 06 Exam Name: Sectio ad/or TA: Do ot remove this aswer page you will retur the whole exam. You will be allowed two hours to complete this test. No books or otes may be used. You may use

More information

Chapter 2 The Monte Carlo Method

Chapter 2 The Monte Carlo Method Chapter 2 The Mote Carlo Method The Mote Carlo Method stads for a broad class of computatioal algorithms that rely o radom sampligs. It is ofte used i physical ad mathematical problems ad is most useful

More information

Estimation for Complete Data

Estimation for Complete Data Estimatio for Complete Data complete data: there is o loss of iformatio durig study. complete idividual complete data= grouped data A complete idividual data is the oe i which the complete iformatio of

More information

Direction: This test is worth 150 points. You are required to complete this test within 55 minutes.

Direction: 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 information

2. The volume of the solid of revolution generated by revolving the area bounded by the

2. The volume of the solid of revolution generated by revolving the area bounded by the IIT JAM Mathematical Statistics (MS) Solved Paper. A eigevector of the matrix M= ( ) is (a) ( ) (b) ( ) (c) ( ) (d) ( ) Solutio: (a) Eigevalue of M = ( ) is. x So, let x = ( y) be the eigevector. z (M

More information

Notation List. For Cambridge International Mathematics Qualifications. For use from 2020

Notation List. For Cambridge International Mathematics Qualifications. For use from 2020 Notatio List For Cambridge Iteratioal Mathematics Qualificatios For use from 2020 Notatio List for Cambridge Iteratioal Mathematics Qualificatios (For use from 2020) Mathematical otatio Eamiatios for CIE

More information

MATH 320: Probability and Statistics 9. Estimation and Testing of Parameters. Readings: Pruim, Chapter 4

MATH 320: Probability and Statistics 9. Estimation and Testing of Parameters. Readings: Pruim, Chapter 4 MATH 30: Probability ad Statistics 9. Estimatio ad Testig of Parameters Estimatio ad Testig of Parameters We have bee dealig situatios i which we have full kowledge of the distributio of a radom variable.

More information

Lecture 7: Properties of Random Samples

Lecture 7: Properties of Random Samples Lecture 7: Properties of Radom Samples 1 Cotiued From Last Class Theorem 1.1. Let X 1, X,...X be a radom sample from a populatio with mea µ ad variace σ

More information

Describing the Relation between Two Variables

Describing the Relation between Two Variables Copyright 010 Pearso Educatio, Ic. Tables ad Formulas for Sulliva, Statistics: Iformed Decisios Usig Data 010 Pearso Educatio, Ic Chapter Orgaizig ad Summarizig Data Relative frequecy = frequecy sum of

More information

Modern Algebra. Previous year Questions from 2017 to Ramanasri

Modern Algebra. Previous year Questions from 2017 to Ramanasri Moder Algebra Previous year Questios from 017 to 199 Ramaasri 017 S H O P NO- 4, 1 S T F L O O R, N E A R R A P I D F L O U R M I L L S, O L D R A J E N D E R N A G A R, N E W D E L H I. W E B S I T E

More information

SCORE. Exam 2. MA 114 Exam 2 Fall 2016

SCORE. Exam 2. MA 114 Exam 2 Fall 2016 Exam 2 Name: Sectio ad/or TA: Do ot remove this aswer page you will retur the whole exam. You will be allowed two hours to complete this test. No books or otes may be used. You may use a graphig calculator

More information

Probability and statistics: basic terms

Probability and statistics: basic terms Probability ad statistics: basic terms M. Veeraraghava August 203 A radom variable is a rule that assigs a umerical value to each possible outcome of a experimet. Outcomes of a experimet form the sample

More information

Econ 325/327 Notes on Sample Mean, Sample Proportion, Central Limit Theorem, Chi-square Distribution, Student s t distribution 1.

