Descriptive Statistics
|
|
- Clara Woods
- 6 years ago
- Views:
Transcription
1 Math 3 Lecture I Descrptve tatstcs Descrptve statstcs are graphcal or umercal methods utlsed to summarze data such a way that mportat features of the sample ca be depcted. tatstcs: tatstcs s cocered wth scetfc methods for collectg, orgazg, summarzg, presetg ad aalyzg data, drawg vald coclusos ad mag reasoable decsos of such aalyss. The term stasstcs s used to deote the data themselves or umbers derved from data, such as averages. As a example Employmet statstcs, Accdet statstcs ca be gve. Populato: I collectg data t s ofte mpossble or mpractcal to observe the etre for example sads o the beach, umber of defectve bolts produced a factory a gve day, all possble outcomes successve tosses of a far co, etc.. Therefore stead of examg the etre group called the Populato (uverse), oe exames a small part of t whch represets the group, called ample. A populato ca be fte or fte. Data: A collecto of values to be used for statstcal aalyss. Raw Data: Collected data whch does ot eed to be umercal..e. weghts of certa set of studets, days of the wee, etc. Array: Arragemet of raw umercal data ascedg or descedg order. Rage: Maxmum data mmum data. Class Iterval: A class terval s a dvso of data for use Hstogram(a type of Bar graph). For stace, t s possble to partto scores o a 00 pot test to class tervals of -5, 6-49, ad The ed umbers are called class lmts; the smaller umbers are Lower Class Lmts (LCL) ad the larger umbers are the Upper Class Lmts (UCL). The umbers , , , are called class boudares. For example 0.5 s a lower class boudary ad 5.5 s a upper class boudary of the frst class. Class Iterval ze (wdthessc): Upper class boudary lower class boudary. Class Frequecy: Number of dvduals belogg to each class. Class Mar(CM): The mdpot of the class terval. LCL + UCL LCB + UCB CM or CM. Frequecy Tables (Frequecy Dstrbutos): The frst step drawg a frequecy dstrbuto s to costruct a frequecy table. A frequecy table s a way of orgazg the data by lstg every possble score (cludg those ot actually obtaed the sample) as a colum of umbers ad the frequecy of occurrece of each score as aother. mply a frequecy dstrbuto s a arragemet of data by classes together wth the correspodg class frequecy. ouç Zorlu
2 Computg the frequecy of a score s smply a matter of coutg the umber of tmes that score appears the set of data. It s ecessary to clude scores wth zero frequecy order to draw the frequecy polygos correctly. Geeral Rules for formg Frequecy Dstrbutos:. step: Fd the rage. step: Dvde the rage to a coveet umber of class tervals havg the same sze (possble umber of class tervals are chagg betwee 5 ad 0). 3.step: fd the class frequeces. Hstograms: A hstogram s draw by plottg the scores (mdpots) o the -axs ad the frequeces o the Y-axs. A bar s draw for each score value, the wdth of the bar correspodg to the real lmts of the terval ad the heght correspodg to the frequecy of the occurrece of the score value Frequecy Polygos: A frequecy polygo s draw exactly le a hstogram except that pots are draw rather tha bars. The -axs begs wth the mdpot of the terval mmedately lower tha the lowest terval, ad eds wth the terval mmedately hgher tha the hghest terval. Relatve Frequecy of a class: It s a percetaeg whch s obtaed by dvdg the frequecy of the class to the total frequecy of all classes. Relatve Frequecy Dstrbuto: Arragemet of data by classes together wth the correspodg relatve frequeces. Cumulatve Frequecy: The total frequecy of all values less tha the upper class boudary of a gve class terval s called the cumulatve frequecy upto ad cludg that class terval. Plottg scores o the -axs ad the cumulatve frequecy o the Y-axs draws the Ogve (cumulatve frequecy polygo). The pots are plotted at the tersecto of the upper class boudary of the terval ad the cumulatve frequecy. Relatve Cumulatve Frequecy: Ths s also called the percetage cumulatve frequecy whch s obtaed by dvdg the cumulatve frequecy to the total frequecy. Drawg the -axs as before ad the relatve cumulatve frequecy o the Y-axs draws the Percetage Ogve (relatve cumulatve frequecy polygo). Ogve: The graph showg the cumulatve frequecy less tha ay upper class boudary s called a cumulatve-frequecy polygo or Ogve. ouç Zorlu
3 Example : The frequecy dstrbuto of the ages of sample of 400 dabetcs obtaed by a research physca are gve below. Age (years) No. of Class mars dabetcs Costruct (a) a Frequecy Hstogram ad a Frequecy Polygo. (b) a Relatve Frequecy Dstrbuto. (c) a Cumulatve Frequecy Dstrbuto ad a Ogve. (d) a Relatve Cumulatve Frequecy Dstrbuto ad a Percetage Ogve. (e) Estmate the percetage of dabetcs whose age s uder 4. Measures of Locato The ample Mea: Oe obvous ad very useful measure s the ample Mea. The mea s smply a umercal average. uppose that observatos a sample are,,...,. The sample mea s The ample Meda: The sample meda s ( ) f s odd + / ( / ) + ( / ) + f s eve ouç Zorlu 3
