DOCSLIB.ORG

Pedro Valero Mora Universitat De València I Have Worn Many Hats

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Pedro Valero Mora Universitat De València I Have Worn Many Hats DYNAMIC INTERACTIVE GRAPHICS PEDRO VALERO MORA UNIVERSITAT DE VALÈNCIA I HAVE WORN MANY HATS • Degree in Psycology • Phd on Human Computer Interaction (HCI): Formal Methods for Evaluation of Interfaces • Teaching Statistics in the Department of Psychology since1990, Full Professor (Catedrático) in 2012 • Research interest in Statistical Graphics, Computational Statistics • Comercial Packages (1995-) Statview, DataDesk, JMP, Systat, SPSS (of course) • Non Comercial 1998- Programming in Lisp-Stat, contributor to ViSta (free statistical package) • Book on Interactive Dynamic Graphics 2006 (with Forrest Young and Michael Friendly). Wiley • Editor of two special issues of the Journal of Statistical Software (User interfaces for R and the Health of Lisp-Stat) • Papers, conferences, seminars, etc. DYNAMIC-INTERACTIVE GRAPHICS • Working on this topic since mid 90s • Other similar topics are visualization, computer graphics, etc. • They are converging in the last few years • I see three periods in DIG • Special hardware • Desktop computers • The internet...and beyond • This presentation will make a review of the History of DIG and will draw lessons from its evolution 1. THE SPECIAL HARDWARE PERIOD • Tukey's Prim 9 • Tukey was not the only one, in 1988 a book summarized the experiences of many researchers on this topic • Lesson#1: Hire a genius and give him unlimited resources, it always works • Or not... • Great ideas but for limited audience, not only because the cost but also the complexity… 2. DESKTOP COMPUTERS • About 1990, the audience augments...macintosh users • Many things only possible previously for people with deep pockets were now possible for…the price of a Mac • Not cheap but affordable • Several packages with a graphical user interface: • Commercial: Statview, DataDesk, SuperANOVA (yes super), JMP, Systat… • Non Commercial: Lisp-Stat, ViSta, XGobi, MANET... 2.1. COMMERCIAL: DATADESK • Excellent for Exploratory Data Analysis, founded by P. F. Velleman (student of Tukey) in 1985 (still kicking) • Linking, selecting, filtering, corkboards, models, fast, • Teaching • An interesting niche was teaching, companion of great textbooks on introductory statistics • However, many people use the textbook with SPSS or other statistical packages. • Lesson #2: Statistical analysis must be taught with the same package that will be used for real analysis • It did not succeed although it is still in the market, but not that many people use or speaks about it. • A mystery to me…reasons? • Audience? Lesson #3: Think in your audience • Presentation graphics? Static graphics? Lesson #4: Once the fun is over you need to freeze the picture 2.2.1 NON-COMMERCIAL: VISTA • Started about 1990 by F. W. Young • Linking, selecting filtering, spreadplots (like corkboards or dashboards) , statistics, fast...the same as DataDesk but less polished • Excellent for Exploratory Data Analysis • Based on XLisp-Stat 2.2.2. NON-COMMERCIAL: LISPSTAT • Programming language for statistics based on Lisp with elements of S. • Developed by Luke Tierney, he works currently in R • Designed specifically for Dynamic Interactive Graphics • Lisp-Stat was like R/S but with interactive graphics and user interface elements (buttons, lists, mouse events, windows...all native not TCL/TK or Java or Python or else) • Lisp is great!