Econ 325/327 Notes on Sample Mean, Sample Proportion, Central Limit Theorem, Chi-square Distribution, Student s t distribution 1. Eco 325/327 Notes o Sample Mea, Sample Proportio, Cetral Limit Theorem, Chi-square Distributio, Studet s t distributio 1 Sample Mea By Hiro Kasahara We cosider a radom sample from a populatio. Defiitio

More information

GULF MATHEMATICS OLYMPIAD 2014 CLASS : XII

GULF MATHEMATICS OLYMPIAD 2014 CLASS : XII GULF MATHEMATICS OLYMPIAD 04 CLASS : XII Date of Eamiatio: Maimum Marks : 50 Time : 0:30 a.m. to :30 p.m. Duratio: Hours Istructios to cadidates. This questio paper cosists of 50 questios. All questios

More information

Probability 2 - Notes 10. Lemma. If X is a random variable and g(x) 0 for all x in the support of f X, then P(g(X) 1) E[g(X)].

Probability 2 - Notes 10. Lemma. If X is a random variable and g(x) 0 for all x in the support of f X, then P(g(X) 1) E[g(X)]. Probability 2 - Notes 0 Some Useful Iequalities. Lemma. If X is a radom variable ad g(x 0 for all x i the support of f X, the P(g(X E[g(X]. Proof. (cotiuous case P(g(X Corollaries x:g(x f X (xdx x:g(x

More information

IE 230 Seat # Name < KEY > Please read these directions. Closed book and notes. 60 minutes.

IE 230 Seat # Name < KEY > Please read these directions. Closed book and notes. 60 minutes. IE 230 Seat # Name < KEY > Please read these directios. Closed book ad otes. 60 miutes. Covers through the ormal distributio, Sectio 4.7 of Motgomery ad Ruger, fourth editio. Cover page ad four pages of

More information

Regn. No. North Delhi : 33-35, Mall Road, G.T.B. Nagar (Opp. Metro Gate No. 3), Delhi-09, Ph: ,

Regn. No. North Delhi : 33-35, Mall Road, G.T.B. Nagar (Opp. Metro Gate No. 3), Delhi-09, Ph: , . Sectio-A cotais 30 Multiple Choice Questios (MCQ). Each questio has 4 choices (a), (b), (c) ad (d), for its aswer, out of which ONLY ONE is correct. From Q. to Q.0 carries Marks ad Q. to Q.30 carries

More information

ECONOMETRIC THEORY. MODULE XIII Lecture - 34 Asymptotic Theory and Stochastic Regressors

ECONOMETRIC THEORY. MODULE XIII Lecture - 34 Asymptotic Theory and Stochastic Regressors ECONOMETRIC THEORY MODULE XIII Lecture - 34 Asymptotic Theory ad Stochastic Regressors Dr. Shalabh Departmet of Mathematics ad Statistics Idia Istitute of Techology Kapur Asymptotic theory The asymptotic

More information

1 of 7 7/16/2009 6:06 AM Virtual Laboratories > 6. Radom Samples > 1 2 3 4 5 6 7 6. Order Statistics Defiitios Suppose agai that we have a basic radom experimet, ad that X is a real-valued radom variable

More information

SCORE. Exam 2. MA 114 Exam 2 Fall 2017

SCORE. Exam 2. MA 114 Exam 2 Fall 2017 Exam Name: Sectio ad/or TA: Do ot remove this aswer page you will retur the whole exam. You will be allowed two hours to complete this test. No books or otes may be used. You may use a graphig calculator

More information

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting Lecture 6 Chi Square Distributio (χ ) ad Least Squares Fittig Chi Square Distributio (χ ) Suppose: We have a set of measuremets {x 1, x, x }. We kow the true value of each x i (x t1, x t, x t ). We would

More information

Since X n /n P p, we know that X n (n. Xn (n X n ) Using the asymptotic result above to obtain an approximation for fixed n, we obtain

Since X n /n P p, we know that X n (n. Xn (n X n ) Using the asymptotic result above to obtain an approximation for fixed n, we obtain Assigmet 9 Exercise 5.5 Let X biomial, p, where p 0, 1 is ukow. Obtai cofidece itervals for p i two differet ways: a Sice X / p d N0, p1 p], the variace of the limitig distributio depeds oly o p. Use the

