Resource Library

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.

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.

This site has the data and shows the code you would use to replicate the examples in Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence, by Judith D. Singer and John B. Willett. It has code in SAS, R, Stata, SPSS, HLM, MLwiN, and Mplus.

This site shows the code you would use to replicate the examples in Applied Survival Analysis, by Hosmer and Lemeshow. It has code in Stata, R, and SAS.

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.

"Availability Heuristic" is a poem by Lawrence Mark Lesser from The University of Texas at El Paso. The poem was originally published in the January 2017 Journal of Humanistic Mathematics. Classroom discussion the poem may spark includes comparing and contrasting mathematical and statistical roles and language for generality, looking up and discussing the meaning of the title (a phenomenon identified by Kahneman and Tversky in 1973), exploring the"someone, somewhere" idea of Utts' Seeing Through Statistics, or discussing how many people have poor intuition with the birthday problem ("probability of at least one match in the room") because they confuse it with the birthdate ("probability at least one match with ME") problem (see letters in the summer 2014 Teaching Statistics).

"A Given A" is a song that Lawrence Mark Lesser from The University of Texas of El Paso adapted from his poem "P(A|A)" that was originally published in the January 2017 Journal of Humanistic Mathematics. In addition to providing a vehicle for general discussion (about pitfalls of post hoc analysis, multiple comparisons, or confusing the direction of causation or conditioning), the song may spark particularly lively discussion with the second verse's reference to the Bible Code, popularized by Michael Drosnin's so-named books and discussed in 1994 and 1999 papers in Statistical Science.

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.

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.

A cartoon to be used in discussing the value of stratified sampling (or blocking in experiments) with diverse populations. The cartoon was used in the January 2017 CAUSE cartoon caption contest (see "The Elevator I" for the cartoon with the winning caption). This caption is a shortened version of a caption submitted anonymously to the contest. The cartoon was drawn by British cartoonist John Landers based on an idea from Dennis Pearl of Penn State University.