Elementary Statistics and Inference. Elementary Statistics and Inference. Chapter 20 Chance Errors in Sampling (cont.) 22S:025 or 7P:025.
|
|
- Rosa Kelly
- 5 years ago
- Views:
Transcription
1 Elemetary Statistics ad Iferece 22S:025 or 7P:025 Lecture 27 1 Elemetary Statistics ad Iferece 22S:025 or 7P:025 Chapter 20 2 D. The Correctio Factor - (page 367) 1992 Presidetial Campaig Texas 12.5 x 106 voters New Mexico 1.2 x 106 voters Radom Sample of 2,500 voters selected i New Mexica & 2,500 voters i Texas New Mexico samples 1 out of 500 Texas samples 1 out 5,
2 It would seem the accuracy i New Mexico would be better because a greater percetage of the populatio is sampled! NOT SO The SE(%) is based o size of sample ot the size of the populatio. p(1 p) SE(%) = 100 where =sample size. 4 Results for SE(%) whe samplig with replacemet whe populatios are large. SE ( without replacemet) = correctio factor SE(with replacemet) umber of tickets i box - umber of draws SE(without replacemet) = SE umber of tickets i box -1 N SE(without replacemet) = p(1 p) N 1 Where N = size of populatio = sample size p = sample proportio 5 Example: Suppose N = 100,000 = 1,000 p =.50 SE(without replacemet) = = 100, , (.5)(.5) 99, (.5)(.5) = ( ) SE of the box The larger the populatio, the closer the correctio factor is to oe. 6 2
3 Note the correctio factor approaches 1 as size of the tickets i box (populatio) icreases. 7 I New Mexico & Texas sample NM TX 46% 1 54% 0 37% 1 63% 0 Democrat Democrat SD = SD = SE = (.50) SE = (.48) Because SE is early the same i both states, the estimated percet of democrats i a radom sample of 2,500 voters will be about the same, give or take about the same %. 8 Exercise Set C (page 370) #2, 3, 4, 5 #3. A survey orgaizatio wats to take a simple radom sample i order to estimate the percetage of people p who have see a certai televisio program. To keep the costs dow, they wat to take as small a sample as possible. But their cliet will oly tolerate chace errors of 1 percetage poit or so i the estimate. Should they use a sample of size 100, 2,500, or 10,000? You may assume the populatio to be very large; past experiece suggests the populatio percetage will be i the rage 20%-40%. 9 3
4 40% 1 60% 0 avg = p =.40 SD = =.489 ~ Wat SE(%) ~1.00 p(1 p 1.00 = = = (10,000) = (.24)(10,000) ~ 2,400 Use sample of about 2, #5. 2 R 8 B p(r)=.20 Draw 4 marbles with replacemet, ad without replacemet. Fid SE(%) of red marbles draw. (a) With replacemet.2.8 SE(%) = 100 = 20%
5 (b) Without replacemet use correctio factor SE(%) = N SE(%) N 1 SE(%) = 10 4 (20) = (.816)(20) = 16.33% 10 1 Note: A big differece i SE(%) whe size of populatio is small. 13 E. The Gallup Poll They sample several thousad voters out of over 200 millio reaso? The size of the chace error (SE) i percet depeds maily o the absolute size of the sample, ad hardly at all o the size of the populatio from which the sample was selected. 14 For a sample of size 2,500 whe sample percet is betwee.40 ad.60, the p(1 p) SE(%) = SE (%) = 100 = 1.00% 2,500 The chace error for a sample of size 2,500 from a populatio of over 200,000 millio would have a SE of percet aroud 1%. If sample percet is 50%, we would coclude results are accurate, give or take 1%. 15 5
6 F. Review Exercises (pp ) #1, 2, 3, 5, 11, E(H) SE(H) E(% Hds) SE(% Hds) =2,500 1, % =10,000 5, % =1,000, , %
7 3. A group of 50,000 tax forms has a average gross icome of $37,000 with a SD of $20,000. Furthermore, 20% of the forms have a gross icome over $50,000. A group of 900 forms is chose at radom for audit. To estimate the chace that betwee 19% ad 21% of the forms chose for audit have gross icomes over $50,000, a box model is eeded. 19 a) Should the umber of tickets i the box be 900 or 50,000? b) Each ticket i the box shows a zero or a oe a gross icome c) True or false: The SE of the box is $20,000. d) True or false: The umber of draws is 900. e) Fid the chace (approximately) that betwee 19% ad 21% of the forms chose for audit have gross icomes over $50, % + 50k 80% - 50k = 900 E(% over 50k) = 20%.2.8 SE(% over 50k) = 100 = 1.33%
8 SE=1.33% M=20% Z = =.75 Z = = % X M Z = SE Use Normal Curve table to fid percetage of scores betwee ±Z=.75. P (.75 Z.75) =.5467 or ~ 55% 22 #11. (page 373) A uiversity has 25,000 studets, of whom 17,000 are udergraduates. The housig office takes a simple radom sample of 500 studets, 357 out of 500 are udergraduates. 17, , ,000 p = =.68 E(umber) = ,000 E(% of udergraduates) = 68% SE(% of udergraduates) = SE(%) = 2.09% Determie the likelihood of obtaiig 357 or more udergraduates (71%) i a sample of 500 studets. SE=2.09 % M=68 71 Z Z = = 1.44 ~