… according to Ross Ihaka the creator of R • It was abandoned almost by everybody about 2003 • What did it happen?: In a single letter -> R • Why? • Static graphics #Lesson 5: Static graphics (did not I say this before?) • Familiarity? #Lesson 6: Make great software part of student’s education • Community? #Lesson 7: You need a community of users and developers 3. THE INTERNET • The impact of the internet has occurred at many levels • Colaboration, distribution, spread, has made non-commercial collaborative products very common • Sometimes is a problem more than a solution: many programs producing solutions for the same problem, projects discontinued, partially developed, etc. • Delivering products in the browser so everybody can play with them easily • I do not know much about the commercial products currently in the market (but I would love to know more…any free licenses there?) • On the non-commercial (libre?), R is the king 3.1 R • It is great! However, there is an area that could be improved • Graphical User Interfaces! (Come on, it is 2016!) • I am talking of something that my colleagues and students in Psychology could use (just copy Statview!) • No, R-Commander is not sufficiently easy • TBH, there are some exciting packages that go in the right direction (Shiny): • However: A good GUI is not only that! • Lesson #8: Hire specialists in human factors if you wish a really good GUI • I am pessimistic about getting this right non-commercially, there are things that do not pay off academically DISCUSSION • Evolution: DIG progressively closer to the masses • Supporting the idea of data as a public good that anybody can use for understanding the world we live, take decisions, evaluate policies, etc. • Current barriers steeper on the human factor than on the technical factor • Testing: What do the people do? What do they understand? • Education: What do the people know? How can people be taught for understanding statistics? • Communication: Can the message still be manipulated? • Cognition: Are human beings able to think statistically? THANKS FOR YOUR ATTENTION.
Load more

Recommended publications
  • Statistics and GIS Assistance Help with Statistics
    Statistics and GIS assistance An arrangement for help and advice with regard to statistics and GIS is now in operation, principally for Master’s students. How do you seek advice? 1. The users, i.e. students at INA, make direct contact with the person whom they think can help and arrange a time for consultation. Remember to be well prepared! 2. Doctoral students and postdocs register the time used in Agresso (if you have questions about this contact Gunnar Jensen). Help with statistics Research scientist Even Bergseng Discipline: Forest economy, forest policies, forest models Statistical expertise: Regression analysis, models with random and fixed effects, controlled/truncated data, some time series modelling, parametric and non-parametric effectiveness analyses Software: Stata, Excel Postdoc. Ole Martin Bollandsås Discipline: Forest production, forest inventory Statistics expertise: Regression analysis, sampling Software: SAS, R Associate Professor Sjur Baardsen Discipline: Econometric analysis of markets in the forest sector Statistical expertise: General, although somewhat “rusty”, expertise in many econometric topics (all-rounder) Software: Shazam, Frontier Associate Professor Terje Gobakken Discipline: GIS og long-term predictions Statistical expertise: Regression analysis, ANOVA and PLS regression Software: SAS, R Ph.D. Student Espen Halvorsen Discipline: Forest economy, forest management planning Statistical expertise: OLS, GLS, hypothesis testing, autocorrelation, ANOVA, categorical data, GLM, ANOVA Software: (partly) Shazam, Minitab og JMP Ph.D. Student Jan Vidar Haukeland Discipline: Nature based tourism Statistical expertise: Regression and factor analysis Software: SPSS Associate Professor Olav Høibø Discipline: Wood technology Statistical expertise: Planning of experiments, regression analysis (linear and non-linear), ANOVA, random and non-random effects, categorical data, multivariate analysis Software: R, JMP, Unscrambler, some SAS Ph.D.
