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Date Created Size MB . تماس بگیر ید 09353344788
Name Software ( Search List Ctrl+F ) Date created Size MB برای سفارش هر یک از نرم افزارها با شماره 09123125449 - 09353344788 تماس بگ ریید . \1\ Simulia Abaqus 6.6.3 2013-06-10 435.07 Files: 1 Size: 456,200,192 Bytes (435.07 MB) \2\ Simulia Abaqus 6.7 EF 2013-06-10 1451.76 Files: 1 Size: 1,522,278,400 Bytes (1451.76 MB) \3\ Simulia Abaqus 6.7.1 2013-06-10 584.92 Files: 1 Size: 613,330,944 Bytes (584.92 MB) \4\ Simulia Abaqus 6.8.1 2013-06-10 3732.38 Files: 1 Size: 3,913,689,088 Bytes (3732.38 MB) \5\ Simulia Abaqus 6.9 EF1 2017-09-28 3411.59 Files: 1 Size: 3,577,307,136 Bytes (3411.59 MB) \6\ Simulia Abaqus 6.9 2013-06-10 2462.25 Simulia Abaqus Doc 6.9 2013-06-10 1853.34 Files: 2 Size: 4,525,230,080 Bytes (4315.60 MB) \7\ Simulia Abaqus 6.9.3 DVD 1 2013-06-11 2463.45 Simulia Abaqus 6.9.3 DVD 2 2013-06-11 1852.51 Files: 2 Size: 4,525,611,008 Bytes (4315.96 MB) \8\ Simulia Abaqus 6.10.1 With Documation 2017-09-28 3310.64 Files: 1 Size: 3,471,454,208 Bytes (3310.64 MB) \9\ Simulia Abaqus 6.10.1.5 2013-06-13 2197.95 Files: 1 Size: 2,304,712,704 Bytes (2197.95 MB) \10\ Simulia Abaqus 6.11 32BIT 2013-06-18 1162.57 Files: 1 Size: 1,219,045,376 Bytes (1162.57 MB) \11\ Simulia Abaqus 6.11 For CATIA V5-6R2012 2013-06-09 759.02 Files: 1 Size: 795,893,760 Bytes (759.02 MB) \12\ Simulia Abaqus 6.11.1 PR3 32-64BIT 2013-06-10 3514.38 Files: 1 Size: 3,685,099,520 Bytes (3514.38 MB) \13\ Simulia Abaqus 6.11.3 2013-06-09 3529.41 Files: 1 Size: 3,700,856,832 Bytes (3529.41 MB) \14\ Simulia Abaqus 6.12.1 2013-06-10 3166.30 Files: 1 Size: 3,320,102,912 Bytes -
Primena Statistike U Kliničkim Istraţivanjima Sa Osvrtom Na Korišćenje Računarskih Programa
UNIVERZITET U BEOGRADU MATEMATIČKI FAKULTET Dušica V. Gavrilović Primena statistike u kliničkim istraţivanjima sa osvrtom na korišćenje računarskih programa - Master rad - Mentor: prof. dr Vesna Jevremović Beograd, 2013. godine Zahvalnica Ovaj rad bi bilo veoma teško napisati da nisam imala stručnu podršku, kvalitetne sugestije i reviziju, pomoć prijatelja, razumevanje kolega i beskrajnu podršku porodice. To su razlozi zbog kojih želim da se zahvalim: . Mom mentoru, prof. dr Vesni Jevremović sa Matematičkog fakulteta Univerziteta u Beogradu, koja je bila ne samo idejni tvorac ovog rada već i dugogodišnja podrška u njegovoj realizaciji. Njena neverovatna upornost, razne sugestije, neiscrpni optimizam, profesionalizam i razumevanje, predstavljali su moj stalni izvor snage na ovom master-putu. Članu komisije, doc. dr Zorici Stanimirović sa Matematičkog fakulteta Univerziteta u Beogradu, na izuzetnoj ekspeditivnosti, stručnoj recenziji, razumevanju, strpljenju i brojnim korisnim savetima. Članu komisije, mr Marku Obradoviću sa Matematičkog fakulteta Univerziteta u Beogradu, na stručnoj i prijateljskoj podršci kao i spremnosti na saradnju. Dipl. mat. Radojki Pavlović, šefu studentske službe Matematičkog fakulteta Univerziteta u Beogradu, na upornosti, snalažljivosti i kreativnosti u pronalaženju raznih ideja, predloga i rešenja na putu realizacije ovog master rada. Dugogodišnje prijateljstvo sa njom oduvek beskrajno cenim i oduvek mi mnogo znači. Dipl. mat. Zorani Bizetić, načelniku Data Centra Instituta za onkologiju i radiologiju Srbije, na upornosti, idejama, detaljnoj reviziji, korisnim sugestijama i svakojakoj podršci. Čak i kada je neverovatno ili dosadno ili pametno uporna, mnogo je i dugo volim – skoro ceo moj život. Mast. biol. Jelici Novaković na strpljenju, reviziji, bezbrojnim korekcijama i tehničkoj podršci svake vrste. Hvala na osmehu, budnom oku u sitne sate, izvrsnoj hrani koja me je vraćala u život, nes-kafi sa penom i transfuziji energije kada sam bila na rezervi. -
Overview-Of-Statistical-Analytics-And
