DOCSLIB.ORG

Design Principles for Data Presentation: Translating Analysis Into Visualization

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Design Principles for Data Presentation: Translating Analysis Into Visualization Design principles for data presentation: Translating analysis into visualization Andrew Eppig, UC Berkeley CAIR Annual Conference 8 November 2012 Overview • Design Principles • Data Sample and Summary • Why Charts Matter • Visualization Process • Chart Selection • Layout • Aesthetics • Self-Sufficiency Check • Institutional Examples and Applications CAIR 2012 2 Andrew Eppig, 8 November 2012 Guiding Design Principles • Good visualizations start with good data and detailed analysis • Know your data • Good visualizations directly answer specific, focused questions • Know what question(s) you are asking • Good visualizations get out of the way of the data • Let the data tell its story without excess clutter or distraction “Too often we pay more attention to ‘pretty’ than to the most important element: information.” -- Dona Wong, The Secrets of Graphics Presentation CAIR 2012 3 Andrew Eppig, 8 November 2012 Data Sample, Summary, and Metrics Name Weight (lb.) Height (in.) BMI Batman 210 74 27.0 Michael Phelps 165 75 20.6 Wonder Woman 130 72 17.6 Hope Solo 140 69 20.7 weight[lb] BMI = 703 x height[in]2 Gender Group N BMI Mean BMI Std. Dev. Comics 1,239 26.0 4.2 Male Athletes 403 23.8 3.5 Models 493 21.6 2.3 Comics 505 20.3 3.4 Female Athletes 254 22.0 3.4 Models 489 18.2 2.7 CAIR 2012 4 Andrew Eppig, 8 November 2012 Default Excel Charting Average BMI by Group and Gender 35.000 30.000 25.000 20.000 Female 15.000 Male 10.000 5.000 0.000 Models Athletes Comics CAIR 2012 5 Andrew Eppig, 8 November 2012 Excessive Chart Junk Average BMI by Group and Gender 27.000 26.000 26.000 25.000 24.000 23.800 23.000 22.000 22.000 BMI 21.600 Models 21.000 20.300 Athletes 20.000 Comics 19.000 18.200 18.000 17.000 Female Male CAIR 2012 6 Andrew Eppig, 8 November 2012 Improved Excel Charting Average BMI by Group and Gender Comics 26! Male! Athletes 24! Models 22! Comics 20! Female! Athletes 22! Models 18! CAIR 2012 7 Andrew Eppig, 8 November 2012 Visualization Checklist • What stories does the data tell? Which story do you want to tell? • What visualization will best aid the story? • Who is the audience? • What metric should you use? • Which type of chart should you use? Junk Charts What is the layout of the visualization? Kaiser Fung, Fung, Kaiser • • How can details enhance the chart? Source: • Font, color, lines/shading, and text “What are the content-reasoning tasks that this display is supposed to help with?” -- Edward Tufte, Beautiful Evidence CAIR 2012 8 Andrew Eppig, 8 November 2012 Super humans - or - Analysis, Writing, Art, and Lettering by: Andrew Eppig Super Models? A nalysis Question: Are Comic Book Superheroes’ bodies more like Top Athletes’ or Top Models’ bodies? Are comparisons the same for both men and women? To account for differences in height and weight, Body Mass Index (BMI) is used: BMI = 703 x weight / height². Comparing the BMI distributions will reveal similarities or differences. Blue Shaded area shows middle 50% of superhero BMI distributions Chart SelectionBody Mass Index Distributions Male Superheroes FeMale Superheroes (N = 1,239) (N = 505) 50% of distribution is 50% of distribution is INSIDE shaded area INSIDE shaded area Median BMI = 25.2 Median BMI = 19.7 Probability Probability 0.0 0.1 0.0 0.0 0.1 0.2 15 20 25 30 35 15 20 25 30 35 Body Mass Index Body Mass Index “Meaningful quantitative information always involves relationships. When displayed in graphs, these relationshipsMale Athletes always boil down to one or more of feMale Athletes eight specific relationships: time series,(N = ranking, 403) part-to-whole, deviation, (N = 