College --Undergrad Lower Division

  • This site did a lot of data visualization on many hot button topics. They provide the raw data that they used to create their graphs at this page. These data sets are kept in Google Doc spreadsheets.
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  • The Census Bureau has made many data visualizations of the data it collects. It is a good collections of maps, treemaps, an age/sex pyramid, and of course more familiar graphs, like bar graphs.
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  • A game to aid in teaching experimental design and significance testing (especially one sample, two sample, and matched pair situations). Tangrams are puzzles in which a person is expected to place geometrically shaped pieces into a particular design. The on-line Tangram Game provides students the opportunity to design many versions of the original game in order to test which variables have the largest effect on game completion time. A full set of student and instructor materials are available and were created by Kevin Comiskey (West Point), Rod Sturdivant (Ohio State University) and Shonda Kuiper (Grinnell College) as part of the Stat2Labs collection.

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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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  • 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 is a youtube video by Jeremy Balka that was published in May 2013. The video presents a discussion of the assumptions when using the t distribution in constructing a confidence interval for the population mean. By considering various population distributions, the effect of different violations of the normality assumption is investigated through simulation.
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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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  • What is correct, what is incorrect, and why?
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