Statistics for Journalism

Size: px
Start display at page:

Download "Statistics for Journalism"

Transcription

1 Statstcs for Jouralsm Fal Eam Studet: Group: Date: Mark the correct aswer wth a X below for each part of Questo 1. Questo 1 a) 1 b) 1 c) 1 d) 1 e) Correct aswer v 1. a) The followg table shows formato about the daly sales of ewspapers for each 1000 habtats of 8 Spash Commutes ad the ecoomc producto of the commuty based o the PIB (Producto Iterór Bruto) per resdet. PIB Sales Fuete: INE. Auaro Estadístco. Supposg a lear relato betwee these varables, we obta the followg regresso le whch eplas the umber of papers sold per 1000 habtats terms of the PIB per resdet 1000 s of euros: y= What would be the predcted sales a commuty wth PIB per resdet equal to euros? a eamples b eamples for each 1000 habtats c eamples d eamples for each 1000 resdets b) I a recet opo poll for the Suday Epress, 11% or people sad that they would vote for the UK Idepedece Party (UKIP) whch wshes the UK to leave Europe. Assumg these results reflect the opo of UK voters, f three voters are chose at radom, what s the probablty (to 3 decmal places) that at least oe of them would vote for the UKIP?. 0,95 1

2 . 0,110. 0,61 v. 0,999 c) The followg resposes come from a late 011 survey by the BBC about Lodoers opos o the Olympcs. Q3. How ected are you about the Olympcs takg place Lodo? Opo Percetage Very ected 30 Farly eted 35 Not very ected 18 Not at all ected 16 Do t kow 01 The survey was carred out usg a sample of 1000 Lodoers. Appromately how may people were farly ected or more? v. 650 d) The followg dagram comes from a 01 artcle the Facal Tmes ettled Vce Rehart does QE really work? ad shows the relato betwee short ad log term terest rates. Whch oe of the followg statemets s correct?. The correlato betwee short ad log term terest rates s egatve ad the covarace s egatve.. The tercept of the regresso le s postve ad the correlato betwee short ad log term terest rates s postve.. The tercept of the regresso le s egatve ad the correlato betwee short ad log term terest rates s postve. v. Noe of the prevous aswers.

3 e) I a recet survey, 100 ctzes of Greece, Spa ad the UK were asked whether they thought the Euro would survve the curret crss. Ther reples are summarzed the followg table. Respose Coutry Greece Spa UK Yes No Do t kow Whch of the followg statemets s correct?. The proporto of people the sample who thk the Euro wll survve s If a perso s chose at radom from the sample, the evets Do t kow ad Come from Spa are depedet.. If a perso chose at radom from the sample comes from a coutry wth the Euro zoe (Greece or Spa), the probablty that they thk the Euro wll ot survve s v. The probablty that a radomly chose perso, who thks the Euro wll ot survve, comes from Spa s 0.05?. I the latest Barometer of the CIS (Aprl 01), oe of the party leaders acheved a pass ratg of 5. The hghest rated was Rosa Dez who acheved a mea ratg of 4.47 wth a stadard devato of.51. The secod rated was Alfredo Perez Rubalcaba wth a mea ratg of 4.11 ad a stadard devato of.74. a. What s the probablty that a radomly chose perso rates Rosa Dez at over 5? (0.75 pots) b. Calculate the probablty that a radomly chose perso gves Alfredo Perez Rubalcaba a ratg of eactly 5. (0.5 pots) c. If three people are chose at radom, what s the probablty that oe of them gve Rosa Dez a pass ratg? (0.5 pots) d. I a sample of 100 people, what would be the epected umber of people who would gve pass grades to Rosa Dez? (0.75 pots) 3

4 3. The followg artcle comes from the MercoPress South Atlatc News Agecy o 3 rd Aprl 01. YPF sezure approved by 6% of Argetes, accordg to opo poll The recet decso by Presdet Crsta Feradez to seze a majorty stake YPF from Spa s Repsol has the approval of 6% of Argetes, whle 3% dsagree accordg to a publc opo poll from Polarquía publshed the Suday edto of La Naco. The poll shows that 6% the captal Bueos Ares ad dfferet ctes of the coutry are very much agreemet wth the decso ad 36% are agreemet. The decso has soured log establshed relatos betwee Argeta ad Spa that has promsed reprsals, ad demads full compesato f the decso s ot revewed. Aother 3% sad they were dsagreemet wth atoalzato, ad 8% rejected pot blak the decso. Asked about the possble mpacts o the Argete ecoomy, 49% sad the decso was postve ad 47% admtted to fears that t would affect egatvely the mage of Argeta overseas. The poll also showed that 49% of resdets from the captal Bueos Ares support the atoalzato, eve whe the mayor of the cty ad the ma oppoet of Crsta Feradez, Maurco Macr rejected the sezure ad wared that the lawmakers from hs party would vote agast the tatve Cogress. I the rest of the coutry the decso had the support of 66% of tervews, where four out of te sad they were very much agreemet. The poll cluded phoe tervews wth over 18 years of age ad the forty ma ctes of Argeta. a. Calculate a 95% cofdece terval for the true proporto of Argetes who thk that the mpact of the atoalzato wll be postve. Is there ay evdece (at a 5% sgfcace level) that ths proporto s dfferet from 50%? Epla your cocluso. (1.5 pots) b. Is there ay evdece that more tha 60% of Argetes approve of the atoalzato of Repsol YPF? Carry out the test at a sgfcace level of 5% ad epla your coclusos. (1.0 pots) 4