254) distribution, correlation, geospatial, nominal39% of comparison.” distribution is 31% of distribution is -- Stephen Few, Designing Effective TablesINSIDE and Graphsshaded area INSIDE shaded area Median BMI = 23.2 Median BMI = 21.4 CAIR 2012 9 Andrew Eppig, 8 November 2012 Probability Probability 0.0 0.1 0.0 0.1 0.2 15 20 25 30 35 15 20 25 30 35 Body Mass Index Body Mass Index Male Models feMale Models (N = 493) (N = 489) 17% of distribution is 28% of distribution is INSIDE shaded area INSIDE shaded area Median BMI = 21.6 Median BMI = 17.7 Probability Probability 0.0 0.1 0.0 0.1 0.2 15 20 25 30 35 15 20 25 30 35 Body Mass Index Body Mass Index S ummary: Male superheroes Tend to have a higher bmi than top MAle Athletes and a much higher bmi than top male models. So Male superheroes are beyond super human - but more like top male athletes than like top male models. Female superheroes tend to have a lower BMI than top female athletes and a higher bmi than top female models. So female superheroes are Neither super humans nor Super models but between Top female athletes and top female models. Female superheroes also have less variation in their BMI than male Superheroes or Top athletes of either gender. Sources: DC Comics (dc.wikia.com); marvel comics (Marvel.com/universe); 2008 US Olympic Team (www.2008.nbcolympics.com); Models.com (models.com) Super humans - or - Analysis, Writing, Art, and Lettering by: Andrew Eppig Super Models? A nalysis Question: Are Comic Book Superheroes’ bodies more like Top Athletes’ or Top Models’ bodies? Are comparisons the same for both men and women? To account for differences in height and Visualizationweight, Body Mass Index (BMI) is used: BMI = 703 x weight Layout / height². Comparing the BMI distributions will reveal similarities or differences. Blue Shaded area shows middle 50% of superhero BMI distributions Body Mass Index Distributions Male Superheroes FeMale Superheroes (N = 1,239) (N = 505) 50% of distribution is 50% of distribution is INSIDE shaded area INSIDE shaded area Median BMI = 25.2 Median BMI = 19.7 Probability Probability 0.0 0.1 0.0 0.1 0.2 15 20 25 30 35 15 20 25 30 35 Body Mass Index Body Mass Index Male Athletes feMale Athletes (N = 403) (N = 254) 39% of distribution is 31% of distribution is INSIDE shaded area INSIDE shaded area Median BMI = 23.2 Median BMI = 21.4 Probability Probability 0.0 0.1 0.0 0.0 0.1 0.2 15 20 25 30 35 15 20 25 30 35 Body Mass Index Body Mass Index Male Models feMale Models (N = 493) (N = 489) 17% of distribution is 28% of distribution is INSIDE shaded area INSIDE shaded area Median BMI = 21.6 Median BMI = 17.7 Probability Probability 0.0 0.1 0.0 0.0 0.1 0.2 15 20 25 30 35 15 20 25 30 35 Body Mass Index Body Mass Index S ummary: Male superheroes Tend to have a higher bmi than top MAle Athletes and a much “Tufte’s (1990)higher bmirecommendation than top male models. So Male superheroes of ‘small are beyond super multiples human - but more like’ [...] uses the top male athletes than like top male models. replicationFemale in superheroes the display tend to have ato lower facilitate BMI than top female athletes comparison and a higher bmi than to the top female models. So female superheroes are Neither super humans nor Super models but implicitbetween modelTop female athletes of and topno female change models. Female superheroes between also have lessthe variation displays.” in their BMI than male Superheroes or Top athletes of either gender. -- AndrewSources: Gelman, DC Comics (dc.wikia.com); marvel Exploratory comics (Marvel.com/universe); 2008 Data US Olympic Team Analysis (www.2008.nbcolympics.com for); Models.com Complex (models.com) Models CAIR 2012 10 Andrew Eppig, 8 November 2012 Aesthetic Considerations • Font: How do you increase legibility and decrease distraction? • Color: Which color