AP Statistics 2006 Free-Response Questions Form B
|
|
- Elfreda Goodwin
- 8 years ago
- Views:
Transcription
1 AP Statstcs 006 Free-Respose Questos Form B The College Board: Coectg Studets to College Success The College Board s a ot-for-proft membershp assocato whose msso s to coect studets to college success ad opportuty. Fouded 1900, the assocato s composed of more tha 5,000 schools, colleges, uverstes, ad other educatoal orgazatos. Each year, the College Board serves seve mllo studets ad ther parets, 3,000 hgh schools, ad 3,500 colleges through major programs ad servces college admssos, gudace, assessmet, facal ad, erollmet, ad teachg ad learg. Amog ts best-kow programs are the SAT, the PSAT/NMSQT, ad the Advaced Placemet Program (AP ). The College Board s commtted to the prcples of ecellece ad equty, ad that commtmet s emboded all of ts programs, servces, actvtes, ad cocers. 006 The College Board. All rghts reserved. College Board, AP Cetral, APCD, Advaced Placemet Program, AP, AP Vertcal Teams, Pre-AP, SAT, ad the acor logo are regstered trademarks of the College Board. Admtted Class Evaluato Servce, CollegeEd, coect to college success, MyRoad, SAT Professoal Developmet, SAT Readess Program, ad Settg the Corerstoes are trademarks owed by the College Board. PSAT/NMSQT s a regstered trademark of the College Board ad Natoal Mert Scholarshp Corporato. All other products ad servces may be trademarks of ther respectve owers. Permsso to use copyrghted College Board materals may be requested ole at: Vst the College Board o the Web: AP Cetral s the offcal ole home for the AP Program: apcetral.collegeboard.com.
2 Formulas beg o page 3. Questos beg o page 6. Tables beg o page 1.
3 Formulas (I) Descrptve Statstcs = s = 1 1 d s p = d d 1 s + s d 1 + d 1 1 y = b + b 0 1 d d y y b = 1 d b = y b 0 1 r 1 = 1 F HG s IF KJ HG y s y y I KJ b 1 = r s y s s b 1 = d y d y 3
4 (II) Probablty P( A B) = P( A) + PB ( ) PA ( B) PAB ( ) = P( A B) PB ( ) E( X ) = µ = p d Var( X) = s = µ p If X has a bomal dstrbuto wth parameters ad p, the: F HG I K J 1 PX ( = k) = k pk ( p) k µ = p s = p( 1 p) µ p = p p( 1 p) s p = If s the mea of a radom sample of sze from a fte populato wth mea µ ad stadard devato s, the: µ = µ s = s 4
5 (III) Iferetal Statstcs Stadardzed test statstc: statstc - parameter stadard devato of statstc Cofdece terval: statstc ± ( crtcal value) ( stadard devato of statstc) Sgle-Sample Statstc Sample Mea Stadard Devato of Statstc σ Sample Proporto p( 1 p) Two-Sample Statstc Dfferece of sample meas Stadard Devato of Statstc σ1 σ + 1 Specal case whe σ σ = σ 1 Dfferece of sample proportos p ( 1 p ) p( 1 p) Ch-square test statstc = a Specal case whe p p 1 p = p b g + 1 observed epected epected f 5
6 STATISTICS SECTION II Part A Questos 1-5 Sped about 65 mutes o ths part of the eam. Percet of Secto II grade 75 Drectos: Show all your work. Idcate clearly the methods you use, because you wll be graded o the correctess of your methods as well as o the accuracy ad completeess of your results ad eplaatos. 1. A large regoal real estate compay keeps records of home sales for each of ts sales agets. Each moth, the compay publshes the sales volume for each aget. Mothly sales volume s defed as the total sales prce of all homes sold by the aget durg a moth. The fgure below dsplays the cumulatve relatve frequecy plot of the most recet mothly sales volume ( hudreds of thousads of dollars) for these agets. (a) I the cotet of ths questo, epla what formato s coveyed by the crcled pot. (b) What proporto of sales agets acheved mothly sales volumes betwee $700,000 ad $800,000? (c) For values betwee 10 ad 11 o the horzotal as, the cumulatve relatve frequecy plot s flat. I the cotet of ths questo, epla what ths meas. (d) A bous s to be gve to 0 percet of the sales agets. Those who acheved the hghest mothly sales volume durg the precedg moth wll receve a bous. What s the mmum mothly sales volume a aget must have acheved to qualfy for the bous? 006 The College Board. All rghts reserved. Vst apcetral.collegeboard.com (for AP professoals) ad (for studets ad parets). 6 GO ON TO THE NEXT PAGE.
7 . A large compay has two shfts a day shft ad a ght shft. Parts produced by the two shfts must meet the same specfcatos. The maager of the compay beleves that there s a dfferece the proportos of parts produced wth specfcatos by the two shfts. To vestgate ths belef, radom samples of parts that were produced o each of these shfts were selected. For the day shft, 188 of ts 00 selected parts met specfcatos. For the ght shft, 180 of ts 00 selected parts met specfcatos. (a) Use a 96 percet cofdece terval to estmate the dfferece the proportos of parts produced wth specfcatos by the two shfts. (b) Based oly o ths cofdece terval, do you thk that the dfferece the proportos of parts produced wth specfcatos by the two shfts s sgfcatly dfferet from 0? Justfy your aswer. 3. Golf balls must meet a set of fve stadards order to be used professoal touramets. Oe of these stadards s dstace traveled. Whe a ball s ht by a mechacal devce, Iro Byro, wth a 10-degree agle of lauch, a backsp of 4 revolutos per secod, ad a ball velocty of 35 feet per secod, the dstace the ball travels may ot eceed 91. yards. Maufacturers wat to develop balls that wll travel as close to the 91. yards as possble wthout eceedg that dstace. A partcular maufacturer has determed that the dstaces traveled for the balls t produces are ormally dstrbuted wth a stadard devato of.8 yards. Ths maufacturer has a ew process that allows t to set the mea dstace the ball wll travel. (a) If the maufacturer sets the mea dstace traveled to be equal to 88 yards, what s the probablty that a ball that s radomly selected for testg wll travel too far? (b) Assume the mea dstace traveled s 88 yards ad that fve balls are depedetly tested. What s the probablty that at least oe of the fve balls wll eceed the mamum dstace of 91. yards? (c) If the maufacturer wats to be 99 percet certa that a radomly selected ball wll ot eceed the mamum dstace of 91. yards, what s the largest mea that ca be used the maufacturg process? 006 The College Board. All rghts reserved. Vst apcetral.collegeboard.com (for AP professoals) ad (for studets ad parets). 7 GO ON TO THE NEXT PAGE.
