An Area Computation Based Method for RAIM Holes Assessment

Size: px
Start display at page:

Download "An Area Computation Based Method for RAIM Holes Assessment"

Transcription

1 Joural of Global Positioig Systems (2006) Vol. 5, No. 1-2:11-16 A Area Computatio Based Method for RAIM Holes Assessmet Shaoju Feg, Washigto Y. Ochieg ad Raier Mautz Cetre for Trasport Studies, Departmet of Civil ad Evirometal Egieerig, Imperial College Lodo, Lodo, Uited Kigdom, SW7 2AZ Abstract. Receiver Autoomous Itegrity Moitorig (RAIM) is a method implemeted withi the receiver to protect users agaist satellite avigatio system failures. Research has show that traditioal methods for the determiatio of RAIM holes (i.e. places where less tha five satellites are visible ad available) based o spatial ad temporal itervals (grids) compromise accuracy due to the costrait of computatio load. Research by the authors of this paper has addressed this ad developed a ew algorithm to determie RAIM holes usig bouded regios istead of approximatio based o grid poits. This paper uses the ew algorithm ad proposes a area based method for the computatio a RAIM satellite availability statistic based o the ratio of the total area of RAIM holes ad the coverage area (regioal or global area). Assessmet over time is based o the iterpolatio usig a model geerated from sapshot spatial statistics at a relatively log temporal iterval. Test results show that the area-based method for the calculatio of the RAIM satellite availability statistic is sigificatly more accurate with less computatioal load tha the traditioal grid poits based approach. Key words: Itegrity moitorig, RAIM hole, GNSS, performace assessmet 1 Backgroud Receiver Autoomous Itegrity Moitorig (RAIM) is a receiver-based scheme to provide timely ad valid warigs to users whe a Global Navigatio Satellite System (GNSS) is ot able to meet the required avigatio performace (RNP). RAIM algorithms are based o statistical cosistecy checks usig redudat measuremets. Cosistecy checks require five or more visible satellites, while i the case of Failure Detectio ad Exclusio (FDE), at least six visible satellites are required. A RAIM hole occurs whe there are isufficiet avigatio satellites i view to provide a itegrity check at a give locatio. It is defied as the area (or period) i which less tha five GNSS satellites are i view above a mask agle of 7.5 degrees (Air Force Space Commad, 1997). RAIM holes are the result of iformatio shortage causig a RAIM algorithm to be uable to perform its fuctio. Accordigly, a FDE hole ca be defied as the area (time) i which less tha six GNSS satellites are i view above a mask agle of 7.5 degrees. The method commoly used is to overlay a grid over the area of iterest ad to search at the grid poits (odes) over time. The spatial ad temporal itervals that have bee used i RAIM availability assessmet iclude: 5 degrees (spatial) ad 5 miutes (temporal) (Ochieg et al., 2002), 3 degrees ad 5 miutes (TSO-C129a,1996, RTCA/Do-229C, 2001) while Eurocotrol employs 0.25 degrees ad 2.5 miutes i the AUGUR software (AUGUR). These samplig itervals are relatively large ad are therefore susceptible to RAIM holes. Hece, if the spatial ad temporal itervals are too large, some RAIM holes could pass udetected if they lie either betwee the spatial or temporal samplig poits. However, smaller itervals result i a large umber of sample poits requirig icreased computatioal resources. The grid-based search method is therefore always depedet upo a trade-off betwee accuracy ad computatioal load. A ew method developed by Feg et al., (2006a) determies RAIM holes with a very high degree of accuracy. The key cosideratios i the ew approach to determiig RAIM holes are: The descriptio of what costitutes a RAIM hole The determiatio of precise satellite coverage boudaries. The determiatio of the itersectio poits of satellites boudaries.

2 12 Joural of Global Positioig Systems Topological aalysis of the regios formed by the itersectio of these coverage boudaries The coverage boudary of a satellite is ormally cosidered to be a curve o the Earth surface. Ay poit o the curve has the same value of elevatio agle as that of maskig agle. A RAIM hole is a polygoal area o the Earth surface formed by the satellite coverage boudaries of less tha five satellites i view. Sice the Earth surface ca be modelled by a ellipsoid, the area of a hole ca be completely described by its borders, which is eclosed by segmets of satellite coverage boudaries as demostrated i figure 1. For each segmet, there is a start poit ad a ed poit. These poits are the itersectios of coverage boudaries shared by the relevat two segmets, e.g. i Figure 1 the itersectio poit A is both the start poit of segmet (AB) ad the ed poit of segmet (CA). The segmets (AB, BC, CA) form a closed area. poits o the segmet betwee two crucial poits pair (start, ed) are referred to as critical poits i this paper. Aalysis shows that there exist at least three crucial poits if a hole exists (Feg et al., 2006b). The method here is to determie a polygoal area formed by the crucial ad critical poits of the segmets o the coverage boudaries as show i figure 2 (if 6). This is a more accurate descriptio of the area of a RAIM hole tha grid-poits based descriptio as demostrated i figure 3. The area with (-2) satellites i view is about 6% of the total area while the grid poit based method gives about 4.7%. The RAIM holes could be missed if the area is very small or the grid is ot dese eough Crucial poit satellites i view B C Segmet -2 Itersectio poit -1 Critical poit -1-1 A -1 Figure 2. Key poits of segmets based method Figure 1. The boudaries itersectio poits ad segmets The topology of the area formed by coverage boudaries is described briefly below: If the boudaries of (m) satellites itersect at oe poit, there are (2m) regios aroud the itersectio poit. The itersectio poit has the maximum umber () of visible satellites. The differece of the umber of visible satellites betwee ay two adjacet regios is 1. For m=2 or 3, there must exist at least oe area where oly (-2) satellites are visible. Based o the RAIM hole descriptio ad topology aalysis, the determiatio of a RAIM hole is trasformed ito the determiatio of itersectio poits with six or less satellites i view ad the adjacet segmets of coverage boudaries. The determiatio of itersectio poits is extremely importat because they defie the start ad ed poit of each segmet. These poits are referred to as crucial poits i this paper. To determie the aalytical solutio of the segmet betwee the start ad ed poit is quite difficult i a ellipsoidal model. However, the segmet ca be described discretely with a umber of poits o the coverage boudary. The Figure 3. Grid poits based area approximatio For a satellite of kow positio, there is a footpoit o the surface of the Earth, which is the itersectio poit of the Earth cetre to satellite vector ad the surface of the Earth as show i figure 4. The positio of a poit o the coverage boudary has a determiistic relatioship with the elevatio agle, the positio of the satellite, ad the relative azimuth to the footpoit either usig a spherical model or a ellipsoidal model of the Earth. However, it is ot straightforward to determie the poit usig a ellipsoidal model. Two steps are ormally used to