5 5 CHULETARIO OFICIAL ) Resultados báscos (basados e ua muestra de tamaño ) X 1 1 ) ( S ) y ( y Cov(X,Y) 1 S S y Cov(X,Y) r(x,y) ) Regresó La recta de mímos cuadrados es y = a + b dode y S S r(x,y) S Cov(X,Y) b ) (b y a ) Itervalos de cofaza de 95% (basada e ua muestra de tamaño N) para a) La meda de ua poblacó ormal (varaza coocda) b) Ua proporcó

6 v) Cotrastes de hpótess de vel de sgfcacó. Z represeta el puto tal que P(Z <Z )=1-dode Z tee ua dstrbucó ormal estádar. a) para la meda de ua poblacó ormal (varaza coocda) H 0 H 1 Regó de rechazo = 0 < 0 = 0 > 0 = 0 0 b) para ua proporcó H 0 H 1 Regó de rechazo p = p 0 p < p 0 p = p 0 p > p 0 p = p 0 p p 0 v) Pot crítcos de la dstrbucó ormal estadár P(Z 1,64) = 0,95 P(Z 1,96) = 0,975. 6

7 TABLAS DE LA DISTRIBUCIÓN NORMAL 7

8 8

Consult the following resources to familiarize yourself with the issues involved in conducting surveys:

Consult the following resources to familiarize yourself with the issues involved in conducting surveys: Cofdece Itervals Learg Objectves: After completo of ths module, the studet wll be able to costruct ad terpret cofdece tervals crtcally evaluate the outcomes of surveys terpret the marg of error the cotext

More information

Mathematics 1307 Sample Placement Examination

Mathematics 1307 Sample Placement Examination Mathematcs 1307 Sample Placemet Examato 1. The two les descrbed the followg equatos tersect at a pot. What s the value of x+y at ths pot of tersecto? 5x y = 9 x 2y = 4 A) 1/6 B) 1/3 C) 0 D) 1/3 E) 1/6

More information

Gene Expression Data Analysis (II) statistical issues in spotted arrays

Gene Expression Data Analysis (II) statistical issues in spotted arrays STATC4 Sprg 005 Lecture Data ad fgures are from Wg Wog s computatoal bology course at Harvard Gee Expresso Data Aalyss (II) statstcal ssues spotted arrays Below shows part of a result fle from mage aalyss

More information

SCEA CERTIFICATION EXAM: PRACTICE QUESTIONS AND STUDY AID

SCEA CERTIFICATION EXAM: PRACTICE QUESTIONS AND STUDY AID SCEA CERTIFICATION EAM: PRACTICE QUESTIONS AND STUDY AID Lear Regresso Formulas Cheat Sheet You ma use the followg otes o lear regresso to work eam questos. Let be a depedet varable ad be a depedet varable

More information

Types of Sampling Plans. Types of Sampling Plans. Sampling Procedures. Probability Samples -Simple Random sample -Stratified sample -Cluster sample

Types of Sampling Plans. Types of Sampling Plans. Sampling Procedures. Probability Samples -Simple Random sample -Stratified sample -Cluster sample Samplg Procedures Defe the Populato Idetfy the Samplg Frame Select a Samplg Procedure Determe the Sample Sze Select the Sample Elemets Collect the Data Types of Samplg Plas o-probablty Samples -Coveece

More information

Random Variables. Discrete Random Variables. Example of a random variable. We will look at: Nitrous Oxide Example. Nitrous Oxide Example

Random Variables. Discrete Random Variables. Example of a random variable. We will look at: Nitrous Oxide Example. Nitrous Oxide Example Radom Varables Dscrete Radom Varables Dr. Tom Ilveto BUAD 8 Radom Varables varables that assume umercal values assocated wth radom outcomes from a expermet Radom varables ca be: Dscrete Cotuous We wll

More information

Probability and Statistical Methods. Chapter 8 Fundamental Sampling Distributions

Probability and Statistical Methods. Chapter 8 Fundamental Sampling Distributions Math 3 Probablty ad Statstcal Methods Chapter 8 Fudametal Samplg Dstrbutos Samplg Dstrbutos I the process of makg a ferece from a sample to a populato we usually calculate oe or more statstcs, such as

More information

Probability and Statistical Methods. Chapter 8 Fundamental Sampling Distributions

Probability and Statistical Methods. Chapter 8 Fundamental Sampling Distributions Math 3 Probablty ad Statstcal Methods Chapter 8 Fudametal Samplg Dstrbutos Samplg Dstrbutos I the process of makg a ferece from a sample to a populato we usually calculate oe or more statstcs, such as

More information

? Economical statistics

? Economical statistics Probablty calculato ad statstcs Probablty calculato Mathematcal statstcs Appled statstcs? Ecoomcal statstcs populato statstcs medcal statstcs etc. Example: blood type Dstrbuto A AB B Elemetary evets: A,

More information

CHAPTER - IV STANDARDIZED CUSUM MEDIAN CONTROL CHART

CHAPTER - IV STANDARDIZED CUSUM MEDIAN CONTROL CHART A Study o Process Varablty usg CUSUM ad Fuzzy Cotrol Charts Ph.D Thess CHAPTER - IV STANDARDIZED CUSUM MEDIAN CONTROL CHART. Itroducto: I motorg e process mea, e Mea ( X ) cotrol charts, ad cumulatve sum

More information

A Test of Normality. Textbook Reference: Chapter 14.2 (eighth edition, pages 591 3; seventh edition, pages 624 6).

