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Data Presentation

  • A cartoon to be used for discussing z-scores. The cartoon was used in the September 2016 CAUSE Cartoon Caption Contest. The winning caption was submitted by Amy Nowacki from Cleveland Clinic/Case Western Reserve University, while the drawing was created by John Landers using an idea from Dennis Pearl. A second winning caption "Even a crash course in model-fitting will need to consider distributions other than normal," was by Eugenie Jackson, a student at University of Wyoming, is well-suited for starting a conversation about the normality assumption in statistical models.(see "Cartoon: Pile-UP I") Honorable mentions that rose to the top of the judging in the September caption contest included "Big pile-up at percentile marker -1.96 on the bell-curve. You might want to take the chi-square curve to avoid these negative values," written by Mickey Dunlap from University of Tennessee at Martin; "Call the nonparametric team! This is not normal!” written by Semra Kilic-Bahi of Colby-Sawyer College; "I assumed the driving conditions today would be normal!" written by John Vogt of Newman University; and "CAUTION: Z- values seem smaller than they appear. Slow down & watch for stopped traffic reading these values,” written by Kevin Schirra, a student at University of Akron.
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  • A song to encourage students to use critical thinking skills in evaluating a statistic published in the media. The 2002 JSM paper (http://www.statlit.org/pdf/2002BestASA.pdf) of sociologist Joel Best and feedback from Milo Schield gave The University of Texas at El Paso’s Lawrence Lesser inspiration to explore what it means to say statistics are socially constructed. The song is a parody of the Beatles' "Lucy in the Sky with Diamonds." The lyrics were originally published in the August 2016 Amstat News.
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  • This software makes it easier to use the R language. It includes a code debugger, editing, and visualization tools.

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  • This is a web application framework for R, in which you can write and publish web apps without knowing HTML, Java, etc. You create two .R files: one that controls the user interface, and one that controls what the app does. The site contains examples of Shiny apps, a tutorial on how to get started, and information on how to have your apps hosted, if you don't have a server.
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  • These slides from the 2014 ICOTS workshop describe a minimal set of R commands for Introductory Statistics. Also, it describes the best way to teach them to students. There are 61 slides that start with plotting, move through modeling, and finish with randomization.
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  • This online booklet, Start Teaching with R, by Randall Pruim, Nicholas J. Horton, and Daniel T. Kaplan comes out of the Mosaic project. It describes how to get started teaching Statistics using R, and gives teaching tips for many ideas in the course, using R commands.

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  • This online booklet comes out of the Mosaic project. It is a guide aimed at students in an introductory statistics class. After a chapter on getting started, the chapters are grouped around what kind of variable is being analyzed. One quantitative variable; one categorical variable; two quantitative variables; two categorical variables; quantitative response, categorical predictor; categorical response, quantitative predictor; and survival time outcomes.
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  • This site is an interactive, online tutorial for R. It asks the user to type in commands at an R prompt, which are then evaluated. Typing the right thing allows the user to continue on, typing the wrong thing yields an error. The user cannot skip the easier lessons. Lessons are: Using R; Vectors; Matrices; Summary Statistics; Factors; Data Frames; Real-World Data; and What’s Next.
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  • This is an e-book tutorial for R. It is organized according to the topics usually taught in an Introductory Statistics course. Topics include: Qualitative Data; Quantitative Data; Numerical Measures; Probability Distributions; Interval Estimation; Hypothesis Testing; Type II Error; Inference about Two Populations; Goodness of Fit; Analysis of Variance; Non-parametric methods; Linear Regression; and Logistic Regression.
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  • A quote to initiate a discussion of the fact that correlation does not imply a causal relationship (especially spurious correlations that happen by coincidence). The quote is by American novelist and poet Siri Hustvedt (1955 - ) from her 2011 novel The Summer Without Men.
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