    [Show full text]
  • Título Del Artículo
    Software for learning and for doing statistics and probability – Looking back and looking forward from a personal perspective Software para aprender y para hacer estadística y probabilidad – Mirando atrás y adelante desde una perspectiva personal Rolf Biehler Universität Paderborn, Germany Abstract The paper discusses requirements for software that supports both the learning and the doing of statistics. It looks back into the 1990s and looks forward to new challenges for such tools stemming from an updated conception of statistical literacy and challenges from big data, the exploding use of data in society and the emergence of data science. A focus is on Fathom, TinkerPlots and Codap, which are looked at from the perspective of requirements for tools for statistics education. Experiences and success conditions for using these tools in various educational contexts are reported, namely in primary and secondary education and pre- service and in-service teacher education. New challenges from data science require new tools for education with new features. The paper finishes with some ideas and experience from a recent project on data science education at the upper secondary level Keywords: software for learning and doing statistics, key attributes of software, data science education, statistical literacy Resumen El artículo discute los requisitos que el software debe cumplir para que pueda apoyar tanto el aprendizaje como la práctica de la estadística. Mira hacia la década de 1990 y hacia el futuro, para identificar los nuevos desafíos que para estas herramientas surgen de una concepción actualizada de la alfabetización estadística y los desafíos que plantean el uso de big data, la explosión del uso masivo de datos en la sociedad y la emergencia de la ciencia de los datos.
    [Show full text]
  • Insight MFR By
    Manufacturers, Publishers and Suppliers by Product Category 11/6/2017 10/100 Hubs & Switches ASCEND COMMUNICATIONS CIS SECURE COMPUTING INC DIGIUM GEAR HEAD 1 TRIPPLITE ASUS Cisco Press D‐LINK SYSTEMS GEFEN 1VISION SOFTWARE ATEN TECHNOLOGY CISCO SYSTEMS DUALCOMM TECHNOLOGY, INC. GEIST 3COM ATLAS SOUND CLEAR CUBE DYCONN GEOVISION INC. 4XEM CORP. ATLONA CLEARSOUNDS DYNEX PRODUCTS GIGAFAST 8E6 TECHNOLOGIES ATTO TECHNOLOGY CNET TECHNOLOGY EATON GIGAMON SYSTEMS LLC AAXEON TECHNOLOGIES LLC. AUDIOCODES, INC. CODE GREEN NETWORKS E‐CORPORATEGIFTS.COM, INC. GLOBAL MARKETING ACCELL AUDIOVOX CODI INC EDGECORE GOLDENRAM ACCELLION AVAYA COMMAND COMMUNICATIONS EDITSHARE LLC GREAT BAY SOFTWARE INC. ACER AMERICA AVENVIEW CORP COMMUNICATION DEVICES INC. EMC GRIFFIN TECHNOLOGY ACTI CORPORATION AVOCENT COMNET ENDACE USA H3C Technology ADAPTEC AVOCENT‐EMERSON COMPELLENT ENGENIUS HALL RESEARCH ADC KENTROX AVTECH CORPORATION COMPREHENSIVE CABLE ENTERASYS NETWORKS HAVIS SHIELD ADC TELECOMMUNICATIONS AXIOM MEMORY COMPU‐CALL, INC EPIPHAN SYSTEMS HAWKING TECHNOLOGY ADDERTECHNOLOGY AXIS COMMUNICATIONS COMPUTER LAB EQUINOX SYSTEMS HERITAGE TRAVELWARE ADD‐ON COMPUTER PERIPHERALS AZIO CORPORATION COMPUTERLINKS ETHERNET DIRECT HEWLETT PACKARD ENTERPRISE ADDON STORE B & B ELECTRONICS COMTROL ETHERWAN HIKVISION DIGITAL TECHNOLOGY CO. LT ADESSO BELDEN CONNECTGEAR EVANS CONSOLES HITACHI ADTRAN BELKIN COMPONENTS CONNECTPRO EVGA.COM HITACHI DATA SYSTEMS ADVANTECH AUTOMATION CORP. BIDUL & CO CONSTANT TECHNOLOGIES INC Exablaze HOO TOO INC AEROHIVE NETWORKS BLACK BOX COOL GEAR EXACQ TECHNOLOGIES INC HP AJA VIDEO SYSTEMS BLACKMAGIC DESIGN USA CP TECHNOLOGIES EXFO INC HP INC ALCATEL BLADE NETWORK TECHNOLOGIES CPS EXTREME NETWORKS HUAWEI ALCATEL LUCENT BLONDER TONGUE LABORATORIES CREATIVE LABS EXTRON HUAWEI SYMANTEC TECHNOLOGIES ALLIED TELESIS BLUE COAT SYSTEMS CRESTRON ELECTRONICS F5 NETWORKS IBM ALLOY COMPUTER PRODUCTS LLC BOSCH SECURITY CTC UNION TECHNOLOGIES CO FELLOWES ICOMTECH INC ALTINEX, INC.