A Test of Normality. Textbook Reference: Chapter 14.2 (eighth edition, pages 591 3; seventh edition, pages 624 6). A Test of Normalty Textbook Referece: Chapter 4. (eghth edto, pages 59 ; seveth edto, pages 64 6). The calculato of p-values for hypothess testg typcally s based o the assumpto that the populato dstrbuto

More information

1036: Probability & Statistics

1036: Probability & Statistics 036: Probablty & Statstcs Lecture 9 Oe- ad Two-Sample Estmato Problems Prob. & Stat. Lecture09 - oe-/two-sample estmato cwlu@tws.ee.ctu.edu.tw 9- Statstcal Iferece Estmato to estmate the populato parameters

More information

= 1. UCLA STAT 13 Introduction to Statistical Methods for the Life and Health Sciences. Parameters and Statistics. Measures of Centrality

= 1. UCLA STAT 13 Introduction to Statistical Methods for the Life and Health Sciences. Parameters and Statistics. Measures of Centrality UCLA STAT Itroducto to Statstcal Methods for the Lfe ad Health Sceces Istructor: Ivo Dov, Asst. Prof. of Statstcs ad Neurolog Teachg Assstats: Brad Shaata & Tffa Head Uverst of Calfora, Los Ageles, Fall

More information

Deriving & Understanding the Variance Formulas

Deriving & Understanding the Variance Formulas Dervg & Uderstadg the Varace Formulas Ma H. Farrell BUS 400 August 28, 205 The purpose of ths hadout s to derve the varace formulas that we dscussed class ad show why take the form they do. I class we

More information

Overview. Linear Models Connectionist and Statistical Language Processing. Numeric Prediction. Example

Overview. Linear Models Connectionist and Statistical Language Processing. Numeric Prediction. Example Overvew Lear Models Coectost ad Statstcal Laguage Processg Frak Keller keller@col.u-sb.de Computerlgustk Uverstät des Saarlades classfcato vs. umerc predcto lear regresso least square estmato evaluatg

More information

Solutions to Problems

Solutions to Problems Solutos to Problems ( Pt Pt + Ct) P5-. LG : Rate of retur: rt Pt Basc ($,000 $0,000 + $,500) a. Ivestmet X: Retur.50% $0,000 Ivestmet Y: Retur ($55,000 $55,000 + $6,800).36% $55,000 b. Ivestmet X should

More information

Valuation of Asian Option

Valuation of Asian Option Mälardales Uversty västerås 202-0-22 Mathematcs ad physcs departmet Project aalytcal face I Valuato of Asa Opto Q A 90402-T077 Jgjg Guo89003-T07 Cotet. Asa opto------------------------------------------------------------------3

More information

MEASURING THE FOREIGN EXCHANGE RISK LOSS OF THE BANK

MEASURING THE FOREIGN EXCHANGE RISK LOSS OF THE BANK Gabrel Bstrceau, It.J.Eco. es., 04, v53, 7 ISSN: 9658 MEASUING THE FOEIGN EXCHANGE ISK LOSS OF THE BANK Gabrel Bstrceau Ecoomst, Ph.D. Face Natoal Bak of omaa Bucharest, Moetary Polcy Departmet, 5 Lpsca

More information

IEOR 130 Methods of Manufacturing Improvement Fall, 2017 Prof. Leachman Solutions to First Homework Assignment

IEOR 130 Methods of Manufacturing Improvement Fall, 2017 Prof. Leachman Solutions to First Homework Assignment IEOR 130 Methods of Maufacturg Improvemet Fall, 2017 Prof. Leachma Solutos to Frst Homework Assgmet 1. The scheduled output of a fab a partcular week was as follows: Product 1 1,000 uts Product 2 2,000

More information

Chapter 4. More Interest Formulas

Chapter 4. More Interest Formulas Chapter 4 More Iterest ormulas Uform Seres Compoud Iterest ormulas Why? May paymets are based o a uform paymet seres. e.g. automoble loas, house paymets, ad may other loas. 2 The Uform aymet Seres s 0

More information

Chapter 4. More Interest Formulas

Chapter 4. More Interest Formulas Chapter 4 More Iterest ormulas Uform Seres Compoud Iterest ormulas Why? May paymets are based o a uform paymet seres. e.g. automoble loas, house paymets, ad may other loas. 2 The Uform aymet Seres s 0

More information

GAUTENG DEPARTMENT OF EDUCATION SENIOR SECONDARY INTERVENTION PROGRAMME MATHEMATICS GRADE 12 SESSION 3 (LEARNER NOTES)

GAUTENG DEPARTMENT OF EDUCATION SENIOR SECONDARY INTERVENTION PROGRAMME MATHEMATICS GRADE 12 SESSION 3 (LEARNER NOTES) MATHEMATICS GRADE SESSION 3 (LEARNER NOTES) TOPIC 1: FINANCIAL MATHEMATICS (A) Learer Note: Ths sesso o Facal Mathematcs wll deal wth future ad preset value autes. A future value auty s a savgs pla for

More information

b. (6 pts) State the simple linear regression models for these two regressions: Y regressed on X, and Z regressed on X.

b. (6 pts) State the simple linear regression models for these two regressions: Y regressed on X, and Z regressed on X. Mat 46 Exam Sprg 9 Mara Frazer Name SOLUTIONS Solve all problems, ad be careful ot to sped too muc tme o a partcular problem. All ecessary SAS fles are our usual folder (P:\data\mat\Frazer\Regresso). You

More information

Inferential: methods using sample results to infer conclusions about a larger population.