3 Feg et al.: A Area Computatio Based Method for RAIM Holes Assessmet 13 determie the positio of a boudary poit o a ellipsoidal model. I the first step, the Earth is cosidered to be a sphere, ad the poit is assumed to be o the surface of the Earth. It is the straightforward to determie a approximate locatio of the poit. I a secod step, a iterative process is carried out to obtai the precise positio of the poit o a ellipsoidal model usig the locatio i spherical model as the iitial value. If the user is ot o the surface of the Earth (e.g. a aircraft) ad 3D locatio is kow the the height iformatio ca be added i the secod step to exted the coverage boudary from the surface of the Earth to ay height of cocer. The process above is used to determie the itersectio poits. Iitially, the itersectio poits are assumed to be o the surface of a spherical model of the Earth. The itersectio poits (As ad Bs as show i figure 4) of two boudaries ca be calculated from the locatio of footpoits (F1, F2) ad parameters derived from the maskig agle usig spherical trigoometry. I a secod step, the itersectio poits (A E, B E ) are calculated iteratively i the ellipsoidal model where local height iformatio ca also be cosidered. The accuracy of the locatio of these poits depeds o the umber iteratios. 4 Oe iteratio ca reach a accuracy level of degrees ad six iteratios ca reach 1 10 degrees level (measured with respect to a fixed maskig agle). North F1 Footpoit Towards satellite Bs AE O Boudary o Sphere Boudary o Ellipsoid BE As Earth Cetre Towards satellite Footpoit Figure 4. Itersectio of two satellite coverage boudaries Amog these itersectio poits, oly those with six or less satellites i view are cosidered to be crucial poits ad eed to be idetified. The positio of the itersectio poit, the umber of visible satellites, ad related idetities (e.g. PRN pair) of satellites whose coverage boudaries itersect at this poit are recorded. The existece of crucial poits oly idicates the existece a hole. There is o direct iformatio about the size ad the umber of crucial poits that ecloses a hole. It is difficult to get a solutio from the related idetities of satellites that itersect at each crucial poit. Oe reaso is that the total umber of crucial poits could be very large ad the hole could be formed by ay umber F2 (more tha two) of crucial poits. The other reaso is the existece of two poits with the same idetity sice there are ormally two itersectio poits for the coverge boudaries of two satellites. The ext step is the determiatio of critical poits at a azimuth iterval alog oe coverage boudary startig from oe crucial poit ad edig at ext crucial poit. The latter crucial poit is take as the start poit for the other coverage boudary which itersects with the previous boudary. The process cotiues util a closed polygoal is foud. 2 Area calculatio ad assessmet of RAIM holes Accordig to the Radio Techical Commissio for Aviatio (RTCA), the grid poits for curret availability test are sampled every three degrees from zero to iety degrees orth (RTCA/Do-229C, 2001). The poits o each latitude circle are separated evely i logitude defied as: 360 log.iter val = (1) ROUND( 360 ) mi(3 degrees / cos( latitude), 360) The logitude iterval determied here eables uiformly distributed grid poits i terms of area. Cosequetly, the area ca be approximated by the umber of poits. Obviously, the direct calculatio of the area covered by RAIM holes is more accurate tha poit based approximatios, ad further eables a more accurate quatificatio of the RAIM satellite availability (Feg et al, 2005). 2.1 Area calculatio of a polygo The calculatio of the polygo area of a RAIM hole o the surface of a ellipsoid is ot straightforward. This is accomplished through a equal area projectio from the ellipsoidal model to the spherical model. The projectio ivolves two aspects, the determiatio of the authalic latitude, which trasforms the latitude from the ellipsoidal model to the spherical model, ad the determiatio of the radius of authalic sphere which has the same area as that i ellipsoidal model (Syder, 1987; Malig, 1992). The authalic latitude β, havig the same surface area o a sphere as the ellipsoid, provides a sphere of equal area (authalic) to the ellipsoid: β = arcsi( q / q ) (2) where, p

4 14 Joural of Global Positioig Systems 2 siφ 1 1 esiφ q = (1 e ) l (3) e si φ 2e 1+ esiφ 2 1 e 1+ e q p = 1+ l 2e 1 e o q p is q evaluated for a φ of 90, φ is the geodetic latitude. e deotes the first eccetricity of the ellipsoid. The radius R q of the sphere havig the same surface area as the ellipsoid is calculated as follows: q p R q = a (5) 2 where a is the semi-major axis of the ellipsoid. The area of a RAIM hole regio ca be calculated usig the umerical itegratio based o Gree s Theorem for a polygoal area o a surface of a sphere. 2.2 Satellite availability assessmet The availability statistic based o the grid poits method is to calculate the ratio of the umber of available spacetime poits Na versus the umber of total sample poits, which ca be expressed as: N total N (4) a A = (6) Ntotal where A is the availability. The accuracy of the method is poor due to the approximatio of a area by uiformly distributed poits with a certai desity. Therefore, it s always a trade-off betwee accuracy ad the umber of total grid poits (desity). I cotrast, the availability assessmet method proposed here determies the exact areas of RAIM holes (uavailable regio), ad further calculates the ratio of the area of availability ad the total area of cocered, which ca be expressed as: AHole A = 1 (7) A c where, A is the availability, A Hole is the area of RAIM holes, A c is the area of the regio cocered, or the surface of the Earth if a global assessmet is performed. Expressio (7) eables a spatial determiatio of satellite availability at a istat i time. To carry out the availability assessmet to cover a full geometry, a umber of temporal samples (time domai) must be take at a certai time iterval. A relatively short time iterval, e.g. oe secod ievitably results i a very large sample size. For example, for the grid-based method the product of the spatio-temporal assessmet would be too large requirig very sigificat computatioal resources. This problem is egated by the area-based method which is able to use a relatively large time iterval e.g. 50 secods. Because of the accuracy of the area-based method ad the predictability of RAIM holes, the performace ibetwee sample times ca be iterpolated by a model derived through a curve fittig process. 3 Results A global RAIM holes calculatio was carried out to verify the algorithm. The omial costellatio of 24 Global Positioig System (GPS) satellites (RTCA/Do- 229C, 2001) was used. The Earth s ellipsoidal model WGS-84 was used. The RAIM holes startig at to Secods of Week (SoW) at a time iterval of 200 secods are show i figures 5a to 5f. The solid lies are the boudaries of satellites footprit. The umber of visible satellites at each itersectio poit is ext to each poit i the figure. Patches with differet shapes ad sizes are formed by the boudaries. The asterisks (*) are the crucial poits ad the dots are the critical poits. The RAIM holes are the areas bouded by the asterisks ad the dots, where oly four or less satellites are i view. Figures 5a to 5f also show how the shapes ad sizes of the RAIM holes chage over time. boudary critical poit crucial poit Figure 5a. RAIM hole at secod Figure 5b. RAIM hole at secod