    [Show full text]
  • Antibiotic De-Escalation Experience in the Setting of Emergency Department: a Retrospective, Observational Study
    Journal of Clinical Medicine Article Antibiotic De-escalation Experience in the Setting of Emergency Department: A Retrospective, Observational Study Silvia Corcione 1,2, Simone Mornese Pinna 1 , Tommaso Lupia 3,* , Alice Trentalange 1, Erika Germanò 1, Rossana Cavallo 4, Enrico Lupia 1 and Francesco Giuseppe De Rosa 1,2 1 Department of Medical Sciences, University of Turin, 10126 Turin, Italy; [email protected] (S.C.); [email protected] (S.M.P.); [email protected] (A.T.); [email protected] (E.G.); [email protected] (E.L.); [email protected] (F.G.D.R.) 2 Tufts University School of Medicine, Boston, MA 02129, USA 3 Infectious Diseases Unit, Cardinal Massaia Hospital, 14100 Asti, Italy 4 Microbiology and Virology Unit, University of Turin, 10126 Turin, Italy; [email protected] * Correspondence: [email protected]; Tel.: +39-0141-486-404 or +39-3462-248-637 Abstract: Background: Antimicrobial de-escalation (ADE) is a part of antimicrobial stewardship strategies aiming to minimize unnecessary or inappropriate antibiotic exposure to decrease the rate of antimicrobial resistance. Information regarding the effectiveness and safety of ADE in the setting of emergency medicine wards (EMW) is lacking. Methods: Adult patients admitted to EMW and receiving empiric antimicrobial treatment were retrospectively studied. The primary outcome was the rate and timing of ADE. Secondary outcomes included factors associated with early ADE, length of stay, and in-hospital mortality. Results: A total of 336 patients were studied. An initial regimen combining two agents was prescribed in 54.8%. Ureidopenicillins and carbapenems were Citation: Corcione, S.; Mornese the most frequently empiric treatment prescribed (25.1% and 13.6%).
    [Show full text]
  • Statistical Softwares: Introduction Team Maarten Jansen 1
    Statistical softwares: introduction Team Maarten Jansen 1. Maarten Jansen and Toufik Zahaf 2. Teaching assistant: Bastien Marquis http://homepages.ulb.ac.be/˜majansen/teaching/STAT-F-413/ c Maarten Jansen STAT-F-413 — Statistical softwares: introduction p.1 Objectives Forbidden data • Retrieve and analyse your own real data Not allowed: • Use at least two different software systems and two different types of analyses (typ- • Time series: time dependence of your data is allowed (longitudinal), but ically ANOVA and regression, but others are equally welcome: principle component analysis etc.) time must not be the dominant explanatory variable • Find your data • Birth weights of babies 1. at a company, hospital, banks, insurance company: this option is by far the best. If you get data, then also try to get to know what sort of business questions the company/organization is trying to answer: use the data to respond to the questions. 2. Otherwise (but less preferable) on the internet, e.g.: government data (such as statbel.gov.be) This option has the drawback that it is harder to be original and harder to focus on specific business questions. The data should be original, in the sense that they must not be popular in scien- tific papers or textbooks as illustration of a method. – Number of births per communality – Macro-economical data; per country, european, regional, provinces etc. – Socio-economical data c Maarten Jansen STAT-F-413 — Statistical softwares: introduction p.2 c Maarten Jansen STAT-F-413 — Statistical softwares: introduction p.3 Why not time-series Note on the data size: large enough..
