DOCSLIB.ORG

Visualiation of Quantitative Information

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Visualiation of Quantitative Information Visualisation of quantitative information Overview 1. Visualisation 2. Approaching data 3. Levels of measurement 4. Principals of graphing 5. Univariate graphs 6. Graphical integrity James Neill, 2011 2 Is Pivot a turning point for web exploration? (Gary Flake) (TED talk - 6 min. ) 4 Approaching Entering & data screening Approaching Exploring, describing, & data graphing Hypothesis testing 5 6 Describing & graphing data THE CHALLENGE: to find a meaningful, accurate way to depict the ‘true story’ of the data 7 Clearly report the data's main features 10 Levels of measurement • Nominal / Categorical • Ordinal • Interval • Ratio 12 Discrete vs. continuous Discrete - - - - - - - - - - Continuous ___________ Each level has the properties of the preceding 14 13 levels, plus something more! Categorical / nominal Ordinal / ranked scale • Conveys a category label • Conveys order , but not distance • (Arbitrary) assignment of #s to e.g. in a race, 1st, 2nd, 3rd, etc. or categories ranking of favourites or preferences e.g. Gender • No useful information, except as labels 15 16 Ordinal / ranked example: Ranked importance Interval scale Rank the following aspects of the university according to what is most • Conveys order & distance important to you (1 = most important • 0 is arbitrary through to 5 = least important) e.g., temperature (degrees C) __ Quality of the teaching and education • Usually treat as continuous for > 5 __ Quality of the social life intervals __ Quality of the campus __ Quality of the administration __ Quality of the university's reputation 17 18 Interval example: 8 point Likert scale Ratio scale • Conveys order & distance • Continuous, with a meaningful 0 point e.g. height, age, weight, time, number of times an event has occurred • Ratio statements can be made e.g. X is twice as old (or high or heavy) 19 as Y 20 Ratio scale: Why do levels of Time measurement matter? Different analytical procedures are used for different levels of data. More powerful statistics can be applied to higher levels 21 22 Graphs Principles of graphing (Edward Tufte) • Visualise data • Reveal data – Describe – Explore – Tabulate – Decorate • Communicate complex ideas with clarity, precision, and efficiency 23 24 Tufte's graphing guidelines Tufte's graphing guidelines • Show the data • Maximise the information-to-ink • Avoid distortion ratio • Focus on substance rather than • Encourage the eye to make method comparisons • Present many numbers in a small • Reveal data at several space levels/layers • Make large data sets coherent • Closely integrate with statistical and verbal descriptions 25 26 Software for Graphing steps data visualisation (graphing) 1. Identify the purpose of the graph 2. Select which type of graph to use 1. Statistical packages 3. Draw a graph ● e.g., SPSS 4. Modify the graph to be clear, 2. Spreadsheet packages non-distorting, and well-labelled. ● e.g., MS Excel 5. Disseminate the graph (e.g., 3. Word-processors include it in a report) ● e.g., MS Word – Insert – Object – Micrograph Graph Chart 27 28 Univariate graphs • Bar graph • Pie chart Univariate graphs • Data plot • Error bar • Stem & leaf plot • Box plot (Box & whisker) • Histogram 29 30 Bar chart (Bar graph) Pie chart • Examine comparative heights of bars • X-axis: Collapse if too many categories • Use a bar chart instead • Y-axis: Count or % or mean? • Hard to read • Consider whether to use data labels –Does not show small differences 13 12 11 12 10 –Rotation / position influences 12 9 8 11 perception 7 11 6 Bio logy Count Count Sociolo gy 5 10 4 10 3 2 9 1 Anthropology 9 0 P sy cholo gy Sociology Information T echnolo Biology Sociology Information T echnolo Biology P sychology Anthropology P sychology Anthropology AREA AREA 31 Information32 T echnolo Data plot & error bar Stem & leaf plot Data plot Error bar ● Alternative to histogram ● Use for ordinal, interval and ratio data ● May look confusing to unfamiliar reader 33 34 Stem & leaf plot Box plot • Contains actual data (Box & • Collapses tails whisker) Frequency Stem & Leaf 7.00 1 . & 192.00 1 . 