5 Feg et al.: A Area Computatio Based Method for RAIM Holes Assessmet 15 correspodig iterpolated availability. The very small errors i the iterpolatio cofirm that a relatively log temporal iterval ca be used with the positive effect of sigificatly reducig the computatioal load. Table1. RAIM hole area ad availability Figure 5c. RAIM hole at secod Figure 5d. RAIM hole at secod Time SoW (s) RAIM hole Area (km 2 ) Availability Figure a b c d e f Figure 5e. RAIM hole at secod Figure 6. Curve fit of availability Table2. Compariso of computed ad iterpolated availability Figure 5f. RAIM hole at secod The areas of the RAIM holes ad the global availability for a time iterval of 100 secods are listed i table 1. I order to create a model to eable spatio-temporal determiatio of RAIM holes usig a relatively large iterval, a curve has bee fitted to the data i table 1. This is show i Figure 6. The asterisks (*) are the satellite availability at each sample time. Table 2 gives example errors betwee computed satellite availability ad the Time SoW (s) Computed availability Iterpolated availability Error

6 16 Joural of Global Positioig Systems Aother example is the RAIM hole at SoW. The RAIM hole is small at oly 1.6 square kilometres. The latitude ad logitude of each of the three crucial poits are: ( , ) ( , ) ( , ). At SoW, the RAIM hole is smaller at oly 36m 2. The latitude ad logitude of the three crucial poits are: ( , ) ( , ) ( , ). The area computatio based method easily captures these RAIM holes, while for the grid based method a very dese grid would be required. I this example, a grid of 5 less tha 1 10 degree is required equivalet to more 15 tha sample poits i order to capture this RAIM hole. 4 Coclusios This paper has preseted a ew algorithm for the quatificatio of satellite availability with a particular focus of RAIM holes. Sice the area of a RAIM hole chages i a determiistic way with the motio of the satellites whose boudaries form a regio, a full geometry assessmet (spatial ad temporal) is possible via iterpolatio i the time domai usig a relatively log temporal iterval. This has the positive effect of deliverig high accuracy with miimal computatioal load. The ew approaches for determiig RAIM holes ad quatifyig the RAIM satellite availability statistic i space ad time, are sigificatly superior to the traditioal grid-based method, both i terms of accuracy ad computatioal load. Refereces Air Force Space Commad (1997) Air force space commad capstoe requiremets documet for global positio, velocity, ad time determiatio capability. July, AUGUR. Feg S., Ochieg W.Y., Mautz R., Brodi G. ad Ioaides R. (2005) RAIM Holes Assessmet via a Accurate ad Efficiet Computatio Method, Proceedigs of the Iteratioal Symposium o GPS/GNSS 2005, Hog Kog. Feg S., Ochieg W.Y., Walsh D. ad Ioaides R. (2006a) A Highly Accurate ad Computatioally Efficiet Method for Predictig RAIM Holes, The Joural of Navigatio, The Royal Istitute of Navigatio, 59 (1), pp Feg S., Ochieg W. (2006b) A Geeral Method for Accurate Assessmet of GNSS FDE Holes, Proceedigs of ION GNSS 2006, Fort Worth, TX, 26-29, September, pp Malig D. H. (1992) Coordiate systems ad map projectios (2d editio), Pergamo Press,1992. Ochieg W.Y., Sherida K.F., Sauer K., ad Ha X. (2002) A assessmet of the RAIM performace of a combied Galileo/GPS avigatio system usig the margially detectable errors (MDE) algorithm. GPS Solutio (2002). Vol. 5No. 3, pp RTCA/DO-229C (2001) Miimum operatioal performace stadards for global positioig system/ wide area augmetatio system airbore equipmet. November, Syder J.P. (1987) Map projectios: a workig maual, U.S. Geological Survey Professioal Paper 1395, TSO-C129a (1996) Airbore supplemetal avigatio equipmet usig the global positioig system (GPS), Departmet of Trasportatio Federal Aviatio Admiistratio Aircraft Certificatio Service, Washigto, DC, February, 1996.

Modified Line Search Method for Global Optimization

Modified Line Search Method for Global Optimization Modified Lie Search Method for Global Optimizatio Cria Grosa ad Ajith Abraham Ceter of Excellece for Quatifiable Quality of Service Norwegia Uiversity of Sciece ad Techology Trodheim, Norway {cria, ajith}@q2s.tu.o

More information

FOUNDATIONS OF MATHEMATICS AND PRE-CALCULUS GRADE 10

FOUNDATIONS OF MATHEMATICS AND PRE-CALCULUS GRADE 10 FOUNDATIONS OF MATHEMATICS AND PRE-CALCULUS GRADE 10 [C] Commuicatio Measuremet A1. Solve problems that ivolve liear measuremet, usig: SI ad imperial uits of measure estimatio strategies measuremet strategies.