Brief Overview of Statistical Analytics and Machine Learning tools for Data Scientists Tom Breur 17 January 2017 It is often said that Excel is the most commonly used analytics tool, and that is hard to argue with: it has a Billion users worldwide. Although not everybody thinks of Excel as a Data Science tool, it certainly is often used for “data discovery”, and can be used for many other tasks, too. There are two “old school” tools, SPSS and SAS, that were founded in 1968 and 1976 respectively. These products have been the hallmark of statistics. Both had early offerings of data mining suites (Clementine, now called IBM SPSS Modeler, and SAS Enterprise Miner) and both are still used widely in Data Science, today. They have evolved from a command line only interface, to more user-friendly graphic user interfaces. What they also share in common is that in the core SPSS and SAS are really COBOL dialects and are therefore characterized by row- based processing. That doesn’t make them inherently good or bad, but it is principally different from set-based operations that many other tools offer nowadays. Similar in functionality to the traditional leaders SAS and SPSS have been JMP and Statistica. Both remarkably user-friendly tools with broad data mining and machine learning capabilities. JMP is, and always has been, a fully owned daughter company of SAS, and only came to the fore when hardware became more powerful. Its initial “handicap” of storing all data in RAM was once a severe limitation, but since computers now have large enough internal memory for most data sets, its computational power and intuitive GUI hold their own. -
Estimating Road Transport Fuel Demand Elasticities in the Uk: an Empirical Investigation of Response Heterogeneity
1 ESTIMATING ROAD TRANSPORT FUEL DEMAND ELASTICITIES IN THE UK: AN EMPIRICAL INVESTIGATION OF RESPONSE HETEROGENEITY Ahmad Razi Ramli Centre for Transport Studies Department of Civil and Environmental Engineering Imperial College London Submitted for the Diploma of the Imperial College (DIC), PhD degree of Imperial College London March 2014 2 DECLARATION OF ORIGINALITY I hereby declare that I am the sole author of this thesis and have personally carried out the work contained within. The contribution of my supervisor was only supervisory and editorial. I further declare that all sources cited or quoted are indicated and acknowledged in the list of references in this thesis. ……………………………………………… Ahmad Razi Ramli 3 COPYRIGHT DECLARATION ‘The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commercial No Derivatives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the licence terms of this work’. 4 ABSTRACT The main aim of this dissertation is to estimate fuel demand elasticities for the UK road transport sector. Despite being extensively studied, there is a renewed need for the estimation of fuel demand elasticities so that they might be more reflective of recent trends and changes in consumption patterns. At present, understanding the fuel demand sensitivities is especially important for policy making purposes. A review of the empirical literature on fuel demand revealed three important areas of concern. -
Investigating Data Management Practices in Australian Universities
Investigating Data Management Practices in Australian Universities Margaret Henty, The Australian National University Belinda Weaver, The University of Queensland Stephanie Bradbury, Queensland University of Technology Simon Porter, The University of Melbourne http://www.apsr.edu.au/investigating_data_management July, 2008 ii Table of Contents Introduction ...................................................................................... 1 About the survey ................................................................................ 1 About this report ................................................................................ 1 The respondents................................................................................. 2 The survey results............................................................................... 2 Tables and Comments .......................................................................... 3 Digital data .................................................................................... 3 Non-digital data forms....................................................................... 3 Types of digital data ......................................................................... 4 Size of data collection ....................................................................... 5 Software used for analysis or manipulation .............................................. 6 Software storage and retention ............................................................ 7 Research Data Management Plans......................................................... -