Inferential: methods using sample results to infer conclusions about a larger population. Chapter 1 Def : Statstcs: 1) are commoly kow as umercal facts ) s a feld of dscple or study Here, statstcs s about varato. 3 ma aspects of statstcs: 1) Desg ( Thk ): Plag how to obta data to aswer questos.

More information

The Measurement and Control of Chinese Administrative Expenses: Perspective into Administrative Expenses

The Measurement and Control of Chinese Administrative Expenses: Perspective into Administrative Expenses Joural of Poltcs ad Law Jue, 9 The Measuremet ad Cotrol of Chese Admstratve Epeses: Perspectve to Admstratve Epeses Xagzhou He Zhejag Uversty Hagzhou 38, Cha E-mal: hez5@6.com Natoal Natural Scece Foudato

More information

TOPIC 7 ANALYSING WEIGHTED DATA

TOPIC 7 ANALYSING WEIGHTED DATA TOPIC 7 ANALYSING WEIGHTED DATA You do t have to eat the whole ox to kow that the meat s tough. Samuel Johso Itroducto dfferet aalyss for sample data Up utl ow, all of the aalyss techques have oly dealt

More information

Forecasting the Movement of Share Market Price using Fuzzy Time Series

Forecasting the Movement of Share Market Price using Fuzzy Time Series Iteratoal Joural of Fuzzy Mathematcs ad Systems. Volume 1, Number 1 (2011), pp. 73-79 Research Ida Publcatos http://www.rpublcato.com Forecastg the Movemet of Share Market Prce usg Fuzzy Tme Seres B.P.

More information

FINANCIAL MATHEMATICS : GRADE 12

FINANCIAL MATHEMATICS : GRADE 12 FINANCIAL MATHEMATICS : GRADE 12 Topcs: 1 Smple Iterest/decay 2 Compoud Iterest/decay 3 Covertg betwee omal ad effectve 4 Autes 4.1 Future Value 4.2 Preset Value 5 Skg Fuds 6 Loa Repaymets: 6.1 Repaymets

More information

STATIC GAMES OF INCOMPLETE INFORMATION

STATIC GAMES OF INCOMPLETE INFORMATION ECON 10/410 Decsos, Markets ad Icetves Lecture otes.11.05 Nls-Herk vo der Fehr SAIC GAMES OF INCOMPLEE INFORMAION Itroducto Complete formato: payoff fuctos are commo kowledge Icomplete formato: at least

More information

Supplemental notes for topic 9: April 4, 6

Supplemental notes for topic 9: April 4, 6 Sta-30: Probablty Sprg 017 Supplemetal otes for topc 9: Aprl 4, 6 9.1 Polyomal equaltes Theorem (Jese. If φ s a covex fucto the φ(ex Eφ(x. Theorem (Beaymé-Chebyshev. For ay radom varable x, ɛ > 0 P( x

More information

FINANCIAL MATHEMATICS GRADE 11

FINANCIAL MATHEMATICS GRADE 11 FINANCIAL MATHEMATICS GRADE P Prcpal aout. Ths s the orgal aout borrowed or vested. A Accuulated aout. Ths s the total aout of oey pad after a perod of years. It cludes the orgal aout P plus the terest.

More information

- Inferential: methods using sample results to infer conclusions about a larger pop n.

- Inferential: methods using sample results to infer conclusions about a larger pop n. Chapter 6 Def : Statstcs: are commoly kow as umercal facts. s a feld of dscple or study. I ths class, statstcs s the scece of collectg, aalyzg, ad drawg coclusos from data. The methods help descrbe ad

More information

The Consumer Price Index for All Urban Consumers (Inflation Rate)

The Consumer Price Index for All Urban Consumers (Inflation Rate) The Cosumer Prce Idex for All Urba Cosumers (Iflato Rate) Itroducto: The Cosumer Prce Idex (CPI) s the measure of the average prce chage of goods ad servces cosumed by Iraa households. Ths measure, as

More information

Application of Portfolio Theory to Support Resource Allocation Decisions for Biosecurity

Application of Portfolio Theory to Support Resource Allocation Decisions for Biosecurity Applcato of Portfolo Theory to Support Resource Allocato Decsos for Bosecurty Paul Mwebaze Ecoomst 11 September 2013 CES/BIOSECURITY FLAGSHIP Presetato outle The resource allocato problem What ca ecoomcs

More information

THE NPV CRITERION FOR VALUING INVESTMENTS UNDER UNCERTAINTY

THE NPV CRITERION FOR VALUING INVESTMENTS UNDER UNCERTAINTY Professor Dael ARMANU, PhD Faculty of Face, Isurace, Baks ad Stock xchage The Bucharest Academy of coomc Studes coomst Leoard LACH TH CRITRION FOR VALUING INVSTMNTS UNDR UNCRTAINTY Abstract. Corporate

More information

Sample Survey Design

Sample Survey Design Sample Survey Desg A Hypotetcal Exposure Scearo () Assume we kow te parameters of a worker s exposure dstrbuto of 8-our TWAs to a cemcal. As t appes, te worker as four dfferet types of days wt regard to

More information

Management Science Letters

Management Science Letters Maagemet Scece Letters (0) 355 36 Cotets lsts avalable at GrowgScece Maagemet Scece Letters homepage: www.growgscece.com/msl A tellget techcal aalyss usg eural etwork Reza Rae a Shapour Mohammad a ad Mohammad

More information

Variance Covariance (Delta Normal) Approach of VaR Models: An Example From Istanbul Stock Exchange