More information

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008 I ite Sequeces Dr. Philippe B. Laval Keesaw State Uiversity October 9, 2008 Abstract This had out is a itroductio to i ite sequeces. mai de itios ad presets some elemetary results. It gives the I ite Sequeces

More information

CHAPTER 3 DIGITAL CODING OF SIGNALS

CHAPTER 3 DIGITAL CODING OF SIGNALS CHAPTER 3 DIGITAL CODING OF SIGNALS Computers are ofte used to automate the recordig of measuremets. The trasducers ad sigal coditioig circuits produce a voltage sigal that is proportioal to a quatity

More information

Systems Design Project: Indoor Location of Wireless Devices

Systems Design Project: Indoor Location of Wireless Devices Systems Desig Project: Idoor Locatio of Wireless Devices Prepared By: Bria Murphy Seior Systems Sciece ad Egieerig Washigto Uiversity i St. Louis Phoe: (805) 698-5295 Email: bcm1@cec.wustl.edu Supervised

More information

Incremental calculation of weighted mean and variance

Incremental calculation of weighted mean and variance Icremetal calculatio of weighted mea ad variace Toy Fich faf@cam.ac.uk dot@dotat.at Uiversity of Cambridge Computig Service February 009 Abstract I these otes I eplai how to derive formulae for umerically

More information

Z-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 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 information

Section 11.3: The Integral Test

Section 11.3: The Integral Test Sectio.3: The Itegral Test Most of the series we have looked at have either diverged or have coverged ad we have bee able to fid what they coverge to. I geeral however, the problem is much more difficult

More information

Determining the sample size

Determining the sample size Determiig the sample size Oe of the most commo questios ay statisticia gets asked is How large a sample size do I eed? Researchers are ofte surprised to fid out that the aswer depeds o a umber of factors

More information

where: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return

where: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return EVALUATING ALTERNATIVE CAPITAL INVESTMENT PROGRAMS By Ke D. Duft, Extesio Ecoomist I the March 98 issue of this publicatio we reviewed the procedure by which a capital ivestmet project was assessed. The

More information

Hypothesis testing. Null and alternative hypotheses

Hypothesis testing. Null and alternative hypotheses Hypothesis testig Aother importat use of samplig distributios is to test hypotheses about populatio parameters, e.g. mea, proportio, regressio coefficiets, etc. For example, it is possible to stipulate

More information

Vladimir N. Burkov, Dmitri A. Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT

Vladimir N. Burkov, Dmitri A. Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT Keywords: project maagemet, resource allocatio, etwork plaig Vladimir N Burkov, Dmitri A Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT The paper deals with the problems of resource allocatio betwee

More information

BENEFIT-COST ANALYSIS Financial and Economic Appraisal using Spreadsheets

BENEFIT-COST ANALYSIS Financial and Economic Appraisal using Spreadsheets BENEIT-CST ANALYSIS iacial ad Ecoomic Appraisal usig Spreadsheets Ch. 2: Ivestmet Appraisal - Priciples Harry Campbell & Richard Brow School of Ecoomics The Uiversity of Queeslad Review of basic cocepts

More information

INVESTMENT PERFORMANCE COUNCIL (IPC)

INVESTMENT PERFORMANCE COUNCIL (IPC) INVESTMENT PEFOMANCE COUNCIL (IPC) INVITATION TO COMMENT: Global Ivestmet Performace Stadards (GIPS ) Guidace Statemet o Calculatio Methodology The Associatio for Ivestmet Maagemet ad esearch (AIM) seeks

More information

Convention Paper 6764

Convention Paper 6764 Audio Egieerig Society Covetio Paper 6764 Preseted at the 10th Covetio 006 May 0 3 Paris, Frace This covetio paper has bee reproduced from the author's advace mauscript, without editig, correctios, or

More information

AP Calculus AB 2006 Scoring Guidelines Form B

AP 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 information

Your organization has a Class B IP address of 166.144.0.0 Before you implement subnetting, the Network ID and Host ID are divided as follows:

Your organization has a Class B IP address of 166.144.0.0 Before you implement subnetting, the Network ID and Host ID are divided as follows: Subettig Subettig is used to subdivide a sigle class of etwork i to multiple smaller etworks. Example: Your orgaizatio has a Class B IP address of 166.144.0.0 Before you implemet subettig, the Network

More information

Chapter 7 Methods of Finding Estimators

Chapter 7 Methods of Finding Estimators Chapter 7 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 011 Chapter 7 Methods of Fidig Estimators Sectio 7.1 Itroductio Defiitio 7.1.1 A poit estimator is ay fuctio W( X) W( X1, X,, X ) of

More information

AP Calculus BC 2003 Scoring Guidelines Form B

AP 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 information

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling Taig DCOP to the Real World: Efficiet Complete Solutios for Distributed Multi-Evet Schedulig Rajiv T. Maheswara, Milid Tambe, Emma Bowrig, Joatha P. Pearce, ad Pradeep araatham Uiversity of Souther Califoria

More information

hp calculators HP 12C Statistics - average and standard deviation Average and standard deviation concepts HP12C average and standard deviation

hp 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 information

NATIONAL SENIOR CERTIFICATE GRADE 12

NATIONAL SENIOR CERTIFICATE GRADE 12 NATIONAL SENIOR CERTIFICATE GRADE MATHEMATICS P EXEMPLAR 04 MARKS: 50 TIME: 3 hours This questio paper cosists of 8 pages ad iformatio sheet. Please tur over Mathematics/P DBE/04 NSC Grade Eemplar INSTRUCTIONS

More information

Cantilever Beam Experiment

Cantilever Beam Experiment Mechaical Egieerig Departmet Uiversity of Massachusetts Lowell Catilever Beam Experimet Backgroud A disk drive maufacturer is redesigig several disk drive armature mechaisms. This is the result of evaluatio

More information

Automatic Tuning for FOREX Trading System Using Fuzzy Time Series

Automatic Tuning for FOREX Trading System Using Fuzzy Time Series utomatic Tuig for FOREX Tradig System Usig Fuzzy Time Series Kraimo Maeesilp ad Pitihate Soorasa bstract Efficiecy of the automatic currecy tradig system is time depedet due to usig fixed parameters which

More information

I. Chi-squared Distributions

I. Chi-squared Distributions 1 M 358K Supplemet to Chapter 23: CHI-SQUARED DISTRIBUTIONS, T-DISTRIBUTIONS, AND DEGREES OF FREEDOM To uderstad t-distributios, we first eed to look at aother family of distributios, the chi-squared distributios.

