DOCSLIB.ORG

Appendix a R

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Appendix a R Appendix A R R (R Core Team, 2015)isthelingua franca of data analysis and graphics. In this book we will learn the fundamentals of the language and immediately use it for the statistical analysis of data. We will graph both the data and the results of the anal- yses. We will work with the basic statistical tools (regression, analysis of variance, and contingency tables) and some more specialized tools. Many of our examples use the additional functions we have provided in the HH package (Appendix B) (Heiberger, 2015). The R code for all tables and graphs in the book is included in the HH package. In later appendices we will also look at the Rcmdr menu system (Appendix C), RExcel integration of R with Excel (Appendix D), and the shiny package for inte- gration of R with interactive html web pages (Appendix E). A.1 Installing R—Initial Installation R is an open-source publicly licensed software system. R is free under the GPL (Gnu Public License). R is available for download on Windows, Macintosh OSX, and Linux com- puter systems. Start at http://www.R-project.org. Click on “download R” in the “Getting Started” box, pick a mirror near you, and download and install the most recent release of R for your operating system. Once R is running, you will download several necessary contributed packages. You get them from a running R session while connected to the internet. This install.packages statement will install all the packages listed and additional packages that these specified packages need. © Springer Science+Business Media New York 2015 699 R.M. Heiberger, B. Holland, Statistical Analysis and Data Display, Springer Texts in Statistics, DOI 10.1007/978-1-4939-2122-5 700 A R A.1.1 Packages Needed for This Book—Macintosh and Linux Start R, then enter install.packages(c("HH","RcmdrPlugin.HH","RcmdrPlugin.mosaic", "fortunes","ggplot2","shiny","gridExtra", "gridBase","Rmpfr","png","XLConnect", "matrixcalc", "sem", "relimp", "lmtest", "markdown", "knitr", "effects", "aplpack", "RODBC", "TeachingDemos", "gridGraphics", "gridSVG"), dependencies=TRUE) ## This is the sufficient list (as of 16 August 2015) of packages ## needed in order to install the HH package. Should ## additional dependencies be declared by any of these packages ## after that date, the first use of "library(HH)" after the ## installation might ask for permission to install some more ## packages. The install.packages command might tell you that it can’t write in a system directory and will then ask for permission to create a personal library. The question might be in a message box that is behind other windows. Should the installation seem to freeze, find the message box and respond “yes” and accept its recommend directory. It might ask you for a CRAN mirror. Take the mirror from which you downloaded R. A.1.2 Packages and Other Software Needed for This Book—Windows A.1.2.1 RExcel If you are running on a Windows machine and have access to Excel, then we rec- ommend that you also install RExcel.TheRExcel software provides a seamless integration of R and Excel. See Appendix D for further information on RExcel, including the download statements and licensing information. See Section A.1.2.2 for information about using Rcmdr with RExcel. A.1 Installing R—Initial Installation 701 A.1.2.2 RExcel Users Need to Install Rcmdr as Administrator Should you choose to install RExcel then you need to install Rcmdr as Adminis- trator. Otherwise you can install Rcmdr as an ordinary user. Start R as Administrator (on Windows 7 and 8 you need to right-click the R icon and click the “Run as administrator” item). In R, run the following commands (again, you must have started R as Administrator to do this) ## Tell Windows that R should have the same access to the ## outside internet that is granted to Internet Explorer. setInternet2() install.packages("Rcmdr", dependencies=TRUE) Close R with q("no"). Answer with n if it asks Save workspace image? [y/n/c]: A.1.2.3 Packages Needed for This Book—Windows The remaining packages can be installed as an ordinary user. Rcmdr is one of the dependencies of RcmdrPlugin.HH, so it will be installed by the following state- ment (unless it was previously installed). Start R, then enter ## Tell Windows that R should have the same access to the ## outside internet that is granted to Internet Explorer. setInternet2() install.packages(c("HH","RcmdrPlugin.HH","RcmdrPlugin.mosaic", "fortunes","ggplot2","shiny","gridExtra", "gridBase","Rmpfr","png","XLConnect", "matrixcalc", "sem", "relimp", "lmtest", "markdown", "knitr", "effects", "aplpack", "RODBC", "TeachingDemos", "gridGraphics", "gridSVG"), dependencies=TRUE) ## This is the sufficient list (as of 16 August 2015) of packages ## needed in order to install the HH package. Should ## additional dependencies be declared by any of these packages ## after that date, the first use of "library(HH)" after the ## installation might ask for permission to install some more ## packages. 