    [Show full text]
  • A Case-Guided Tutorial to Statistical Analysis and Plagiarism Detection
    38HIPPOKRATIA 2010, 14 (Suppl 1): 38-48 PASCHOS KA REVIEW ARTICLE Revisiting Information Technology tools serving authorship and editorship: a case-guided tutorial to statistical analysis and plagiarism detection Bamidis PD, Lithari C, Konstantinidis ST Lab of Medical Informatics, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece Abstract With the number of scientific papers published in journals, conference proceedings, and international literature ever increas- ing, authors and reviewers are not only facilitated with an abundance of information, but unfortunately continuously con- fronted with risks associated with the erroneous copy of another’s material. In parallel, Information Communication Tech- nology (ICT) tools provide to researchers novel and continuously more effective ways to analyze and present their work. Software tools regarding statistical analysis offer scientists the chance to validate their work and enhance the quality of published papers. Moreover, from the reviewers and the editor’s perspective, it is now possible to ensure the (text-content) originality of a scientific article with automated software tools for plagiarism detection. In this paper, we provide a step-by- step demonstration of two categories of tools, namely, statistical analysis and plagiarism detection. The aim is not to come up with a specific tool recommendation, but rather to provide useful guidelines on the proper use and efficiency of either category of tools. In the context of this special issue, this paper offers a useful tutorial to specific problems concerned with scientific writing and review discourse. A specific neuroscience experimental case example is utilized to illustrate the young researcher’s statistical analysis burden, while a test scenario is purpose-built using open access journal articles to exemplify the use and comparative outputs of seven plagiarism detection software pieces.
    [Show full text]
  • Spatial Tools for Econometric and Exploratory Analysis
    Spatial Tools for Econometric and Exploratory Analysis Michael F. Goodchild University of California, Santa Barbara Luc Anselin University of Illinois at Urbana-Champaign http://csiss.org Outline ¾A Quick Tour of a GIS ¾Spatial Data Analysis ¾CSISS Tools Spatial Data Analysis Principles: 1. Integration ¾Linking data through common location the layer cake ¾Linking processes across disciplines spatially explicit processes e.g. economic and social processes interact at common locations 2. Spatial analysis ¾Social data collected in cross- section longitudinal data are difficult to construct ¾Cross-sectional perspectives are rich in context can never confirm process though they can perhaps falsify useful source of hypotheses, insights 3. Spatially explicit theory ¾Theory that is not invariant under relocation ¾Spatial concepts (location, distance, adjacency) appear explicitly ¾Can spatial concepts ever explain, or are they always surrogates for something else? 4. Place-based analysis ¾Nomothetic - search for general principles ¾Idiographic - description of unique properties of places ¾An old debate in Geography The Earth's surface ¾Uncontrolled variance ¾There is no average place ¾Results depend explicitly on bounds ¾Places as samples ¾Consider the model: y = a + bx Tract Pop Location Shape 1 3786 x,y 2 2966 x,y 3 5001 x,y 4 4983 x,y 5 4130 x,y 6 3229 x,y 7 4086 x,y 8 3979 x,y Iij = EiAjf (dij) / ΣkAkf (dik) Aj d Ei ij Types of Spatial Data Analysis ¾ Exploratory Spatial Data Analysis • exploring the structure of spatial data • determining
    [Show full text]
  • APPENDICES Appendix A