9 85.29% About 85.29% of scores i Normal Distributio are betwee ±Z=1.45. About 14.71% of scores are either less tha Z=-1.45 or greater tha Z=1.45. About 7.35% of scores are greater tha Z=1.45. Result: The probability (chace) of obtaiig 357 udergrads or more i a sample of 500 studets is about 7%. Z 25 9
Estimating Proportions with Confidence
Aoucemets: Discussio today is review for midterm, o credit. You may atted more tha oe discussio sectio. Brig sheets of otes ad calculator to midterm. We will provide Scatro form. Homework: (Due Wed Chapter
More informationBasic formula for confidence intervals. Formulas for estimating population variance Normal Uniform Proportion
Basic formula for the Chi-square test (Observed - Expected ) Expected Basic formula for cofidece itervals sˆ x ± Z ' Sample size adjustmet for fiite populatio (N * ) (N + - 1) Formulas for estimatig populatio
More informationCHAPTER 8 Estimating with Confidence
CHAPTER 8 Estimatig with Cofidece 8.2 Estimatig a Populatio Proportio The Practice of Statistics, 5th Editio Stares, Tabor, Yates, Moore Bedford Freema Worth Publishers Estimatig a Populatio Proportio
More informationTopic-7. Large Sample Estimation
Topic-7 Large Sample Estimatio TYPES OF INFERENCE Ò Estimatio: É Estimatig or predictig the value of the parameter É What is (are) the most likely values of m or p? Ò Hypothesis Testig: É Decidig about
More informationStandard Deviations for Normal Sampling Distributions are: For proportions For means _
Sectio 9.2 Cofidece Itervals for Proportios We will lear to use a sample to say somethig about the world at large. This process (statistical iferece) is based o our uderstadig of samplig models, ad will
More informationLecture 5: Sampling Distribution
Lecture 5: Samplig Distributio Readigs: Sectios 5.5, 5.6 Itroductio Parameter: describes populatio Statistic: describes the sample; samplig variability Samplig distributio of a statistic: A probability
More informationMath 124: Lecture for Week 10 of 17
What we will do toight 1 Lecture for of 17 David Meredith Departmet of Mathematics Sa Fracisco State Uiversity 2 3 4 April 8, 2008 5 6 II Take the midterm. At the ed aswer the followig questio: To be revealed
More informationSampling Distributions and Estimation
Samplig Distributios ad Estimatio T O P I C # Populatio Proportios, π π the proportio of the populatio havig some characteristic Sample proportio ( p ) provides a estimate of π : x p umber of successes
More informationStatistics for Economics & Business
Statistics for Ecoomics & Busiess Cofidece Iterval Estimatio Learig Objectives I this chapter, you lear: To costruct ad iterpret cofidece iterval estimates for the mea ad the proportio How to determie
More informationLecture 4: Probability (continued)
Lecture 4: Probability (cotiued) Desity Curves We ve defied probabilities for discrete variables (such as coi tossig). Probabilities for cotiuous or measuremet variables also are evaluated usig relative
More informationChapter 8: Estimation of Mean & Proportion. Introduction
Chapter 8: Estimatio of Mea & Proportio 8.1 Estimatio, Poit Estimate, ad Iterval Estimate 8.2 Estimatio of a Populatio Mea: σ Kow 8.3 Estimatio of a Populatio Mea: σ Not Kow 8.4 Estimatio of a Populatio
More informationChapter 8. Confidence Interval Estimation. Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 8, Slide 1
Chapter 8 Cofidece Iterval Estimatio Copyright 2015, 2012, 2009 Pearso Educatio, Ic. Chapter 8, Slide 1 Learig Objectives I this chapter, you lear: To costruct ad iterpret cofidece iterval estimates for
More informationToday: Finish Chapter 9 (Sections 9.6 to 9.8 and 9.9 Lesson 3)
Today: Fiish Chapter 9 (Sectios 9.6 to 9.8 ad 9.9 Lesso 3) ANNOUNCEMENTS: Quiz #7 begis after class today, eds Moday at 3pm. Quiz #8 will begi ext Friday ad ed at 10am Moday (day of fial). There will be
More informationInferential Statistics and Probability a Holistic Approach. Inference Process. Inference Process. Chapter 8 Slides. Maurice Geraghty,
Iferetial Statistics ad Probability a Holistic Approach Chapter 8 Poit Estimatio ad Cofidece Itervals This Course Material by Maurice Geraghty is licesed uder a Creative Commos Attributio-ShareAlike 4.0
More informationConfidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the.