702 A R A.1.2.4 Rtools Rtools provides all the standard Unix utilities (C and Fortran compilers, command- line editing tools such as grep, awk, diff, and many others) that are not in- cluded with the Windows operating system. These utilities are needed in two circumstances. 1. Should you decide to collect your R functions and datasets into a package, you will need Rtools to build the package. See Appendix F for more information. 2. Should you need to use the ediff command in Emacs for visual comparison of two different versions of a file (yesterday’s version and today’s after some editing, for example), you will need Rtools. See Section M.1.2 for an example of visual comparison of files. You may download Rtools from the Windows download page at CRAN. Please see the references from that page for more details. A.1.2.5 Windows Potential Complications: Internet, Firewall, and Proxy When install.packages on a Windows machine gives an Error message that includes the phrase “unable to connect”, then you are probably working behind a company firewall. You will need the R statement setInternet2() before the install.packages statement. This statement tells the firewall to give R the same access to the outside internet that is granted to Internet Explorer. When the install.packages gives a Warning message that says you don’t have write access to one directory, but it will install the packages in a different directory, that is normal and the installation is successful. When the install.packages gives an Error message that says you don’t have write access and doesn’t offer an alternative, then you will have to try the package installation as Administrator. Close R, then reopen R by right-clicking the R icon, and selecting “Run as administrator”. If this still doesn’t allow the installation, then 1. Run the R line sessionInfo() 2. Run the install.packages lines. 3. Highlight and pick up the entire contents of the R console and save it in a text file. Screenshots are usually not helpful. 4. Show your text listing to an R expert. A.1 Installing R—Initial Installation 703 A.1.3 Installation Problems—Any Operating System The most likely source of installation problems is settings (no write access to res- tricted directories on your machine, or system-wide firewalls to protect against offsite internet problems) that your computer administrator has placed on your machine. Check the FAQ (Frequently Asked Questions) files linked to at http://cran.r-project.org/faqs.html. For Windows, see also Section A.1.2.5. If outside help is needed, then save the contents of the R console window to show to your outside expert. In addition to the lines and results leading to the problem, including ALL messages that R produces, you must include the line (and its results) sessionInfo() in the material you show the expert. Screenshots are not a good way to capture information. The informative way to get the contents of the console window is by highlighting the entire window (including the off-screen part) and saving it in a text file. Show your text listing to an R expert. A.1.4 XLConnect: All Operating Systems The XLConnect package lets you read MS Excel files directly from R on any oper- ating system. You may use an R statement similar to the following library(XLConnect) WB <- ## pathname of file with some additional information loadWorkbook( ## "c:/Users/rmh/MyWorkbook.xlsx" ## rmh pathname in Windows "~/MyWorkbook.xlsx" ## rmh pathname in Macintosh ) mydata <- readWorksheet(WB, sheet="Sheet1", region="A1:D11") If you get an error from library(XLConnect) of the form Error : .onLoad failed in loadNamespace() for ’rJava’ then you need to install java on your machine from http://java.com.Thejava installer will ask you if you want to install ask as your default search provider. You may deselect both checkboxes to retain your preferred search provider. 704 A R A.2 Installing R—Updating R is under constant development with new releases every few months. At this writ- ing (August 2015) the current release is R-3.2.2 (2015-08-14). See the FAQ for general update information, and in addition the Windows FAQ or MacOs X FAQ for those operating systems. Links to all three FAQs are available at http://cran.r-project.org/faqs.html. The FAQ files are included in the documentation placed on your machine during installation. The update.packages mechanism works for packages on CRAN. It does not work for packages downloaded from elsewhere. Specifically, Windows users with RExcel installed will need to update the RExcel packages by reinstalling them as described in Section D.1.2.