Variance Covariance (Delta Normal) Approach of VaR Models: An Example From Istanbul Stock Exchange ISSN 2222-697 (Paper) ISSN 2222-2847 (Ole) Vol.7, No.3, 206 Varace Covarace (Delta Normal) Approach of VaR Models: A Example From Istabul Stock Exchage Dr. Ihsa Kulal Iformato ad Commucato Techologes Authorty,

More information

LECTURE 5: Quadratic classifiers

LECTURE 5: Quadratic classifiers LECURE 5: Quadratc classfers Bayes classfers for Normally dstrbuted classes Case : σ I Case : ( daoal) Case : ( o-daoal) Case : σ I Case 5: j eeral case Numercal example Lear ad quadratc classfers: coclusos

More information

Sorting. Data Structures LECTURE 4. Comparison-based sorting. Sorting algorithms. Quick-Sort. Example (1) Pivot

Sorting. Data Structures LECTURE 4. Comparison-based sorting. Sorting algorithms. Quick-Sort. Example (1) Pivot Data Structures, Sprg 004. Joskowcz Data Structures ECUE 4 Comparso-based sortg Why sortg? Formal aalyss of Quck-Sort Comparso sortg: lower boud Summary of comparso-sortg algorthms Sortg Defto Iput: A

More information

The Firm. The Firm. Maximizing Profits. Decisions. ECON 370: Microeconomic Theory Summer 2004 Rice University Stanley Gilbert

The Firm. The Firm. Maximizing Profits. Decisions. ECON 370: Microeconomic Theory Summer 2004 Rice University Stanley Gilbert The Frm The Frm ECON 370: Mcroecoomc Theory Summer 004 Rce Uversty Staley Glbert A Frm s a mechasm for covertg labor, captal ad raw materals to desrable goods A frm s owed by cosumers ad operated for the

More information

CHAPTER 8. r E( r ) m e. Reduces the number of inputs for diversification. Easier for security analysts to specialize

CHAPTER 8. r E( r ) m e. Reduces the number of inputs for diversification. Easier for security analysts to specialize CHATE 8 Idex odels cgra-hll/ir Copyrght 0 by The cgra-hll Compaes, Ic. All rghts reserved. 8- Advatages of the Sgle Idex odel educes the umber of puts for dversfcato Easer for securty aalysts to specalze

More information

Confidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the.

Confidence 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 information

0.07 (12) i 1 1 (12) 12n. *Note that N is always the number of payments, not necessarily the number of years. Also, for

0.07 (12) i 1 1 (12) 12n. *Note that N is always the number of payments, not necessarily the number of years. Also, for Chapter 3, Secto 2 1. (S13HW) Calculate the preset value for a auty that pays 500 at the ed of each year for 20 years. You are gve that the aual terest rate s 7%. 20 1 v 1 1.07 PV Qa Q 500 5297.01 0.07

More information

Robust Statistical Analysis of Long-Term Performance For Sharia-Compliant Companies in Malaysia Stock Exchange

Robust Statistical Analysis of Long-Term Performance For Sharia-Compliant Companies in Malaysia Stock Exchange Iteratoal Joural of Maagemet Scece ad Busess Admstrato Volume 3, Issue 3, March 07, Pages 49-66 DOI: 0.8775/jmsba.849-5664-549.04.33.006 URL: http://dx.do.org/0.8775/jmsba.849-5664-549.04.33.006 Robust

More information

Measures of Dispersion

Measures of Dispersion Chapter IV Meaure of Dpero R. 4.. The meaure of locato cate the geeral magtue of the ata a locate oly the cetre of a trbuto. They o ot etablh the egree of varablty or the prea out or catter of the vual

More information

8.0% E(R) 6.0% Lend. Borrow 4.0% 2.0% rf rf 0.0% 0.0% 1.0% 2.0% 3.0% 4.0% STD(R) E(R) Long A and Short B. Long A and Long B. Short A and Long B

8.0% E(R) 6.0% Lend. Borrow 4.0% 2.0% rf rf 0.0% 0.0% 1.0% 2.0% 3.0% 4.0% STD(R) E(R) Long A and Short B. Long A and Long B. Short A and Long B F8000 Valuato of Facal ssets Sprg Semester 00 Dr. Isabel Tkatch ssstat Professor of Face Ivestmet Strateges Ledg vs. orrowg rsk-free asset) Ledg: a postve proporto s vested the rsk-free asset cash outflow

More information

AMS Final Exam Spring 2018

AMS Final Exam Spring 2018 AMS57.1 Fal Exam Sprg 18 Name: ID: Sgature: Istructo: Ths s a close book exam. You are allowed two pages 8x11 formula sheet (-sded. No cellphoe or calculator or computer or smart watch s allowed. Cheatg

More information

The Complexity of General Equilibrium

The Complexity of General Equilibrium Prof. Ja Bhattachara Eco --Sprg 200 Welfare Propertes of Market Outcomes Last tme, we covered equlbrum oe market partal equlbrum. We foud that uder perfect competto, the equlbrum prce ad quatt mamzed the

More information

Monetary fee for renting or loaning money.

Monetary fee for renting or loaning money. Ecoomcs Notes The follow otes are used for the ecoomcs porto of Seor Des. The materal ad examples are extracted from Eeer Ecoomc alyss 6 th Edto by Doald. Newa, Eeer ress. Notato Iterest rate per perod.