8 4. The developers of a trag program desged to mprove maual deterty clam that people who complete the 6-week program wll crease ther maual deterty. A radom sample of 1 people erolled the trag program was selected. A measure of each perso s deterty o a scale from 1 (lowest) to 9 (hghest) was recorded just before the start of ad just after the completo of the 6-week program. The data are show the table below. Perso Before Program After Program A B C D E F G H I J K L Total Ca oe coclude that the mea maual deterty for people who have completed the 6-week trag program has sgfcatly creased? Support your cocluso wth approprate statstcal evdece. 006 The College Board. All rghts reserved. Vst apcetral.collegeboard.com (for AP professoals) ad (for studets ad parets). 8 GO ON TO THE NEXT PAGE.
9 5. Whe a tractor pulls a plow through a agrcultural feld, the eergy eeded to pull that plow s called the draft. The draft s affected by evrometal codtos such as sol type, terra, ad mosture. A study was coducted to determe whether a ewly developed htch would be able to reduce draft compared to the stadard htch. (A htch s used to coect the plow to the tractor.) Two large plots of lad were used ths study. It was radomly determed whch plot was to be plowed usg the stadard htch. As the tractor plowed that plot, a measuremet devce o the tractor automatcally recorded the draft at 5 radomly selected pots the plot. After the plot was plowed, the htch was chaged from the stadard oe to the ew oe, a process that takes a substatal amout of tme. The the secod plot was plowed usg the ew htch. Twety-fve measuremets of draft were also recorded at radomly selected pots ths plot. (a) What was the respose varable ths study? Idetfy the treatmets. What were the epermetal uts? (b) Gve that the goal of the study s to determe whether a ewly developed htch reduces draft compared to the stadard htch, was radomzato used properly ths study? Justfy your aswer. (c) Gve that the goal of the study s to determe whether a ewly developed htch reduces draft compared to the stadard htch, was replcato used properly ths study? Justfy your aswer. (d) Plot of lad s a cofoudg varable ths epermet. Epla why. 006 The College Board. All rghts reserved. Vst apcetral.collegeboard.com (for AP professoals) ad (for studets ad parets). 9 GO ON TO THE NEXT PAGE.
10 STATISTICS SECTION II Part B Questo 6 Sped about 5 mutes o ths part of the eam. Percet of Secto II grade 5 Drectos: Show all your work. Idcate clearly the methods you use, because you wll be graded o the correctess of your methods as well as o the accuracy ad completeess of your results ad eplaatos. 6. Sushe Farms wats to kow whether there s a dfferece cosumer preferece for two ew juce products Ctrus Fresh ad Tropcal Taste. I a tal bld taste test, 8 radomly selected cosumers were gve umarked samples of the two juces. The product that each cosumer tasted frst was radomly decded by the flp of a co. After tastg the two juces, each cosumer was asked to choose whch juce he or she preferred, ad the results were recorded. (a) Let p represet the populato proporto of cosumers who prefer Ctrus Fresh. I terms of p, state the hypotheses that Sushe Farms s terested testg. (b) Oe mght cosder usg a oe-proporto z-test to test the hypotheses part (a). Epla why ths would ot be a reasoable procedure for ths sample. (c) Let X represet the umber of cosumers the sample who prefer Ctrus Fresh. Assumg there s o dfferece cosumer preferece, fd the probablty for each possble value of X. Record the -values ad the correspodg probabltes the table below. p( ) 006 The College Board. All rghts reserved. Vst apcetral.collegeboard.com (for AP professoals) ad (for studets ad parets). 10 GO ON TO THE NEXT PAGE.