22223333333 ● Useful for 541.00 1 . 444444444444444455555555555555 610.00 1 . 6666666666666677777777777777777777 849.00 1 . 88888888888888888888888888899999999999999999999 interval and 614.00 2 . 0000000000000000111111111111111111 602.00 2 . 222222222222222233333333333333333 ratio data 447.00 2 . 4444444444444455555555555 291.00 2 . 66666666677777777 ● Represents 240.00 2 . 88888889999999 167.00 3 . 000001111 min., max, 146.00 3 . 22223333 153.00 3 . 44445555 118.00 3 . 666777 median, 99.00 3 . 888999 106.00 4 . 000111 quartiles, & 54.00 4 . 222 339.00 Extremes (>=43) outliers 35 36 Box plot Histogram • Alternative to histogram • For continuous data • Useful for screening • X-axis needs a happy medium for • Useful for comparing variables # of categories • Can get messy - too much info • Y-axis matters (can exaggerate) • Confusing to unfamiliar reader 3 00 0 10 2 00 0 60 0 8 50 0 1 00 0 40 0 1000 6 Std. Dev = 309.16 0 Mean = 24 .0 800 0 N = 5 57 5.00 20 0 4 28294482267253154120226228451504231939983902646355221793020527435314997364541416412902548168628144167196326144171955174443826882822262617931747148 1 2 .5 2 2.5 32 .5 4 2. 5 52 .5 62 .5 218736735510399522434250553623594998649620510638344230032962562527 44954162578259628414042044353275182341862330517623006559128211495 600 35644317149302843626902101233519693009296541539905538229314216883634 3201419358828475475400198324512898200336473 Participant Age 569 27433593251521081985531655582138303424526783352317 52157129504268724318255928345427211669040523444 10 0 St d. Dev = 9.16 5491 2480296024926454284316542285186 4423423635403519067273946893137 M ean = 24.0 419324766472662291 3562338330403962312229 400 2 226520672270403852527688296021515564300430321938532836535506271835192336608405435012183292849986302224518624385114882241 6084308 1503275241623466255243493045 12255255545 0 N = 5 57 5.00 27806412743294423212570661146542792576430229232476 17 304032431371222596415943511907247380 2385410773323584004 4028 8.0 13.0 18.0 23.0 28.0 33.0 38.0 43. 0 48.0 53.0 58.0 63. 0 231214932334 2699 18082659 4308292014254307 3556334 197862231372721142861 552433515563 T ime Management-T1 200 Std. Dev = 9.16 Participant Age 0 Self-Confidence-T1 Mean = 24 0 N = 5 575.00 Missing Male Female 9 17 25 33 41 49 57 65 1 3 21 29 37 45 53 61 Participant Gender 37 Participant Age 38 Non-normal distributions Histogram of male & female heights 39 40 Non-normal distributions Histogram of weight Histogram 8 6 4 2 Std. Dev = 17.10 Mean = 69.6 0 N = 20.00 Frequency 40.0 50.0 60.0 70.0 80.0 90.0 100.0 110.0 WEIGHT 41 42 Histogram of daily calorie intake Histogram of fertility 43 44 Example ‘normal’ distribution Example ‘normal’ distribution 60 1 2 60 50 40 40 Count 30 Frequency 20 20 10 Mean =81.21 Std. Dev. =18.228 0 N =188 Very feminine Fairly feminine Androgynous Fairly masculine Very masculine 0 45 46 0 20 40 60 80 100 120 140 Femininity-Masculinity 2 Effects of skew on measures of 60 central tendency 40 Count 20 0 Very feminine Fairly feminine Androgynous Fairly masculine Very masculine Femininity-Masculinity Gender: male Gender: male 50 50 40 40 30 30 Count Count 20 20 10 10 0 0 Fairly feminine Androgynous Fairly masculine Very masculine Fairly feminine Androgynous Fairly masculine Very masculine Femininity-Masculinity Femininity-Masculinity 47 48 Line graph Summary: • Alternative to histogram Graphs & levels of measurement • Implies continuity e.g., time NOIR • Can show multiple lines Bar chart & pie chart NOI 8.0 Histogram IR 7.5 Stem & leaf IR 7.0 6.5 Mean Data plot & box plot IR 6.0 Error-bar IR 5.5 Line graph IR 5.0 OVERALL SCALES-T0 OVERALL SCALES-T 2 OVERALL SCALES-T 1 OVERALL SCALES-T 3 49 50 Graphing can be like Graphical a bikini. What they integrity reveal is suggestive, (part of academic but