4 Measures of Varablty(Dsperso) Dsperso: The degree to whch mercal data ted to spread about a average value s called the dsperso or varato. The most commo measures of dspersos are rage,varace ad stadard devato. max ample Rage: The smplest measure of varablty (dsperso) s the sample rage. m ample Varace: Let,,..., deote sample values, the quatty ( ) or s called the sample varace. ample tadard Devato: The sample stadard devato s ( ). Example : A maufacturer of electroc compoets s terested determg the lfetme of a certa type of battery. A sample, hour of lfe, s as follows: 3, 6,, 0, 75, 6, 5,, 8, 7 (a) Fd the sample mea ad meda. 43 The sample mea s Arrage the data a Array as: 0,,6,7,8,,3,5,6,75. ( ) + 5 ( 6) 8 + ce 0 s eve, the meda s 0. (b) Fd the sample varace, stadard devato ad the rage ( ) The sample varace s ( ) ouç Zorlu 4
5 The sample stadard devato s ( ) The rage s max m Mea ad Varace computed from Grouped Data: Mea: If,,..., occur f, f,..., f tmes, respectvely (.e. occur wth frequeces f, f,..., f ), the arthmetc mea s where N s the total frequecy ad f N represets the class amr of the th class. Varace: If,,..., occur wth frequeces f, f,..., f respectvely, the varace ca be wrtte as f ( ) N or N f f N( N ). Example 3: Use the gve frequecy dstrbuto of the weghts of the 00 studets at YZ Uversty to fd the mea, varace ad the stadard devato. Weght(g) Frequecy( f ) Class mars( ) f ( ) f ( ) N f 00 f Mea f g N 00 ouç Zorlu 5
6 Varace f ( ) g N 99 f ( ) tadard Devato N g Example 4: The followg data represets the age of the buldgs ( years) of a gve area Lefosa (a) Costruct the frequecy table usg the followg classes: -7, 8-3, 4-9, 0-5, 6-3, (b) Draw the relatve cumulatve frequecy Hstogram ad the Percetage Ogve. (c) Estmate the percetage of houses whose age s uder 5 years. (a) Class Tally Freq.( f ) Cum Rel. Rel. cum boudary Freq( cf ) f Freq( N ) cf freq( N ) /30 6/ /30 6/ /30 3/ /30 7/ /30 9/ /30 30/30 6 f 30 ouç Zorlu 6
7 Example 5: Thrty AA Batteres were tested to determe how log they would last. The results, to the earest mute, were recorded as follows Costruct (a) a Frequecy Dstrbuto. (b) a Cumulatve Frequecy Dstrbuto. Example 6: I a -weely study of the productvty of worers, the followg data were obtaed o the total umber of acceptable peces whch 40 worers produced (a) Costruct a frequecy dstrbuto usg 6 classes. (b) Draw a frequecy hstogram. (c) Costruct a relatve frequecy hstogram. (d) Draw the percetage ogve. Example 7: The followg table shows a frequecy dstrbuto of the weely wages of 65 employees at the P&R Compay. Wages No. of employees $ $ $ $ $ $ $ N65 (a) Costruct a frequecy hstogram (b) Costruct a cumulatve-frequecy dstrbuto (c) Costruct a ogve (d) Evaluate the umber of employees earg () less tha $88.00 per wee () at least $63.00 per wee but less tha $75.00 per wee (e) Compute ad for grouped data. ouç Zorlu 7
The Institute of Chartered Accountants of Sri Lanka
The Isttute of Chartered Accoutats of Sr Laka Executve Dploma Accoutg, Busess ad Strategy Quattatve Methods for Busess Studes Hadout 0: Presetato ad Aalyss of data Presetato of Data Arragg Data The arragemet
More informationSixth Edition. Chapter 7 Point Estimation of Parameters and Sampling Distributions Mean Squared Error of an 7-2 Sampling Distributions and
3//06 Appled Statstcs ad Probablty for Egeers Sth Edto Douglas C. Motgomery George C. Ruger Chapter 7 Pot Estmato of Parameters ad Samplg Dstrbutos Copyrght 04 Joh Wley & Sos, Ic. All rghts reserved. 7
More informationApplied Statistics and Probability for Engineers, 6 th edition December 31, 2013 CHAPTER 6. Section 6-1
Appled Statstcs ad Probablty for Egeers, 6 th edto December 31, 013 CHAPTER 6 Secto 6-1 6-1. No, usually ot. For eample, f the sample s {, 3} the mea s.5 whch s ot a observato the sample. 6-3. No, usually
More informationShort Note: Merging Secondary Variables for Geophysical Data Integration
Short Note: Mergg Secodary Varables for Geophyscal Data Itegrato Steve Lyster ad Clayto V. Deutsch Departmet of Cvl & Evrometal Egeerg Uversty of Alberta Abstract Multple secodary data from geophyscal
More informationA CONTROL CHART FOR HEAVY TAILED DISTRIBUTIONS. K. Thaga. Department of Statistics University of Botswana, Botswana
A CONTROL CHART FOR HEAVY TAILED DISTRIBUTIONS K. Thaga Departmet of Statstcs Uversty of Botswaa, Botswaa thagak@mopp.ub.bw ABSTRACT Stadard cotrol charts wth cotrol lmts determed by the mea ad stadard
More informationMeasures of variation or measures of spread: is a descriptive measure that describes how much variation or spread there is in a data set.
Secto.6 Meaure o Dpero Meaure o varato or meaure o pread: a decrptve meaure that decrbe how much varato or pread there a data et. Wh th mportat? Whch Catheter IV mauacturer would ou preer to ue or purchag
More informationLECTURE 4 QUANTITATIVE MODELS FOR FACILITY LOCATION: SERVICE FACILITY ON A LINE OR ON A PLANE
LECTUE 4 QUANTITATIVE MODELS FO FACILITY LOCATION: SEVICE FACILITY ON A LINE O ON A PLANE Learg objectve 1. To demostrate the quattatve approach to locate faclt o a le ad o a plae 6.10 Locatg Faclt o a
More informationLecture6: Lossless Compression Techniques. Conditional Human Codes
ecture6: ossless Compresso Techques Codtoal uma Codes -Cosder statoary dscrete arov process, = { s, s, s } wth codtoal pmfs P P s s wth,, tates o Ps/so.9.5.5 Ps/s.5.8.5 Ps/s.5.5.6 -The margal probabltes
More informationSIMPLE RANDOM SAMPLING
UIT IMPL RADOM AMPLIG mple Radom amplg tructure. Itroducto Obectves. Methods of electo of a ample Lottery Method Radom umber Method Computer Radom umber Geerato Method.3 Propertes of mple Radom amplg Merts
More informationK-Map 1. In contrast, Karnaugh map (K-map) method provides a straightforward procedure for simplifying Boolean functions.