More information

INSTRUCTIONS (A) 1.22 (B) 0.74 (C) 4.93 (D) 1.18 (E) 2.43

INSTRUCTIONS (A) 1.22 (B) 0.74 (C) 4.93 (D) 1.18 (E) 2.43 PAPER NO.: 444, 445 PAGE NO.: Page 1 of 1 INSTRUCTIONS I. You have bee provided with: a) the examiatio paper i two parts (PART A ad PART B), b) a multiple choice aswer sheet (for PART A), c) selected formulae

More information

Random Variables, Sampling and Estimation

Random Variables, Sampling and Estimation Chapter 1 Radom Variables, Samplig ad Estimatio 1.1 Itroductio This chapter will cover the most importat basic statistical theory you eed i order to uderstad the ecoometric material that will be comig

More information

DEEPAK SERIES DEEPAK SERIES DEEPAK SERIES FREE BOOKLET CSIR-UGC/NET MATHEMATICAL SCIENCES

DEEPAK SERIES DEEPAK SERIES DEEPAK SERIES FREE BOOKLET CSIR-UGC/NET MATHEMATICAL SCIENCES DEEPAK SERIES DEEPAK SERIES DEEPAK SERIES FREE BOOKLET DEEPAK SERIES CSIR-UGC/NET MATHEMATICAL SCIENCES SOLVED PAPER DEC- DEEPAK SERIES DEEPAK SERIES DEEPAK SERIES Note : This material is issued as complimetary

More information

Because it tests for differences between multiple pairs of means in one test, it is called an omnibus test.

Because it tests for differences between multiple pairs of means in one test, it is called an omnibus test. Math 308 Sprig 018 Classes 19 ad 0: Aalysis of Variace (ANOVA) Page 1 of 6 Itroductio ANOVA is a statistical procedure for determiig whether three or more sample meas were draw from populatios with equal

More information

Continuous Data that can take on any real number (time/length) based on sample data. Categorical data can only be named or categorised

Continuous Data that can take on any real number (time/length) based on sample data. Categorical data can only be named or categorised Questio 1. (Topics 1-3) A populatio cosists of all the members of a group about which you wat to draw a coclusio (Greek letters (μ, σ, Ν) are used) A sample is the portio of the populatio selected for

More information

EXAMINATIONS OF THE ROYAL STATISTICAL SOCIETY

EXAMINATIONS OF THE ROYAL STATISTICAL SOCIETY EXAMINATIONS OF THE ROYAL STATISTICAL SOCIETY HIGHER CERTIFICATE IN STATISTICS, 017 MODULE 4 : Liear models Time allowed: Oe ad a half hours Cadidates should aswer THREE questios. Each questio carries

More information

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting Lecture 6 Chi Square Distributio (χ ) ad Least Squares Fittig Chi Square Distributio (χ ) Suppose: We have a set of measuremets {x 1, x, x }. We kow the true value of each x i (x t1, x t, x t ). We would

More information

Resampling Methods. X (1/2), i.e., Pr (X i m) = 1/2. We order the data: X (1) X (2) X (n). Define the sample median: ( n.

Resampling Methods. X (1/2), i.e., Pr (X i m) = 1/2. We order the data: X (1) X (2) X (n). Define the sample median: ( n. Jauary 1, 2019 Resamplig Methods Motivatio We have so may estimators with the property θ θ d N 0, σ 2 We ca also write θ a N θ, σ 2 /, where a meas approximately distributed as Oce we have a cosistet estimator

More information

7-1. Chapter 4. Part I. Sampling Distributions and Confidence Intervals

7-1. Chapter 4. Part I. Sampling Distributions and Confidence Intervals 7-1 Chapter 4 Part I. Samplig Distributios ad Cofidece Itervals 1 7- Sectio 1. Samplig Distributio 7-3 Usig Statistics Statistical Iferece: Predict ad forecast values of populatio parameters... Test hypotheses

More information

Lecture 6 Simple alternatives and the Neyman-Pearson lemma

Lecture 6 Simple alternatives and the Neyman-Pearson lemma STATS 00: Itroductio to Statistical Iferece Autum 06 Lecture 6 Simple alteratives ad the Neyma-Pearso lemma Last lecture, we discussed a umber of ways to costruct test statistics for testig a simple ull

More information

1 Inferential Methods for Correlation and Regression Analysis

1 Inferential Methods for Correlation and Regression Analysis 1 Iferetial Methods for Correlatio ad Regressio Aalysis I the chapter o Correlatio ad Regressio Aalysis tools for describig bivariate cotiuous data were itroduced. The sample Pearso Correlatio Coefficiet

More information

Topic 9: Sampling Distributions of Estimators

Topic 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 information

This exam contains 19 pages (including this cover page) and 10 questions. A Formulae sheet is provided with the exam.