what they integrity) conceal is vital. (aka Aaron Levenstein) 51 52 "Like good writing, good graphical Cleveland’s hierarchy displays of data communicate ideas with clarity, precision, and efficiency. Like poor writing, bad graphical displays distort or obscure the data, make it harder to understand or compare, or otherwise thwart the communicative effect which the graph should convey." Michael Friendly – Gallery of Data Visualisation 53 54 Cleveland’s hierarchy: Best to worst Tufte’s graphical integrity 1.Position along a common scale • Some lapses intentional, some not 2.Position along identical, non aligned scales • Lie Factor = size of effect in graph size of effect in data 3.Length • Misleading uses of area 4.Angle-slope • Misleading uses of perspective 5.Area • Leaving out important context 6.Volume • Lack of taste and aesthetics 7.Color hue - color saturation - density 55 56 Review questions Can you complete this table? 1.If a survey question produces a Level Properties Examples Descriptive Graphs ‘floor effect’, where will the mean, Statistics median and mode lie in relation to Nominal one another? /Categorical Ordinal / 2.Over the last century, the Rank performance of the best baseball Interval hitters has declined. Does this imply Ratio that the overall performance of baseball batters has decreased? Answers: http://wilderdom.com/research/Summary_Levels_Measurement.html 57 58 Links References • Presenting Data – Statistics Glossary v1.1 - 1. Cleveland, W. S. (1985). The elements http://www.cas.lancs.ac.uk/glossary_v1.1/presdata.html of graphing data . Monterey, CA: • A Periodic Table of Visualisation Methods - Wadsworth. http://www.visual-literacy.org/periodic_table/periodic_table.html • Gallery of Data Visualization - 2. Jones, G. E. (2006). How to lie with http://www.math.yorku.ca/SCS/Gallery/ charts . Santa Monica, CA: LaPuerta. • Univariate Data Analysis – The Best & Worst of Statistical Graphs - http://www.csulb.edu/~msaintg/ppa696/696uni.htm 3. Tufte, E. (1983). The visual display of • Pitfalls of Data Analysis – quantitative information . Cheshire, CT: http://www.vims.edu/~david/pitfalls/pitfalls.htm Graphics Press. • Statistics for the Life Sciences – http://www.math.sfu.ca/~cschwarz/Stat-301/Handouts/Handouts.html 59 60 .
Load more

Recommended publications
  • 2. Graphing Distributions
    2. Graphing Distributions A. Qualitative Variables B. Quantitative Variables 1. Stem and Leaf Displays 2. Histograms 3. Frequency Polygons 4. Box Plots 5. Bar Charts 6. Line Graphs 7. Dot Plots C. Exercises Graphing data is the first and often most important step in data analysis. In this day of computers, researchers all too often see only the results of complex computer analyses without ever taking a close look at the data themselves. This is all the more unfortunate because computers can create many types of graphs quickly and easily. This chapter covers some classic types of graphs such bar charts that were invented by William Playfair in the 18th century as well as graphs such as box plots invented by John Tukey in the 20th century. 65 by David M. Lane Prerequisites • Chapter 1: Variables Learning Objectives 1. Create a frequency table 2. Determine when pie charts are valuable and when they are not 3. Create and interpret bar charts 4. Identify common graphical mistakes When Apple Computer introduced the iMac computer in August 1998, the company wanted to learn whether the iMac was expanding Apple’s market share. Was the iMac just attracting previous Macintosh owners? Or was it purchased by newcomers to the computer market and by previous Windows users who were switching over? To find out, 500 iMac customers were interviewed. Each customer was categorized as a previous Macintosh owner, a previous Windows owner, or a new computer purchaser. This section examines graphical methods for displaying the results of the interviews. We’ll learn some general lessons about how to graph data that fall into a small number of categories.