More information

ST 305: Exam 2 Fall 2014

ST 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 information

The Prediction Error of Bornhuetter-Ferguson

The Prediction Error of Bornhuetter-Ferguson The Predcto Error of Borhuetter-Ferguso Thomas Mac Abstract: Together wth the Cha Ladder (CL method, the Borhuetter-Ferguso ( method s oe of the most popular clams reservg methods. Whereas a formula for

More information

Notes on Expected Revenue from Auctions

Notes 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 information

Today: Finish Chapter 9 (Sections 9.6 to 9.8 and 9.9 Lesson 3)

Today: 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 information

CREDIT MANAGEMENT 3 - (SWC) CRM33B3 FINAL ASSESSMENT OPPORTUNITY. Date of examination: 5 NOVEMBER 2015

CREDIT MANAGEMENT 3 - (SWC) CRM33B3 FINAL ASSESSMENT OPPORTUNITY. Date of examination: 5 NOVEMBER 2015 Departmet of Commercal Accoutg CREDIT MANAGEMENT 3 - (SWC) CRM33B3 FINAL ASSESSMENT OPPORTUNITY Date of examato: 5 NOVEMBER 05 Tme: 3 hours Marks: 00 Assessor: Iteral Moderator: Exteral Moderator: Fred

More information

The Application of Asset Pricing to Portfolio Management

The Application of Asset Pricing to Portfolio Management Clemso Ecoomcs The Applcato of Asset Prcg to Portfolo Maagemet The Nature of the Problem Portfolo maagers have two basc problems. Frst they must determe whch assets to hold a portfolo, ad secod, they must

More information

Lecture 9 February 21

Lecture 9 February 21 Math 239: Dscrete Mathematcs for the Lfe Sceces Sprg 2008 Lecture 9 February 21 Lecturer: Lor Pachter Scrbe/ Edtor: Sudeep Juvekar/ Alle Che 9.1 What s a Algmet? I ths lecture, we wll defe dfferet types

More information

Actuarial principles of the cotton insurance in Uzbekistan

Actuarial principles of the cotton insurance in Uzbekistan Actuaral prcples of the cotto surace Uzeksta Topc : Rsk evaluato Shamsuddov Bakhodr The Tashket rach of Russa ecoomc academy, the departmet of hgher mathematcs ad formato techology 763, Uzekstasky street

More information

S. No. 1. Yemo No. Promoter and Promoter Group Public Group Promoter and Promoter Group

S. No. 1. Yemo No. Promoter and Promoter Group Public Group Promoter and Promoter Group .,. L'. Name of Lsted Etty:GTPL Hathway Lmted. Scrp Code/Name of Scrp/Class of Securty: GTPL. Share Holdg Patter Fled uder: b. Share Holdg Patter as o : Mar8 5. Declarato: The Lsted etty s requred to submt

More information

Accounting 303 Exam 2, Chapters 4, 6, and 18A Fall 2014

Accounting 303 Exam 2, Chapters 4, 6, and 18A Fall 2014 Accoutg 303 Exam 2, Chapters 4, 6, ad 18A Fall 2014 Name Row I. Multple Choce Questos. (2 pots each, 34 pots total) Read each questo carefully ad dcate your aswer by crclg the letter precedg the oe best

More information

Review. Statistics and Quantitative Analysis U4320. Review: Sampling. Review: Sampling (cont.) Population and Sample Estimates:

Review. Statistics and Quantitative Analysis U4320. Review: Sampling. Review: Sampling (cont.) Population and Sample Estimates: Stattc ad Quattatve Aaly U430 Segmet 6: Cofdece Iterval Prof. Shary O Hallora URL: http://www.columba.edu/tc/pa/u430y-003/ Revew Populato ad Sample Etmate: Populato Sample N X X Mea = = µ = X = N Varace

More information

Poverty indices. P(k;z; α ) = P(k;z; α ) /(z) α. If you wish to compute the FGT index of poverty, follow these steps:

Poverty indices. P(k;z; α ) = P(k;z; α ) /(z) α. If you wish to compute the FGT index of poverty, follow these steps: Poverty dces DAD offers four possbltes for fxg the poverty le: - A determstc poverty le set by the user. 2- A poverty le equal to a proporto l of the mea. 3- A poverty le equal to a proporto m of a quatle

More information

BASIC STATISTICS ECOE 1323

BASIC 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 information

Portfolio Optimization via Pair Copula-GARCH-EVT-CVaR Model

Portfolio Optimization via Pair Copula-GARCH-EVT-CVaR Model Avalable ole at www.scecedrect.com Systems Egeerg Proceda 2 (2011) 171 181 Portfolo Optmzato va Par Copula-GARCH-EVT-CVaR Model Lg Deg, Chaoqu Ma, Weyu Yag * Hua Uversty, Hua, Chagsha 410082, PR Cha Abstract

More information

Estimating Proportions with Confidence

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 information

COMPARISON OF APPROACHES TO TESTING EQUALITY OF EXPECTATIONS AMONG SAMPLES FROM POISSON AND NEGATIVE BINOMIAL DISTRIBUTION

COMPARISON OF APPROACHES TO TESTING EQUALITY OF EXPECTATIONS AMONG SAMPLES FROM POISSON AND NEGATIVE BINOMIAL DISTRIBUTION ACTA UNIVERSITATIS AGRICULTURAE ET SILVICULTURAE MENDELIANAE BRUNENSIS Volume 66 0 Number 4, 08 https://do.org/0.8/actau08660405 COMPARISON OF APPROACHES TO TESTING EQUALITY OF EXPECTATIONS AMONG SAMPLES

More information

0.07. i PV Qa Q Q i n. Chapter 3, Section 2

0.07. i PV Qa Q Q i n. Chapter 3, Section 2 Chapter 3, Secto 2 1. (S13HW) Calculate the preset value for a auty that pays 500 at the ed of each year for 20 years. You are gve that the aual terest rate s 7%. 20 1 v 1 1.07 PV Qa Q 500 5297.01 0.07

More information

PORTFOLIO OPTIMIZATION IN THE FRAMEWORK MEAN VARIANCE -VAR

PORTFOLIO OPTIMIZATION IN THE FRAMEWORK MEAN VARIANCE -VAR Lecturer Floret SERBAN, PhD Professor Vorca STEFANESCU, PhD Departmet of Mathematcs The Bucharest Academy of Ecoomc Studes Professor Massmlao FERRARA, PhD Departmet of Mathematcs Uversty of Reggo Calabra,

More information

. (The calculated sample mean is symbolized by x.)

