Resource Library

What is correct, what is incorrect, and why?

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.

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 for discussing the F test in ANOVA and for discussing general student anxiety about statistics. The cartoon was used in the December 2016 CAUSE Cartoon Caption Contest. The winning caption was submitted by Larry Lesser at The University of Texas at El Paso, while the drawing was created by John Landers using an idea from Dennis Pearl. A second winning caption "Mark was pleased to note that he was a significant outlier. Little did he know it was a two-sided test..." written by Robert Garrett, a student at Miami University is well-suited to stimulate a discussion of statistical hypothesis testing and the effect of outliers (see "Cartoon: The Exam I")

A cartoon to be used for discussing statistical hypothesis testing and the effect of outliers. The cartoon was used in the December 2016 CAUSE Cartoon Caption Contest. The winning caption was submitted by Robert Garrett, a student at Miami University, while the drawing was created by John Landers using an idea from Dennis Pearl. A second winning caption "The sadistic ANOVA problem made most students feel headed for an F test," written by Larry Lesser from University of Texas at El Paso is well-suited to stimulate a discussion of the F test in ANOVA and about general student anxiety about statistics (see "Cartoon: The Exam II")