    [Show full text]
  • Chapter 16.Tst
    Chapter 16 Statistical Quality Control- Prof. Dr. Samir Safi TRUE/FALSE. Write 'T' if the statement is true and 'F' if the statement is false. 1) Statistical process control uses regression and other forecasting tools to help control processes. 1) 2) It is impossible to develop a process that has zero variability. 2) 3) If all of the control points on a control chart lie between the UCL and the LCL, the process is always 3) in control. 4) An x-bar chart would be appropriate to monitor the number of defects in a production lot. 4) 5) The central limit theorem provides the statistical foundation for control charts. 5) 6) If we are tracking quality of performance for a class of students, we should plot the individual 6) grades on an x-bar chart, and the pass/fail result on a p-chart. 7) A p-chart could be used to monitor the average weight of cereal boxes. 7) 8) If we are attempting to control the diameter of bowling bowls, we will find a p-chart to be quite 8) helpful. 9) A c-chart would be appropriate to monitor the number of weld defects on the steel plates of a 9) ship's hull. MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) Which of the following is not a popular definition of quality? 1) A) Quality is fitness for use. B) Quality is the totality of features and characteristics of a product or service that bears on its ability to satisfy stated or implied needs.
    [Show full text]
  • RUNNING HEAD: Turkish Emotional Word Norms
    RUNNING HEAD: Turkish Emotional Word Norms Turkish Emotional Word Norms for Arousal, Valence, and Discrete Emotion Categories Aycan Kapucu1, Aslı Kılıç2, Yıldız Özkılıç Kartal3, and Bengisu Sarıbaz1 1 Department of Psychology, Ege University 2 Department of Psychology, Middle Eastern Technical University 3Department of Psychology, Uludag University Figures: 3 Tables: 5 Corresponding Author: Aycan Kapucu Department of Psychology, Ege University Ege Universitesi Edebiyat Fakultesi Psikoloji Bolumu, Kampus, Bornova, 35400, Izmir, Turkey Phone: +902323111340 Email: [email protected] Turkish Emotional Word Norms 2 Abstract The present study combined dimensional and categorical approaches to emotion to develop normative ratings for a large set of Turkish words on two major dimensions of emotion: arousal and valence, as well as on five basic emotion categories of happiness, sadness, anger, fear, and disgust. A set of 2031 Turkish words obtained by translating ANEW (Bradley & Lang, 1999) words to Turkish and pooling from the Turkish Word Norms (Tekcan & Göz, 2005) were rated by a large sample of 1685 participants. This is the first comprehensive and standardized word set in Turkish offering discrete emotional ratings in addition to dimensional ratings along with concreteness judgments. Consistent with ANEW and word databases in several other languages, arousal increased as valence became more positive or more negative. As expected, negative emotions (anger, sadness, fear, and disgust) were positively correlated with each other; whereas the
    [Show full text]
  • Section 2.2: Bar Charts and Pie Charts
    Section 2.2: Bar Charts and Pie Charts 1 Raw Data is Ugly Graphical representations of data always look pretty in newspapers, magazines and books. What you haven’tseen is the blood, sweat and tears that it some- times takes to get those results. http://seatgeek.com/blog/mlb/5-useful-charts-for-baseball-fans-mlb-ticket-prices- by-day-time 1 Google listing for Ru San’sKennesaw location 2 The previous examples are informative graphical displays of data. They all started life as bland data sets. 2 Bar Charts Bar charts are a visual way to organize data. The height or length of a bar rep- resents the number of points of data (frequency distribution) in a particular category. One can also let the bar represent the percentage of data (relative frequency distribution) in a category. Example 1 From (http://vgsales.wikia.com/wiki/Halo) consider the sales …g- ures for Microsoft’s videogame franchise, Halo. Here is the raw data. Year Game Units Sold (in millions) 2001 Halo: Combat Evolved 5.5 2004 Halo 2 8 2007 Halo 3 12.06 2009 Halo Wars 2.54 2009 Halo 3: ODST 6.32 2010 Halo: Reach 9.76 2011 Halo: Combat Evolved Anniversary 2.37 2012 Halo 4 9.52 2014 Halo: The Master Chief Collection 2.61 2015 Halo 5 5 Let’s construct a bar chart based on units sold. Translating frequencies into relative frequencies is an easy process. A rela- tive frequency is the frequency divided by total number of data points. Example 2 Create a relative frequency chart for Halo games.