Cofidece Itervals A cofidece iterval is a iterval whose purpose is to estimate a parameter (a umber that could, i theory, be calculated from the populatio, if measuremets were available for the whole populatio).
More information1. Find the area under the standard normal curve between z = 0 and z = 3. (a) (b) (c) (d)
STA 2023 Practice 3 You may receive assistace from the Math Ceter. These problems are iteded to provide supplemetary problems i preparatio for test 3. This packet does ot ecessarily reflect the umber,
More informationpoint estimator a random variable (like P or X) whose values are used to estimate a population parameter
Estimatio We have oted that the pollig problem which attempts to estimate the proportio p of Successes i some populatio ad the measuremet problem which attempts to estimate the mea value µ of some quatity
More informationSampling Distributions and Estimation
Cotets 40 Samplig Distributios ad Estimatio 40.1 Samplig Distributios 40. Iterval Estimatio for the Variace 13 Learig outcomes You will lear about the distributios which are created whe a populatio is
More informationii. Interval estimation:
1 Types of estimatio: i. Poit estimatio: Example (1) Cosider the sample observatios 17,3,5,1,18,6,16,10 X 8 X i i1 8 17 3 5 118 6 16 10 8 116 8 14.5 14.5 is a poit estimate for usig the estimator X ad
More informationA point estimate is the value of a statistic that estimates the value of a parameter.
Chapter 9 Estimatig the Value of a Parameter Chapter 9.1 Estimatig a Populatio Proportio Objective A : Poit Estimate A poit estimate is the value of a statistic that estimates the value of a parameter.
More informationBASIC STATISTICS ECOE 1323
BASIC STATISTICS ECOE 33 SPRING 007 FINAL EXAM NAME: ID NUMBER: INSTRUCTIONS:. Write your ame ad studet ID.. You have hours 3. This eam must be your ow work etirely. You caot talk to or share iformatio
More informationLecture 4: Parameter Estimation and Confidence Intervals. GENOME 560 Doug Fowler, GS
Lecture 4: Parameter Estimatio ad Cofidece Itervals GENOME 560 Doug Fowler, GS (dfowler@uw.edu) 1 Review: Probability Distributios Discrete: Biomial distributio Hypergeometric distributio Poisso distributio
More informationChapter 8 Interval Estimation. Estimation Concepts. General Form of a Confidence Interval
Chapter 8 Iterval Estimatio Estimatio Cocepts Usually ca't take a cesus, so we must make decisios based o sample data It imperative that we take the risk of samplig error ito accout whe we iterpret sample
More informationChapter 10 - Lecture 2 The independent two sample t-test and. confidence interval
Assumptios Idepedet Samples - ukow σ 1, σ - 30 or m 30 - Upooled case Idepedet Samples - ukow σ 1, σ - 30 or m 30 - Pooled case Idepedet samples - Pooled variace - Large samples Chapter 10 - Lecture The
More informationCHAPTER 8: CONFIDENCE INTERVAL ESTIMATES for Means and Proportions
CHAPTER 8: CONFIDENCE INTERVAL ESTIMATES for Meas ad Proportios Itroductio: I this chapter we wat to fid out the value of a parameter for a populatio. We do t kow the value of this parameter for the etire
More information1 Random Variables and Key Statistics
Review of Statistics 1 Radom Variables ad Key Statistics Radom Variable: A radom variable is a variable that takes o differet umerical values from a sample space determied by chace (probability distributio,
More informationSampling Distributions & Estimators
API-209 TF Sessio 2 Teddy Svoroos September 18, 2015 Samplig Distributios & Estimators I. Estimators The Importace of Samplig Radomly Three Properties of Estimators 1. Ubiased 2. Cosistet 3. Efficiet I
More informationB = A x z
114 Block 3 Erdeky == Begi 6.3 ============================================================== 1 / 8 / 2008 1 Correspodig Areas uder a ormal curve ad the stadard ormal curve are equal. Below: Area B = Area
More informationCHAPTER 8: CONFIDENCE INTERVAL ESTIMATES for Means and Proportions
CHAPTER 8: CONFIDENCE INTERVAL ESTIMATES for Meas ad Proportios Itroductio: We wat to kow the value of a parameter for a populatio. We do t kow the value of this parameter for the etire populatio because
More informationOutline. Populations. Defs: A (finite) population is a (finite) set P of elements e. A variable is a function v : P IR. Population and Characteristics
Outlie Populatio Characteristics Types of Samples Sample Characterstics Sample Aalogue Estimatio Populatios Defs: A (fiite) populatio is a (fiite) set P of elemets e. A variable is a fuctio v : P IR. Examples
More information2. Find the annual percentage yield (APY), to the nearest hundredth of a %, for an account with an APR of 12% with daily compounding.