This exam contains 19 pages (including this cover page) and 10 questions. A Formulae sheet is provided with the exam. Probability ad Statistics FS 07 Secod Sessio Exam 09.0.08 Time Limit: 80 Miutes Name: Studet ID: This exam cotais 9 pages (icludig this cover page) ad 0 questios. A Formulae sheet is provided with the

More information

Tests of Hypotheses Based on a Single Sample (Devore Chapter Eight)

Tests of Hypotheses Based on a Single Sample (Devore Chapter Eight) Tests of Hypotheses Based o a Sigle Sample Devore Chapter Eight MATH-252-01: Probability ad Statistics II Sprig 2018 Cotets 1 Hypothesis Tests illustrated with z-tests 1 1.1 Overview of Hypothesis Testig..........

More information

Lecture 19: Convergence

Lecture 19: Convergence Lecture 19: Covergece Asymptotic approach I statistical aalysis or iferece, a key to the success of fidig a good procedure is beig able to fid some momets ad/or distributios of various statistics. I may

More information

Stat 200 -Testing Summary Page 1

Stat 200 -Testing Summary Page 1 Stat 00 -Testig Summary Page 1 Mathematicias are like Frechme; whatever you say to them, they traslate it ito their ow laguage ad forthwith it is somethig etirely differet Goethe 1 Large Sample Cofidece

More information

THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA STATISTICAL THEORY AND METHODS PAPER I

THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA STATISTICAL THEORY AND METHODS PAPER I THE ROYAL STATISTICAL SOCIETY 5 EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA STATISTICAL THEORY AND METHODS PAPER I The Society provides these solutios to assist cadidates preparig for the examiatios i future

More information

TAMS24: Notations and Formulas

TAMS24: Notations and Formulas TAMS4: Notatios ad Formulas Basic otatios ad defiitios X: radom variable stokastiska variabel Mea Vätevärde: µ = X = by Xiagfeg Yag kpx k, if X is discrete, xf Xxdx, if X is cotiuous Variace Varias: =

More information

Statistical Inference (Chapter 10) Statistical inference = learn about a population based on the information provided by a sample.

Statistical Inference (Chapter 10) Statistical inference = learn about a population based on the information provided by a sample. Statistical Iferece (Chapter 10) Statistical iferece = lear about a populatio based o the iformatio provided by a sample. Populatio: The set of all values of a radom variable X of iterest. Characterized

More information

MATHEMATICS. The assessment objectives of the Compulsory Part are to test the candidates :

MATHEMATICS. The assessment objectives of the Compulsory Part are to test the candidates : MATHEMATICS INTRODUCTION The public assessmet of this subject is based o the Curriculum ad Assessmet Guide (Secodary 4 6) Mathematics joitly prepared by the Curriculum Developmet Coucil ad the Hog Kog

More information

Open book and notes. 120 minutes. Cover page and six pages of exam. No calculators.

Open book and notes. 120 minutes. Cover page and six pages of exam. No calculators. IE 330 Seat # Ope book ad otes 120 miutes Cover page ad six pages of exam No calculators Score Fial Exam (example) Schmeiser Ope book ad otes No calculator 120 miutes 1 True or false (for each, 2 poits

More information

Joint Probability Distributions and Random Samples. Jointly Distributed Random Variables. Chapter { }

Joint Probability Distributions and Random Samples. Jointly Distributed Random Variables. Chapter { } UCLA STAT A Applied Probability & Statistics for Egieers Istructor: Ivo Diov, Asst. Prof. I Statistics ad Neurology Teachig Assistat: Neda Farziia, UCLA Statistics Uiversity of Califoria, Los Ageles, Sprig

More information

IE 230 Probability & Statistics in Engineering I. Closed book and notes. No calculators. 120 minutes.