palette is appropriate? • Line/Shading: Which weight, color, and style will enhance the final product? • Text: Can adding labels and narrative Plate Corp. ChemicalColor Source: provide useful context? “Hue contrast is easy to overuse to the point of visual clutter. A better approach is to use a few high chroma colors as color contrast in a presentation consisting primarily of grays and muted colors.” -- Maureen Stone, Choosing Colors for Data Visualization CAIR 2012 11 Andrew Eppig, 8 November 2012 Final Infographic Super humans SuperHeroes - or - Analysis, Writing, Art, and Lettering by: Andrew Eppig Super Models? A nalysis Question: Are Comic Book Superheroes’ bodies more like Top Athletes’ or Top Models’ bodies? Are comparisons the same for both men and women? To account for differences in height and weight, Body Mass Index (BMI) is used: BMI = 703 x weight / height². Comparing the BMI distributions will reveal similarities or differences. Blue Shaded area shows middle 50% of superhero BMI distributions Body Mass Index Distributions Male Superheroes FeMale Superheroes (N = 1,239) (N = 505) 50% of distribution is 50% of distribution is INSIDE shaded area INSIDE shaded area Median BMI = 25.2 Median BMI = 19.7 Probability Probability 0.0 0.1 0.0 0.1 0.2 15 20 25 30 35 15 20 25 30 35 Body Mass Index Body Mass Index Male Athletes feMale Athletes (N = 403) (N = 254) 39% of distribution is 31% of distribution is INSIDE shaded area INSIDE shaded area Median BMI = 23.2 Median BMI = 21.4 Probability Probability 0.0 0.1 0.0 0.0 0.1 0.2 15 20 25 30 35 15 20 25 30 35 Body Mass Index Body Mass Index Male Models feMale Models (N = 493) (N = 489) 17% of distribution is 28% of distribution is INSIDE shaded area INSIDE shaded area Median BMI = 21.6 Median BMI = 17.7 Probability Probability 0.0 0.1 0.0 0.0 0.1 0.2 15 20 25 30 35 15 20 25 30 35 Body Mass Index Body Mass Index S ummary: Male superheroes Tend to have a higher bmi than top MAle Athletes and a much higher bmi than top male models.
Load more

Recommended publications
  • The Savvy Survey #16: Data Analysis and Survey Results1 Milton G
    AEC409 The Savvy Survey #16: Data Analysis and Survey Results1 Milton G. Newberry, III, Jessica L. O’Leary, and Glenn D. Israel2 Introduction In order to evaluate the effectiveness of a program, an agent or specialist may opt to survey the knowledge or Continuing the Savvy Survey Series, this fact sheet is one of attitudes of the clients attending the program. To capture three focused on working with survey data. In its most raw what participants know or feel at the end of a program, he form, the data collected from surveys do not tell much of a or she could implement a posttest questionnaire. However, story except who completed the survey, partially completed if one is curious about how much information their clients it, or did not respond at all. To truly interpret the data, it learned or how their attitudes changed after participation must be analyzed. Where does one begin the data analysis in a program, then either a pretest/posttest questionnaire process? What computer program(s) can be used to analyze or a pretest/follow-up questionnaire would need to be data? How should the data be analyzed? This publication implemented. It is important to consider realistic expecta- serves to answer these questions. tions when evaluating a program. Participants should not be expected to make a drastic change in behavior within the Extension faculty members often use surveys to explore duration of a program. Therefore, an agent should consider relevant situations when conducting needs and asset implementing a posttest/follow up questionnaire in several assessments, when planning for programs, or when waves in a specific timeframe after the program (e.g., six assessing customer satisfaction.
