Resource Library

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 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 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.

The Comprehensive Epidemiologic Data Resource is a collection of data sets. It includes definitions of each variable in the data set. It requires a login to retrieve the data sets. Registering involves giving your name and address and the name of the study and a detailed description of the intended use of the data.

This complete lesson plan, which includes assessments, is based upon a data set partially discussed in the article "Female Hurricanes are Deadlier than Male Hurricanes." The data set contains archival data on actual fatalities caused by hurricanes in the United States between 1950 and 2012. Students analyze and explore this hurricane data in order to formulate a question, design and implement a plan to collect data, analyze the data by measures and graphs, and interpret the results in the context of the original question.

A statistical analysis, properly conducted, is a delicate dissection of uncertainties, a surgery of suppositions. is a quote from Statistician Michael J. Moroney (1940 - ). The quote appears in his 1951 book "Facts from Figures".

This is a song suitable for middle school level statistics in reinforcing key elements of the scientific method. College-level use might include playing before a lecture to lighten the mood while setting up. The song's lyrics and music were composed by Jeff Hall audio file is a performance by the scientific jam band (see www.scientificjam.com/scijam2.htm)

Indeed, the laws of chance are just as necessary as the causal laws themselves. is a quote of quantum physicist David J. Bohm (1917- 1992). The quote appears on page 23 of his 1957 book "Causality and Chance in Modern Physics". The quote also appears in "Statistically Speaking: A dictionary of quotations" compiled by Carl Gaither and Alma Cavazos-Gaither.

Everyone believes in the normal law, the experimenters because they imagine that it is a mathematical theorem, and the mathematicians because they think it is an experimental fact. is a quote by French physicist Jonas Ferdinand Gabriel Lippmann (1945-1921). The quote may used in a class discussion of the assumption of normality. It can be found in Henri Poincare's 1896 book "Calcul de Probabilities" (in French).

No matter how much reverence is paid to anything purporting to be statistics," the term has no meaning unless the source, relevance, and truth are all checked." is a quote by American English professor Tom B. Burnam (1913-1991). The quote is found on page 244 of his 1975 book "The Dictionary of Misinformation".