11 (d) Whe testg the hypotheses part (a), Sushe Farms wll coclude that there s a cosumer preferece f too may or too few dvduals prefer Ctrus Fresh. Based o your probabltes part (c), s t possble for the sgfcace level (probablty of rejectg the ull hypothess whe t s true) for ths test to be eactly 0.05? Justfy your aswer. (e) The preferece data for the 8 radomly selected cosumers are gve the table below. Idvdual Juce Preferece 1 Tropcal Taste Ctrus Fresh 3 Tropcal Taste 4 Tropcal Taste 5 Tropcal Taste 6 Ctrus Fresh 7 Tropcal Taste 8 Tropcal Taste Based o these prefereces ad your prevous work, test the hypotheses part (a). (f) Sushe Farms plas to add oe of these two ew juces Ctrus Fresh or Tropcal Taste to ts producto schedule. A follow-up study wll be coducted to decde whch of the two juces to produce. Make oe recommedato for the follow-up study that would make t better tha the tal study. Provde a statstcal justfcato for your recommedato the cotet of the problem. STOP END OF EXAM 006 The College Board. All rghts reserved. Vst apcetral.collegeboard.com (for AP professoals) ad (for studets ad parets). 11
12 Probablty Table etry for z s the probablty lyg below z. z Table A Stadard ormal probabltes z
13 Probablty Table etry for z s the probablty lyg below z. Table A (Cotued) z z
14 Table etry for p ad C s the pot t* wth probablty p lyg above t ad probablty C lyg betwee t * ad t*. Probablty p t* Table B t dstrbuto crtcal values Tal probablty p df % 60% 70% 80% 90% 95% 96% 98% 99% 99.5% 99.8% 99.9% Cofdece level C 14
15 Table C Table etry for p s the pot ( χ ) wth probablty p lyg above t. χ crtcal values Tal probablty p (χ ) Probablty p df
Simple Linear Regression
Smple Lear Regresso Regresso equato a equato that descrbes the average relatoshp betwee a respose (depedet) ad a eplaator (depedet) varable. 6 8 Slope-tercept equato for a le m b (,6) slope. (,) 6 6 8
More informationClassic Problems at a Glance using the TVM Solver
C H A P T E R 2 Classc Problems at a Glace usg the TVM Solver The table below llustrates the most commo types of classc face problems. The formulas are gve for each calculato. A bref troducto to usg the
More informationMDM 4U PRACTICE EXAMINATION
MDM 4U RCTICE EXMINTION Ths s a ractce eam. It does ot cover all the materal ths course ad should ot be the oly revew that you do rearato for your fal eam. Your eam may cota questos that do ot aear o ths
More informationANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data
ANOVA Notes Page Aalss of Varace for a Oe-Wa Classfcato of Data Cosder a sgle factor or treatmet doe at levels (e, there are,, 3, dfferet varatos o the prescrbed treatmet) Wth a gve treatmet level there
More informationn. We know that the sum of squares of p independent standard normal variables has a chi square distribution with p degrees of freedom.
UMEÅ UNIVERSITET Matematsk-statstska sttutoe Multvarat dataaalys för tekologer MSTB0 PA TENTAMEN 004-0-9 LÖSNINGSFÖRSLAG TILL TENTAMEN I MATEMATISK STATISTIK Multvarat dataaalys för tekologer B, 5 poäg.
More informationBanking (Early Repayment of Housing Loans) Order, 5762 2002 1
akg (Early Repaymet of Housg Loas) Order, 5762 2002 y vrtue of the power vested me uder Secto 3 of the akg Ordace 94 (hereafter, the Ordace ), followg cosultato wth the Commttee, ad wth the approval of
More informationThe Analysis of Development of Insurance Contract Premiums of General Liability Insurance in the Business Insurance Risk
The Aalyss of Developmet of Isurace Cotract Premums of Geeral Lablty Isurace the Busess Isurace Rsk the Frame of the Czech Isurace Market 1998 011 Scetfc Coferece Jue, 10. - 14. 013 Pavla Kubová Departmet
More informationSTATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1
STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ
More informationCHAPTER 13. Simple Linear Regression LEARNING OBJECTIVES. USING STATISTICS @ Sunflowers Apparel
CHAPTER 3 Smple Lear Regresso USING STATISTICS @ Suflowers Apparel 3 TYPES OF REGRESSION MODELS 3 DETERMINING THE SIMPLE LINEAR REGRESSION EQUATION The Least-Squares Method Vsual Exploratos: Explorg Smple
More informationFINANCIAL MATHEMATICS 12 MARCH 2014
FINNCIL MTHEMTICS 12 MRCH 2014 I ths lesso we: Lesso Descrpto Make use of logarthms to calculate the value of, the tme perod, the equato P1 or P1. Solve problems volvg preset value ad future value autes.
More informationSHAPIRO-WILK TEST FOR NORMALITY WITH KNOWN MEAN
SHAPIRO-WILK TEST FOR NORMALITY WITH KNOWN MEAN Wojcech Zelńsk Departmet of Ecoometrcs ad Statstcs Warsaw Uversty of Lfe Sceces Nowoursyowska 66, -787 Warszawa e-mal: wojtekzelsk@statystykafo Zofa Hausz,
More informationAn Effectiveness of Integrated Portfolio in Bancassurance
A Effectveess of Itegrated Portfolo Bacassurace Taea Karya Research Ceter for Facal Egeerg Isttute of Ecoomc Research Kyoto versty Sayouu Kyoto 606-850 Japa arya@eryoto-uacp Itroducto As s well ow the
More information1. The Time Value of Money
Corporate Face [00-0345]. The Tme Value of Moey. Compoudg ad Dscoutg Captalzato (compoudg, fdg future values) s a process of movg a value forward tme. It yelds the future value gve the relevat compoudg
More informationAverage Price Ratios
Average Prce Ratos Morgstar Methodology Paper August 3, 2005 2005 Morgstar, Ic. All rghts reserved. The formato ths documet s the property of Morgstar, Ic. Reproducto or trascrpto by ay meas, whole or
More informationAPPENDIX III THE ENVELOPE PROPERTY