More information

The following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles

The following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles The followig eample will help us uderstad The Samplig Distributio of the Mea Review: The populatio is the etire collectio of all idividuals or objects of iterest The sample is the portio of the populatio

More information

Sequences and Series

Sequences and Series CHAPTER 9 Sequeces ad Series 9.. Covergece: Defiitio ad Examples Sequeces The purpose of this chapter is to itroduce a particular way of geeratig algorithms for fidig the values of fuctios defied by their

More information

NEW HIGH PERFORMANCE COMPUTATIONAL METHODS FOR MORTGAGES AND ANNUITIES. Yuri Shestopaloff,

NEW HIGH PERFORMANCE COMPUTATIONAL METHODS FOR MORTGAGES AND ANNUITIES. Yuri Shestopaloff, NEW HIGH PERFORMNCE COMPUTTIONL METHODS FOR MORTGGES ND NNUITIES Yuri Shestopaloff, Geerally, mortgage ad auity equatios do ot have aalytical solutios for ukow iterest rate, which has to be foud usig umerical

More information

Theorems About Power Series

Theorems About Power Series Physics 6A Witer 20 Theorems About Power Series Cosider a power series, f(x) = a x, () where the a are real coefficiets ad x is a real variable. There exists a real o-egative umber R, called the radius

More information

Domain 1: Designing a SQL Server Instance and a Database Solution

Domain 1: Designing a SQL Server Instance and a Database Solution Maual SQL Server 2008 Desig, Optimize ad Maitai (70-450) 1-800-418-6789 Domai 1: Desigig a SQL Server Istace ad a Database Solutio Desigig for CPU, Memory ad Storage Capacity Requiremets Whe desigig a

More information

Research Article Sign Data Derivative Recovery

Research Article Sign Data Derivative Recovery Iteratioal Scholarly Research Network ISRN Applied Mathematics Volume 0, Article ID 63070, 7 pages doi:0.540/0/63070 Research Article Sig Data Derivative Recovery L. M. Housto, G. A. Glass, ad A. D. Dymikov

More information

CHAPTER 7: Central Limit Theorem: CLT for Averages (Means)

CHAPTER 7: Central Limit Theorem: CLT for Averages (Means) CHAPTER 7: Cetral Limit Theorem: CLT for Averages (Meas) X = the umber obtaied whe rollig oe six sided die oce. If we roll a six sided die oce, the mea of the probability distributio is X P(X = x) Simulatio:

More information

1 Correlation and Regression Analysis

1 Correlation and Regression Analysis 1 Correlatio ad Regressio Aalysis I this sectio we will be ivestigatig the relatioship betwee two cotiuous variable, such as height ad weight, the cocetratio of a ijected drug ad heart rate, or the cosumptio

More information

CHAPTER 3 THE TIME VALUE OF MONEY

CHAPTER 3 THE TIME VALUE OF MONEY CHAPTER 3 THE TIME VALUE OF MONEY OVERVIEW A dollar i the had today is worth more tha a dollar to be received i the future because, if you had it ow, you could ivest that dollar ad ear iterest. Of all

More information

Lecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009)

Lecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009) 18.409 A Algorithmist s Toolkit October 27, 2009 Lecture 13 Lecturer: Joatha Keler Scribe: Joatha Pies (2009) 1 Outlie Last time, we proved the Bru-Mikowski iequality for boxes. Today we ll go over the

More information

Department of Computer Science, University of Otago

Department of Computer Science, University of Otago Departmet of Computer Sciece, Uiversity of Otago Techical Report OUCS-2006-09 Permutatios Cotaiig May Patters Authors: M.H. Albert Departmet of Computer Sciece, Uiversity of Otago Micah Colema, Rya Fly

More information

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is 0_0605.qxd /5/05 0:45 AM Page 470 470 Chapter 6 Additioal Topics i Trigoometry 6.5 Trigoometric Form of a Complex Number What you should lear Plot complex umbers i the complex plae ad fid absolute values

More information

Properties of MLE: consistency, asymptotic normality. Fisher information.

Properties of MLE: consistency, asymptotic normality. Fisher information. Lecture 3 Properties of MLE: cosistecy, asymptotic ormality. Fisher iformatio. I this sectio we will try to uderstad why MLEs are good. Let us recall two facts from probability that we be used ofte throughout

More information

Normal Distribution.

Normal Distribution. Normal Distributio www.icrf.l Normal distributio I probability theory, the ormal or Gaussia distributio, is a cotiuous probability distributio that is ofte used as a first approimatio to describe realvalued

More information

Maximum Likelihood Estimators.

Maximum Likelihood Estimators. Lecture 2 Maximum Likelihood Estimators. Matlab example. As a motivatio, let us look at oe Matlab example. Let us geerate a radom sample of size 00 from beta distributio Beta(5, 2). We will lear the defiitio

More information

Lesson 15 ANOVA (analysis of variance)

Lesson 15 ANOVA (analysis of variance) Outlie Variability -betwee group variability -withi group variability -total variability -F-ratio Computatio -sums of squares (betwee/withi/total -degrees of freedom (betwee/withi/total -mea square (betwee/withi

More information

Domain 1 - Describe Cisco VoIP Implementations

Domain 1 - Describe Cisco VoIP Implementations Maual ONT (642-8) 1-800-418-6789 Domai 1 - Describe Cisco VoIP Implemetatios Advatages of VoIP Over Traditioal Switches Voice over IP etworks have may advatages over traditioal circuit switched voice etworks.

More information

Security Functions and Purposes of Network Devices and Technologies (SY0-301) 1-800-418-6789. Firewalls. Audiobooks

Security Functions and Purposes of Network Devices and Technologies (SY0-301) 1-800-418-6789. Firewalls. Audiobooks Maual Security+ Domai 1 Network Security Every etwork is uique, ad architecturally defied physically by its equipmet ad coectios, ad logically through the applicatios, services, ad idustries it serves.

