Resource Library

A quote to initiate a discussion of sampling and the value of randomization in avoiding bias. The quote is by economist and blogger Jaana M Goodrich (1955 - ) writing in her blog under the pseudonym Echidne of the Snakes. The quote is found at http://echidneofthesnakes.blogspot.com/2005/01/exit-polls-make-my-heart-beat-faster.html

A quote that can be used in discussing how data are more convincing to people if they align with current beliefs. The quote is by American mathematician Mary Gray (1938 - ) and comes from the title of her 1993 (v. 8, page 144) Statistical Science "Can Statistics Tell Us What We Do Not Want to Hear? The Case of Complex Salary Structures." The paper, the commentaries on the paper, and Dr. Gray’s rejoinder to the commentaries include discussions of many statistical issues and is very approachable for undergraduate students.

A quote that can be used in discussing the value and wide applicability of simulation for understanding statistical concepts and applying statistical methods. The quote is by American Statistical educator Christine Franklin (1956 - ) and is found in a 2013 interview with her conducted by Allan Rossman in the Journal of Statistics Education (volume 21, number 3).

A quote to initiate discussions of model building By British Statistician and Epidemiologist Hilda Mary Woods (1892-1971). The quote is from her paper "The influence of external factors on the mortality from pneumonia in childhood and later adult life" in the Journal of Hygiene 1927 pages 36-43 (quote is on page 42).

What is correct, what is incorrect, and why?

A quote to motivate discussions of the importance of statistics for critical thinking. The quote is by Deborah J. Rumsey (1961 - ), The Ohio State University. The quote appears in Chapter 1 page 10 of her book, Statistics For Dummies 2nd edition, 2011

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.