IE 230 Probability & Statistics in Engineering I. Closed book and notes. No calculators. 120 minutes. Closed book ad otes. No calculators. 120 miutes. Cover page, five pages of exam, ad tables for discrete ad cotiuous distributios. Score X i =1 X i / S X 2 i =1 (X i X ) 2 / ( 1) = [i =1 X i 2 X 2 ] / (

More information

Lecture 4. Random variable and distribution of probability

Lecture 4. Random variable and distribution of probability Itroductio to theory of probability ad statistics Lecture. Radom variable ad distributio of probability dr hab.iż. Katarzya Zarzewsa, prof.agh Katedra Eletroii, AGH e-mail: za@agh.edu.pl http://home.agh.edu.pl/~za

More information

Expectation and Variance of a random variable

Expectation and Variance of a random variable Chapter 11 Expectatio ad Variace of a radom variable The aim of this lecture is to defie ad itroduce mathematical Expectatio ad variace of a fuctio of discrete & cotiuous radom variables ad the distributio

More information

EXAMINATIONS OF THE ROYAL STATISTICAL SOCIETY

EXAMINATIONS OF THE ROYAL STATISTICAL SOCIETY EXAMINATIONS OF THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA, 016 MODULE : Statistical Iferece Time allowed: Three hours Cadidates should aswer FIVE questios. All questios carry equal marks. The umber

More information

Discrete Random Variables and Probability Distributions. Random Variables. Discrete Models

Discrete Random Variables and Probability Distributions. Random Variables. Discrete Models UCLA STAT 35 Applied Computatioal ad Iteractive Probability Istructor: Ivo Diov, Asst. Prof. I Statistics ad Neurology Teachig Assistat: Chris Barr Uiversity of Califoria, Los Ageles, Witer 006 http://www.stat.ucla.edu/~diov/

More information

MAT1026 Calculus II Basic Convergence Tests for Series

MAT1026 Calculus II Basic Convergence Tests for Series MAT026 Calculus II Basic Covergece Tests for Series Egi MERMUT 202.03.08 Dokuz Eylül Uiversity Faculty of Sciece Departmet of Mathematics İzmir/TURKEY Cotets Mootoe Covergece Theorem 2 2 Series of Real

More information

Problems from 9th edition of Probability and Statistical Inference by Hogg, Tanis and Zimmerman:

Problems from 9th edition of Probability and Statistical Inference by Hogg, Tanis and Zimmerman: Math 224 Fall 2017 Homework 4 Drew Armstrog Problems from 9th editio of Probability ad Statistical Iferece by Hogg, Tais ad Zimmerma: Sectio 2.3, Exercises 16(a,d),18. Sectio 2.4, Exercises 13, 14. Sectio

More information

AH Checklist (Unit 3) AH Checklist (Unit 3) Matrices

AH Checklist (Unit 3) AH Checklist (Unit 3) Matrices AH Checklist (Uit 3) AH Checklist (Uit 3) Matrices Skill Achieved? Kow that a matrix is a rectagular array of umbers (aka etries or elemets) i paretheses, each etry beig i a particular row ad colum Kow

More information

Lecture 5. Random variable and distribution of probability

Lecture 5. Random variable and distribution of probability Itroductio to theory of probability ad statistics Lecture 5. Radom variable ad distributio of probability prof. dr hab.iż. Katarzya Zarzewsa Katedra Eletroii, AGH e-mail: za@agh.edu.pl http://home.agh.edu.pl/~za

More information

Complex Analysis Spring 2001 Homework I Solution

Complex Analysis Spring 2001 Homework I Solution Complex Aalysis Sprig 2001 Homework I Solutio 1. Coway, Chapter 1, sectio 3, problem 3. Describe the set of poits satisfyig the equatio z a z + a = 2c, where c > 0 ad a R. To begi, we see from the triagle