K-Map Lesso Objectves: Eve though Boolea expressos ca be smplfed by algebrac mapulato, such a approach lacks clear regular rules for each succeedg step ad t s dffcult to determe whether the smplest expresso
More informationAssignment#4 Due: 5pm on the date stated in the course outline. Hand in to the assignment box on the 3 rd floor of CAB.
MATH Assgmet#4 Due: 5pm o the date stated the course outle. Had to the assgmet box o the 3 rd floor of CAB.. Let deote the umber of teror regos of a covex polygo wth sdes, dvded by all ts dagoals, f o
More informationOn the Techniques for Constructing Even-order Magic Squares using Basic Latin Squares
Iteratoal Joural of Scetfc ad Research Publcatos, Volume, Issue 9, September 0 ISSN 50-353 O the Techques for Costructg Eve-order Magc Squares usg Basc Lat Squares Tomba I. Departmet of Mathematcs, Mapur
More informationAn ANOVA-Based GPS Multipath Detection Algorithm Using Multi-Channel Software Receivers
A ANOVA-Based GPS Multpath Detecto Algorthm Usg Mult-Chael Software Recevers M.T. Breema, Y.T. Morto, ad Q. Zhou Dept. of Electrcal ad Computer Egeerg Mam Uversty Oxford, OH 4556 Abstract: We preset a
More informationModule 6. Channel Coding. Version 2 ECE IIT, Kharagpur
Module 6 Chael Codg Lesso 36 Coded Modulato Schemes After readg ths lesso, you wll lear about Trells Code Modulato; Set parttog TCM; Decodg TCM; The modulated waveform a covetoal ucoded carrer modulato
More informationSTATISTICS. is given by. i i. = total frequency, d i. = x i a ANIL TUTORIALS. = total frequency and d i. = total frequency, h = class-size
STATISTICS ImPORTANT TERmS, DEFINITIONS AND RESULTS l The mean x of n values x 1, x 2, x 3,... x n s gven by x1+ x2 + x3 +... + xn x = n l mean of grouped data (wthout class-ntervals) () Drect method :
More informationOPTIMAL BUS DISPATCHING POLICY UNDER VARIABLE DEMAND OVER TIME AND ROUTE LENGTH
OPTIMAL BUS DISPATCHING POLICY UNDER VARIABLE DEMAND OVER TIME AND ROUTE LENGTH Amal S. Kumarage, Professor of Cvl Egeerg, Uversty of Moratuwa, Sr Laka H.A.C. Perera, Cetral Egeerg Cosultacy Bureau, Sr
More informationComparison of Measurement and Prediction of ITU-R Recommendation P.1546
Comparso of Measuremet ad Predcto of ITU-R Recommedato P.546 Chag-Hoo Lee *, Nam-Ryul Jeo *, Seog-Cheol Km *, Jug-m Lm * Isttute of New Meda ad Commucatos, Seoul Natoal Uversty, Cosoldated Mateace Depot,
More information5. Random Processes. 5-3 Deterministic and Nondeterministic Random Processes
5. Radom Processes 5- Itroducto 5- Cotuous ad Dscrete Radom Processes 5-3 Determstc ad Nodetermstc Radom Processes 5-4 Statoary ad Nostatoary Radom Processes 5-5 rgodc ad Noergodc Radom Processes 5-6 Measuremet
More informationGeometric Distribution as a Randomization Device: Implemented to the Kuk s Model
It. J. Cotem. Math. Sceces, Vol. 8, 03, o. 5, 43-48 HIKARI Ltd, www.m-hkar.com Geometrc Dstrbuto as a Radomzato Devce: Imlemeted to the Kuk s Model Sarjder Sgh Deartmet of Mathematcs Texas A&M Uversty-Kgsvlle
More informationA New Mathematical Model for a Redundancy Allocation Problem with Mixing Components Redundant and Choice of Redundancy Strategies
Appled Mathematcal Sceces, Vol, 2007, o 45, 222-2230 A New Mathematcal Model for a Redudacy Allocato Problem wth Mxg Compoets Redudat ad Choce of Redudacy Strateges R Tavakkol-Moghaddam Departmet of Idustral
More informationComparison of Estimators of Extreme Value Distributions for Wind Data Analysis
Bofrg Iteratoal Joural of Data g, Vol., o. 3, September 0 6 Comparso of Estmators of Extreme Value Dstrbutos for d Data Aalyss. Vvekaada Abstract--- Estmato of extreme wd speed potetal at a rego s of mportace
More informationThe Institute of Chartered Accountants of Sri Lanka
The Istitute of Chartered Accoutats of Sri Laka Postgraduate Diploma i Busiess ad Fiace Quatitative Techiques for Busiess Hadout 02:Presetatio ad Aalysis of data Presetatio of Data The Stem ad Leaf Display
More informationDYNAMIC BROADCAST SCHEDULING IN ASYMMETRIC COMMUNICATION SYSTEMS: PUSH AND PULL DATA BASED ON SCHEDULING INDEX AND OPTIMAL CUT-OFF POINT YUFEI GUO
DYNAMIC BROADCAST SCHEDULING IN ASYMMETRIC COMMUNICATION SYSTEMS: PUSH AND PULL DATA BASED ON SCHEDULING INDEX AND OPTIMAL CUT-OFF POINT by YUFEI GUO Preseted to the Faculty of the Graduate School of The