Apped III APPENDIX III THE ENVELOPE PROPERTY Optmzato mposes a very strog structure o the problem cosdered Ths s the reaso why eoclasscal ecoomcs whch assumes optmzg behavour has bee the most successful
More informationADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN
Colloquum Bometrcum 4 ADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN Zofa Hausz, Joaa Tarasńska Departmet of Appled Mathematcs ad Computer Scece Uversty of Lfe Sceces Lubl Akademcka 3, -95 Lubl
More informationAP Calculus AB 2006 Scoring Guidelines Form B
AP Calculus AB 6 Scorig Guidelies Form B The College Board: Coectig Studets to College Success The College Board is a ot-for-profit membership associatio whose missio is to coect studets to college success
More information10.5 Future Value and Present Value of a General Annuity Due
Chapter 10 Autes 371 5. Thomas leases a car worth $4,000 at.99% compouded mothly. He agrees to make 36 lease paymets of $330 each at the begg of every moth. What s the buyout prce (resdual value of the
More informationCommercial Pension Insurance Program Design and Estimated of Tax Incentives---- Based on Analysis of Enterprise Annuity Tax Incentives
Iteratoal Joural of Busess ad Socal Scece Vol 5, No ; October 204 Commercal Peso Isurace Program Desg ad Estmated of Tax Icetves---- Based o Aalyss of Eterprse Auty Tax Icetves Huag Xue, Lu Yatg School
More informationGreen Master based on MapReduce Cluster
Gree Master based o MapReduce Cluster Mg-Zh Wu, Yu-Chag L, We-Tsog Lee, Yu-Su L, Fog-Hao Lu Dept of Electrcal Egeerg Tamkag Uversty, Tawa, ROC Dept of Electrcal Egeerg Tamkag Uversty, Tawa, ROC Dept of
More informationHow To Value An Annuity
Future Value of a Auty After payg all your blls, you have $200 left each payday (at the ed of each moth) that you wll put to savgs order to save up a dow paymet for a house. If you vest ths moey at 5%
More informationChapter 3. AMORTIZATION OF LOAN. SINKING FUNDS R =
Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS Objectves of the Topc: Beg able to formalse ad solve practcal ad mathematcal problems, whch the subjects of loa amortsato ad maagemet of cumulatve fuds are
More informationStatistical Pattern Recognition (CE-725) Department of Computer Engineering Sharif University of Technology
I The Name of God, The Compassoate, The ercful Name: Problems' eys Studet ID#:. Statstcal Patter Recogto (CE-725) Departmet of Computer Egeerg Sharf Uversty of Techology Fal Exam Soluto - Sprg 202 (50
More informationLoad and Resistance Factor Design (LRFD)
53:134 Structural Desg II Load ad Resstace Factor Desg (LRFD) Specfcatos ad Buldg Codes: Structural steel desg of buldgs the US s prcpally based o the specfcatos of the Amerca Isttute of Steel Costructo
More informationRUSSIAN ROULETTE AND PARTICLE SPLITTING
RUSSAN ROULETTE AND PARTCLE SPLTTNG M. Ragheb 3/7/203 NTRODUCTON To stuatos are ecoutered partcle trasport smulatos:. a multplyg medum, a partcle such as a eutro a cosmc ray partcle or a photo may geerate
More informationof the relationship between time and the value of money.
TIME AND THE VALUE OF MONEY Most agrbusess maagers are famlar wth the terms compoudg, dscoutg, auty, ad captalzato. That s, most agrbusess maagers have a tutve uderstadg that each term mples some relatoshp
More informationThe Gompertz-Makeham distribution. Fredrik Norström. Supervisor: Yuri Belyaev
The Gompertz-Makeham dstrbuto by Fredrk Norström Master s thess Mathematcal Statstcs, Umeå Uversty, 997 Supervsor: Yur Belyaev Abstract Ths work s about the Gompertz-Makeham dstrbuto. The dstrbuto has
More informationANNEX 77 FINANCE MANAGEMENT. (Working material) Chief Actuary Prof. Gaida Pettere BTA INSURANCE COMPANY SE
ANNEX 77 FINANCE MANAGEMENT (Workg materal) Chef Actuary Prof. Gada Pettere BTA INSURANCE COMPANY SE 1 FUNDAMENTALS of INVESTMENT I THEORY OF INTEREST RATES 1.1 ACCUMULATION Iterest may be regarded as
More informationSettlement Prediction by Spatial-temporal Random Process
Safety, Relablty ad Rs of Structures, Ifrastructures ad Egeerg Systems Furuta, Fragopol & Shozua (eds Taylor & Fracs Group, Lodo, ISBN 978---77- Settlemet Predcto by Spatal-temporal Radom Process P. Rugbaapha
More informationNumerical Methods with MS Excel
TMME, vol4, o.1, p.84 Numercal Methods wth MS Excel M. El-Gebely & B. Yushau 1 Departmet of Mathematcal Sceces Kg Fahd Uversty of Petroleum & Merals. Dhahra, Saud Araba. Abstract: I ths ote we show how
More informationISyE 512 Chapter 7. Control Charts for Attributes. Instructor: Prof. Kaibo Liu. Department of Industrial and Systems Engineering UW-Madison
ISyE 512 Chapter 7 Cotrol Charts for Attrbutes Istructor: Prof. Kabo Lu Departmet of Idustral ad Systems Egeerg UW-Madso Emal: klu8@wsc.edu Offce: Room 3017 (Mechacal Egeerg Buldg) 1 Lst of Topcs Chapter
More informationQuestions? Ask Prof. Herz, herz@ucsd.edu. General Classification of adsorption
Questos? Ask rof. Herz, herz@ucsd.edu Geeral Classfcato of adsorpto hyscal adsorpto - physsorpto - dsperso forces - Va der Waals forces - weak - oly get hgh fractoal coerage of surface at low temperatures
More informationReport 52 Fixed Maturity EUR Industrial Bond Funds
Rep52, Computed & Prted: 17/06/2015 11:53 Report 52 Fxed Maturty EUR Idustral Bod Fuds From Dec 2008 to Dec 2014 31/12/2008 31 December 1999 31/12/2014 Bechmark Noe Defto of the frm ad geeral formato:
More informationIDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki
IDENIFICAION OF HE DYNAMICS OF HE GOOGLE S RANKING ALGORIHM A. Khak Sedgh, Mehd Roudak Cotrol Dvso, Departmet of Electrcal Egeerg, K.N.oos Uversty of echology P. O. Box: 16315-1355, ehra, Ira sedgh@eetd.ktu.ac.r,