GPL Reference Guide for IBM SPSS Statistics
GPL Reference Guide for IBM SPSS Statistics Note Before using this information and the product it supports, read the information in “Notices” on page 415. Product Information This edition applies to version 162261647161, release 0, modification 0 of IBM SPSS Modeler BatchIBM SPSS ModelerIBM SPSS Modeler ServerIBM SPSS StatisticsIBM SPSS Statistics ServerIBM SPSS AmosIBM SPSS SmartreaderIBM SPSS Statistics Base Integrated Student EditionIBM SPSS Collaboration and Deployment ServicesIBM SPSS Visualization DesignerIBM SPSS Modeler Text AnalyticsIBM SPSS Text Analytics for Surveys IBM Analytical Decision ManagementIBM SPSS Modeler Social Network AnalysisIBM SPSS Analytic Server and to all subsequent releases and modifications until otherwise indicated in new editions. Contents Chapter 1. Introduction to GPL .....1 color.hue Function (For GPL Guides) .....90 The Basics ...............1 color.saturation Function (For GPL Graphic GPL Syntax Rules.............3 Elements) ..............90 GPL Concepts ..............3 color.saturation Function (For GPL Guides) . 91 Brief Overview of GPL Algebra .......3 csvSource Function ...........92 How Coordinates and the GPL Algebra Interact . 6 dataMaximum Function .........92 Common Tasks .............14 dataMinimum Function .........93 How to Add Stacking to a Graph ......14 delta Function ............93 How to Add Faceting (Paneling) to a Graph . 15 density.beta Function ..........93 How to Add Clustering to a Graph .....17 density.chiSquare Function ........96 How to Use Aesthetics .........18 density.exponential -
Used.Html#Sbm Mv
http://www.kdnuggets.com/2015/05/poll-analytics-data-mining-data-science-software- used.html#sbm_mv Vote in KDnuggets 16th Annual Poll: What Analytics, Data Mining, Data Science software/tools you used in the past 12 months for a real project?. We will clean and analyze the results and publish our trend analysis afterward What Analytics, Data Mining, Data Science software/tools you used in the past 12 months for a real project (not just evaluation)? 4 categories: Platforms, Languages, Deep Learning, Hadoop. Analytics / Data Mining Platforms / Suites: Actian Alpine Data Labs Alteryx Angoss Amazon Machine Learning Ayasdi BayesiaLab BigML Birst C4.5/C5.0/See5 Datameer Dataiku Dato (former Graphlab) Dell (including Statistica) FICO Model Builder Gnu Octave GoodData H2O (0xdata) IBM Cognos IBM SPSS Modeler IBM SPSS Statistics IBM Watson Analytics JMP KNIME Lavastorm Lexalytics MATLAB Mathematica Megaputer Polyanalyst/TextAnalyst MetaMind Microsoft Azure ML Microsoft SQL Server Microsoft Power BI MicroStrategy Miner3D Ontotext Oracle Data Miner Orange Pentaho Predixion Software QlikView RapidInsight/Veera RapidMiner Rattle Revolution Analytics (now part of Microsoft) SAP (including former KXEN) SAS Enterprise Miner Salford SPM/CART/Random Forests/MARS/TreeNet scikit-learn Skytree SiSense Splunk/ Hunk Stata TIBCO Spotfire Tableau Vowpal Wabbit Weka WordStat XLSTAT for Excel Zementis Other free analytics/data mining tools Other paid analytics/data mining/data science software Languages / low-level tools: C/C++ Clojure Excel F# Java Julia Lisp Perl Python R Ruby SAS base SQL Scala Unix shell/awk/gawk WPS: World Programming System Other programming languages Deep Learning tools Caffe Cuda-convnet Deeplearning4j Torch Theano Pylearn2 Other Deep Learning tools Hadoop-based Analytics-related tools Hadoop HBase Hive Pig Spark Mahout MLlib SQL on Hadoop tools Other Hadoop/HDFS-based tools. -
IBM SPSS Modeler Data Sheet