More information

Case Study. Normal and t Distributions. Density Plot. Normal Distributions

Case Study. Normal and t Distributions. Density Plot. Normal Distributions Case Study Normal ad t Distributios Bret Halo ad Bret Larget Departmet of Statistics Uiversity of Wiscosi Madiso October 11 13, 2011 Case Study Body temperature varies withi idividuals over time (it ca

More information

3. Greatest Common Divisor - Least Common Multiple

3. Greatest Common Divisor - Least Common Multiple 3 Greatest Commo Divisor - Least Commo Multiple Defiitio 31: The greatest commo divisor of two atural umbers a ad b is the largest atural umber c which divides both a ad b We deote the greatest commo gcd

More information

HCL Dynamic Spiking Protocol

HCL Dynamic Spiking Protocol ELI LILLY AND COMPANY TIPPECANOE LABORATORIES LAFAYETTE, IN Revisio 2.0 TABLE OF CONTENTS REVISION HISTORY... 2. REVISION.0... 2.2 REVISION 2.0... 2 2 OVERVIEW... 3 3 DEFINITIONS... 5 4 EQUIPMENT... 7

More information

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n We will cosider the liear regressio model i matrix form. For simple liear regressio, meaig oe predictor, the model is i = + x i + ε i for i =,,,, This model icludes the assumptio that the ε i s are a sample

More information

Analyzing Longitudinal Data from Complex Surveys Using SUDAAN

Analyzing Longitudinal Data from Complex Surveys Using SUDAAN Aalyzig Logitudial Data from Complex Surveys Usig SUDAAN Darryl Creel Statistics ad Epidemiology, RTI Iteratioal, 312 Trotter Farm Drive, Rockville, MD, 20850 Abstract SUDAAN: Software for the Statistical

More information

Swaps: Constant maturity swaps (CMS) and constant maturity. Treasury (CMT) swaps

Swaps: Constant maturity swaps (CMS) and constant maturity. Treasury (CMT) swaps Swaps: Costat maturity swaps (CMS) ad costat maturity reasury (CM) swaps A Costat Maturity Swap (CMS) swap is a swap where oe of the legs pays (respectively receives) a swap rate of a fixed maturity, while

More information

A probabilistic proof of a binomial identity

A probabilistic proof of a binomial identity A probabilistic proof of a biomial idetity Joatho Peterso Abstract We give a elemetary probabilistic proof of a biomial idetity. The proof is obtaied by computig the probability of a certai evet i two

More information

FEATURE BASED RECOGNITION OF TRAFFIC VIDEO STREAMS FOR ONLINE ROUTE TRACING

FEATURE BASED RECOGNITION OF TRAFFIC VIDEO STREAMS FOR ONLINE ROUTE TRACING FEATURE BASED RECOGNITION OF TRAFFIC VIDEO STREAMS FOR ONLINE ROUTE TRACING Christoph Busch, Ralf Dörer, Christia Freytag, Heike Ziegler Frauhofer Istitute for Computer Graphics, Computer Graphics Ceter

More information

*The most important feature of MRP as compared with ordinary inventory control analysis is its time phasing feature.

*The most important feature of MRP as compared with ordinary inventory control analysis is its time phasing feature. Itegrated Productio ad Ivetory Cotrol System MRP ad MRP II Framework of Maufacturig System Ivetory cotrol, productio schedulig, capacity plaig ad fiacial ad busiess decisios i a productio system are iterrelated.

More information

NATIONAL SENIOR CERTIFICATE GRADE 11

NATIONAL SENIOR CERTIFICATE GRADE 11 NATIONAL SENIOR CERTIFICATE GRADE MATHEMATICS P NOVEMBER 007 MARKS: 50 TIME: 3 hours This questio paper cosists of 9 pages, diagram sheet ad a -page formula sheet. Please tur over Mathematics/P DoE/November

More information

Statistical inference: example 1. Inferential Statistics

Statistical inference: example 1. Inferential Statistics Statistical iferece: example 1 Iferetial Statistics POPULATION SAMPLE A clothig store chai regularly buys from a supplier large quatities of a certai piece of clothig. Each item ca be classified either

More information

Escola Federal de Engenharia de Itajubá

Escola Federal de Engenharia de Itajubá Escola Federal de Egeharia de Itajubá Departameto de Egeharia Mecâica Pós-Graduação em Egeharia Mecâica MPF04 ANÁLISE DE SINAIS E AQUISÇÃO DE DADOS SINAIS E SISTEMAS Trabalho 02 (MATLAB) Prof. Dr. José

More information

Domain 1: Configuring Domain Name System (DNS) for Active Directory

Domain 1: Configuring Domain Name System (DNS) for Active Directory Maual Widows Domai 1: Cofigurig Domai Name System (DNS) for Active Directory Cofigure zoes I Domai Name System (DNS), a DNS amespace ca be divided ito zoes. The zoes store ame iformatio about oe or more

More information

Approximating Area under a curve with rectangles. To find the area under a curve we approximate the area using rectangles and then use limits to find

Approximating Area under a curve with rectangles. To find the area under a curve we approximate the area using rectangles and then use limits to find 1.8 Approximatig Area uder a curve with rectagles 1.6 To fid the area uder a curve we approximate the area usig rectagles ad the use limits to fid 1.4 the area. Example 1 Suppose we wat to estimate 1.

More information

Output Analysis (2, Chapters 10 &11 Law)

Output Analysis (2, Chapters 10 &11 Law) B. Maddah ENMG 6 Simulatio 05/0/07 Output Aalysis (, Chapters 10 &11 Law) Comparig alterative system cofiguratio Sice the output of a simulatio is radom, the comparig differet systems via simulatio should

More information

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES Read Sectio 1.5 (pages 5 9) Overview I Sectio 1.5 we lear to work with summatio otatio ad formulas. We will also itroduce a brief overview of sequeces,

More information

Finding the circle that best fits a set of points

Finding the circle that best fits a set of points Fidig the circle that best fits a set of poits L. MAISONOBE October 5 th 007 Cotets 1 Itroductio Solvig the problem.1 Priciples............................... Iitializatio.............................

More information

Lesson 17 Pearson s Correlation Coefficient

Lesson 17 Pearson s Correlation Coefficient Outlie Measures of Relatioships Pearso s Correlatio Coefficiet (r) -types of data -scatter plots -measure of directio -measure of stregth Computatio -covariatio of X ad Y -uique variatio i X ad Y -measurig

More information

Soving Recurrence Relations

Soving Recurrence Relations Sovig Recurrece Relatios Part 1. Homogeeous liear 2d degree relatios with costat coefficiets. Cosider the recurrece relatio ( ) T () + at ( 1) + bt ( 2) = 0 This is called a homogeeous liear 2d degree

More information

Using Four Types Of Notches For Comparison Between Chezy s Constant(C) And Manning s Constant (N)

Using Four Types Of Notches For Comparison Between Chezy s Constant(C) And Manning s Constant (N) INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH OLUME, ISSUE, OCTOBER ISSN - Usig Four Types Of Notches For Compariso Betwee Chezy s Costat(C) Ad Maig s Costat (N) Joyce Edwi Bategeleza, Deepak