Load more

Recommended publications
  • Rkward: a Comprehensive Graphical User Interface and Integrated Development Environment for Statistical Analysis with R
    JSS Journal of Statistical Software June 2012, Volume 49, Issue 9. http://www.jstatsoft.org/ RKWard: A Comprehensive Graphical User Interface and Integrated Development Environment for Statistical Analysis with R Stefan R¨odiger Thomas Friedrichsmeier Charit´e-Universit¨atsmedizin Berlin Ruhr-University Bochum Prasenjit Kapat Meik Michalke The Ohio State University Heinrich Heine University Dusseldorf¨ Abstract R is a free open-source implementation of the S statistical computing language and programming environment. The current status of R is a command line driven interface with no advanced cross-platform graphical user interface (GUI), but it includes tools for building such. Over the past years, proprietary and non-proprietary GUI solutions have emerged, based on internal or external tool kits, with different scopes and technological concepts. For example, Rgui.exe and Rgui.app have become the de facto GUI on the Microsoft Windows and Mac OS X platforms, respectively, for most users. In this paper we discuss RKWard which aims to be both a comprehensive GUI and an integrated devel- opment environment for R. RKWard is based on the KDE software libraries. Statistical procedures and plots are implemented using an extendable plugin architecture based on ECMAScript (JavaScript), R, and XML. RKWard provides an excellent tool to manage different types of data objects; even allowing for seamless editing of certain types. The objective of RKWard is to provide a portable and extensible R interface for both basic and advanced statistical and graphical analysis, while not compromising on flexibility and modularity of the R programming environment itself. Keywords: GUI, integrated development environment, plugin, R.
    [Show full text]
  • R-Based Strategies for DH in English Linguistics: a Case Study
    R-based strategies for DH in English Linguistics: a case study Nicolas Ballier Paula Lissón Université Paris Diderot Université Paris Diderot UFR Études Anglophones UFR Études Anglophones CLILLAC-ARP (EA 3967) CLILLAC-ARP (EA 3967) nicolas.ballier@univ- [email protected] paris-diderot.fr paris-diderot.fr language for research in linguistics, both in a Abstract quantitative and qualitative approach. Developing a culture based on the program- This paper is a position statement advocating ming language R (R Core Team 2016) for NLP the implementation of the programming lan- among MA and PhD students doing English Lin- guage R in a curriculum of English Linguis- guistics is no easy task. Broadly speaking, most tics. This is an illustration of a possible strat- of the students have little background in mathe- egy for the requirements of Natural Language matics, statistics, or programming, and usually Processing (NLP) for Digital Humanities (DH) studies in an established curriculum. R feel reluctant to study any of these disciplines. plays the role of a Trojan Horse for NLP and While most PhD students in English linguistics statistics, while promoting the acquisition of are former students with a Baccalauréat in Sci- a programming language. We report an over- ences, some MA students pride themselves on view of existing practices implemented in an having radically opted out of Maths. However, MA and PhD programme at the University of we believe that students should be made aware of Paris Diderot in the recent years. We empha- the growing use of statistical and NLP methods size completed aspects of the curriculum and in linguistics and to be able to interpret and im- detail existing teaching strategies rather than plement these techniques.
    [Show full text]
  • An Introduction to the SAS System
    An Introduction to the SAS System Dileep K. Panda Directorate of Water Management Bhubaneswar-751023 [email protected] Introduction The SAS – Statistical Analysis System (erstwhile expansion of SAS) - is the one of the most widely used Statistical Software package by the academic circles and Industry. The SAS software was developed in late 1960s at North Carolina State University and in 1976 SAS Institute was formed. The SAS system is a collection of products, available from the SAS Institute in North Carolina. SAS software is a combination of a statistical package, a data – base management system, and a high level programming language. The SAS is an integrated system of software solutions that performs the following tasks: Data entry, retrieval, and management Report writing and graphics design Statistical and mathematical analysis Business forecasting and decision support Operations research and project management Applications development At the core of the SAS System is the Base SAS software. The Base SAS software includes a fourth-generation programming language and ready-to-use programs called procedures. These integrated procedures handle data manipulation, information storage and retrieval, statistical analysis, and report writing. Additional components offer capabilities for data entry, retrieval, and management; report writing and graphics; statistical and mathematical analysis; business planning, forecasting, and decision support; operations research and project management; quality improvement; and applications development. In general, the Base SAS software has the following capabilities A data management facility A programming language Data analysis and reporting utilities Learning to use Base SAS enables you to work with these features of SAS. It also prepares you to learn other SAS products, because all SAS products follow the same basic rules.