More informationHandbook on precision requirements and variance estimation for ESS households surveys
ISSN 977-0375 Methodologes ad Workg papers Hadbook o precso requremets ad varace estmato for ESS households surveys 03 edto Methodologes ad Workg papers Hadbook o precso requremets ad varace estmato for
More informationDeinterleaving of Interfering Radars Signals in Identification Friend or Foe Systems
8 Telecommucatos forum TEFOR Serba, Belgrade, ovember -5, Deterleavg of Iterferg Radars Sgals Idetfcato Fred or Foe Systems Youes Ahmad amal Mohamedpour Moe Ahmad Abstract I a dese moder electroc warfare
More informationChapter 3. Geographical Data Broadcast Cost Models
Chapter Geographcal ata Broadcast Cost Models s dscussed Secto. T s further dvded to to compoets amel Probe Wat ad Bcast Wat. We argue that t mght be more approprate to dvde T to four compoets: Ide-Probe
More informationBER ANALYSIS OF V-BLAST MIMO SYSTEMS UNDER VARIOUS CHANNEL MODULATION TECHNIQUES IN MOBILE RADIO CHANNELS
202 Iteratoal Coferece o Computer Techology ad Scece (ICCTS 202) IPCSIT vol. 47 (202) (202) IACSIT Press, Sgapore DOI: 0.7763/IPCSIT.202.V47.24 BER ANALYSIS OF V-BLAST MIMO SYSTEMS UNDER VARIOUS CANNEL
More informationPerformance Comparison of Two Inner Coding Structures in Concatenated Codes for Frequency-Hopping Spread Spectrum Multiple-Access Communications
Iteratoal Joural o Recet ad Iovato Treds Computg ad Commucato IN: 31-8169 Volume: 3 Issue: 741-745 erformace Comparso of Two Ier Codg tructures Cocateated Codes for Frequecy-Hoppg pread pectrum Multple-Access
More informationCHAPTER-4 WIDE BAND PASS FILTER DESIGN 4.1 INTRODUCTION
CHAPTER-4 WIDE BAND PASS FILTER DESIGN 4. INTRODUCTION The bad pass flters suested last chapter are hav the FBW less tha the 2%. I cotrast of that ths chapter deals wth the des of wde bad pass flter whch
More informationTime-Frequency Entropy Analysis of Arc Signal in Non-Stationary Submerged Arc Welding
Egeerg, 211, 3, 15-19 do:1.4236/eg.211.3213 Publshed Ole February 211 (http://www.scrp.org/joural/eg) Tme-Frequecy Etropy Aalyss of Arc Sgal o-statoary Submerged Arc Weldg Abstract Kuafag He 1, Swe Xao
More informationAn Improved DV-Hop Localization Algorithm Based on the Node Deployment in Wireless Sensor Networks
Iteratoal Joural of Smart Home Vol. 9, No. 0, (05), pp. 97-04 http://dx.do.org/0.457/jsh.05.9.0. A Improved DV-Hop Localzato Algorthm Based o the Node Deploymet Wreless Sesor Networks Jam Zhag, Ng Guo
More informationTHE FOURIER SERIES USED IN ANALYSE OF THE CAM MECHANISMS FOR THE SHOEMAKING MACHINES (PART I)
ANNALS OF HE UNIVERSIY OF ORADEA FASCICLE OF EXILES, LEAHERWORK HE FOURIER SERIES USED IN ANALYSE OF HE CAM MECHANISMS FOR HE SHOEMAKING MACHINES (PAR I) IOVAN-DRAGOMIR Ala, DRIȘCU Maraa, Gheorghe Asach
More informationEfficient Utilization of FlexRay Network Using Parameter Optimization Method
Iteratoal Joural of Egeerg ad Techology, Vol. 8, No. 6, December 2016 Effcet Utlzato of FlexRay Network Usg Parameter Optmzato Method Y. X. Wag, Y. H. Xu, ad Y. N. Xu Abstract FlexRay s a hgh rate of bus
More informationA New and Efficient Proposed Approach to Find Initial Basic Feasible Solution of a Transportation Problem
Amerca Joural of Appled Mathematcs ad Statstcs, 27, Vol., No. 2, 4-6 Avalable ole at http://pubs.scepub.com/ajams//2/3 Scece ad Educato Publshg DOI:.269/ajams--2-3 A New ad Effcet Proposed Approach to
More informationAdvances in SAR Change Detection
Lesle M. ovak Scetfc Sstems Compa, Ic. 500 West Cummgs Park, Sute 3000 Wobur, MA 080 UITED STATES E-mal: lovak@ssc.com, ovakl@charter.et ABSTRACT SAR chage detecto performace usg coheret chage detecto
More informationEvolutionary Algorithm With Experimental Design Technique
Evolutoary Algorthm Wth Expermetal Desg Techque Qgfu Zhag Departmet of Computer Scece Uversty of Essex Wvehoe Park Colchester, CO4 3SQ Uted Kgdom Abstract: - Major steps evolutoary algorthms volve samplg
More informationShort Term Load Forecasting using Multiple Linear Regression
Short Term Load Forecastg usg Multple Lear Regresso N. Amral, C.S. Özvere, D Kg Uversty of Abertay Dudee, UK Abstract I ths paper we preset a vestgato for the short term (up 4 hours load forecastg of the
More informationZigbee wireless sensor network localization evaluation scheme with weighted centroid method