    [Show full text]
  • Evolution of the Infographic
    EVOLUTION OF THE INFOGRAPHIC: Then, now, and future-now. EVOLUTION People have been using images and data to tell stories for ages—long before the days of the Internet, smartphones, and Excel. In fact, the history of infographics pre-dates the web by more than 30,000 years with the earliest forms of these visuals being cave paintings that helped early humans find food, resources, and shelter. But as technology has advanced, so has our ability to tell meaningful stories. Here’s a look into the evolution of modern infographics—where they’ve been, how they’ve evolved, and where they’re headed. Then: Printed, static infographics The 20th Century introduced the infographic—a staple for how we communicate, visualize, and share information today. Early on, these print graphics married illustration and data to communicate information in a revolutionary way. ADVANTAGE Design elements enable people to quickly absorb information previously confined to long paragraphs of text. LIMITATION Static infographics didn’t allow for deeper dives into the data to explore granularities. Hoping to drill down for more detail or context? Tough luck—what you see is what you get. Source: http://www.wired.co.uk/news/archive/2012-01/16/painting- by-numbers-at-london-transport-museum INFOGRAPHICS THROUGH THE AGES DOMO 03 Now: Web-based, interactive infographics While the first wave of modern infographics made complex data more consumable, web-based, interactive infographics made data more explorable. These are everywhere today. ADVANTAGE Everyone looking to make data an asset, from executives to graphic designers, are now building interactive data stories that deliver additional context and value.
    [Show full text]
  • Using Survey Data Author: Jen Buckley and Sarah King-Hele Updated: August 2015 Version: 1
    ukdataservice.ac.uk Using survey data Author: Jen Buckley and Sarah King-Hele Updated: August 2015 Version: 1 Acknowledgement/Citation These pages are based on the following workbook, funded by the Economic and Social Research Council (ESRC). Williamson, Lee, Mark Brown, Jo Wathan, Vanessa Higgins (2013) Secondary Analysis for Social Scientists; Analysing the fear of crime using the British Crime Survey. Updated version by Sarah King-Hele. Centre for Census and Survey Research We are happy for our materials to be used and copied but request that users should: • link to our original materials instead of re-mounting our materials on your website • cite this as an original source as follows: Buckley, Jen and Sarah King-Hele (2015). Using survey data. UK Data Service, University of Essex and University of Manchester. UK Data Service – Using survey data Contents 1. Introduction 3 2. Before you start 4 2.1. Research topic and questions 4 2.2. Survey data and secondary analysis 5 2.3. Concepts and measurement 6 2.4. Change over time 8 2.5. Worksheets 9 3. Find data 10 3.1. Survey microdata 10 3.2. UK Data Service 12 3.3. Other ways to find data 14 3.4. Evaluating data 15 3.5. Tables and reports 17 3.6. Worksheets 18 4. Get started with survey data 19 4.1. Registration and access conditions 19 4.2. Download 20 4.3. Statistics packages 21 4.4. Survey weights 22 4.5. Worksheets 24 5. Data analysis 25 5.1. Types of variables 25 5.2. Variable distributions 27 5.3.
    [Show full text]
  • Monte Carlo Likelihood Inference for Missing Data Models
    MONTE CARLO LIKELIHOOD INFERENCE FOR MISSING DATA MODELS By Yun Ju Sung and Charles J. Geyer University of Washington and University of Minnesota Abbreviated title: Monte Carlo Likelihood Asymptotics We describe a Monte Carlo method to approximate the maximum likeli- hood estimate (MLE), when there are missing data and the observed data likelihood is not available in closed form. This method uses simulated missing data that are independent and identically distributed and independent of the observed data. Our Monte Carlo approximation to the MLE is a consistent and asymptotically normal estimate of the minimizer ¤ of the Kullback- Leibler information, as both a Monte Carlo sample size and an observed data sample size go to in¯nity simultaneously. Plug-in estimates of the asymptotic variance are provided for constructing con¯dence regions for ¤. We give Logit-Normal generalized linear mixed model examples, calculated using an R package that we wrote. AMS 2000 subject classi¯cations. Primary 62F12; secondary 65C05. Key words and phrases. Asymptotic theory, Monte Carlo, maximum likelihood, generalized linear mixed model, empirical process, model misspeci¯cation 1 1. Introduction. Missing data (Little and Rubin, 2002) either arise naturally|data that might have been observed are missing|or are intentionally chosen|a model includes random variables that are not observable (called latent variables or random e®ects). A mixture of normals or a generalized linear mixed model (GLMM) is an example of the latter. In either case, a model is speci¯ed for the complete data (x; y), where x is missing and y is observed, by their joint den- sity f(x; y), also called the complete data likelihood (when considered as a function of ).