. (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 information

Measuring Restrictiveness of Agricultural Trade Policies in Iran

Measuring Restrictiveness of Agricultural Trade Policies in Iran World Appled Sceces Joural 19 (3): 34-39, 01 ISSN 1818-495; IDOSI Publcatos, 01 DOI: 10.589/dos.wasj.01.19.03.1006 Measurg Restrctveess of Agrcultural Trade Polces Ira 1 1 Ghasem Norouz, Reza Moghaddas

More information

Math 124: Lecture for Week 10 of 17

Math 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 information

ON MAXIMAL IDEAL OF SKEW POLYNOMIAL RINGS OVER A DEDEKIND DOMAIN

ON MAXIMAL IDEAL OF SKEW POLYNOMIAL RINGS OVER A DEDEKIND DOMAIN Far East Joural of Mathematcal Sceces (FJMS) Volume, Number, 013, Pages Avalable ole at http://pphmj.com/jourals/fjms.htm Publshed by Pushpa Publshg House, Allahabad, INDIA ON MAXIMAL IDEAL OF SKEW POLYNOMIAL

More information

May 2005 Exam Solutions

May 2005 Exam Solutions May 005 Exam Soluto 1 E Chapter 6, Level Autes The preset value of a auty-mmedate s: a s (1 ) v s By specto, the expresso above s ot equal to the expresso Choce E. Soluto C Chapter 1, Skg Fud The terest

More information

Measuring the degree to which probability weighting affects risk-taking. Behavior in financial decisions

Measuring the degree to which probability weighting affects risk-taking. Behavior in financial decisions Joural of Face ad Ivestmet Aalyss, vol., o.2, 202, -39 ISSN: 224-0988 (prt verso), 224-0996 (ole) Iteratoal Scetfc Press, 202 Measurg the degree to whch probablty weghtg affects rsk-takg Behavor facal

More information

Chapter 8. Confidence Interval Estimation. Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 8, Slide 1

Chapter 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 information

A point estimate is the value of a statistic that estimates the value of a parameter.

A 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 information

Optimal Reliability Allocation

Optimal Reliability Allocation Optmal Relablty Allocato Yashwat K. Malaya malaya@cs.colostate.edu Departmet of Computer Scece Colorado State Uversty Relablty Allocato Problem Allocato the relablty values to subsystems to mmze the total

More information

Classification of Firms into Industries Using Market Data. Michael J. Gibbs. and. Dan W. French. University of Missouri

Classification of Firms into Industries Using Market Data. Michael J. Gibbs. and. Dan W. French. University of Missouri 1 Classfcato of Frms to dustres Usg Market Data Mchael J. Gbbs ad Da W. Frech Uversty of Mssour Cotact: Da W. Frech Departmet of Face Robert J. Trulaske, Sr. College of Busess Uversty of Mssour Columba,

More information

ANALYSING COMMON STOCKS PERFORMANCE FROM THE OPTIMAL EX-POST PORTFOLIO WEIGHTS. This version: October 2014

ANALYSING COMMON STOCKS PERFORMANCE FROM THE OPTIMAL EX-POST PORTFOLIO WEIGHTS. This version: October 2014 ANALYSING COON STOCKS PERFORANCE FRO THE OPTIAL EX-POST PORTFOLIO WEIGHTS d Face Forum Zaragoza 0 th November 014 Ths verso: October 014 ara-teresa Bosch-Bada Vstg Researcher Uverstat de Groa Departmet

More information

Emergency Food Security Assessments (EFSAs) Technical Guidance Sheet No. 11 1

Emergency Food Security Assessments (EFSAs) Technical Guidance Sheet No. 11 1 Emergecy Food Securty Assessmets (EFSAs) Techcal gudace sheet. Usg the T-square samplg method to estmate populato sze, demographcs ad other characterstcs emergecy food securty assessmets (EFSAs) Table

More information

Standard Deviations for Normal Sampling Distributions are: For proportions For means _

Standard 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 information

Method for Assessment of Sectoral Efficiency of Investments Based on Input-Output Models 1

Method for Assessment of Sectoral Efficiency of Investments Based on Input-Output Models 1 Global Joural of Pure ad Appled Mathematcs. ISSN 0973-1768 Volume 12, Number 1 (2016), pp. 19-32 Research Ida Publcatos http://www.rpublcato.com Method for Assessmet of Sectoral Effcecy of Ivestmets Based

More information

Mathematical Background and Algorithms

Mathematical Background and Algorithms (Scherhet ud Zuverlässgket egebetteter Systeme) Fault Tree Aalyss Mathematcal Backgroud ad Algorthms Prof. Dr. Lggesmeyer, 0 Deftos of Terms Falure s ay behavor of a compoet or system that devates from