Zgbee wreless sesor etwork localzato evaluato scheme wth weghted cetrod method Loesy Thammavog 1, Khamphog Khogsomboo 1, Thaadol Tegthog 2 ad Sathapor Promwog 2,* 1 Departmet of Electrocs ad Telecommucato
More informationDISTRIBUTION VOLTAGE MONITORING AND CONTROL UTILIZING SMART METERS
4 th Iteratoal Coferece o Electrcty Dstrbuto Glasgow, -5 Jue 07 DISTRIBUTION VOLTAGE MONITORING AND CONTROL UTILIZING SMART METERS Yoshhto. KINOSHITA Kazuor. IWABUCHI Yasuyuk. MIYAZAKI Toshba Japa Toshba
More informationInformation Theory and Coding
Iformato heory ad Codg Itroducto What s t all aout? Refereces: C..hao, A Mathematcal heory of Commucato, he Bell ystem echcal Joural, Vol. 7, pp. 379 43, 63 656, July, Octoer, 948. C..hao Commucato the
More informationK-sorted Permutations with Weakly Restricted Displacements
K-sorted Permutatos wth Weakly Restrcted Dsplacemets Tg Kuo Departmet of Marketg Maagemet, Takmg Uversty of Scece ad Techology Tape 5, Tawa, ROC tkuo@takmg.edu.tw Receved February 0; Revsed 5 Aprl 0 ;
More informationA HIGH ACCURACY HIGH THROUGHPUT JITTER TEST SOLUTION ON ATE FOR 3GBPS AND 6GBPS SERIAL-ATA
A HIGH ACCURACY HIGH THROUGHPUT JITTER TEST SOLUTION ON ATE FOR 3GBPS AND 6GBPS SERIAL-ATA Yogqua Fa, Y Ca ad Zeljko Zlc LSI Corporato 0 Amerca Parkway NE, Alletow, Pesylvaa 809 Emal: y.ca@ls.com Departmet
More informationAn Enhanced Posterior Probability Anti-Collision Algorithm Based on Dynamic Frame Slotted ALOHA for EPCglobal Class1 Gen2
Joural of Commucatos Vol. 9,. 0, October 204 A Ehaced Posteror Probablty At-Collso Algorthm Based o Dyamc Frame Slotted ALOHA for EPCglobal Class Ge2 Lta Dua,Wewe Pag 2, ad Fu Dua 2 College of Iformato
More informationMultiset Permutations in Lexicographic Order
Webste: www.jetae.com ISSN 2250-2459, ISO 9001:2008 Certfed Joural, Volume 4, Issue 1, Jauary 2014 Multset Permutatos Lexcographc Order Tg Kuo Departmet of Marketg Maagemet, Takmg Uversty of Scece ad Techology,
More informationBlock-based Feature-level Multi-focus Image Fusion
Block-based Feature-level Mult-focus Image Fuso Abdul Bast Sddqu, M. Arfa Jaffar Natoal Uversty of Computer ad Emergg Sceces Islamabad, Paksta {bast.sddqu,arfa.affar}@u.edu.pk Ayyaz Hussa, Awar M. Mrza
More informationInstallation and Dispatch of the Traffic Patrol Service Platform
Iteratoal Joural of Statstcs ad Probablty; Vol. 6, No. 1; Jauary 2017 ISSN 1927-7032 E-ISSN 1927-7040 Publshed by Caada Ceter of Scece ad Educato Istallato ad Dspatch of the Traffc Patrol Servce Platform
More informationA New Aggregation Policy for RSS Services
A New Aggregato Polcy for RSS Servces Youg Geu Ha Sag Ho Lee Jae Hw Km Yaggo Km 2 School of Computg, Soogsl Uversty Seoul, Korea {youggeu,shlee99,oassdle}@gmal.com 2 Dept. of Computer ad Iformato Sceces,
More information606. Research of positioning accuracy of robot Motoman SSF2000
606. Research of postog accuracy of robot Motoma SSF2000 A. Klkevčus, M. Jurevčus 2, V. Vekters 3, R. Maskeluas 4, J. Stakūas 5, M. Rybokas 6, P. Petroškevčus 7 Vlus Gedmas Techcal Uversty, Departmet of
More informationSchedule. ECEN 301 Discussion #24 DAC 1. Date Day Class No. Title Chapters HW Due date 24 Nov Mon 24 DAC Exam. Lab Due date
Schedule Date Day Class No. Ttle Chapters HW Due date 4 No Mo 4 DAC 5.4 Lab Due date Exam 5 No Tue ectato HW 6 No Wed Thaksgg 7 No Thu Thaksgg 8 No Fr Thaksgg 9 No Sat 3 No Su Dec Mo Fal eew Dec Tue LAB
More informationAllocating Travel Times Recorded from Sparse GPS Probe Vehicles into Individual Road Segments
Avalable ole at www.scecedrect.com SceceDrect Trasportato Research Proceda 25C (207) 223 2226 www.elsever.com/locate/proceda World Coferece o Trasport Research - WCTR 206 Shagha. 0-5 July 206 Allocatg
More informationLesson 6: Queues and Markov Chains
lde supportg materal Lesso 6: Queues ad Markov Chas Gova Gambee Queug Theor ad Telecommucatos: Networks ad Applcatos d edto, prger All rghts reserved 3 Queug Theor ad Telecommucatos: Networks ad Applcatos
More informationA Two Stage Methodology for Siting and Sizing of DG for Minimum Loss in Radial Distribution System using RCGA
teratoal Joural of Computer Applcatos (0975 8887) Volume 5 No., July 0 A Two Stage Methodology for Stg ad Szg of for Mmum Loss adal Dstrbuto System usg CGA Dr.M.adma ltha EEE Member rofessor & HOD Dept.