More information

Formulas and Tables for Gerstman

Formulas and Tables for Gerstman Formulas ad Tables for Gerstma Measuremet ad Study Desig Biostatistics is more tha a compilatio of computatioal techiques! Measuremet scales: quatitative, ordial, categorical Iformatio quality is primary

More information

TABLES AND FORMULAS FOR MOORE Basic Practice of Statistics

TABLES AND FORMULAS FOR MOORE Basic Practice of Statistics TABLES AND FORMULAS FOR MOORE Basic Practice of Statistics Explorig Data: Distributios Look for overall patter (shape, ceter, spread) ad deviatios (outliers). Mea (use a calculator): x = x 1 + x 2 + +

More information

Apply change-of-basis formula to rewrite x as a linear combination of eigenvectors v j.

Apply change-of-basis formula to rewrite x as a linear combination of eigenvectors v j. Eigevalue-Eigevector Istructor: Nam Su Wag eigemcd Ay vector i real Euclidea space of dimesio ca be uiquely epressed as a liear combiatio of liearly idepedet vectors (ie, basis) g j, j,,, α g α g α g α

More information

(6) Fundamental Sampling Distribution and Data Discription

(6) Fundamental Sampling Distribution and Data Discription 34 Stat Lecture Notes (6) Fudametal Samplig Distributio ad Data Discriptio ( Book*: Chapter 8,pg5) Probability& Statistics for Egieers & Scietists By Walpole, Myers, Myers, Ye 8.1 Radom Samplig: Populatio:

More information

Large Sample Theory. Convergence. Central Limit Theorems Asymptotic Distribution Delta Method. Convergence in Probability Convergence in Distribution

Large Sample Theory. Convergence. Central Limit Theorems Asymptotic Distribution Delta Method. Convergence in Probability Convergence in Distribution Large Sample Theory Covergece Covergece i Probability Covergece i Distributio Cetral Limit Theorems Asymptotic Distributio Delta Method Covergece i Probability A sequece of radom scalars {z } = (z 1,z,

More information

11 Correlation and Regression

11 Correlation and Regression 11 Correlatio Regressio 11.1 Multivariate Data Ofte we look at data where several variables are recorded for the same idividuals or samplig uits. For example, at a coastal weather statio, we might record

More information

Linear regression. Daniel Hsu (COMS 4771) (y i x T i β)2 2πσ. 2 2σ 2. 1 n. (x T i β y i ) 2. 1 ˆβ arg min. β R n d

Linear regression. Daniel Hsu (COMS 4771) (y i x T i β)2 2πσ. 2 2σ 2. 1 n. (x T i β y i ) 2. 1 ˆβ arg min. β R n d Liear regressio Daiel Hsu (COMS 477) Maximum likelihood estimatio Oe of the simplest liear regressio models is the followig: (X, Y ),..., (X, Y ), (X, Y ) are iid radom pairs takig values i R d R, ad Y

More information

Parameter, Statistic and Random Samples

Parameter, Statistic and Random Samples Parameter, Statistic ad Radom Samples A parameter is a umber that describes the populatio. It is a fixed umber, but i practice we do ot kow its value. A statistic is a fuctio of the sample data, i.e.,

More information

Chapter 6 Infinite Series

Chapter 6 Infinite Series Chapter 6 Ifiite Series I the previous chapter we cosidered itegrals which were improper i the sese that the iterval of itegratio was ubouded. I this chapter we are goig to discuss a topic which is somewhat

More information

MTH 133 Solutions to Exam 2 November 16th, Without fully opening the exam, check that you have pages 1 through 12.

MTH 133 Solutions to Exam 2 November 16th, Without fully opening the exam, check that you have pages 1 through 12. Name: Sectio: Recitatio Istructor: INSTRUCTIONS Fill i your ame, etc. o this first page. Without fully opeig the exam, check that you have pages through. Show all your work o the stadard respose questios.

More information

MOST PEOPLE WOULD RATHER LIVE WITH A PROBLEM THEY CAN'T SOLVE, THAN ACCEPT A SOLUTION THEY CAN'T UNDERSTAND.