More informationChapter Eight. f : R R
Chapter Eght f : R R 8. Itroducto We shall ow tur our atteto to the very mportat specal case of fuctos that are real, or scalar, valued. These are sometmes called scalar felds. I the very, but mportat,
More informationECONOMIC CHOICE OF OPTIMUM FEEDER CABLE CONSIDERING RISK ANALYSIS. University of Brasilia (UnB) and The Brazilian Regulatory Agency (ANEEL), Brazil
ECONOMIC CHOICE OF OPTIMUM FEEDER CABE CONSIDERING RISK ANAYSIS I Camargo, F Fgueredo, M De Olvera Uversty of Brasla (UB) ad The Brazla Regulatory Agecy (ANEE), Brazl The choce of the approprate cable
More informationCHAPTER 2. Time Value of Money 6-1
CHAPTER 2 Tme Value of Moey 6- Tme Value of Moey (TVM) Tme Les Future value & Preset value Rates of retur Autes & Perpetutes Ueve cash Flow Streams Amortzato 6-2 Tme les 0 2 3 % CF 0 CF CF 2 CF 3 Show
More informationMaintenance Scheduling of Distribution System with Optimal Economy and Reliability
Egeerg, 203, 5, 4-8 http://dx.do.org/0.4236/eg.203.59b003 Publshed Ole September 203 (http://www.scrp.org/joural/eg) Mateace Schedulg of Dstrbuto System wth Optmal Ecoomy ad Relablty Syua Hog, Hafeg L,
More informationProjection model for Computer Network Security Evaluation with interval-valued intuitionistic fuzzy information. Qingxiang Li
Iteratoal Joural of Scece Vol No7 05 ISSN: 83-4890 Proecto model for Computer Network Securty Evaluato wth terval-valued tutostc fuzzy formato Qgxag L School of Software Egeerg Chogqg Uversty of rts ad
More informationOnline Appendix: Measured Aggregate Gains from International Trade
Ole Appedx: Measured Aggregate Gas from Iteratoal Trade Arel Burste UCLA ad NBER Javer Cravo Uversty of Mchga March 3, 2014 I ths ole appedx we derve addtoal results dscussed the paper. I the frst secto,
More informationThe Time Value of Money
The Tme Value of Moey 1 Iversemet Optos Year: 1624 Property Traded: Mahatta Islad Prce : $24.00, FV of $24 @ 6%: FV = $24 (1+0.06) 388 = $158.08 bllo Opto 1 0 1 2 3 4 5 t ($519.37) 0 0 0 0 $1,000 Opto
More informationThe analysis of annuities relies on the formula for geometric sums: r k = rn+1 1 r 1. (2.1) k=0
Chapter 2 Autes ad loas A auty s a sequece of paymets wth fxed frequecy. The term auty orgally referred to aual paymets (hece the ame), but t s ow also used for paymets wth ay frequecy. Autes appear may
More informationModels for Selecting an ERP System with Intuitionistic Trapezoidal Fuzzy Information
JOURNAL OF SOFWARE, VOL 5, NO 3, MARCH 00 75 Models for Selectg a ERP System wth Itutostc rapezodal Fuzzy Iformato Guwu We, Ru L Departmet of Ecoomcs ad Maagemet, Chogqg Uversty of Arts ad Sceces, Yogchua,
More informationCurve Fitting and Solution of Equation
UNIT V Curve Fttg ad Soluto of Equato 5. CURVE FITTING I ma braches of appled mathematcs ad egeerg sceces we come across epermets ad problems, whch volve two varables. For eample, t s kow that the speed
More informationReinsurance and the distribution of term insurance claims
Resurace ad the dstrbuto of term surace clams By Rchard Bruyel FIAA, FNZSA Preseted to the NZ Socety of Actuares Coferece Queestow - November 006 1 1 Itroducto Ths paper vestgates the effect of resurace
More informationAutomated Event Registration System in Corporation
teratoal Joural of Advaces Computer Scece ad Techology JACST), Vol., No., Pages : 0-0 0) Specal ssue of CACST 0 - Held durg 09-0 May, 0 Malaysa Automated Evet Regstrato System Corporato Zafer Al-Makhadmee
More informationRegression Analysis. 1. Introduction
. Itroducto Regresso aalyss s a statstcal methodology that utlzes the relato betwee two or more quattatve varables so that oe varable ca be predcted from the other, or others. Ths methodology s wdely used
More informationA DISTRIBUTED REPUTATION BROKER FRAMEWORK FOR WEB SERVICE APPLICATIONS
L et al.: A Dstrbuted Reputato Broker Framework for Web Servce Applcatos A DISTRIBUTED REPUTATION BROKER FRAMEWORK FOR WEB SERVICE APPLICATIONS Kwe-Jay L Departmet of Electrcal Egeerg ad Computer Scece
More informationAP Calculus BC 2003 Scoring Guidelines Form B
AP Calculus BC Scorig Guidelies Form B The materials icluded i these files are iteded for use by AP teachers for course ad exam preparatio; permissio for ay other use must be sought from the Advaced Placemet
More informationAn Evaluation of Naïve Bayesian Anti-Spam Filtering Techniques
Proceedgs of the 2007 IEEE Workshop o Iformato Assurace Uted tates Mltary Academy, West Pot, Y 20-22 Jue 2007 A Evaluato of aïve Bayesa At-pam Flterg Techques Vkas P. Deshpade, Robert F. Erbacher, ad Chrs
More informationAbraham Zaks. Technion I.I.T. Haifa ISRAEL. and. University of Haifa, Haifa ISRAEL. Abstract
Preset Value of Autes Uder Radom Rates of Iterest By Abraham Zas Techo I.I.T. Hafa ISRAEL ad Uversty of Hafa, Hafa ISRAEL Abstract Some attempts were made to evaluate the future value (FV) of the expected
More information6.7 Network analysis. 6.7.1 Introduction. References - Network analysis. Topological analysis
6.7 Network aalyss Le data that explctly store topologcal formato are called etwork data. Besdes spatal operatos, several methods of spatal aalyss are applcable to etwork data. Fgure: Network data Refereces
More informationThe simple linear Regression Model
The smple lear Regresso Model Correlato coeffcet s o-parametrc ad just dcates that two varables are assocated wth oe aother, but t does ot gve a deas of the kd of relatoshp. Regresso models help vestgatg
More informationCredibility Premium Calculation in Motor Third-Party Liability Insurance