1. Suppose that you ivest $4,000 i a accout that ears iterest at a of 5%, compouded mothly, for 58 years. `Show the formula that you would use to determie the accumulated balace, ad determie the accumulated
More information1. Suppose X is a variable that follows the normal distribution with known standard deviation σ = 0.3 but unknown mean µ.
Chapter 9 Exercises Suppose X is a variable that follows the ormal distributio with kow stadard deviatio σ = 03 but ukow mea µ (a) Costruct a 95% cofidece iterval for µ if a radom sample of = 6 observatios
More informationFixed Income Securities
Prof. Stefao Mazzotta Keesaw State Uiversity Fixed Icome Securities FIN4320. Fall 2006 Sample First Midterm Exam Last Name: First Name: Studet ID Number: Exam time is: 80 miutes. Total poits for this exam
More informationChance Error in Sampling
1 Chance Error in Sampling How different is the sample percentage from the population percentage? The purpose of this chapter is to show how box models can be used to understand the error in simple random
More informationThe Idea of a Confidence Interval
AP Statistics Ch. 8 Notes Estimatig with Cofidece I the last chapter, we aswered questios about what samples should look like assumig that we kew the true values of populatio parameters (like μ, σ, ad
More informationA random variable is a variable whose value is a numerical outcome of a random phenomenon.
The Practice of Statistics, d ed ates, Moore, ad Stares Itroductio We are ofte more iterested i the umber of times a give outcome ca occur tha i the possible outcomes themselves For example, if we toss
More informationSCHOOL OF ACCOUNTING AND BUSINESS BSc. (APPLIED ACCOUNTING) GENERAL / SPECIAL DEGREE PROGRAMME
All Right Reserved No. of Pages - 10 No of Questios - 08 SCHOOL OF ACCOUNTING AND BUSINESS BSc. (APPLIED ACCOUNTING) GENERAL / SPECIAL DEGREE PROGRAMME YEAR I SEMESTER I (Group B) END SEMESTER EXAMINATION
More informationCHAPTER 2 PRICING OF BONDS
CHAPTER 2 PRICING OF BONDS CHAPTER SUARY This chapter will focus o the time value of moey ad how to calculate the price of a bod. Whe pricig a bod it is ecessary to estimate the expected cash flows ad
More information. (The calculated sample mean is symbolized by x.)
Stat 40, sectio 5.4 The Cetral Limit Theorem otes by Tim Pilachowski If you have t doe it yet, go to the Stat 40 page ad dowload the hadout 5.4 supplemet Cetral Limit Theorem. The homework (both practice
More informationNOTES ON ESTIMATION AND CONFIDENCE INTERVALS. 1. Estimation
NOTES ON ESTIMATION AND CONFIDENCE INTERVALS MICHAEL N. KATEHAKIS 1. Estimatio Estimatio is a brach of statistics that deals with estimatig the values of parameters of a uderlyig distributio based o observed/empirical
More informationConfidence Intervals Introduction
Cofidece Itervals Itroductio A poit estimate provides o iformatio about the precisio ad reliability of estimatio. For example, the sample mea X is a poit estimate of the populatio mea μ but because of
More informationLecture 5 Point Es/mator and Sampling Distribu/on
Lecture 5 Poit Es/mator ad Samplig Distribu/o Fall 03 Prof. Yao Xie, yao.xie@isye.gatech.edu H. Milto Stewart School of Idustrial Systems & Egieerig Georgia Tech Road map Poit Es/ma/o Cofidece Iterval
More informationParametric Density Estimation: Maximum Likelihood Estimation
Parametric Desity stimatio: Maimum Likelihood stimatio C6 Today Itroductio to desity estimatio Maimum Likelihood stimatio Itroducto Bayesia Decisio Theory i previous lectures tells us how to desig a optimal
More informationISBN Copyright 2015 The Continental Press, Inc.