    [Show full text]
  • Information Technology Laboratory Technical Accomplishments
    CONTENTS Director’s Foreword 1 ITL at a Glance 4 ITL Research Blueprint 6 Accomplishments of our Research Program 7 Foundation Research Areas 8 Selected Cross-Cutting Themes 26 Industry and International Interactions 36 Publications 44 NISTIR 7169 Conferences 47 February 2005 Staff Recognition 50 U.S. DEPARTMENT OF COMMERCE Carlos M. Gutierrez, Secretary Technology Administration Phillip J. Bond Under Secretary of Commerce for Technology National Institute of Standards and Technology Hratch G. Semerjian, Jr., Acting Director About ITL For more information about ITL, contact: Information Technology Laboratory National Institute of Standards and Technology 100 Bureau Drive, Stop 8900 Gaithersburg, MD 20899-8900 Telephone: (301) 975-2900 Facsimile: (301) 840-1357 E-mail: [email protected] Website: http://www.itl.nist.gov INFORMATION TECHNOLOGY LABORATORY D IRECTOR’S F OREWORD n today’s complex technology-driven world, the Information Technology Laboratory (ITL) at the National Institute of Standards and Technology has the broad mission of supporting U.S. industry, government, and Iacademia with measurements and standards that enable new computational methods for scientific inquiry, assure IT innovations for maintaining global leadership, and re-engineer complex societal systems and processes through insertion of advanced information technology. Through its efforts, ITL seeks to enhance productivity and public safety, facilitate trade, and improve the Dr. Shashi Phoha, quality of life. ITL achieves these goals in areas of Director, Information national priority by drawing on its core capabilities in Technology Laboratory cyber security, software quality assurance, advanced networking, information access, mathematical and computational sciences, and statistical engineering. utilizing existing and emerging IT to meet national Information technology is the acknowledged engine for priorities that reflect the country’s broad based social, national and regional economic growth.
    [Show full text]
  • R for Statistics: First Steps
    R for statistics: first steps CAR meeting, Raleigh, Feb 26 2011 Peter Aldhous, San Francisco Bureau Chief [email protected] A note of caution before using any stats package: Beware running with scissors! You can bamboozle yourself, and your readers, by misusing statistics. Make sure you understand the methods you are using, in particular the assumptions that must be met for them to be valid (e.g. normal distribution for many common tests). So before rushing in, consult: • IRE tipsheets (e.g. Donald & LaFleur, #2752; Donald & Hacker, #2731) • Statistical textbooks (e.g. http://www.statsoft.com/textbook/ is free online) • Experts who can provide a reality check on your analysis! Getting started: Download R, instructions at: http://www.r-project.org/ Start the program: Prepare the data: 1) Save as a csv file 2) Point R at the data First steps in the R command line: Load and examine the data: View a basic summary: What is the mean age and salary for CEOs in each sector? An alternative, computing mean and standard deviation in one go: Is there a correlation between age and salary? Draw a graph to explore the correlation analysis: Does mean CEO age differ significantly across sectors? Does mean CEO salary differ significantly across sectors? Draw a graph to explore the distribution of salary by sector: Moving beyond the basic functions: R packages The R community has written 2800+ extensions to R’s basic statistical and graphical functions, available from the CRAN repository: http://cran.r-project.org/web/packages/ Follow the links to find a PDF Reference Manual for each package.
    [Show full text]
  • Deducer: a Data Analysis GUI for R
    JSS Journal of Statistical Software June 2012, Volume 49, Issue 8. http://www.jstatsoft.org/ Deducer: A Data Analysis GUI for R Ian Fellows University of California, Los Angeles Abstract While R has proven itself to be a powerful and flexible tool for data exploration and analysis, it lacks the ease of use present in other software such as SPSS and Minitab. An easy to use graphical user interface (GUI) can help new users accomplish tasks that would otherwise be out of their reach, and improves the efficiency of expert users by replacing fifty key strokes with five mouse clicks. With this in mind, Deducer presents dialogs that are understandable for the beginner, and yet contain all (or most) of the options that an experienced statistician, performing the same task, would want. An Excel-like spreadsheet is included for easy data viewing and editing. Deducer is based on Java's Swing GUI library and can be used on any common operating system. The GUI is independent of the specific R console and can easily be used by calling a text-based menu system. Graphical menus are provided for the JGR console and the Windows R GUI. Keywords: GUI, R. 1. Introduction R (R Development Core Team 2012) is a powerful statistical programming language that places the latest statistical techniques at one's fingertips through thousands of add-on packages available on the Comprehensive R Archive Network (CRAN) download servers. The price for all of this power is complexity. Because R analyses must be called as text commands, the user is required to find out the name of the function that will accomplish their task, and then remember that name along with the names of the variables to feed it, and its argument options.