More information

Confidence Intervals for One Mean

Confidence Intervals for One Mean Chapter 420 Cofidece Itervals for Oe Mea Itroductio This routie calculates the sample size ecessary to achieve a specified distace from the mea to the cofidece limit(s) at a stated cofidece level for a

More information

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth Questio 1: What is a ordiary auity? Let s look at a ordiary auity that is certai ad simple. By this, we mea a auity over a fixed term whose paymet period matches the iterest coversio period. Additioally,

More information

An Efficient Polynomial Approximation of the Normal Distribution Function & Its Inverse Function

An Efficient Polynomial Approximation of the Normal Distribution Function & Its Inverse Function A Efficiet Polyomial Approximatio of the Normal Distributio Fuctio & Its Iverse Fuctio Wisto A. Richards, 1 Robi Atoie, * 1 Asho Sahai, ad 3 M. Raghuadh Acharya 1 Departmet of Mathematics & Computer Sciece;

More information

Definition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean

Definition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean 1 Social Studies 201 October 13, 2004 Note: The examples i these otes may be differet tha used i class. However, the examples are similar ad the methods used are idetical to what was preseted i class.

More information

Chapter 7: Confidence Interval and Sample Size

Chapter 7: Confidence Interval and Sample Size Chapter 7: Cofidece Iterval ad Sample Size Learig Objectives Upo successful completio of Chapter 7, you will be able to: Fid the cofidece iterval for the mea, proportio, ad variace. Determie the miimum

More information

SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx

SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx SAMPLE QUESTIONS FOR FINAL EXAM REAL ANALYSIS I FALL 006 3 4 Fid the followig usig the defiitio of the Riema itegral: a 0 x + dx 3 Cosider the partitio P x 0 3, x 3 +, x 3 +,......, x 3 3 + 3 of the iterval

More information

Quadrat Sampling in Population Ecology

Quadrat Sampling in Population Ecology Quadrat Samplig i Populatio Ecology Backgroud Estimatig the abudace of orgaisms. Ecology is ofte referred to as the "study of distributio ad abudace". This beig true, we would ofte like to kow how may

More information

5 Boolean Decision Trees (February 11)

5 Boolean Decision Trees (February 11) 5 Boolea Decisio Trees (February 11) 5.1 Graph Coectivity Suppose we are give a udirected graph G, represeted as a boolea adjacecy matrix = (a ij ), where a ij = 1 if ad oly if vertices i ad j are coected

More information

The Stable Marriage Problem

The Stable Marriage Problem The Stable Marriage Problem William Hut Lae Departmet of Computer Sciece ad Electrical Egieerig, West Virgiia Uiversity, Morgatow, WV William.Hut@mail.wvu.edu 1 Itroductio Imagie you are a matchmaker,

More information

How To Solve An Old Japanese Geometry Problem

How To Solve An Old Japanese Geometry Problem 116 Taget circles i the ratio 2 : 1 Hiroshi Okumura ad Masayuki Wataabe I this article we cosider the followig old Japaese geometry problem (see Figure 1), whose statemet i [1, p. 39] is missig the coditio

More information

Chapter 5 Unit 1. IET 350 Engineering Economics. Learning Objectives Chapter 5. Learning Objectives Unit 1. Annual Amount and Gradient Functions

Chapter 5 Unit 1. IET 350 Engineering Economics. Learning Objectives Chapter 5. Learning Objectives Unit 1. Annual Amount and Gradient Functions Chapter 5 Uit Aual Amout ad Gradiet Fuctios IET 350 Egieerig Ecoomics Learig Objectives Chapter 5 Upo completio of this chapter you should uderstad: Calculatig future values from aual amouts. Calculatig

More information

Ekkehart Schlicht: Economic Surplus and Derived Demand

Ekkehart Schlicht: Economic Surplus and Derived Demand Ekkehart Schlicht: Ecoomic Surplus ad Derived Demad Muich Discussio Paper No. 2006-17 Departmet of Ecoomics Uiversity of Muich Volkswirtschaftliche Fakultät Ludwig-Maximilias-Uiversität Müche Olie at http://epub.ub.ui-mueche.de/940/

More information

A Faster Clause-Shortening Algorithm for SAT with No Restriction on Clause Length

A Faster Clause-Shortening Algorithm for SAT with No Restriction on Clause Length Joural o Satisfiability, Boolea Modelig ad Computatio 1 2005) 49-60 A Faster Clause-Shorteig Algorithm for SAT with No Restrictio o Clause Legth Evgey Datsi Alexader Wolpert Departmet of Computer Sciece

More information

Engineering Data Management

Engineering Data Management BaaERP 5.0c Maufacturig Egieerig Data Maagemet Module Procedure UP128A US Documetiformatio Documet Documet code : UP128A US Documet group : User Documetatio Documet title : Egieerig Data Maagemet Applicatio/Package

More information

Supply Chain Management

Supply Chain Management Supply Chai Maagemet LOA Uiversity October 9, 205 Distributio D Distributio Authorized to Departmet of Defese ad U.S. DoD Cotractors Oly Aim High Fly - Fight - Wi Who am I? Dr. William A Cuigham PhD Ecoomics

More information

Tradigms of Astundithi and Toyota

Tradigms of Astundithi and Toyota Tradig the radomess - Desigig a optimal tradig strategy uder a drifted radom walk price model Yuao Wu Math 20 Project Paper Professor Zachary Hamaker Abstract: I this paper the author iteds to explore

More information

GOOD PRACTICE CHECKLIST FOR INTERPRETERS WORKING WITH DOMESTIC VIOLENCE SITUATIONS

GOOD PRACTICE CHECKLIST FOR INTERPRETERS WORKING WITH DOMESTIC VIOLENCE SITUATIONS GOOD PRACTICE CHECKLIST FOR INTERPRETERS WORKING WITH DOMESTIC VIOLENCE SITUATIONS I the sprig of 2008, Stadig Together agaist Domestic Violece carried out a piece of collaborative work o domestic violece

More information

Convexity, Inequalities, and Norms

Convexity, Inequalities, and Norms Covexity, Iequalities, ad Norms Covex Fuctios You are probably familiar with the otio of cocavity of fuctios. Give a twicedifferetiable fuctio ϕ: R R, We say that ϕ is covex (or cocave up) if ϕ (x) 0 for

More information

Designing Incentives for Online Question and Answer Forums

Designing Incentives for Online Question and Answer Forums Desigig Icetives for Olie Questio ad Aswer Forums Shaili Jai School of Egieerig ad Applied Scieces Harvard Uiversity Cambridge, MA 0238 USA shailij@eecs.harvard.edu Yilig Che School of Egieerig ad Applied

More information

1 Computing the Standard Deviation of Sample Means

1 Computing the Standard Deviation of Sample Means Computig the Stadard Deviatio of Sample Meas Quality cotrol charts are based o sample meas ot o idividual values withi a sample. A sample is a group of items, which are cosidered all together for our aalysis.