TABLE OF CONTENTS Itroductio 3 Format of Books 4 Suggestios for Use 7 Aotated Aswer Key ad Extesio Activities 9 Reproducible Tool Set 183 ISBN 978-0-8454-7897-4 Copyright 2015 The Cotietal Press, Ic. Exceptig
More informationChapter 17 Sampling Distribution Models
Chapter 17 Samplig Distributio Models 353 Chapter 17 Samplig Distributio Models 1. Sed moey. All of the histograms are cetered aroud p 0.05. As gets larger, the shape of the histograms get more uimodal
More informationAPPLIED STATISTICS Complementary Course of BSc Mathematics - IV Semester CUCBCSS Admn onwards Question Bank
Prepared by: Prof (Dr) K.X. Joseph Multiple Choice Questios 1. Statistical populatio may cosists of (a) a ifiite umber of items (b) a fiite umber of items (c) either of (a) or (b) Module - I (d) oe of
More informationIntroduction to Probability and Statistics Chapter 7
Itroductio to Probability ad Statistics Chapter 7 Ammar M. Sarha, asarha@mathstat.dal.ca Departmet of Mathematics ad Statistics, Dalhousie Uiversity Fall Semester 008 Chapter 7 Statistical Itervals Based
More informationDepartment of Mathematics, S.R.K.R. Engineering College, Bhimavaram, A.P., India 2
Skewess Corrected Cotrol charts for two Iverted Models R. Subba Rao* 1, Pushpa Latha Mamidi 2, M.S. Ravi Kumar 3 1 Departmet of Mathematics, S.R.K.R. Egieerig College, Bhimavaram, A.P., Idia 2 Departmet
More informationCHAPTER 8 CONFIDENCE INTERVALS
CHAPTER 8 CONFIDENCE INTERVALS Cofidece Itervals is our first topic i iferetial statistics. I this chapter, we use sample data to estimate a ukow populatio parameter: either populatio mea (µ) or populatio
More informationVariance and Standard Deviation (Tables) Lecture 10
Variace ad Stadard Deviatio (Tables) Lecture 10 Variace ad Stadard Deviatio Theory I this lesso: 1. Calculatig stadard deviatio with ugrouped data.. Calculatig stadard deviatio with grouped data. What
More informationUnbiased estimators Estimators
19 Ubiased estimators I Chapter 17 we saw that a dataset ca be modeled as a realizatio of a radom sample from a probability distributio ad that quatities of iterest correspod to features of the model distributio.
More informationSTRAND: FINANCE. Unit 3 Loans and Mortgages TEXT. Contents. Section. 3.1 Annual Percentage Rate (APR) 3.2 APR for Repayment of Loans
CMM Subject Support Strad: FINANCE Uit 3 Loas ad Mortgages: Text m e p STRAND: FINANCE Uit 3 Loas ad Mortgages TEXT Cotets Sectio 3.1 Aual Percetage Rate (APR) 3.2 APR for Repaymet of Loas 3.3 Credit Purchases
More informationREINSURANCE ALLOCATING RISK
6REINSURANCE Reisurace is a risk maagemet tool used by isurers to spread risk ad maage capital. The isurer trasfers some or all of a isurace risk to aother isurer. The isurer trasferrig the risk is called
More informationFixed Income Securities
Prof. Stefao Mazzotta Keesaw State Uiversity Fixed Icome Securities Sample First Midterm Exam Last Name: First Name: Studet ID Number: Exam time is: 80 miutes. Total poits for this exam is: 400 poits Prelimiaries
More informationRevolving Credit Facility. Flexible Funds for Flexible Needs
Revolvig Credit Facility Flexible Fuds for Flexible Needs Freddie Mac Multifamily Revolvig Credit Facility Compellig Reasos To choose the Revolvig Credit Facility Success i maagig multifamily property
More informationCONSUMER PRICE INDEX
REPUBLIC OF THE MARSHALL ISLANDS CONSUMER PRICE INDEX Ecoomic Policy, Plaig ad Statistics Office Office of the Presidet August 2004 3 rd Quarter Majuro, RMI CPI Costructio ad Methodology Survey Overview:
More informationOverlapping Generations
Eco. 53a all 996 C. Sims. troductio Overlappig Geeratios We wat to study how asset markets allow idividuals, motivated by the eed to provide icome for their retiremet years, to fiace capital accumulatio
More information5 Statistical Inference
5 Statistical Iferece 5.1 Trasitio from Probability Theory to Statistical Iferece 1. We have ow more or less fiished the probability sectio of the course - we ow tur attetio to statistical iferece. I statistical
More informationMATH : EXAM 2 REVIEW. A = P 1 + AP R ) ny
MATH 1030-008: EXAM 2 REVIEW Origially, I was havig you all memorize the basic compoud iterest formula. I ow wat you to memorize the geeral compoud iterest formula. This formula, whe = 1, is the same as