    [Show full text]
  • R in a Nutshell
    R IN A NUTSHELL Second Edition Joseph Adler Beijing • Cambridge • Farnham • Köln • Sebastopol • Tokyo R in a Nutshell, Second Edition by Joseph Adler Copyright © 2012 Joseph Adler. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://my.safaribooksonline.com). For more infor- mation, contact our corporate/institutional sales department: 800-998-9938 or [email protected]. Editors: Mike Loukides and Meghan Blanchette Indexer: Fred Brown Production Editor: Holly Bauer Cover Designer: Karen Montgomery Proofreader: Julie Van Keuren Interior Designer: David Futato Illustrators: Robert Romano and Re- becca Demarest September 2009: First Edition. October 2012: Second Edition. Revision History for the Second Edition: 2012-09-25 First release See http://oreilly.com/catalog/errata.csp?isbn=9781449312084 for release details. Nutshell Handbook, the Nutshell Handbook logo, and the O’Reilly logo are registered trade- marks of O’Reilly Media, Inc. R in a Nutshell, the image of a harpy eagle, and related trade dress are trademarks of O’Reilly Media, Inc. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and O’Reilly Media, Inc., was aware of a trademark claim, the designations have been printed in caps or initial caps. While every precaution has been taken in the preparation of this book, the publisher and author assume no responsibility for errors or omissions, or for damages resulting from the use of the information contained herein.
    [Show full text]
  • Eviews 9 Command and Programming Reference Eviews 9 Command and Programming Reference Copyright © 1994–2015 IHS Global Inc
    EViews 9 Command and Programming Reference EViews 9 Command and Programming Reference Copyright © 1994–2015 IHS Global Inc. All Rights Reserved ISBN: 978-1-880411-29-2 This software product, including program code and manual, is copyrighted, and all rights are reserved by IHS Global Inc. The distribution and sale of this product are intended for the use of the original purchaser only. Except as permitted under the United States Copyright Act of 1976, no part of this product may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written permission of IHS Global Inc. Disclaimer The authors and IHS Global Inc. assume no responsibility for any errors that may appear in this manual or the EViews program. The user assumes all responsibility for the selection of the pro- gram to achieve intended results, and for the installation, use, and results obtained from the pro- gram. Trademarks EViews® is a registered trademark of IHS Global Inc. Windows, Excel, PowerPoint, and Access are registered trademarks of Microsoft Corporation. PostScript is a trademark of Adobe Corpora- tion. X11.2 and X12-ARIMA Version 0.2.7, and X-13ARIMA-SEATS are seasonal adjustment pro- grams developed by the U. S. Census Bureau. Tramo/Seats is copyright by Agustin Maravall and Victor Gomez. Info-ZIP is provided by the persons listed in the infozip_license.txt file. Please refer to this file in the EViews directory for more information on Info-ZIP. Zlib was written by Jean-loup Gailly and Mark Adler.
    [Show full text]
  • The Use of Statistical Software to Teach Nonparametric Curve Estimation: from Excel to R
    ICOTS8 (2010) Invited Paper Refereed Cao & Naya THE USE OF STATISTICAL SOFTWARE TO TEACH NONPARAMETRIC CURVE ESTIMATION: FROM EXCEL TO R Ricardo Cao and Salvador Naya Research Group MODES, Department of Mathematics, University of A Coruña, Spain [email protected] The advantages of using R and Excel for teaching nonparametric curve estimation are presented in this paper. The use of these two tools for teaching nonparametric curve estimation is illustrated by means of several well-known data sets. Computation of histogram and kernel density estimators as well as kernel and local polynomial regression estimators is presented using Excel and R. Interactive changes in the sample and the smoothing parameter are illustrated using both tools. R incorporates sophisticated routines for crucial issues in nonparametric curve estimation, as smoothing parameter selection. The paper concludes summarizing the relative merits of these two tools for teaching nonparametric curve estimation and presenting RExcel, a free add-in for Excel that can be downloaded from the R distribution network. INTRODUCTION There has been an enormous expansion, over the past few years, on the use of computer and communication technologies for teaching statistics at different levels. Microsoft Excel is the most popular spreadsheet program that is used to store information in columns and rows, which can then be organized and/or processed. Many authors consider Microsoft Excel as an excellent tool for statistical education (see, for example, Giles, 2002). On the other hand, the use of free software is also one of the most interesting available tools for teaching statistics. Universal access to the Internet enables easy installation of free software.