Advaces Mathematcal ad Computatoal Methods Credblty remum Calculato Motor Thrd-arty Lablty Isurace BOHA LIA, JAA KUBAOVÁ epartmet of Mathematcs ad Quattatve Methods Uversty of ardubce Studetská 95, 53
More informationA New Bayesian Network Method for Computing Bottom Event's Structural Importance Degree using Jointree
, pp.277-288 http://dx.do.org/10.14257/juesst.2015.8.1.25 A New Bayesa Network Method for Computg Bottom Evet's Structural Importace Degree usg Jotree Wag Yao ad Su Q School of Aeroautcs, Northwester Polytechcal
More informationMathematics of Finance
CATE Mathematcs of ace.. TODUCTO ths chapter we wll dscuss mathematcal methods ad formulae whch are helpful busess ad persoal face. Oe of the fudametal cocepts the mathematcs of face s the tme value of
More informationMeasures of Central Tendency: Basic Statistics Refresher. Topic 1 Point Estimates
Basc Statstcs Refresher Basc Statstcs: A Revew by Alla T. Mese, Ph.D., PE, CRE Ths s ot a tetbook o statstcs. Ths s a refresher that presumes the reader has had some statstcs backgroud. There are some
More informationMeasuring the Quality of Credit Scoring Models
Measur the Qualty of Credt cor Models Mart Řezáč Dept. of Matheatcs ad tatstcs, Faculty of cece, Masaryk Uversty CCC XI, Edurh Auust 009 Cotet. Itroducto 3. Good/ad clet defto 4 3. Measur the qualty 6
More information10/19/2011. Financial Mathematics. Lecture 24 Annuities. Ana NoraEvans 403 Kerchof AnaNEvans@virginia.edu http://people.virginia.
Math 40 Lecture 24 Autes Facal Mathematcs How ready do you feel for the quz o Frday: A) Brg t o B) I wll be by Frday C) I eed aother week D) I eed aother moth Aa NoraEvas 403 Kerchof AaNEvas@vrga.edu http://people.vrga.edu/~as5k/
More informationThe paper presents Constant Rebalanced Portfolio first introduced by Thomas
Itroducto The paper presets Costat Rebalaced Portfolo frst troduced by Thomas Cover. There are several weakesses of ths approach. Oe s that t s extremely hard to fd the optmal weghts ad the secod weakess
More informationRQM: A new rate-based active queue management algorithm
: A ew rate-based actve queue maagemet algorthm Jeff Edmods, Suprakash Datta, Patrck Dymod, Kashf Al Computer Scece ad Egeerg Departmet, York Uversty, Toroto, Caada Abstract I ths paper, we propose a ew
More informationChapter 3 0.06 = 3000 ( 1.015 ( 1 ) Present Value of an Annuity. Section 4 Present Value of an Annuity; Amortization
Chapter 3 Mathematcs of Face Secto 4 Preset Value of a Auty; Amortzato Preset Value of a Auty I ths secto, we wll address the problem of determg the amout that should be deposted to a accout ow at a gve
More informationForecasting Trend and Stock Price with Adaptive Extended Kalman Filter Data Fusion
2011 Iteratoal Coferece o Ecoomcs ad Face Research IPEDR vol.4 (2011 (2011 IACSIT Press, Sgapore Forecastg Tred ad Stoc Prce wth Adaptve Exteded alma Flter Data Fuso Betollah Abar Moghaddam Faculty of
More informationCyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Telecommunications (JSAT), January Edition, 2011
Cyber Jourals: Multdscplary Jourals cece ad Techology, Joural of elected Areas Telecommucatos (JAT), Jauary dto, 2011 A ovel rtual etwork Mappg Algorthm for Cost Mmzg ZHAG hu-l, QIU Xue-sog tate Key Laboratory
More informationAn Approach to Evaluating the Computer Network Security with Hesitant Fuzzy Information
A Approach to Evaluatg the Computer Network Securty wth Hestat Fuzzy Iformato Jafeg Dog A Approach to Evaluatg the Computer Network Securty wth Hestat Fuzzy Iformato Jafeg Dog, Frst ad Correspodg Author
More informationOptimal multi-degree reduction of Bézier curves with constraints of endpoints continuity
Computer Aded Geometrc Desg 19 (2002 365 377 wwwelsevercom/locate/comad Optmal mult-degree reducto of Bézer curves wth costrats of edpots cotuty Guo-Dog Che, Guo-J Wag State Key Laboratory of CAD&CG, Isttute
More informationPreparation of Calibration Curves
Preparato of Calbrato Curves A Gude to Best Practce September 3 Cotact Pot: Lz Prchard Tel: 8943 7553 Prepared by: Vck Barwck Approved by: Date: The work descrbed ths report was supported uder cotract
More informationApplications of Support Vector Machine Based on Boolean Kernel to Spam Filtering
Moder Appled Scece October, 2009 Applcatos of Support Vector Mache Based o Boolea Kerel to Spam Flterg Shugag Lu & Keb Cu School of Computer scece ad techology, North Cha Electrc Power Uversty Hebe 071003,
More informationIntegrating Production Scheduling and Maintenance: Practical Implications
Proceedgs of the 2012 Iteratoal Coferece o Idustral Egeerg ad Operatos Maagemet Istabul, Turkey, uly 3 6, 2012 Itegratg Producto Schedulg ad Mateace: Practcal Implcatos Lath A. Hadd ad Umar M. Al-Turk
More informationSession 4: Descriptive statistics and exporting Stata results
Itrduct t Stata Jrd Muñz (UAB) Sess 4: Descrptve statstcs ad exprtg Stata results I ths sess we are gg t wrk wth descrptve statstcs Stata. Frst, we preset a shrt trduct t the very basc statstcal ctets
More informationA Parallel Transmission Remote Backup System
2012 2d Iteratoal Coferece o Idustral Techology ad Maagemet (ICITM 2012) IPCSIT vol 49 (2012) (2012) IACSIT Press, Sgapore DOI: 107763/IPCSIT2012V495 2 A Parallel Trasmsso Remote Backup System Che Yu College
More informationPay for the Continuous Workday
Uted States epartmet of Arculture Food Safety ad Ispecto Servce FSIS rectve 4550.7 Pay for the Cotuous Workday PAY FOR THE CONTINUOUS WORKAY TABLE OF CONTENTS Ttle Pae No. I. PURPOSE....................