    [Show full text]
  • Interactive Statistical Graphics/ When Charts Come to Life
    Titel Event, Date Author Affiliation Interactive Statistical Graphics When Charts come to Life [email protected] www.theusRus.de Telefónica Germany Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 2 www.theusRus.de What I do not talk about … Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 3 www.theusRus.de … still not what I mean. Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 4 www.theusRus.de Interactive Graphics ≠ Dynamic Graphics • Interactive Graphics … uses various interactions with the plots to change selections and parameters quickly. Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 4 www.theusRus.de Interactive Graphics ≠ Dynamic Graphics • Interactive Graphics … uses various interactions with the plots to change selections and parameters quickly. • Dynamic Graphics … uses animated / rotating plots to visualize high dimensional (continuous) data. Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 4 www.theusRus.de Interactive Graphics ≠ Dynamic Graphics • Interactive Graphics … uses various interactions with the plots to change selections and parameters quickly. • Dynamic Graphics … uses animated / rotating plots to visualize high dimensional (continuous) data. 1973 PRIM-9 Tukey et al. Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 4 www.theusRus.de Interactive Graphics ≠ Dynamic Graphics • Interactive Graphics … uses various interactions with the plots to change selections and parameters quickly. • Dynamic Graphics … uses animated / rotating plots to visualize high dimensional (continuous) data.
    [Show full text]
  • Infovis and Statistical Graphics: Different Goals, Different Looks1
    Infovis and Statistical Graphics: Different Goals, Different Looks1 Andrew Gelman2 and Antony Unwin3 20 Jan 2012 Abstract. The importance of graphical displays in statistical practice has been recognized sporadically in the statistical literature over the past century, with wider awareness following Tukey’s Exploratory Data Analysis (1977) and Tufte’s books in the succeeding decades. But statistical graphics still occupies an awkward in-between position: Within statistics, exploratory and graphical methods represent a minor subfield and are not well- integrated with larger themes of modeling and inference. Outside of statistics, infographics (also called information visualization or Infovis) is huge, but their purveyors and enthusiasts appear largely to be uninterested in statistical principles. We present here a set of goals for graphical displays discussed primarily from the statistical point of view and discuss some inherent contradictions in these goals that may be impeding communication between the fields of statistics and Infovis. One of our constructive suggestions, to Infovis practitioners and statisticians alike, is to try not to cram into a single graph what can be better displayed in two or more. We recognize that we offer only one perspective and intend this article to be a starting point for a wide-ranging discussion among graphics designers, statisticians, and users of statistical methods. The purpose of this article is not to criticize but to explore the different goals that lead researchers in different fields to value different aspects of data visualization. Recent decades have seen huge progress in statistical modeling and computing, with statisticians in friendly competition with researchers in applied fields such as psychometrics, econometrics, and more recently machine learning and “data science.” But the field of statistical graphics has suffered relative neglect.
    [Show full text]
  • Questionnaire Analysis Using SPSS
    Questionnaire design and analysing the data using SPSS page 1 Questionnaire design. For each decision you make when designing a questionnaire there is likely to be a list of points for and against just as there is for deciding on a questionnaire as the data gathering vehicle in the first place. Before designing the questionnaire the initial driver for its design has to be the research question, what are you trying to find out. After that is established you can address the issues of how best to do it. An early decision will be to choose the method that your survey will be administered by, i.e. how it will you inflict it on your subjects. There are typically two underlying methods for conducting your survey; self-administered and interviewer administered. A self-administered survey is more adaptable in some respects, it can be written e.g. a paper questionnaire or sent by mail, email, or conducted electronically on the internet. Surveys administered by an interviewer can be done in person or over the phone, with the interviewer recording results on paper or directly onto a PC. Deciding on which is the best for you will depend upon your question and the target population. For example, if questions are personal then self-administered surveys can be a good choice. Self-administered surveys reduce the chance of bias sneaking in via the interviewer but at the expense of having the interviewer available to explain the questions. The hints and tips below about questionnaire design draw heavily on two excellent resources. SPSS Survey Tips, SPSS Inc (2008) and Guide to the Design of Questionnaires, The University of Leeds (1996).