IBM Analytics IBM SPSS Modeler Data sheet IBM SPSS Modeler Accelerate time to value with visual data science and machine learning Overview Highlights In a business environment, the main objective of analytics is to improve a business outcome. These outcomes can include: • Exploit the power of visual and programmatic data science with IBM® SPSS® Modeler for IBM Data • Acquiring and retaining customers Science Experience, or get started with • Driving operational efficiency a subscription standalone deployment • Minimizing and mitigating fraud of IBM SPSS Modeler • Optimizing hiring processes and reducing attrition • Harness the value and find patterns in more data sources including text, flat • Creating new business models files, databases, data warehouses and Hadoop distributions in a multi-cloud When you apply data science and machine learning to improve a environment decision, the result is likely to be a better outcome. • Put 40+ out-of-box machine learning algorithms to work for ease of model development and management Data science is the process of using analytical techniques to uncover patterns in data and applying the results for business value. Descriptive • Integrate with Apache Spark for fast in-memory computing analysis, predictive modeling, text analytics, geospatial analytics, entity • Optimize productivity of data science analytics, decision management and optimization are used to identify and business teams with programmatic, patterns and deploy predictive models into operational systems. visual and other skills Systems and people can use these patterns and models to derive insights • Speed data analysis with in-database that enable them to consistently make the right decision at the point of performance and minimized data impact. -
Statistický Software
Statistický software 1 Software AcaStat GAUSS MRDCL RATS StatsDirect ADaMSoft GAUSS NCSS RKWard[4] Statistix Analyse-it GenStat OpenEpi SalStat SYSTAT The ASReml GoldenHelix Origin SAS Unscrambler Oxprogramming Auguri gretl language SOCR UNISTAT BioStat JMP OxMetrics Stata VisualStat BrightStat MacAnova Origin Statgraphics Winpepi Dataplot Mathematica Partek STATISTICA WinSPC EasyReg Matlab Primer StatIt XLStat EpiInfo MedCalc PSPP StatPlus XploRe EViews modelQED R SPlus Excel Minitab R Commander[4] SPSS 2 Data miningovýsoftware n Cca 20 až30 dodavatelů n Hlavníhráči na trhu: n Clementine, IBM SPSS Modeler n IBM’s Intelligent Miner, (PASW Modeler) n SGI’sMineSet, n SAS’s Enterprise Miner. n Řada vestavěných produktů: n fraud detection: n electronic commerce applications, n health care, n customer relationship management 3 Software-SAS n : www.sas.com 4 SAS n Společnost SAS Institute n Vznik 1976 v univerzitním prostředí n Dnes:největšísoukromásoftwarováspolečnost na světě (více než11.000 zaměstnanců) n přes 45.000 instalací n cca 9 milionů uživatelů ve 118 zemích n v USA okolo 1.000 akademických zákazníků (SAS používávětšina vyšších a vysokých škol a výzkumných pracovišť) 5 SAS 6 SAS 7 SAS q Statistickáanalýza: Ø Popisnástatistika Ø Analýza kontingenčních (frekvenčních) tabulek Ø Regresní, korelační, kovariančníanalýza Ø Logistickáregrese Ø Analýza rozptylu Ø Testováníhypotéz Ø Diskriminačníanalýza Ø Shlukováanalýza Ø Analýza přežití Ø … 8 SAS q Analýza časových řad: Ø Regresnímodely Ø Modely se sezónními faktory Ø Autoregresnímodely Ø -
Stručný Návod K Ovládání IBM SPSS Statistics a IBM SPSS Modeler Autor Doc
STRUČNÝ NÁVOD K OVLÁDÁNÍ IBM SPSS Statistics 19 a IBM SPSS Modeler 14 Pavel PETR Ústav systémového inženýrství a informatiky Fakulta ekonomicko – správní UNIVERZITA PARDUBICE 2012 Obsah 1. Základní informace o programu IBM SPSS Statistics .............................................. 4 1.1. Základní moduly IBM SPSS Statistics 19 ........................................................ 4 2. Program IBM SPSS Statistics ................................................................................... 6 2.1. Prostředí programu IBM SPSS Statistics.......................................................... 6 2.2. Okna v programu IBM SPSS Statistics ............................................................ 8 2.2.1. Datové okno .............................................................................................. 8 2.2.2. Výstupové okno ...................................................................................... 13 2.3. Základní ovládání programu IBM SPSS Statistics ......................................... 19 2.3.1. Soubor (File) ........................................................................................... 20 2.3.2. Úpravy (Edit) .......................................................................................... 22 2.3.3. Pohled (View) ......................................................................................... 24 3. Základní informace o programu IBM SPSS Modeler ............................................ 26 3.1. Fáze CRISP-DM ............................................................................................ -