More informationPERMUTATION AND COMBINATION
MPC 1 PERMUTATION AND COMBINATION Syllabus : Fudametal priciples of coutig; Permutatio as a arragemet ad combiatio as selectio, Meaig of P(, r) ad C(, r). Simple applicatios. Permutatios are arragemets
More informationCONSTRUCTION OF AN OPTIMAL MATHEMATICAL MODEL OF FUNCTIONING OF THE MANUFACTURING INDUSTRY OF THE REPUBLIC OF KAZAKHSTAN
CONSTRUCTION OF AN OPTIMAL MATHEMATICAL MODEL OF FUNCTIONING OF THE MANUFACTURING INDUSTRY OF THE REPUBLIC OF KAZAKHSTAN SEILKHAN N. BORANBAYEV, ASKAR B. NURBEKOV L.N. Gumlyov Eurasa Natoal Uversty, Satpayev
More informationWeighted Centroid Correction Localization in Cellular Systems
Amerca J. of Egeerg ad Appled Sceces 4 (): 37-4, 20 ISSN 94-7020 200 Scece Publcatos Weghted Cetrod Correcto Localzato Cellular Systems Rog-Zheg L, X-Log Luo ad Ja-Ru L Key Laboratory of Uversal Wreless
More informationSwitching Angle Design for Pulse Width Modulation AC Voltage Controller Using Genetic Algorithm and Distributed Artificial Neural Network
Swtchg Agle Desg for Pulse Wdth Modulato AC Voltage Cotroller Usg Geetc Algorthm ad Dstrbuted Artfcal Neural Network Pattarapor Jtta, Somyot Katwadvla ad Atthapol Ngaoptakkul Abstract. Ths paper proposes
More informationDistributed Online Matching Algorithm For Multi-Path Planning of Mobile Robots
Proect Paper for 6.854 embers: Seugkook Yu (yusk@mt.edu) Sooho Park (dreameo@mt.edu) Dstrbuted Ole atchg Algorthm For ult-path Plag of oble Robots 1. Itroducto Curretly, we are workg o moble robots whch
More informationOn the Development of an Intelligent Computer Player for CLUE : a Case Study on Preposterior Decision Analysis
Proceedgs of the 2006 Amerca Cotrol Coferece Meapols, Mesota, USA, Jue 14-16, 2006 FrA05.2 O the Developmet of a Itellget Computer Player for CLUE: a Case Study o Preposteror Decso Aalyss Cheghu Ca ad
More informationNew Difference Estimator in Two-phase Sampling using Arbitrary Probabilities
IN 1684-8403 Joural o tatstcs olume 15, 008,. 7-16 Abstract New Derece Estmator Two-hase amlg usg Arbtrar Probabltes Asa Kamal 1 ad Muhammad Qaser hahbaz A ew derece estmator has bee costructed two-hase
More informationImplementing wavelet packet transform for valve failure detection using vibration and acoustic emission signals
Joural of Physcs: Coferece Seres Implemetg wavelet packet trasform for valve falure detecto usg vbrato ad acoustc emsso sgals To cte ths artcle: H Y Sm et al 1 J. Phys.: Cof. Ser. 364 186 Vew the artcle
More informationThermometer-to-binary Encoder with Bubble Error Correction (BEC) Circuit for Flash Analog-to-Digital Converter (FADC)
Thermometer-to-bary Ecoder wth Bubble Error Correcto (BEC) Crcut for Flash Aalog-to-Dgtal Coverter (FADC) Bu Va Heu, Seughyu Beak, Seughwa Cho +, Jogkook Seo ±, Takyeog Ted. Jeog,* Dept. of Electroc Egeerg,
More informationColor Image Enhancement using Modify Retinex and Histogram Equalization Algorithms Depending on a Bright Channel Prior
Iteratoal Joural of Applcato or Iovato Egeerg & Maagemet (IJAIEM) Web Ste: www.jaem.org Emal: edtor@jaem.org Color Image Ehacemet usg Modfy Retex ad Hstogram Equalzato Algorthms Depedg o a Brght Chael
More informationSimulation of rainfall-runoff process by artificial neural networks and HEC-HMS model (case study Zard river basin)
Proceedgs of The Fourth Iteratoal Ira & Russa Coferece 43 Smulato of rafall-ruoff process by artfcal eural etworks ad HEC-HMS model (case study Zard rver bas Mehrdad Akbarpour MSc. Graguate, Water Structures
More informationVoltage Contingency Ranking for IEEE 39-Bus System using Newton- Raphson Method
WSEAS TRANSACTIONS o OWER SSTEMS Haer m, Asma Meddeb, Souad Chebb oltage Cotgecy Rag for IEEE 39-Bus System usg Newto- Raphso Method HAER MII, ASMA MEDDEB ad SOUAD CHEBBI Natoal Hgh School of Egeers of
More informationA Spectrally Efficient Frequency Division Multiplexing Based Communications System M. R. D. Rodrigues and I. Darwazeh
IOWo'03, 8th Iteratoal OFDM-Workshop, Proceedgs, Hamburg, DE, Sep 24-25, 2003 (Prepublcato draft) A Spectrally Effcet Frequecy Dvso Multplexg Based Commucatos System M. R. D. Rodrgues ad I. Darwazeh Laboratory
More informationFace Recognition Algorithm Using Muti-direction Markov Stationary Features and Adjacent Pixel Intensity Difference Quantization Histogram
ICSNC 2012 : The Seveth Iteratoal Coferece o Systems ad Networks Commucatos Face Recogto Algorthm Usg Mut-drecto Markov Statoary Features ad Adjacet xel Itesty Dfferece Quatzato Hstogram Fefe Lee, Koj
More informationFUZZY IMAGE SEGMENTATION USING LOCATION AND INTENSITY INFORMATION
FUZZY AGE SEGENTATON USNG OCATON AND NTENSTY NFOATON Ameer Al, aurece S Dooley ad Gour C Karmakar Gppslad School of Computg & formato Techology, oash Uversty, Australa Emal: {AmeerAl, aurecedooley ad GourKarmakar}@fotechmoasheduau
More informationThe optimization of emergency resource-mobilization based on harmony search algorithm
Avalable ole www.ocpr.com Joural of Chemcal ad Pharmaceutcal Research, 04, 6(7):483-487 Research Artcle ISS : 0975-7384 CODE(USA) : JCPRC5 The optmzato of emergecy resource-moblzato based o harmoy search
More informationOn the Robustness of Next Generation GNSS Phase-only Real-Time Kinematic Positioning
O the Robustess of Next Geerato GNSS Phase-oly Real-Tme Kematc Postog Leard HUISMAN 1, Peter J.G. TEUNISSEN 1,2, Des ODIJK 1 1 Curt Uversty of Techology GNSS Research Lab Ket Street, Betley WA 6845, Perth,
More informationReliability Allocation
Relablty Allocato Yashwat K. Malaya omputer Scece Dept. olorado State Uversty Fort olls O 80523 USA malaya@cs.colostate.edu Phoe: 970-49-703, 970-49-2466 Abstract---A system s geerally desged as a assembly
More informationA New Method for Detection and Evaluation of Winding Mechanical Faults in Transformer through Transfer Function Measurements
[Dowloaded from www.aece.ro o Wedesday, Jue 0, 20 at 6:04:09 (UTC) by 27.28.226.42. Redstrbuto subject to AECE lcese or copyrght. Ole dstrbuto s expressly prohbted.] Advaces Electrcal ad Computer Egeerg
More informationCS519K: M ULTIMEDIA SYSTEMS STUDENT PROJECTS DATE ANNOUNCED: OCTOBER 25, 2002 DUE DATE:
CS519K: M ULTIMEDIA SYSTEMS STUDENT PROJECTS DATE ANNOUNCED: OCTOBER 25, 2002 DUE DATE: DECEMBER 5, 2002, 11:59PM Basc gropg: Groups of 2 studets each; (or dvdual 1-member groups) There are 6 projects.