MOST PEOPLE WOULD RATHER LIVE WITH A PROBLEM THEY CAN'T SOLVE, THAN ACCEPT A SOLUTION THEY CAN'T UNDERSTAND. XI-1 (1074) MOST PEOPLE WOULD RATHER LIVE WITH A PROBLEM THEY CAN'T SOLVE, THAN ACCEPT A SOLUTION THEY CAN'T UNDERSTAND. R. E. D. WOOLSEY AND H. S. SWANSON XI-2 (1075) STATISTICAL DECISION MAKING Advaced

More information

Module 1 Fundamentals in statistics

Module 1 Fundamentals in statistics Normal Distributio Repeated observatios that differ because of experimetal error ofte vary about some cetral value i a roughly symmetrical distributio i which small deviatios occur much more frequetly

More information

Inverse Matrix. A meaning that matrix B is an inverse of matrix A.

Inverse Matrix. A meaning that matrix B is an inverse of matrix A. Iverse Matrix Two square matrices A ad B of dimesios are called iverses to oe aother if the followig holds, AB BA I (11) The otio is dual but we ofte write 1 B A meaig that matrix B is a iverse of matrix

More information

1 Models for Matched Pairs

1 Models for Matched Pairs 1 Models for Matched Pairs Matched pairs occur whe we aalyse samples such that for each measuremet i oe of the samples there is a measuremet i the other sample that directly relates to the measuremet i

More information

Chapter 6 Sampling Distributions

Chapter 6 Sampling Distributions Chapter 6 Samplig Distributios 1 I most experimets, we have more tha oe measuremet for ay give variable, each measuremet beig associated with oe radomly selected a member of a populatio. Hece we eed to

More information

Lecture 22: Review for Exam 2. 1 Basic Model Assumptions (without Gaussian Noise)

Lecture 22: Review for Exam 2. 1 Basic Model Assumptions (without Gaussian Noise) Lecture 22: Review for Exam 2 Basic Model Assumptios (without Gaussia Noise) We model oe cotiuous respose variable Y, as a liear fuctio of p umerical predictors, plus oise: Y = β 0 + β X +... β p X p +

More information

UNIVERSITY OF TORONTO Faculty of Arts and Science APRIL/MAY 2009 EXAMINATIONS ECO220Y1Y PART 1 OF 2 SOLUTIONS

UNIVERSITY OF TORONTO Faculty of Arts and Science APRIL/MAY 2009 EXAMINATIONS ECO220Y1Y PART 1 OF 2 SOLUTIONS PART of UNIVERSITY OF TORONTO Faculty of Arts ad Sciece APRIL/MAY 009 EAMINATIONS ECO0YY PART OF () The sample media is greater tha the sample mea whe there is. (B) () A radom variable is ormally distributed

More information

BITSAT MATHEMATICS PAPER III. For the followig liear programmig problem : miimize z = + y subject to the costraits + y, + y 8, y, 0, the solutio is (0, ) ad (, ) (0, ) ad ( /, ) (0, ) ad (, ) (d) (0, )

More information

Pearson Edexcel Level 3 Advanced Subsidiary and Advanced GCE in Statistics

Pearson Edexcel Level 3 Advanced Subsidiary and Advanced GCE in Statistics Pearso Edecel Level 3 Advaced Subsidiary ad Advaced GCE i Statistics Statistical formulae ad tables For first certificatio from Jue 018 for: Advaced Subsidiary GCE i Statistics (8ST0) For first certificatio

More information

Econ 325 Notes on Point Estimator and Confidence Interval 1 By Hiro Kasahara

Econ 325 Notes on Point Estimator and Confidence Interval 1 By Hiro Kasahara Poit Estimator Eco 325 Notes o Poit Estimator ad Cofidece Iterval 1 By Hiro Kasahara Parameter, Estimator, ad Estimate The ormal probability desity fuctio is fully characterized by two costats: populatio

More information

Math 116 Practice for Exam 3

Math 116 Practice for Exam 3 Math 6 Practice for Eam 3 Geerated April 4, 26 Name: SOLUTIONS Istructor: Sectio Number:. This eam has questios. Note that the problems are ot of equal difficulty, so you may wat to skip over ad retur

More information

Properties and Hypothesis Testing

Properties and Hypothesis Testing Chapter 3 Properties ad Hypothesis Testig 3.1 Types of data The regressio techiques developed i previous chapters ca be applied to three differet kids of data. 1. Cross-sectioal data. 2. Time series data.