    [Show full text]
  • Reviews Edited by Beth Notzon and Edith Paal
    Reviews edited by Beth Notzon and Edith Paal People have been trying to display infor- dull statistician or economist: he dabbled mation graphically ever since our ancestors in fields as varied as engineering, journal- depicted hunts on the cave walls. From ism, and blackmail, the reader discovers. those early efforts through the graphs and There is essentially no limit to the types tables in modern scientific publications, of data display that can be influenced by the attempts have met with various degrees good design, as Wainer makes clear by of success, as Howard Wainer describes in the variety of examples he presents. The Graphic Discovery: A Trout in the Milk and college acceptance letter Wainer’s son Other Visual Adventures. received is cited as a successful display. The This book is no dry, academic tome word “YES,” which is really the only infor- about data. The conversational tone, well- mation the reader cares about in such a chosen illustrations, and enriching asides letter, is printed in large type in the middle combine to create a delightful presentation of the page, with two short, smaller-type of the highlights and lowlights of graphic lines of congratulatory text at the bottom. display. And good data displays will con- As Wainer points out, that tells readers tinue to be important in our current, data- what they want to know without making inundated society. them hunt for it. (He leaves unaddressed Like any good story, this one has its hero. what that school’s rejection letters looked Although writers have used pictures to like that year.
    [Show full text]
  • How Well Can We Equate Test Forms That Are Constructed by Examinees? Program Statistics Research
    DOCUMENT RESUME ED 385 579 TM 024 017 AUTHOR Wainer, Howard; And Others TITLE How Well Can We Equate Test Forms That Are Constructed by Examinees? Program Statistics Research. INSTITUTION Educational Testing Service, Princeton, N.J. REPORT NO ETS-RR-91-57; ETS-TR-91-15 PUB DATE Oct 91 NOTE 29p. PUB TYPE Reports Research/Technical (143) EDRS PRICE MF01/PCO2 Plus Postage. DESCRIPTORS *Adaptive Testing; Chemistry; ComparativeAnalysis; Computer Assisted Testing; *Constructed Response; Difficulty Level; *Equated Scores; *ItemResponse Theory; Models; Selection; Test Format; Testing; *Test Items IDENTIFIERS Advanced Placement Examinations (CEEB); *Unidimensionality (Tests) ABSTRACT When an examination consists, in wholeor in part, of constructed response items, it isa common-practice to allow the examinee to choose amonga variety of questions. This procedure is usually adopted so that the limited number ofitems that can be completed in the allotted time does not unfairlyaffect the examinee. This results in the de facto administrationof several different test forms, where the exact structure ofany particular form is determined by the examinee. When different formsare administered, a canon of good testing practice requires that thoseforms be equated to adjust for differences in their difficulty.When the items are chosen by the examinee, traditional equating proceduresdo not strictly apply. In this paper, how one might equate withan item response theory (IRT) framework is explored. The procedure isillustrated with data from the College Board's Advanced PlacementTest in Chemistry taken by a sample of 18,431 examinees. Comparablescores can be produced in the context of choice to the extent thatresponses may be characterized with a unidimensional IRT model.Seven tables and five figures illustrate the discussion.
    [Show full text]
  • STANDARDS and GUIDELINES for STATISTICAL SURVEYS September 2006
    OFFICE OF MANAGEMENT AND BUDGET STANDARDS AND GUIDELINES FOR STATISTICAL SURVEYS September 2006 Table of Contents LIST OF STANDARDS FOR STATISTICAL SURVEYS ....................................................... i INTRODUCTION......................................................................................................................... 1 SECTION 1 DEVELOPMENT OF CONCEPTS, METHODS, AND DESIGN .................. 5 Section 1.1 Survey Planning..................................................................................................... 5 Section 1.2 Survey Design........................................................................................................ 7 Section 1.3 Survey Response Rates.......................................................................................... 8 Section 1.4 Pretesting Survey Systems..................................................................................... 9 SECTION 2 COLLECTION OF DATA................................................................................... 9 Section 2.1 Developing Sampling Frames................................................................................ 9 Section 2.2 Required Notifications to Potential Survey Respondents.................................... 10 Section 2.3 Data Collection Methodology.............................................................................. 11 SECTION 3 PROCESSING AND EDITING OF DATA...................................................... 13 Section 3.1 Data Editing ........................................................................................................