    APPENDICES Appendix A Analysis of Industry Crude and Product Oil Spills on the Alaska North Slope and Estimates of Potential Spills for the Liberty Development Project _____________________________________________________ Appendix A. Analysis of Industry Crude and Product Oil Spills on the Alaska North Slope and Estimates of Potential Spills for the Liberty Development Project July 2, 2007 SUBMITTED TO: BP Exploration (Alaska) Inc. P.O. Box 196612 Anchorage, Alaska 99519-6612 SUBMITTED BY: Everest Consulting Associates 15 North Main Street Cranbury, NJ 08512 __________________________________________________ Appendix A. Analysis of Industry Crude and Product Oil Spills on the Alaska North Slope and Estimates of Potential Spills for the Liberty Development Project Table of Contents: Summary ............................................................................................................................................... 1 Introduction ........................................................................................................................................... 4 A brief description of Typical ANS Oil and Gas facilities.................................................................... 5 Types of spills ....................................................................................................................................... 6 The spill database .................................................................................................................................. 7 -Updating the oil spill database
    [Show full text]
  • The Role of Adipokines in Obesity-Associated Asthma
    The role of adipokines in obesity-associated asthma By David Gibeon A thesis submitted to Imperial College London for the degree of Doctor of Philosophy in the Faculty of Medicine 2016 National Heart and Lung Institute Dovehouse Street, London SW3 6LY 1 Author’s declaration The following tests on patients were carried out by myself and nurses in the asthma clinic at the Royal Brompton Hospital: lung function measurements, exhaled nitric oxide (FENO) measurement, skin prick testing, methacholine provocation test (PC20), weight, height, waist/hip measurement, bioelectrical impedance analysis (BIA), and skin fold measurements. Fibreoptic bronchoscopies were performed by me and previous research fellows in the Airways Disease Section at the National Heart and Lung Institute (NHLI), Imperial College London. The experiments carried out in this PhD were carried out using the latest equipment available to me at the NHLI. The staining of bronchoalveolar lavage cytospins with Oil red O (Chapter 4) and the staining of endobronchial biopsies with perilipin (Chapter 5) was performed by Dr. Jie Zhu. The multiplex assay (Chapter 7) was performed by Dr. Christos Rossios. 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. Signed………………………………… David Gibeon 2 Abstract Obesity is a risk factor for the development of asthma and plays a role in disease control, severity and airway inflammation.
    [Show full text]
  • Data Analytics (DAT)
    Data Analytics (DAT) DAT 7723. Querying SQL. 0.0 Hours. Class-440.0. Clinical-0.0. Lab-0.0. Data Analytics (DAT) Work-0.0 Learning SQL can be one of the greatest career decisions you make. DAT 7711. Database Fundamentals. 0.0 Hours. Class-440.0. Between the potential salary, no longer relying on others to give you Clinical-0.0. Lab-0.0. Work-0.0 information, and being able to ask any question about your business, CPCC login required. Understanding how relational databases are learning SQL enables you to do so much more than you have done designed for maximum data manipulation is the first step in the field of previously. Simply put, SQL is the language you use to interact with a data management and analytics. You will learn core database concepts, database and is the most sought after skill set regardless of your role. and how to create database objects and manipulate data. Maps to Corequisites: Take DAT 7711 with a minimum grade of S Microsoft Technology Associate exam #98-364. DAT 7724. Developing SQL. 0.0 Hours. Class-440.0. Clinical-0.0. DAT 7712. Business Intelligence Fundamentals. 0.0 Hours. Lab-0.0. Work-0.0 Class-440.0. Clinical-0.0. Lab-0.0. Work-0.0 This class is intended for individuals who have completed Querying Discover the best practices to access and analyze data while gaining SQL and who are ready to move beyond the fundamentals of querying the confidence to dive right into your processes to improve and optimize and advance their skills in programmability, data definition (DDL) and business decisions and performance.