More information

Lecture 5: Sampling Distribution

Lecture 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 information

1 Random Variables and Key Statistics

1 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 information

The Research on Credit Risk Assessment Model of Agriculture-Related Organizations Based on Set of Theoretical

The Research on Credit Risk Assessment Model of Agriculture-Related Organizations Based on Set of Theoretical Maagemet Scece ad Egeerg Vol. 6, No. 4, 202, pp. 5-9 DOI:0.3968/j.mse.93035X2020604.805 ISSN 93-034 [Prt] ISSN 93-035X [Ole] www.cscaada.et www.cscaada.org The Research o Credt Rsk Assessmet Model of Agrculture-Related

More information

Determination of Optimal Portfolio by Using Tangency Portfolio and Sharpe Ratio

Determination of Optimal Portfolio by Using Tangency Portfolio and Sharpe Ratio Research Joural of Face ad Accoutg ISSN 2222-697 (Paper) ISSN 2222-2847 (Ole) Vol.7, No.5, 206 Determato of Optmal Portfolo by Usg Tagecy Portfolo ad Sharpe Rato Dr. Haka Blr Bahçeşehr Uversty, Turkey

More information

B = A x z

B = 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 information

6. Loss systems. ELEC-C7210 Modeling and analysis of communication networks 1

6. Loss systems. ELEC-C7210 Modeling and analysis of communication networks 1 ELEC-C72 Modelg ad aalyss of commucato etwors Cotets Refresher: Smple teletraffc model Posso model customers, servers Applcato to flow level modellg of streamg data traffc Erlag model customers, ; servers

More information

The Gravity Equation. The gravity equation. Generally the equation is formulated as the relationship:

The Gravity Equation. The gravity equation. Generally the equation is formulated as the relationship: The Gravty Euato I emrcal trade ecoomc, the gravty euato ha a etablhed role a a workhore model. The relatoh ay that blateral trade betwee ay two coutre ad,, a otve fucto of the roduct of the GDP the two

More information

Decoupling and Contagion

Decoupling and Contagion Decouplg ad Cotago By ANTON KORINEK, AGUSTÍN ROITMAN AND CARLOS A. VÉGH The acal crss that has egulfed the world over the past three years started out a relatvely small set of sectors a select umber of

More information

DEGRESSIVE PROPORTIONALITY IN THE EUROPEAN PARLIAMENT

DEGRESSIVE PROPORTIONALITY IN THE EUROPEAN PARLIAMENT M A T H E M A T I C A L E C O N O M I C S No. 7(4) 20 DEGRESSIVE PROPORTIONALITY IN THE EUROPEAN PARLIAMENT Katarzya Cegełka Abstract. The dvso of madates to the Europea Parlamet has posed dffcultes sce

More information

Comparison between the short-term observed and long-term estimated wind power density using Artificial Neural Networks.

Comparison between the short-term observed and long-term estimated wind power density using Artificial Neural Networks. Comparso betwee the short-term observed ad log-term estmated wd power desty usg Artfcal Neural Networks. A case study S Velázquez, JA. Carta 2 Departmet of Electrocs ad Automatcs Egeerg, Uversty of Las

More information

Jewelry as a Kind of Household Savings of Uzbekistan

Jewelry as a Kind of Household Savings of Uzbekistan Advaces Ecoomcs ad Busess 5(6): 346-35, 7 DOI:.389/aeb.7.565 http://www.hrpub.org Jewelry as a Kd of Household Savgs of Uzbeksta Ia Steceko,*, Avar Irchaev Baltc Iteratoal Academy, Doctoral Program, Regoal

More information

Accounting 303 Exam 2, Chapters 5, 6, 7 Fall 2015

Accounting 303 Exam 2, Chapters 5, 6, 7 Fall 2015 Accoutg 303 Exam 2, Chapters 5, 6, 7 Fall 2015 Name Row I. Multple Choce Questos. (2 pots each, 30 pots total) Read each questo carefully ad dcate your aswer by crclg the letter precedg the oe best aswer.

More information

ETSI TS V1.2.1 ( )

ETSI TS V1.2.1 ( ) TS 0 50-6 V.. (004-0) Techcal Specfcato Speech Processg, Trasmsso ad Qualty Aspects (STQ); QoS aspects for popular servces GSM ad 3G etworks; Part 6: Post processg ad statstcal methods TS 0 50-6 V.. (004-0)

More information

Basic formula for confidence intervals. Formulas for estimating population variance Normal Uniform Proportion

Basic 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 information

1. Suppose X is a variable that follows the normal distribution with known standard deviation σ = 0.3 but unknown mean µ.

1. 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 information

DO ENVIOUS CEOS CAUSE MERGER WAVES?

DO ENVIOUS CEOS CAUSE MERGER WAVES? Forthcomg Revew of Facal Studes DO ENVIOUS CEOS CAUSE MERGER WAVES? by Aad M. Goel * ad Aja V. Thakor ** Ackowledgmet: We thak a aoymous referee ad partcularly Matthew Spegel (the edtor) for helpful commets

More information

MOMENTS EQUALITIES FOR NONNEGATIVE INTEGER-VALUED RANDOM VARIABLES

MOMENTS EQUALITIES FOR NONNEGATIVE INTEGER-VALUED RANDOM VARIABLES MOMENTS EQUALITIES FOR NONNEGATIVE INTEGER-VALUED RANDOM VARIABLES MOHAMED I RIFFI ASSOCIATE PROFESSOR OF MATHEMATICS DEPARTMENT OF MATHEMATICS ISLAMIC UNIVERSITY OF GAZA GAZA, PALESTINE Abstract. We preset

More information