More information

MTH 142 Exam 3 Spr 2011 Practice Problem Solutions 1

MTH 142 Exam 3 Spr 2011 Practice Problem Solutions 1 MTH 42 Exam 3 Spr 20 Practice Problem Solutios No calculators will be permitted at the exam. 3. A pig-pog ball is lauched straight up, rises to a height of 5 feet, the falls back to the lauch poit ad bouces

More information

of the matrix is =-85, so it is not positive definite. Thus, the first

of the matrix is =-85, so it is not positive definite. Thus, the first BOSTON COLLEGE Departmet of Ecoomics EC771: Ecoometrics Sprig 4 Prof. Baum, Ms. Uysal Solutio Key for Problem Set 1 1. Are the followig quadratic forms positive for all values of x? (a) y = x 1 8x 1 x

More information

AMS570 Lecture Notes #2

AMS570 Lecture Notes #2 AMS570 Lecture Notes # Review of Probability (cotiued) Probability distributios. () Biomial distributio Biomial Experimet: ) It cosists of trials ) Each trial results i of possible outcomes, S or F 3)

More information

Power and Type II Error

Power and Type II Error Statistical Methods I (EXST 7005) Page 57 Power ad Type II Error Sice we do't actually kow the value of the true mea (or we would't be hypothesizig somethig else), we caot kow i practice the type II error

More information

PRACTICE PROBLEMS FOR THE FINAL

PRACTICE PROBLEMS FOR THE FINAL PRACTICE PROBLEMS FOR THE FINAL Math 36Q Fall 25 Professor Hoh Below is a list of practice questios for the Fial Exam. I would suggest also goig over the practice problems ad exams for Exam ad Exam 2 to

More information

Test of Statistics - Prof. M. Romanazzi

Test of Statistics - Prof. M. Romanazzi 1 Uiversità di Veezia - Corso di Laurea Ecoomics & Maagemet Test of Statistics - Prof. M. Romaazzi 19 Jauary, 2011 Full Name Matricola Total (omial) score: 30/30 (2 scores for each questio). Pass score:

More information

Topic 9: Sampling Distributions of Estimators

Topic 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 information

We are mainly going to be concerned with power series in x, such as. (x)} converges - that is, lims N n

We are mainly going to be concerned with power series in x, such as. (x)} converges - that is, lims N n Review of Power Series, Power Series Solutios A power series i x - a is a ifiite series of the form c (x a) =c +c (x a)+(x a) +... We also call this a power series cetered at a. Ex. (x+) is cetered at

More information

HOMEWORK I: PREREQUISITES FROM MATH 727

HOMEWORK I: PREREQUISITES FROM MATH 727 HOMEWORK I: PREREQUISITES FROM MATH 727 Questio. Let X, X 2,... be idepedet expoetial radom variables with mea µ. (a) Show that for Z +, we have EX µ!. (b) Show that almost surely, X + + X (c) Fid the

More information

AAEC/ECON 5126 FINAL EXAM: SOLUTIONS

AAEC/ECON 5126 FINAL EXAM: SOLUTIONS AAEC/ECON 5126 FINAL EXAM: SOLUTIONS SPRING 2015 / INSTRUCTOR: KLAUS MOELTNER This exam is ope-book, ope-otes, but please work strictly o your ow. Please make sure your ame is o every sheet you re hadig

More information

Goodness-of-Fit Tests and Categorical Data Analysis (Devore Chapter Fourteen)

Goodness-of-Fit Tests and Categorical Data Analysis (Devore Chapter Fourteen) Goodess-of-Fit Tests ad Categorical Data Aalysis (Devore Chapter Fourtee) MATH-252-01: Probability ad Statistics II Sprig 2019 Cotets 1 Chi-Squared Tests with Kow Probabilities 1 1.1 Chi-Squared Testig................

More information