Resource Library

Using cooperative learning methods, this activity helps students develop a better intuitive understanding of what is meant by variability in statistics. Emphasis is placed on the standard deviation as a measure of variability. This lesson also helps students to discover that the standard deviation is a measure of the density of values about the mean of a distribution. As such, students become more aware of how clusters, gaps, and extreme values affect the standard deviation.

This site contains 100 modules designed to introduce concepts in statistics. The modules are divided into categories such as Descriptive Statistics, Inferential Statistics, Related Measures, Enumeration Statistics, and ANOVA. Click the green button on the side to start the modules, then click "Main Menu" at the top to see a list of topics. Topics include Describing Numbers, Normal Curve, Sampling Distributions, Hypothesis Testing, Regression, and Chi-Square. The site also includes a glossary, statistical tables and simulations, and a personalized progress report. Key Words: Collection; Central Tendency; Spread; Correlation.

Legal proceedings are like statistics. If you manipulate them, you can prove anything. A quote by Bristish-born Canadian novelist Arthur Hailey (1920 - 2004). The quote is found in the novel "Airpot" (1968; Doubleday, p. 385). The quote also appears in "Statistically Speaking: A dictionary of quotations" compiled by Carl Gaither and Alma Cavazos-Gaither.

This text based website provides an explanation of some coincidences that are often discussed. It gives an explanation of the birthday problem along with a graphic display of the probability of birthday matches vs. the number of people included. It also discussess other popular coincidences such as the similarities between John F. Kennedy and Abraham Lincoln. It goes on to discuss steaks of heads and tails along with random features of stocks and the stock market prices.

This tutorial introduces mean, median, mode, variance, and standard deviation using sports statistics from the Internet and class-generated statistics. Students should understand stem-and-leaf plots before using this tutorial. This material is intended for class use. Excel spreadsheets with sample data are also available for download. The relation links to a letter for teachers.

This page uses Bayes' Theorem to calculate the probability of a hypothesis given a datum. An example about cancer is given to help users understand Bayes' Theorem and the calculator. Key Word: Conditional Probability.

This glossary defines and explains statistical terms for introductory students. The glossary can be shown in alphabetical order or in suggested learning order. Click on the topic of interest to see the definition. Use the arrows at the bottom to proceed to the next topic or click the blue dot to return to the contents page.

These pages from the University of Melbourne explain statistical concepts using various examples from medicine, science, sports, and finance. The intent is not computational skill but conceptual understanding. Some pages also contain data.

This tutorial opens with a survey on polling. Upon completing the survey, students are taken through an election example which uses polling to explain random sampling, bias, margin of error, and confidence intervals.

This online resource is intended to help students understand concepts from probability and statistics and covers many topics from introductory to advanced. You can follow the progression of the text, or you can click on a topic on the left. Key Words: Alpha Reliability; Analysis of Covariance (ANCOVA); Analysis of Variance (ANOVA); Bayesian Analysis; Bias; Binomial regression; Bonferroni adjustment; Bootstrapping; Categorical modeling; Central limit theorem; Chi-squared test; Clinical significance; Cluster analysis; Coefficient of variation; Confidence Intervals; Contingency Table; Controlled trial; Confounders; Correlation; Dimension reduction; Discriminant function analysis; Frequency; Normal; Poisson; Probability Distribution; Effect; Error; Factor Analysis; Goodness of Fit; Heteroscedasticity; Hypothesis Testing; Independence, Interactions; Kappa Coefficient; Latin Squares; Least Squares Means; Likert scales; Linear Regression; Logistic Regression; Multivariate ANOVA (MANOVA); Mixed Modeling; Multiple Linear Regression; Nonparametric models; Odds ratio; P Values; Path Analysis; Percentiles; Polynomial Regression; Power; PRESS; Probability; Relative Frequency; Repeated Measures; Sample Size; Sampling; Sensitivity; Stepwise regression; Structural equation modeling; T Test; Transformation; Validity.