    [Show full text]
  • Appendix C Some Details of Matrix.Xla(M)
    Appendix C Some details of Matrix.xla(m) C.1 Matrix nomenclature For the sake of notational compactness, we will denote a square diagonal matrix by D with elements dii, a square tridiagonal matrix by T with elements tij where | j – i | ≤ 1, most other square matrices by S, rectangular matrices by R, and all matrix elements by mij. A vector will be shown as v, with elements vi, and a scalar as s. Particular values are denoted by x when real, and by z when complex. All optional pa- rameters are shown in straight brackets, [ ]. All matrices, vectors, and scalars are assumed to be real, ex- cept when specified otherwise. All matrices are restricted to two dimensions, and vectors to one dimen- sion. Table C.1 briefly explains some matrix terms that will be used in subsequent tables. With some functions, the user is given the integer option Int of applying integer arithmetic. When a matrix only contains integer elements, selecting integer arithmetic may avoid most round-off problems. On the other hand, the range of integer arithmetic is limited, so that overflow errors may result if the ma- trix is large and/or contains large numbers. Another common option it Tiny, which defines the absolute value of quantities that can be regarded as most likely resulting from round-off errors, and are therefore set to zero. When not activated, the routine will use its user-definable default value. Condition of a matrix: ratio of its largest to smallest singular value Diagonal of a square matrix: the set of terms mij where i = j Diagonal matrix D square matrix with mij = 0 for all off-diagonal elements i ≠ j.
    [Show full text]
  • Paquetes Estadísticos Con Licencia Libre (I) Free Statistical
    2013, Vol. 18 No 2, pp. 12-33 http://www.uni oviedo.es/reunido /index.php/Rema Paquetes estadísticos con licencia libre (I) Free statistical software (I) Carlos Carleos Artime y Norberto Corral Blanco Departamento de Estadística e I. O. y D.M.. Universidad de Oviedo RESUMEN El presente artículo, el primero de una serie de trabajos, presenta un panorama de los principales paquetes estadísticos disponibles en la actualidad con licencia libre, entendiendo este concepto a partir de los grandes proyectos informáticos así autodefinidos que han cristalizado en los últimos treinta años. En esta primera entrega se presta atención fundamentalmente al programa R. Palabras clave : paquetes estadísticos, software libre, R. ABSTRACT This article, the first in a series of works, presents an overview of the major statistical packages available today with free license, understanding this concept from the large computer and self-defined projects that have crystallized in the last thirty years. In this first paper we pay attention mainly to the program R. Keywords : statistical software, free software, R. Contacto: Norberto Corral Blanco Email: [email protected] . 12 Revista Electrónica de Metodología Aplicada (2013), Vol. 18 No 2, pp. 12-33 1.- Introducción La palabra “libre” es usada, y no siempre de manera adecuada, en múltiples campos de conocimiento así que la informática no iba a ser una excepción. La palabra es especialmente problemática porque su traducción al inglés, “free”, es aun más polisémica, e incluye el concepto “gratis”. En 1985 se fundó la Free Software Foundation (FSF) para defender una definición rigurosa del sófguar libre. La propia palabra “sófguar” (del inglés “software”, por oposición a “hardware”, quincalla, aplicado al soporte físico de la informática) es en sí misma problemática.
    [Show full text]
  • The Statistics Tutor's Pocket Book Guide
    statstutor community project encouraging academics to share statistics support resources All stcp resources are released under a Creative Commons licence Stcp-marshall_owen-pocket The Statistics Tutor’s Pocket Book Guide to Statistics Resources Version 1.0 Dated 25/08/2016 Please note that if printed, this guide has been designed to be printed as an A5 booklet double sided on A4 paper. © Alun Owen (University of Worcester), Ellen Marshall (University of Sheffield) www.statstutor.ac.uk and Jonathan Gillard (Cardiff University). Reviewer: Mark Johnston (University of Worcester) Page 2 of 51 Contents INTRODUCTION ................................................................................................................................................................................. 4 SECTION 1 MOST POPULAR RESOURCES .................................................................................................................................... 7 THE MOST RECOMMENDED STATISTICS BOOKS ......................................................................................................................................... 8 THE MOST RECOMMENDED ONLINE STATISTICS RESOURCES ........................................................................................................................ 9 SECTION 2 DESIGNING A STUDY AND CHOOSING A TEST ......................................................................................................... 11 DESIGNING AN EXPERIMENT OR SURVEY AND CHOOSING A TEST ...............................................................................................................
    [Show full text]