Graphical Displays

  • This poem, written in July 2024 by Lawrence M. Lesser of The University of Texas at El Paso, is in the form of a bimodal distribution, reflected in the poem’s real-world context.  Before showing the poem, a teacher could first ask students to reflect on what they would expect a histogram of ages of pedestrians killed (or severely injured) to have and why (chances are some of their suggested rationale will  be captured in the poem!).

    Afterwards, students wanting to examine or discuss real-world evidence of such a distribution may look for data on their own, or be shown section 1.1.3 of 

    Roe, M., Shin, H., Ukkusuri, S., Blatt, A., Majka, K. et al. (2010), “The New York City Pedestrian Safety Study and Action Plan Technical Supplement,” New York City Department of Transportation. http://www.nyc.gov/html/dot/downloads/pdf/nyc_ped_safety_study_action_plan_technical_supplement.pdf . 

    This visual poem may also inspire students to write their own short statistics poem using (and connecting to) a data set with a differently shaped distribution.

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  • A cartoon that can be used to help start a class conversation about how good visualizations are important for understanding and communicating data analyses. The cartoon was used in the September 2021 CAUSE cartoon caption contest and the winning caption was written by Ciaran Evans from Wake Forest University. The cartoon was drawn by British cartoonist John Landers (www.landers.co.uk) based on an idea by Dennis Pearl from Penn State University.

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  • A cartoon that can be used to highlight various features of the times series plots shown such as the seasonal trends perhaps signaling the oncoming storm in the cartoon. The cartoon was used in the July 2021 CAUSE cartoon caption contest and the winning caption was written by Larry Lesser from The University of Texas at El Paso. The cartoon was drawn by British cartoonist John Landers (www.landers.co.uk) based on an idea by Dennis Pearl from Penn State University.

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  • A cartoon that provides a clever way to introduce the statistical field of sabermetrics.  The cartoon was used in the May 2020 CAUSE cartoon caption contest and the winning caption was written by Larry Lesser from the The University of Texas at El Paso. The cartoon was drawn by British cartoonist John Landers (www.landers.co.uk) based on an idea by Dennis Pearl from Penn State University.

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  • A humorous cartoon to initiate a conversation about time series plots. The cartoon was drawn by American cartoonist Jon Carter in 2017.

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  • A humorous cartoon to initiate a conversation about interpreting a time series plot (e.g. discussing trend versus random components). The cartoon was drawn by American cartoonist Jon Carter in 2014.

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  • A humorous cartoon by American cartoonist Jon Carter in 2018 which may be used for in-class discussions about interpreting time series plots. The drawing indicates confusion about what each axes represents, since the plot itself indicates the  x-axes labels time, but the axes itself says "customer intelligence"  and there is no scale on either axesThe cartoon is free to use in non-profit educational settings.

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  • A cartoon that can help in discussing how context matters in thinking about trend and "Seasonal" patterns in time series.The cartoon was used in the July 2018 CAUSE cartoon caption contest and the winning caption was written by Karsten Luebke from FOM University in Germany. The cartoon was drawnby British cartoonist John Landers (www.landers.co.uk) based on an idea by Dennis Pearl from Penn State University.

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  • Examples of real data/studies and their analyses and interpretation.

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  • RStudio Cloud makes it easy for professionals, hobbyists, trainers, teachers and students to do, share, teach and learn data science using R.  Create analyses using RStudio directly from your browser - there is no software to install and nothing to configure on your computer.  Share your projects - and access those of others - without worrying about data transfer or package installation. Each project defines its own environment, and RStudio Cloud automatically reproduces that environment whenever anyone accesses the project.  It’s easy to share analyses with the world - but it’s also simple to collaborate with a select group in a private space. You control who can enter a space - and via roles, you have fine grained control over what each user can do.  There are also many learning materials available: interactive tutorials covering the basics of data science, cheatsheets for working with popular R packages, links to Datacamp courses, and a guide to using RStudio Cloud.

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