    [Show full text]
  • Bar Charts and Box Plots Normal Poisson Exponential Creating a Simple Yet Effective Plot Requires an 02468 C 1.0 Exponential Uniform Understanding of Data and Tasks
    THIS MONTH a Uniform POINTS OF VIEW Normal Poisson Exponential 02468 b Uniform Bar charts and box plots Normal Poisson Exponential Creating a simple yet effective plot requires an 02468 c 1.0 Exponential Uniform understanding of data and tasks. λ = 1 Min = 5.5 Normal Max = 6.5 0.5 Poisson σμ= 1, = 5 μ = 2 Bar charts and box plots are omnipresent in the scientific literature. 0 02468 They are typically used to visualize quantities associated with a set of Figure 2 | Representation of four distributions with bar charts and box plots. items. Representing the data accurately, however, requires choosing (a) Bar chart showing sample means (n = 1,000) with standard-deviation the appropriate plot according to the nature of the data and the task at error bars. (b) Box plot (n = 1,000) with whiskers extending to ±1.5 × IQR. hand. Bar charts are appropriate for counts, whereas box plots should (c) Probability density functions of the distributions in a and b. λ, rate; be used to represent the characteristics of a distribution. µ, mean; σ, standard deviation. Bar charts encode quantities by length, which is a highly accurate visual encoding and preferred over the angle-based strategy used in to represent such uncertainty. However, because the bars always start pie charts (Fig. 1a). Often the counts that we want to represent are at zero, they can be misleading: for example, part of the range covered sums over multiple categories. There are several options to visualize by the bar might have never been observed in the sample.
    [Show full text]
  • Measurement & Transformations
    Chapter 3 Measurement & Transformations 3.1 Measurement Scales: Traditional Classifica- tion Statisticians call an attribute on which observations differ a variable. The type of unit on which a variable is measured is called a scale.Traditionally,statisticians talk of four types of measurement scales: (1) nominal,(2)ordinal,(3)interval, and (4) ratio. 3.1.1 Nominal Scales The word nominal is derived from nomen,theLatinwordforname.Nominal scales merely name differences and are used most often for qualitative variables in which observations are classified into discrete groups. The key attribute for anominalscaleisthatthereisnoinherentquantitativedifferenceamongthe categories. Sex, religion, and race are three classic nominal scales used in the behavioral sciences. Taxonomic categories (rodent, primate, canine) are nomi- nal scales in biology. Variables on a nominal scale are often called categorical variables. For the neuroscientist, the best criterion for determining whether a variable is on a nominal scale is the “plotting” criteria. If you plot a bar chart of, say, means for the groups and order the groups in any way possible without making the graph “stupid,” then the variable is nominal or categorical. For example, were the variable strains of mice, then the order of the means is not material. Hence, “strain” is nominal or categorical. On the other hand, if the groups were 0 mgs, 10mgs, and 15 mgs of active drug, then having a bar chart with 15 mgs first, then 0 mgs, and finally 10 mgs is stupid. Here “milligrams of drug” is not anominalorcategoricalvariable. 1 CHAPTER 3. MEASUREMENT & TRANSFORMATIONS 2 3.1.2 Ordinal Scales Ordinal scales rank-order observations.