More informationAn Empirical Study of the Behaviour of the Sample Kurtosis in Samples from Symmetric Stable Distributions
A Empirical Study of the Behaviour of the Sample Kurtosis i Samples from Symmetric Stable Distributios J. Marti va Zyl Departmet of Actuarial Sciece ad Mathematical Statistics, Uiversity of the Free State,
More information5. Best Unbiased Estimators
Best Ubiased Estimators http://www.math.uah.edu/stat/poit/ubiased.xhtml 1 of 7 7/16/2009 6:13 AM Virtual Laboratories > 7. Poit Estimatio > 1 2 3 4 5 6 5. Best Ubiased Estimators Basic Theory Cosider agai
More informationVETERINARY PATHOLOGIST EMPLOYER DEMOGRAPHIC SURVEY: ADDENDUM
VETERINARY PATHOLOGIST EMPLOYER DEMOGRAPHIC SURVEY: ADDENDUM Prepared for the America College of Veteriary, the Society of Toxicologic Pathology, ad the America Society for Veteriary Cliical Pathology
More informationTwitter: @Owe134866 www.mathsfreeresourcelibrary.com Prior Kowledge Check 1) State whether each variable is qualitative or quatitative: a) Car colour Qualitative b) Miles travelled by a cyclist c) Favourite
More informationThese characteristics are expressed in terms of statistical properties which are estimated from the sample data.
0. Key Statistical Measures of Data Four pricipal features which characterize a set of observatios o a radom variable are: (i) the cetral tedecy or the value aroud which all other values are buched, (ii)
More informationSUPPLEMENTAL MATERIAL
A SULEMENTAL MATERIAL Theorem (Expert pseudo-regret upper boud. Let us cosider a istace of the I-SG problem ad apply the FL algorithm, where each possible profile A is a expert ad receives, at roud, a
More informationST 305: Exam 2 Fall 2014
ST 305: Exam Fall 014 By hadig i this completed exam, I state that I have either give or received assistace from aother perso durig the exam period. I have used o resources other tha the exam itself ad
More informationStatistics for Business and Economics
Statistics for Busiess ad Ecoomics Chapter 8 Estimatio: Additioal Topics Copright 010 Pearso Educatio, Ic. Publishig as Pretice Hall Ch. 8-1 8. Differece Betwee Two Meas: Idepedet Samples Populatio meas,
More informationA Note About Maximum Likelihood Estimator in Hypergeometric Distribution
Comuicacioes e Estadística Juio 2009, Vol. 2, No. 1 A Note About Maximum Likelihood Estimator i Hypergeometric Distributio Ua ota sobre los estimadores de máxima verosimilitud e la distribució hipergeométrica
More informationMonetary Economics: Problem Set #5 Solutions
Moetary Ecoomics oblem Set #5 Moetary Ecoomics: oblem Set #5 Solutios This problem set is marked out of 1 poits. The weight give to each part is idicated below. Please cotact me asap if you have ay questios.
More informationBIOSTATS 540 Fall Estimation Page 1 of 72. Unit 6. Estimation. Use at least twelve observations in constructing a confidence interval
BIOSTATS 540 Fall 015 6. Estimatio Page 1 of 7 Uit 6. Estimatio Use at least twelve observatios i costructig a cofidece iterval - Gerald va Belle What is the mea of the blood pressures of all the studets
More informationProblem Set 1a - Oligopoly
Advaced Idustrial Ecoomics Sprig 2014 Joha Steek 6 may 2014 Problem Set 1a - Oligopoly 1 Table of Cotets 2 Price Competitio... 3 2.1 Courot Oligopoly with Homogeous Goods ad Differet Costs... 3 2.2 Bertrad
More informationLabour Force Survey in Belarus: determination of sample size, sample design, statistical weighting
Labour Force urvey i Belarus: determiatio of sample size, sample desig, statistical weightig Natallia Boku Belarus tate Ecoomic Uiversity, e-mail: ataliaboku@rambler.ru Abstract The first experiece of
More informationREITInsight. In this month s REIT Insight:
REITIsight Newsletter February 2014 REIT Isight is a mothly market commetary by Resource Real Estate's Global Portfolio Maager, Scott Crowe. It discusses our perspectives o major evets ad treds i real
More informationThis article is part of a series providing
feature Bryce Millard ad Adrew Machi Characteristics of public sector workers SUMMARY This article presets aalysis of public sector employmet, ad makes comparisos with the private sector, usig data from
More information1 Estimating the uncertainty attached to a sample mean: s 2 vs.