More informationA particle Swarm Optimization-based Framework for Agile Software Effort Estimation
The Iteratoal Joural Of Egeerg Ad Scece (IJES) olume 3 Issue 6 Pages 30-36 204 ISSN (e): 239 83 ISSN (p): 239 805 A partcle Swarm Optmzato-based Framework for Agle Software Effort Estmato Maga I, & 2 Blamah
More informationDynamic Two-phase Truncated Rayleigh Model for Release Date Prediction of Software
J. Software Egeerg & Applcatos 3 63-69 do:.436/jsea..367 Publshed Ole Jue (http://www.scrp.org/joural/jsea) Dyamc Two-phase Trucated Raylegh Model for Release Date Predcto of Software Lafe Qa Qgchua Yao
More informationIP Network Topology Link Prediction Based on Improved Local Information Similarity Algorithm
Iteratoal Joural of Grd Dstrbuto Computg, pp.141-150 http://dx.do.org/10.14257/jgdc.2015.8.6.14 IP Network Topology Lk Predcto Based o Improved Local Iformato mlarty Algorthm Che Yu* 1, 2 ad Dua Zhem 1
More informationOptimal Packetization Interval for VoIP Applications Over IEEE 802.16 Networks
Optmal Packetzato Iterval for VoIP Applcatos Over IEEE 802.16 Networks Sheha Perera Harsha Srsea Krzysztof Pawlkowsk Departmet of Electrcal & Computer Egeerg Uversty of Caterbury New Zealad sheha@elec.caterbury.ac.z
More informationCapacitated Production Planning and Inventory Control when Demand is Unpredictable for Most Items: The No B/C Strategy
SCHOOL OF OPERATIONS RESEARCH AND INDUSTRIAL ENGINEERING COLLEGE OF ENGINEERING CORNELL UNIVERSITY ITHACA, NY 4853-380 TECHNICAL REPORT Jue 200 Capactated Producto Plag ad Ivetory Cotrol whe Demad s Upredctable
More informationSecurity Analysis of RAPP: An RFID Authentication Protocol based on Permutation
Securty Aalyss of RAPP: A RFID Authetcato Protocol based o Permutato Wag Shao-hu,,, Ha Zhje,, Lu Sujua,, Che Da-we, {College of Computer, Najg Uversty of Posts ad Telecommucatos, Najg 004, Cha Jagsu Hgh
More informationHow To Make A Supply Chain System Work
Iteratoal Joural of Iformato Techology ad Kowledge Maagemet July-December 200, Volume 2, No. 2, pp. 3-35 LATERAL TRANSHIPMENT-A TECHNIQUE FOR INVENTORY CONTROL IN MULTI RETAILER SUPPLY CHAIN SYSTEM Dharamvr
More informationThe impact of service-oriented architecture on the scheduling algorithm in cloud computing
Iteratoal Research Joural of Appled ad Basc Sceces 2015 Avalable ole at www.rjabs.com ISSN 2251-838X / Vol, 9 (3): 387-392 Scece Explorer Publcatos The mpact of servce-oreted archtecture o the schedulg
More informationNumerical Comparisons of Quality Control Charts for Variables
Global Vrtual Coferece Aprl, 8. - 2. 203 Nuercal Coparsos of Qualty Cotrol Charts for Varables J.F. Muñoz-Rosas, M.N. Pérez-Aróstegu Uversty of Graada Facultad de Cecas Ecoócas y Epresarales Graada, pa
More informationCH. V ME256 STATICS Center of Gravity, Centroid, and Moment of Inertia CENTER OF GRAVITY AND CENTROID
CH. ME56 STTICS Ceter of Gravt, Cetrod, ad Momet of Ierta CENTE OF GITY ND CENTOID 5. CENTE OF GITY ND CENTE OF MSS FO SYSTEM OF PTICES Ceter of Gravt. The ceter of gravt G s a pot whch locates the resultat
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 informationCSSE463: Image Recognition Day 27
CSSE463: Image Recogto Da 27 Ths week Toda: Alcatos of PCA Suda ght: roject las ad relm work due Questos? Prcal Comoets Aalss weght grth c ( )( ) ( )( ( )( ) ) heght sze Gve a set of samles, fd the drecto(s)
More informationOn formula to compute primes and the n th prime
Joural's Ttle, Vol., 00, o., - O formula to compute prmes ad the th prme Issam Kaddoura Lebaese Iteratoal Uversty Faculty of Arts ad ceces, Lebao Emal: ssam.addoura@lu.edu.lb amh Abdul-Nab Lebaese Iteratoal
More informationT = 1/freq, T = 2/freq, T = i/freq, T = n (number of cash flows = freq n) are :
Bullets bods Let s descrbe frst a fxed rate bod wthout amortzg a more geeral way : Let s ote : C the aual fxed rate t s a percetage N the otoal freq ( 2 4 ) the umber of coupo per year R the redempto of
More informationFix or Evict? Loan Modifications Return More Value Than Foreclosures