    [Show full text]
  • Chapter 6 Presenting Your Data in Graphic Form Political Orientations
    UNIVARIATE ANALYSIS Chapter 6 Presenting Your Data in Graphic Form Political Orientations Now let’s turn our attention from religion to politics. Some people feel so strongly about politics that they joke about it being a religion. The General Social Survey distribute(GSS) data set has several items that reflect political issues. Two are key political items: POLVIEWS and PARTYID. These items will be the primary focus in this chapter. In the process of examining these variables, we are going to learn not only more about the politicalor orientations of respon- dents to the 2012 GSS but also how to use SPSS Statistics to produce and interpret data in graphic form. You will recall that in the previous chapter, we focused on a variety of ways of displaying univariate distributions (frequency tables) and summarizing them (measures of central ten- dency and dispersion). In this chapter, we are going to build on that discussion by focusing on several ways of presenting your data graphically. We will begin by focusing on two charts thatpost, are useful for variables at the nominal and ordinal levels: bar charts and pie charts. We will then consider two graphs appropriate for interval/ratio variables: histograms and line charts. Graphing Data With Direct “Legacy” Dialogs SPSS Statistics gives us acopy, variety of ways to present data graphically. Clicking the Graphs option on the menu bar will give you three options for building charts: Chart Builder, Interactive, and Legacy Dialogs. These take you to two chart-building methods developed by SPSS over the past 20 years. Chart Builder is the most recent.
    [Show full text]
  • Stacked Bar Charts……………………………………………12
    Effective Use of Bar Charts Purpose This tool provides guidelines and tips on how to effectively use bar charts to communicate research findings. Format This tool provides guidance on bar charts and their purposes, shows examples of preferred practices and practical tips for bar charts, and provides cautions and examples of misuse and poor use of bar charts and how to make corrections. Audience This tool is designed primarily for researchers from the Model Systems that are funded by the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR). The tool can be adapted by other NIDILRR-funded grantees and the general public. The contents of this tool were developed under a grant from the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR grant number 90DP0012-01-00). The contents of this fact sheet do not necessarily represent the policy of Department of Health and Human Services, and you should not assume endorsement by the Federal Government. 1 Overview and Organization Simple Bar Charts……………………………………..……....3 Clustered Bar Charts…………………………………………10 Stacked Bar Charts……………………………………………12 Paired Bar Charts…………………………………………..…14 Simple Bar Chart – Categorical Comparisons A bar chart is basically a vertical column chart oriented horizontally instead. Data values are displayed as horizontal bars. The magnitude of each data element is represented by the length of the bar. Can be used to display values for categorical items (diabetes prevalence by state, hospital performance rankings on a preferred clinical practice measure etc). Shows comparisons among the categorical groups on the measure. Categories displayed on the vertical axis. Simple Bar Chart – Categorical Comparisons Bar charts are preferred over column charts: When the categorical axis labels are lengthy; When you have 12 or more categories; When the metric to be displayed is duration (such as clinic lobby wait time per health center).
    [Show full text]
  • Which Chart Or Graph Is Right for You?
    Which chart or graph is right for you? Tracy Rodgers, Product Marketing Manager Robert Bloom, Tableau Engineer You’ve got data and you’ve got questions. You know there’s a chart or graph out there that will find the answer you’re looking for, but it’s not always easy knowing which one is best without some trial and error. This paper pairs appropriate charts with the type of data you’re analyzing and questions you want to answer. But it won’t stop there. Stranding your data in isolated, static graphs limits the number and depth of questions you can answer. Let your analysis become your organization’s centerpiece by using it to fuel exploration. Combine related charts. Add a map. Provide filters to dig deeper. The impact? Immediate business insight and answers to questions that actually drive decisions. So, which chart is right for you? Transforming data into an effective visualization (any kind of chart or graph) or dashboard is the first step towards making your data make an impact. Here are some best practices for conducting meaningful visual analysis: 2 Bar chart Bar charts are one of the most common data visualizations. With them, you can quickly highlight differences between categories, clearly show trends and outliers, and reveal historical highs and lows at a glance. Bar charts are especially effective when you have data that can be split into multiple categories. Use bar charts to: • Compare data across categories. Bar charts are best suited for data that can be split into several groups. For example, volume of shirts in different sizes, website traffic by referrer, and percent of spending by department.