    [Show full text]
  • TMS320C6652 and TMS320C6654 Fixed and Floating-Point Digital Signal Processor Datasheet
    Product Order Technical Tools & Support & Folder Now Documents Software Community TMS320C6652, TMS320C6654 SPRS841E –MARCH 2012–REVISED OCTOBER 2019 TMS320C6652 and TMS320C6654 Fixed and Floating-Point Digital Signal Processor 1 Device Overview 1.1 Features 1 • One TMS320C66x DSP Core Subsystem – 32-Bit DDR3 Interface (CorePac) – DDR3-1066 – C66x Fixed- and Floating-Point CPU Core: Up – 4GB of Addressable Memory Space to 850 MHz for C6654 and 600 MHz for C6652 – 16-Bit EMIF • Multicore Shared Memory Controller (MSMC) – Universal Parallel Port – Memory Protection Unit for DDR3_EMIF – Two Channels of 8 Bits or 16 Bits Each • Multicore Navigator – Supports SDR and DDR Transfers – 8192 Multipurpose Hardware Queues with – Two UART Interfaces Queue Manager – Two Multichannel Buffered Serial Ports – Packet-Based DMA for Zero-Overhead (McBSPs) Transfers – I2C Interface • Peripherals – 32 GPIO Pins – PCIe Gen2 (C6654 Only) – SPI Interface – Single Port Supporting 1 or 2 Lanes – Semaphore Module – Supports up to 5 GBaud Per Lane – Eight 64-Bit Timers – Gigabit Ethernet (GbE) Subsystem (C6654 – Two On-Chip PLLs Only) • Commercial Temperature: – One SGMII Port (C6654 Only) – 0°C to 85°C – Supports 10-, 100-, and 1000-Mbps • Extended Temperature: Operation – –40°C to 100°C 1.2 Applications • Power Protection Systems • Medical Imaging • Avionics and Defense • Other Embedded Systems • Currency Inspection and Machine Vision • Industrial Transportation Systems 1.3 Description The C6654 and C6652 are high performance fixed- and floating-point DSPs that are based on TI's KeyStone multicore architecture. Incorporating the new and innovative C66x DSP core, this device can run at a core speed of up to 850 MHz for C6654 and 600 MHz for C6652.
    [Show full text]
  • Análisis Y Proceso De Datos Aplicado a La Psicología ---Práctica Con
    Análisis y proceso de datos aplicado a la Psicología -----Práctica con ordenador----- Primera sesión de contenidos: El paquete estadístico SPSS: Descripción general y aplicación al proceso de datos Profs.: J. Gabriel Molina María F. Rodrigo Documento PowerPoint de uso exclusivo para los alumnos matriculados en la asignatura y con los profesores arriba citados. No está permitida la distribución o utilización no autorizada de este documento. El paquete estadístico SPSS: Descripción general y aplicación al proceso de datos. Contenidos: 1 El paquete estadístico SPSS: aspectos generales y descripción del funcionamiento. 2 Proceso de datos con SPSS: introducción, edición y almacenamiento de archivos de datos. 1.1 Estructura general: barra de menús, ventanas, barras de herramientas, barra de estado... (1) Programas orientados al análisis de datos : los paquetes estadísticos (BMDP, R, S-PLUS, DataDesk, Jump, Minitab, SAS, Statgraphics, Statview, Systat, ViSta...) (2) Puesta en marcha del programa SPSS Inicio >> Programas >> SPSS for Windows >> SPSS 11.0 para Windows (3) Abrir el archivo de datos ‘demo.sav’ Menú Archivo >> Abrir >> Datos... (C:) / Archivos de Programas / SPSS / tutorial / sample files / 1.2 Introducción a la utilización de SPSS: un recorrido por las funciones básicas del programa. Menú ? >> Tutorial >> Libro ‘Introducción’ 1.3 Tipos de documento asociados al programa • Documentos de datos (.sav) << Editor de datos de SPSS (Documento SPSS) • Documentos de resultados (.spo) << Visor SPSS (Documento de Visor) Visualizacion de documentos de datos y de resultados (1) Abrir el fichero de resultados ‘viewertut.spo’ Menú Archivo >> Abrir >> Resultados... (C:) / Archivos de Programas / SPSS / tutorial / sample files / (2) Abrir el fichero de datos ‘Ansiedad.sav’ Menú Archivo >> Abrir >> Datos..
    [Show full text]