More informationInfinite Series Forms of Double Integrals
Iteratoal Joural of Data Evelopmet Aalyss ad *Operatos Research*, 4, Vol., No., 6- Avalable ole at http://pubs.scepub.com/jdeaor/// Scece ad Educato Publshg DOI:.69/jdeaor--- Ifte Seres Forms of Double
More informationEngineering Oriented Dependability Evaluation: MEADEP and Its Applications
997 Pacfc Rm Iteratoal ymposum o Fault-olerat ystems, ape, awa, Dec. 5-6, 997, pp. 85-90. Egeerg Oreted Depedablty Evaluato: MEADEP ad Its Applcatos Dog ag, Myro Hecht, Jeffrey Agro, Jeffrey Mller ad Herbert
More informationOPTIMAL DG PLACEMENT FOR MAXIMUM LOSS REDUCTION IN RADIAL DISTRIBUTION SYSTEM USING ABC ALGORITHM
teratoal Joural of Revews Computg 009-00 JRC & LLS. All rghts reserved. JRC SSN: 076-338 www.jrc.org E-SSN: 076-3336 OPTMAL PLACEMENT FOR MAXMUM LOSS REDUCTON N RADAL DSTRBUTON SYSTEM USNG ABC ALGORTHM
More informationImpact of Carding Parameters and Draw Frame Speed on Migration Characteristics of Ring Spun Yarns ABSTRACT
Impact of Cardg Parameters ad Draw Frame Speed o Mgrato Characterstcs of Rg Spu Yars A. Kumar 1, S. M. Ishtaque ad A. Mukhopadhaya 3 Volume 6, Issue 4, Fall 010 1 Departmet of Textle Techology, GZS College
More informationThe Use of Genetic Algorithms in Validating the System Model and Determining Worst-case Transients in Capacitor Switching Simulation Studies
The Use of Geetc Algorthms Valdatg the System Model ad Determg Worst-case Trasets Capactor Swtchg Smulato Studes Mlade Kezuovc, Fellow, IEEE Yua Lao, Studet Member, IEEE Texas A&M Uversty Departmet of
More informationLong Number Bit-Serial Squarers
Log Number Bt-Seral Squarers E. Chaotaks, P. Kalvas ad K. Z. Pekmestz are th the Natoal Techcal Uversty of Athes, 7 73 Zographou, Athes, Greece. E-mal: lchaot, paraskevas, pekmes@mcrolab.tua.gr Abstract
More informationSVD-based Collaborative Filtering with Privacy
SVD-based Collaboratve Flterg wth Prvacy Husey Polat Departmet of Electrcal Egeerg ad Computer Scece Syracuse Uversty, 121 L Hall Syracuse, NY 13244-1240, USA Phoe: +1 315 443 4124 hpolat@ecs.syr.edu Welag
More information20. CONFIDENCE INTERVALS FOR THE MEAN, UNKNOWN VARIANCE
20. CONFIDENCE INTERVALS FOR THE MEAN, UNKNOWN VARIANCE If the populatio tadard deviatio σ i ukow, a it uually will be i practice, we will have to etimate it by the ample tadard deviatio. Sice σ i ukow,
More informationAppendix A from T. Yamanaka et al., Generation Separation in Simple Structured Life Cycles: Models and 48 Years of Field Data on a Tea Tortrix Moth
211 by The Uversty of Chcago. All rghts reserved. DOI: 1.186/66321 Appedx A from T. Yamaaka et al., Geerato Separato Smple Structured Lfe Cycles: Models ad 48 Years of Feld Data o a Tea Tortrx Moth Am.