More information

THIN SEQUENCES AND THE GRAM MATRIX PAMELA GORKIN, JOHN E. MCCARTHY, SANDRA POTT, AND BRETT D. WICK

THIN SEQUENCES AND THE GRAM MATRIX PAMELA GORKIN, JOHN E. MCCARTHY, SANDRA POTT, AND BRETT D. WICK THIN SEQUENCES AND THE GRAM MATRIX PAMELA GORKIN, JOHN E MCCARTHY, SANDRA POTT, AND BRETT D WICK Abstract We provide a ew proof of Volberg s Theorem characterizig thi iterpolatig sequeces as those for

More information

Evaluation of Different Fitness Functions for the Evolutionary Testing of an Autonomous Parking System

Evaluation of Different Fitness Functions for the Evolutionary Testing of an Autonomous Parking System Evaluatio of Differet Fitess Fuctios for the Evolutioary Testig of a Autoomous Parkig System Joachim Wegeer 1, Oliver Bühler 2 1 DaimlerChrysler AG, Research ad Techology, Alt-Moabit 96 a, D-1559 Berli,

More information

(VCP-310) 1-800-418-6789

(VCP-310) 1-800-418-6789 Maual VMware Lesso 1: Uderstadig the VMware Product Lie I this lesso, you will first lear what virtualizatio is. Next, you ll explore the products offered by VMware that provide virtualizatio services.

More information

Basic Measurement Issues. Sampling Theory and Analog-to-Digital Conversion

Basic Measurement Issues. Sampling Theory and Analog-to-Digital Conversion Theory ad Aalog-to-Digital Coversio Itroductio/Defiitios Aalog-to-digital coversio Rate Frequecy Aalysis Basic Measuremet Issues Reliability the extet to which a measuremet procedure yields the same results

More information

Predictive Modeling Data. in the ACT Electronic Student Record

Predictive Modeling Data. in the ACT Electronic Student Record Predictive Modelig Data i the ACT Electroic Studet Record overview Predictive Modelig Data Added to the ACT Electroic Studet Record With the release of studet records i September 2012, predictive modelig

More information

INVESTMENT PERFORMANCE COUNCIL (IPC) Guidance Statement on Calculation Methodology

INVESTMENT PERFORMANCE COUNCIL (IPC) Guidance Statement on Calculation Methodology Adoptio Date: 4 March 2004 Effective Date: 1 Jue 2004 Retroactive Applicatio: No Public Commet Period: Aug Nov 2002 INVESTMENT PERFORMANCE COUNCIL (IPC) Preface Guidace Statemet o Calculatio Methodology

More information

Amendments to employer debt Regulations

Amendments to employer debt Regulations March 2008 Pesios Legal Alert Amedmets to employer debt Regulatios The Govermet has at last issued Regulatios which will amed the law as to employer debts uder s75 Pesios Act 1995. The amedig Regulatios

More information

ODBC. Getting Started With Sage Timberline Office ODBC

ODBC. Getting Started With Sage Timberline Office ODBC ODBC Gettig Started With Sage Timberlie Office ODBC NOTICE This documet ad the Sage Timberlie Office software may be used oly i accordace with the accompayig Sage Timberlie Office Ed User Licese Agreemet.

More information

Nr. 2. Interpolation of Discount Factors. Heinz Cremers Willi Schwarz. Mai 1996

Nr. 2. Interpolation of Discount Factors. Heinz Cremers Willi Schwarz. Mai 1996 Nr 2 Iterpolatio of Discout Factors Heiz Cremers Willi Schwarz Mai 1996 Autore: Herausgeber: Prof Dr Heiz Cremers Quatitative Methode ud Spezielle Bakbetriebslehre Hochschule für Bakwirtschaft Dr Willi

More information

Baan Service Master Data Management

Baan Service Master Data Management Baa Service Master Data Maagemet Module Procedure UP069A US Documetiformatio Documet Documet code : UP069A US Documet group : User Documetatio Documet title : Master Data Maagemet Applicatio/Package :

More information

EUROCONTROL PRISMIL. EUROCONTROL civil-military performance monitoring system

EUROCONTROL PRISMIL. EUROCONTROL civil-military performance monitoring system EUROCONTROL PRISMIL EUROCONTROL civil-military performace moitorig system Itroductio What is PRISMIL? PRISMIL is a olie civil-military performace moitorig system which facilitates the combied performace

More information

INFINITE SERIES KEITH CONRAD

INFINITE SERIES KEITH CONRAD INFINITE SERIES KEITH CONRAD. Itroductio The two basic cocepts of calculus, differetiatio ad itegratio, are defied i terms of limits (Newto quotiets ad Riema sums). I additio to these is a third fudametal

More information

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method Chapter 6: Variace, the law of large umbers ad the Mote-Carlo method Expected value, variace, ad Chebyshev iequality. If X is a radom variable recall that the expected value of X, E[X] is the average value

More information

Agency Relationship Optimizer

Agency Relationship Optimizer Decideware Developmet Agecy Relatioship Optimizer The Leadig Software Solutio for Cliet-Agecy Relatioship Maagemet supplier performace experts scorecards.deploymet.service decide ware Sa Fracisco Sydey

More information

THE problem of fitting a circle to a collection of points

THE problem of fitting a circle to a collection of points IEEE TRANACTION ON INTRUMENTATION AND MEAUREMENT, VOL. XX, NO. Y, MONTH 000 A Few Methods for Fittig Circles to Data Dale Umbach, Kerry N. Joes Abstract Five methods are discussed to fit circles to data.

More information

Hypergeometric Distributions

Hypergeometric Distributions 7.4 Hypergeometric Distributios Whe choosig the startig lie-up for a game, a coach obviously has to choose a differet player for each positio. Similarly, whe a uio elects delegates for a covetio or you

More information