Political Sciece 100a/200a Fall 2001 Cofidece itervals ad hypothesis testig, Part I 1 1 Estimatig the ucertaity attached to a sample mea: s 2 vs. σ 2 Recall the problem of descriptive iferece: We wat to
More informationForecasting bad debt losses using clustering algorithms and Markov chains
Forecastig bad debt losses usig clusterig algorithms ad Markov chais Robert J. Till Experia Ltd Lambert House Talbot Street Nottigham NG1 5HF {Robert.Till@uk.experia.com} Abstract Beig able to make accurate
More informationEVIDENCE ON THE DISTRIBUTIONAL EFFECTS OF A LAND VALUE TAX ON RESIDENTIAL HOUSEHOLDS. Elizabeth Plummer
Natioal Tax Joural, March 2010, 63 (1), 63 92 EVIDENCE ON THE DISTRIBUTIONAL EFFECTS OF A LAND VALUE TAX ON RESIDENTIAL HOUSEHOLDS Elizabeth Plummer This study examies how replacig a uiform property tax
More informationBinomial Model. Stock Price Dynamics. The Key Idea Riskless Hedge
Biomial Model Stock Price Dyamics The value of a optio at maturity depeds o the price of the uderlyig stock at maturity. The value of the optio today depeds o the expected value of the optio at maturity
More informationWhen you click on Unit V in your course, you will see a TO DO LIST to assist you in starting your course.
UNIT V STUDY GUIDE Percet Notatio Course Learig Outcomes for Uit V Upo completio of this uit, studets should be able to: 1. Write three kids of otatio for a percet. 2. Covert betwee percet otatio ad decimal
More informationStructuring the Selling Employee/ Shareholder Transition Period Payments after a Closely Held Company Acquisition
Icome Tax Isights Structurig the Sellig Employee/ Shareholder Trasitio Period Paymets after a Closely Held Compay Acquisitio Robert F. Reilly, CPA Corporate acquirers ofte acquire closely held target compaies.
More informationDOWLING COLLEGE: School of Education Department of Educational Administration, Leadership, and Technology
1. Doe 2. Doe 3. Doe 4. Doe DOWLING COLLEGE: School of Educatio Departmet of Educatioal Admiistratio, Leadership, ad Techology 5. Calculate meas ad stadard deviatios for per capita icome ad total reveues
More informationMODIFICATION OF HOLT S MODEL EXEMPLIFIED BY THE TRANSPORT OF GOODS BY INLAND WATERWAYS TRANSPORT
The publicatio appeared i Szoste R.: Modificatio of Holt s model exemplified by the trasport of goods by ilad waterways trasport, Publishig House of Rzeszow Uiversity of Techology No. 85, Maagemet ad Maretig
More informationThe material in this chapter is motivated by Experiment 9.
Chapter 5 Optimal Auctios The material i this chapter is motivated by Experimet 9. We wish to aalyze the decisio of a seller who sets a reserve price whe auctioig off a item to a group of bidders. We begi
More informationMonopoly vs. Competition in Light of Extraction Norms. Abstract
Moopoly vs. Competitio i Light of Extractio Norms By Arkadi Koziashvili, Shmuel Nitza ad Yossef Tobol Abstract This ote demostrates that whether the market is competitive or moopolistic eed ot be the result
More informationSystematic and Complex Sampling!
Systematic ad Complex Samplig! Professor Ro Fricker! Naval Postgraduate School! Moterey, Califoria! Readig Assigmet:! Scheaffer, Medehall, Ott, & Gerow! Chapter 7.1-7.4! 1 Goals for this Lecture! Defie
More informationOptimal Risk Classification and Underwriting Risk for Substandard Annuities
1 Optimal Risk Classificatio ad Uderwritig Risk for Substadard Auities Nadie Gatzert, Uiversity of Erlage-Nürberg Gudru Hoerma, Muich Hato Schmeiser, Istitute of Isurace Ecoomics, Uiversity of St. Galle
More informationWorking conditions in Korea: Survey highlights
Workig coditios i Korea: Survey highlights Itroductio Methodology Mai fidigs Traiig Health ad safety advice Use of iformatio techology Satisfactio with workig coditios Workig time Commetary Aex Wyattville
More information4.5 Generalized likelihood ratio test
4.5 Geeralized likelihood ratio test A assumptio that is used i the Athlete Biological Passport is that haemoglobi varies equally i all athletes. We wish to test this assumptio o a sample of k athletes.