Fx or Evct? Loa Modfcatos etur More Value Tha Foreclosures We L ad Soa arrso March, 0 www.resposbleledg.org Fx or Evct? Loa Modfcatos etur More Value Tha Foreclosures We L ad Soa arrso Ceter for esposble
More informationOne way to organize workers that lies between traditional assembly lines, where workers are specialists,
MANUFACTURING & SERVICE OPERATIONS MANAGEMENT Vol. 7, No. 2, Sprg 2005, pp. 121 129 ss 1523-4614 ess 1526-5498 05 0702 0121 forms do 10.1287/msom.1040.0059 2005 INFORMS Usg Bucket Brgades to Mgrate from
More informationAgent-based modeling and simulation of multiproject
Aget-based modelg ad smulato of multproject schedulg José Alberto Araúzo, Javer Pajares, Adolfo Lopez- Paredes Socal Systems Egeerg Cetre (INSISOC) Uversty of Valladold Valladold (Spa) {arauzo,pajares,adolfo}ssoc.es
More informationZ-TEST / Z-STATISTIC: used to test hypotheses about. µ when the population standard deviation is unknown
Z-TEST / Z-STATISTIC: used to test hypotheses about µ whe the populatio stadard deviatio is kow ad populatio distributio is ormal or sample size is large T-TEST / T-STATISTIC: used to test hypotheses about
More informationHow To Balance Load On A Weght-Based Metadata Server Cluster
WLBS: A Weght-based Metadata Server Cluster Load Balacg Strategy J-L Zhag, We Qa, Xag-Hua Xu *, Ja Wa, Yu-Yu Y, Yog-Ja Re School of Computer Scece ad Techology Hagzhou Daz Uversty, Cha * Correspodg author:xhxu@hdu.edu.c
More informationA COMPARATIVE STUDY BETWEEN POLYCLASS AND MULTICLASS LANGUAGE MODELS
A COMPARATIVE STUDY BETWEEN POLYCLASS AND MULTICLASS LANGUAGE MODELS I Ztou, K Smaïl, S Delge, F Bmbot To cte ths verso: I Ztou, K Smaïl, S Delge, F Bmbot. A COMPARATIVE STUDY BETWEEN POLY- CLASS AND MULTICLASS
More informationOn Error Detection with Block Codes
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 9, No 3 Sofa 2009 O Error Detecto wth Block Codes Rostza Doduekova Chalmers Uversty of Techology ad the Uversty of Gotheburg,
More informationA particle swarm optimization to vehicle routing problem with fuzzy demands
A partcle swarm optmzato to vehcle routg problem wth fuzzy demads Yag Peg, Ye-me Qa A partcle swarm optmzato to vehcle routg problem wth fuzzy demads Yag Peg 1,Ye-me Qa 1 School of computer ad formato
More informationOptimization Model in Human Resource Management for Job Allocation in ICT Project
Optmzato Model Huma Resource Maagemet for Job Allocato ICT Project Optmzato Model Huma Resource Maagemet for Job Allocato ICT Project Saghamtra Mohaty Malaya Kumar Nayak 2 2 Professor ad Head Research
More informationA Real-time Visual Tracking System in the Robot Soccer Domain
Proceedgs of EUEL obotcs-, Salford, Eglad, th - th Aprl A eal-tme Vsual Trackg System the obot Soccer Doma Bo L, Edward Smth, Huosheg Hu, Lbor Spacek Departmet of Computer Scece, Uversty of Essex, Wvehoe
More informationSpeeding up k-means Clustering by Bootstrap Averaging
Speedg up -meas Clusterg by Bootstrap Averagg Ia Davdso ad Ashw Satyaarayaa Computer Scece Dept, SUNY Albay, NY, USA,. {davdso, ashw}@cs.albay.edu Abstract K-meas clusterg s oe of the most popular clusterg
More informationhp calculators HP 12C Statistics - average and standard deviation Average and standard deviation concepts HP12C average and standard deviation
HP 1C Statistics - average ad stadard deviatio Average ad stadard deviatio cocepts HP1C average ad stadard deviatio Practice calculatig averages ad stadard deviatios with oe or two variables HP 1C Statistics
More informationProceedings of the 2010 Winter Simulation Conference B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds.
Proceedgs of the 21 Wter Smulato Coferece B. Johasso, S. Ja, J. Motoya-Torres, J. Huga, ad E. Yücesa, eds. EMPIRICAL METHODS OR TWO-ECHELON INVENTORY MANAGEMENT WITH SERVICE LEVEL CONSTRAINTS BASED ON
More informationThe premium for mandatory house insurance in Romania considerations regarding its financial solvability
Avalable ole at www.scecedrect.com Proceda Ecoomcs ad Face 3 ( 202 ) 829 836 Emergg Markets Queres Face ad Busess The premum for madatory house surace Romaa cosderatos regardg ts facal solvablty Raluca
More information