More informationCOVERAGE ESTIMATION FOR MOBILE CELLULAR NETWORKS FROM SIGNAL STRENGTH MEASUREMENTS. Kanagalu R. Manoj, BE and MS DISSERTATION
COVERAGE ESTIMATION FOR MOBILE CELLULAR NETWORKS FROM SIGNAL STRENGTH MEASUREMENTS by Kaagalu R. Maoj, BE ad MS DISSERTATION Preseted to the Faculty of The Uversty of Texas at Dallas Partal Fulfllmet of
More informationEnhancing Topology Control Algorithms in Wireless Sensor Network using Non-Isotropic Radio Models
IJCSNS Iteratoal Joural of Computer Scece ad Network Securty, VOL.6 No.8B, August 6 5 Ehacg Topology Cotrol Algorthms Wreless Sesor Network usg No-Isotropc Rado Models Ma.Vctora Que ad Wo-Joo Hwag Departmet
More informationSpeculative Completion for the Design of High-Performance Asynchronous Dynamic Adders
I: 1997 IEEE Iteratoal Symposum o Advaced Research Asychroous Crcuts ad Systems ( Asyc97 Symposum), Edhove, The Netherlads Speculatve Completo for the Desg of Hgh-Performace Asychroous Dyamc Adders Steve
More informationMeasuring Correlation between Microarray Time-series Data Using Dominant Spectral Component
Measurg Correlato betwee Mcroarray Tme-seres Data Usg Domat Spectral Compoet Lap Ku Yeug 1, Hog Ya 1, 2, Ala Wee-Chug Lew 1, Lap Keug Szeto 1, Mchael Yag 3 ad Rchard Kog 3 1 Departmet of Computer Egeerg
More informationRobot Path Planning Based on Random Coding Particle Swarm Optimization
(IJACSA) Iteratoal Joural of Advaced Computer Scece ad Applcatos, Vol. 6, No. 4, 5 Robot Path Pla Based o Radom Cod Partcle Swarm Optmzato Ku Su Collee of Electroc ad Electrcal Eeer Shaha Uversty of Eeer
More informationObjectives. Some Basic Terms. Analog and Digital Signals. Analog-to-digital conversion. Parameters of ADC process: Related terms
Objectives. A brief review of some basic, related terms 2. Aalog to digital coversio 3. Amplitude resolutio 4. Temporal resolutio 5. Measuremet error Some Basic Terms Error differece betwee a computed
More informationFUZZY MEASURES FOR STUDENTS MATHEMATICAL MODELLING SKILLS
Iteratoal Joural of uzzy Logc Systems (IJLS) Vol., No., Aprl 0 UZZY MEASURES OR STUDENTS MATHEMATICAL MODELLING SKILLS Mchael Gr. Voskoglou School of Techologcal Applcatos Graduate Techologcal Educatoal
More informationExam. Real-time systems, basic course, CDT315. Grading: Swedish grades: ECTS grades:
6 Exam eal-tme systems, basc course, 35 eacher: amr Isovc Phoe: 0 3 73 Exam durato: 08:30 3:30 Help alloed: Pots: calculator ad a laguage dctoary 48 p Gradg: Sedsh grades: ES grades: Importat formato:
More informationAn Anycast Routing Algorithm Based on Genetic Algorithm
A Aycast Routg Algorthm Based o Geetc Algorthm CHUN ZHU, MIN JIN Computer Scece ad Iformato Techology College Zhejag Wal Uversty No.8, South Q ahu Road, Ngbo P.R.CHINA http://www.computer.zwu.edu.c Abstract:
More informationLow Complexity LMMSE Channel Estimation on GPP
0 7th Iteratoal ICT Coferece o Commucatos ad etworkg Cha (CIACOM) Low Complexty LMME Chael Estmato o GPP We h, Tao Peg, Rogrog Qa Key Lab. Of Uversal Wreless Commucato, Mstry of Educato, BUPT Bejg 00876,
More informationGalileo E1/E5 Measurement Monitoring - Theory, Testing and Analysis
2018 IEEE. Ths materal s posted here wth permsso of the IEEE. Iteral or persoal use of ths materal s permtted. However, permsso to reprt/republsh ths materal for advertsg or promotoal purposes or for creatg
More informationNATIONAL RADIO ASTRONOMY OBSERVATORY Green Bank, West Virginia SPECTRAL PROCESSOR MEMO NO. 25. MEMORANDUM February 13, 1985
NATONAL RADO ASTRONOMY OBSERVATORY Green Bank, West Vrgna SPECTRAL PROCESSOR MEMO NO. 25 MEMORANDUM February 13, 1985 To: Spectral Processor Group From: R. Fsher Subj: Some Experments wth an nteger FFT
More informationLogarithms APPENDIX IV. 265 Appendix
APPENDIX IV Logarithms Sometimes, a umerical expressio may ivolve multiplicatio, divisio or ratioal powers of large umbers. For such calculatios, logarithms are very useful. They help us i makig difficult
More informationOptimal Power Allocation in Zero-forcing MIMO-OFDM Downlink with Multiuser Diversity
Optmal Power Allocato Zero-forcg IO-OFD Dowl wth ultuser Dversty Peter W. C. Cha ad oger S.. Cheg Abstract hs paper cosders the optmal power allocato for a multuser IO-OFD dowl system usg zero-forcg multplexg
More informationMath 140 Introductory Statistics
6. Probability Distributio from Data Math Itroductory Statistics Professor Silvia Ferádez Chapter 6 Based o the book Statistics i Actio by A. Watkis, R. Scheaffer, ad G. Cobb. We have three ways of specifyig
More informationGeneral Model :Algorithms in the Real World. Applications. Block Codes
Geeral Model 5-853:Algorithms i the Real World Error Correctig Codes I Overview Hammig Codes Liear Codes 5-853 Page message (m) coder codeword (c) oisy chael decoder codeword (c ) message or error Errors
More informationElectron Trajectory in an Undulator with Dipole Field and BPM Errors
LCLS-T-99-4 May 4,999 Electro Traectory a Udlator wth Dpole Feld ad PM Errors Pal Emma SLAC ASTRACT A statstcal aalyss of a corrected electro traectory throgh a plaar dlator s preseted. The dlator s composed
More informationMulti-target and Fuzzy Cloud computing Resource Scheduling
Advaced Scece ad Techology Letters Vol.111 (NGCIT 15), pp.94-98 http://dx.do.org/1.147/astl.15.111.19 Mult-target ad Fuzzy Cloud coputg Resource Schedulg Yu Zhag School of forato scece ad techology, Zheg
More informationSeismic Design and Performance of Dual Moment and Eccentrically Braced Frame System Using PBPD Method
441 Sesmc Desg ad Performace of Dual Momet ad Eccetrcally Braced Frame System Usg PBPD Method Abstract Most structural desg codes use elastc aalyss to calculate ad dstrbute sesmc base shear over the heght.
More information