    [Show full text]
  • A Brief History of Data Visualization
    ABriefHistory II.1 of Data Visualization Michael Friendly 1.1 Introduction ........................................................................................ 16 1.2 Milestones Tour ................................................................................... 17 Pre-17th Century: Early Maps and Diagrams ............................................. 17 1600–1699: Measurement and Theory ..................................................... 19 1700–1799: New Graphic Forms .............................................................. 22 1800–1850: Beginnings of Modern Graphics ............................................ 25 1850–1900: The Golden Age of Statistical Graphics................................... 28 1900–1950: The Modern Dark Ages ......................................................... 37 1950–1975: Rebirth of Data Visualization ................................................. 39 1975–present: High-D, Interactive and Dynamic Data Visualization ........... 40 1.3 Statistical Historiography .................................................................. 42 History as ‘Data’ ...................................................................................... 42 Analysing Milestones Data ...................................................................... 43 What Was He Thinking? – Understanding Through Reproduction.............. 45 1.4 Final Thoughts .................................................................................... 48 16 Michael Friendly It is common to think of statistical graphics and data
    [Show full text]
  • Which Chart Or Graph Is Right for You? Tell Impactful Stories with Data
    Which chart or graph is right for you? Tell impactful stories with data Authors: Maila Hardin, Daniel Hom, Ross Perez, & Lori Williams January 2012 p2 You’ve got data and you’ve got questions. Creating a Interact with your data chart or graph links the two, but sometimes you’re not sure which type of chart will get the answer you seek. Once you see your data in a visualization, it inherently leads to more questions. Your bar graph reveals that This paper answers questions about how to select the sales tanked in the second quarter in the Southeast. best charts for the type of data you’re analyzing and the A scatter plot shows an unexpected concentration of questions you want to answer. But it won’t stop there. product defects in one category. Donations from older Stranding your data in isolated, static graphs limits the alumni are significantly down according to a heat map. number of questions you can answer. Let your data In each example, your reaction is the same: why? become the centerpiece of decision making by using it Equip yourself to answer these questions by making to tell a story. Combine related charts. Add a map. your visualization interactive. Doing so creates the Provide filters to dig deeper. The impact? Business opportunity for you and others to analyze your data insight and answers to questions at the speed of visually and in real-time, letting you answer questions Which chart or graph is right for you? Tell impactful stories with data Tell Which chart or graph is right for you? thought.
    [Show full text]
  • Bar Charts This Set of Notes Shows How to Construct a Bar Chart Using
    Revised July 2018 Bar Charts This set of notes shows how to construct a bar chart using Stata. It assumes that you have set Stata up on your computer (see the “Getting Started with Stata” handout), and that you have read in the set of data that you want to analyze (see the “Reading in Stata Format (.dta) Data Files” handout). In Stata, most tasks can be performed either by issuing commands within the “Stata command” window, or by using the menus. These notes illustrate both approaches, using the data file “GSS2016.DTA” (this data file is posted here: https://canvas.harvard.edu/courses/53958). Bar charts display differences in means for a quantitative variable across two or more groups, as opposed to histogram comparisons which display percentage distributions of a categorical variable within groups. For example, we can construct a bar chart of the mean hours per day of television viewing by self-attributed class: 1 Simple bar charts can be constructed easily via the Stata command line. The general command structure is graph bar <varname1>, over(<varname2>) where you enter a quantitative variable in place of “varname1" and a categorical variable in place of “varname2". Using the variables in the above graph, you would issue the command graph bar tvhours, over(class) This command produces a bare-bones graph with no enhancements. To enhance and improve the display, it is probably easier to use the Stata menus. For a bar chart, Click on “Graphics” Click on “Bar chart” When you do this, the “graph bar – Bar chart” window shown below opens up: 2 Under “Type of data” select “Graph of summary statistics.” Enter the name of your quantitative variable in the “Variable(s)” box in the middle of the window.
    [Show full text]