Health Physical Education and Recreation Exercise and Sport
Volume 11, 2 October 1998 A Subject and Author Index of Dissertations and Theses Including Abstracts Health Physical Education and Recreation Exercise and Sport Sciences Microform Publications Bulletin Microform Publications of Human Movement Studies INTERNATIONAL INSTITUTE FOR SPORT AND HUMAN PERFORMANCE UNIVERSITY OF OREGON Eugene, Oregon Microform Publications—University of Oregon MICROFICHE CHARACTERISTICS Reduction ratio: 24:1; 98 pages; NMA #1 format Fiche type: Silver halide, polyester base, meets pH and ANSI standards for archival purposes Polarity: Negative Replacement Policy: Guaranteed if fiche is defective Foreign Checks Cannot be Accepted Payment must be one of the following: U.S. Bank Account U.S. Dollar International Money Order U.S. Dollar World Money Order or by VISA or MASTERCARD Send orders to: Microform Publications International Institute for Sport and Human Performance 1243 University of Oregon Eugene, Oregon 97403-1243 , USA Phone: (541) 346-4117 Fax: (541) 346-0935 ii Microform Publications—University of Oregon M I C R O F O R M P U B L I C A T I O N S HEALTH, PHYSICAL EDUCATION, RECREATION, AND EXERCISE AND SPORT SCIENCES GENERAL INFORMATION physiology, biomechanics, and sport medicine Microform Publications of Human Movement topics covering research, clinical, and lay publi- Studies is a component of the International cations. Institute for Sport and Human Performance at the University of Oregon. Since its inception in BULLETIN 11, 2 1949, Microform Publications has been providing This publication is the second issue of Bulletin a service to the professional academic commu- 11. The bulletin represents microfiche published nity worldwide. Its focus is on the dissemination in October 1998. -
Comparison of Commonly Used Statistics Package Programs
Black Sea Journal of Engineering and Science 2(1): 26-32 (2019) Black Sea Journal of Engineering and Science Open Access Journal e-ISSN: 2619-8991 BSPublishers Review Volume 2 - Issue 1: 26-32 / January 2019 COMPARISON OF COMMONLY USED STATISTICS PACKAGE PROGRAMS Ömer Faruk KARAOKUR1*, Fahrettin KAYA2, Esra YAVUZ1, Aysel YENİPINAR1 1Kahramanmaraş Sütçü Imam University, Faculty of Agriculture, Department of Animal Science, 46040, Onikişubat, Kahramanmaraş, Turkey 2Kahramanmaraş Sütçü Imam University, Andırın Vocational School, Computer Technology Department, 46410, Andırın, Kahramanmaras, Turkey. Received: October 17, 2018; Accepted: December 04, 2018; Published: January 01, 2019 Abstract The specialized computer programs used in the collection, organization, analysis, interpretation and presentation of the data are known as statistical software. Descriptive statistics and inferential statistics are two main statistical methodologies in some of the software used in data analysis. Descriptive statistics summarizes data in a sample using indices such as mean or standard deviation. Inferential statistics draws conclusions that are subject to random variables such as observational errors and sampling variation. In this study, statistical software used in data analysis is examined under two main headings as open source (free) and licensed (paid). For this purpose, 5 most commonly used software were selected from each groups. Statistical analyzes and analysis outputs of this selected software have been examined comparatively. As a result of this study, the features of licensed and unlicensed programs are presented to the researchers in a comparative way. Keywords: Statistical software, Statistical analysis, Data analysis *Corresponding author: Kahramanmaraş Sütçü Imam University, Faculty of Agriculture, Department of Animal Science, 46040, Onikişubat, Kahramanmaraş, Turkey E mail: [email protected] (Ö.