More informationChapter Four 1/15/2018. Learning Objectives. The Meaning of Interest Rates Future Value, Present Value, and Interest Rates Chapter 4, Part 1.
Chapter Four The Meaig of Iterest Rates Future Value, Preset Value, ad Iterest Rates Chapter 4, Part 1 Preview Develop uderstadig of exactly what the phrase iterest rates meas. I this chapter, we see that
More informationRisk Assessment for Project Plan Collapse
518 Proceedigs of the 8th Iteratioal Coferece o Iovatio & Maagemet Risk Assessmet for Project Pla Collapse Naoki Satoh 1, Hiromitsu Kumamoto 2, Norio Ohta 3 1. Wakayama Uiversity, Wakayama Uiv., Sakaedai
More informationSection 3.3 Exercises Part A Simplify the following. 1. (3m 2 ) 5 2. x 7 x 11
123 Sectio 3.3 Exercises Part A Simplify the followig. 1. (3m 2 ) 5 2. x 7 x 11 3. f 12 4. t 8 t 5 f 5 5. 3-4 6. 3x 7 4x 7. 3z 5 12z 3 8. 17 0 9. (g 8 ) -2 10. 14d 3 21d 7 11. (2m 2 5 g 8 ) 7 12. 5x 2
More informationInternational Journal of Management (IJM), ISSN (Print), ISSN (Online) Volume 1, Number 2, July - Aug (2010), IAEME
Iteratioal Joural of Maagemet (IJM), ISSN 0976 6502(Prit), ISSN 0976 6510(Olie) Volume 1, Number 2, July - Aug (2010), pp. 09-13 IAEME, http://www.iaeme.com/ijm.html IJM I A E M E AN ANALYSIS OF STABILITY
More informationSIMPLE INTEREST, COMPOUND INTEREST INCLUDING ANNUITY
Chapter SIMPLE INTEREST, COMPOUND INTEREST INCLUDING ANNUITY 006 November. 8,000 becomes 0,000 i two years at simple iterest. The amout that will become 6,875 i years at the same rate of iterest is:,850
More informationOnline appendices from Counterparty Risk and Credit Value Adjustment a continuing challenge for global financial markets by Jon Gregory
Olie appedices from Couterparty Risk ad Credit Value Adjustmet a APPENDIX 8A: Formulas for EE, PFE ad EPE for a ormal distributio Cosider a ormal distributio with mea (expected future value) ad stadard
More informationQuantitative Analysis
EduPristie www.edupristie.com Modellig Mea Variace Skewess Kurtosis Mea: X i = i Mode: Value that occurs most frequetly Media: Midpoit of data arraged i ascedig/ descedig order s Avg. of squared deviatios
More informationNotes on Expected Revenue from Auctions
Notes o Epected Reveue from Auctios Professor Bergstrom These otes spell out some of the mathematical details about first ad secod price sealed bid auctios that were discussed i Thursday s lecture You
More informationIntroduction to Statistical Inference
Itroductio to Statistical Iferece Fial Review CH1: Picturig Distributios With Graphs 1. Types of Variable -Categorical -Quatitative 2. Represetatios of Distributios (a) Categorical -Pie Chart -Bar Graph
More informationSummary. Recap. Last Lecture. .1 If you know MLE of θ, can you also know MLE of τ(θ) for any function τ?
Last Lecture Biostatistics 60 - Statistical Iferece Lecture Cramer-Rao Theorem Hyu Mi Kag February 9th, 03 If you kow MLE of, ca you also kow MLE of τ() for ay fuctio τ? What are plausible ways to compare
More informationJournal of Statistical Software
JSS Joural of Statistical Software Jue 2007, Volume 19, Issue 6. http://www.jstatsoft.org/ Ratioal Arithmetic Mathematica Fuctios to Evaluate the Oe-sided Oe-sample K-S Cumulative Samplig Distributio J.
More informationCorrelation possibly the most important and least understood topic in finance
Correlatio...... possibly the most importat ad least uderstood topic i fiace 2014 Gary R. Evas. May be used oly for o-profit educatioal purposes oly without permissio of the author. The first exam... Eco
More information