    [Show full text]
  • Improving Critical Thinking Through Data Analysis*
    Midwest Manufacturing Accounting Conference Improving Critical Thinking Through Data Analysis* Kurt Reding May 17, 2018 *This presentation is based on the article, “Improving Critical Thinking Through Data Analysis,” published in the June 2017 issue of Strategic Finance. Outline . Critical thinking . Diagnostic data analysis . Merging critical thinking with diagnostic data analysis 2 Critical Thinking . “Bosses Seek ‘Critical Thinking,’ but What Is That?” (emphasis added) “It’s one of those words—like diversity was, like big data is— where everyone talks about it but there are 50 different ways to define it,” says Dan Black, Americas director of recruiting at the accounting firm and consultancy EY. (emphasis added) “Critical thinking may be similar to U.S. Supreme Court Justice Porter Stewart’s famous threshold for obscenity: You know it when you see it,” says Jerry Houser, associate dean and director of career services at Williamette University in Salem, Ore. (emphasis added) http://www.wsj.com/articles/bosses-seek-critical-thinking-but-what-is-that-1413923730 3 Critical Thinking . Critical thinking is not… − Going-through-the-motions thinking. − Quick thinking. 4 Critical Thinking . Proposed definition: Critical thinking is a manner of thinking that employs curiosity, creativity, skepticism, analysis, and logic, where: − Curiosity means wanting to learn, − Creativity means viewing information from multiple perspectives, − Skepticism means maintaining a ‘trust but verify’ mindset; − Analysis means systematically examining and evaluating evidence, and − Logic means reaching well-founded conclusions. 5 Critical Thinking . Although some people are innately more curious, creative, and/or skeptical than others, everyone can exercise these personal attributes to some degree. Likewise, while some people are naturally better analytical and logical thinkers, everyone can improve these skills through practice, education, and training.
    [Show full text]
  • Notices of the American Mathematical
    ISSN 0002-9920 Notices of the American Mathematical Society AMERICAN MATHEMATICAL SOCIETY Graduate Studies in Mathematics Series The volumes in the GSM series are specifically designed as graduate studies texts, but are also suitable for recommended and/or supplemental course reading. With appeal to both students and professors, these texts make ideal independent study resources. The breadth and depth of the series’ coverage make it an ideal acquisition for all academic libraries that of the American Mathematical Society support mathematics programs. al January 2010 Volume 57, Number 1 Training Manual Optimal Control of Partial on Transport Differential Equations and Fluids Theory, Methods and Applications John C. Neu FROM THE GSM SERIES... Fredi Tro˝ltzsch NEW Graduate Studies Graduate Studies in Mathematics in Mathematics Volume 109 Manifolds and Differential Geometry Volume 112 ocietty American Mathematical Society Jeffrey M. Lee, Texas Tech University, Lubbock, American Mathematical Society TX Volume 107; 2009; 671 pages; Hardcover; ISBN: 978-0-8218- 4815-9; List US$89; AMS members US$71; Order code GSM/107 Differential Algebraic Topology From Stratifolds to Exotic Spheres Mapping Degree Theory Matthias Kreck, Hausdorff Research Institute for Enrique Outerelo and Jesús M. Ruiz, Mathematics, Bonn, Germany Universidad Complutense de Madrid, Spain Volume 110; 2010; approximately 215 pages; Hardcover; A co-publication of the AMS and Real Sociedad Matemática ISBN: 978-0-8218-4898-2; List US$55; AMS members US$44; Española (RSME). Order code GSM/110 Volume 108; 2009; 244 pages; Hardcover; ISBN: 978-0-8218- 4915-6; List US$62; AMS members US$50; Ricci Flow and the Sphere Theorem The Art of Order code GSM/108 Simon Brendle, Stanford University, CA Mathematics Volume 111; 2010; 176 pages; Hardcover; ISBN: 978-0-8218- page 8 Training Manual on Transport 4938-5; List US$47; AMS members US$38; and Fluids Order code GSM/111 John C.
    [Show full text]