Resource Library

This tutorial introduces the basic concepts of probability using various examples. Topics include interpreting probability, calibration experiments, interpreting odds, sample space, basic rules, equally likely outcomes, constructing probability tables, unions and complements, mean, and two-way probability tables. A link to activities is also given.

This article introduces Radial Basis Function (RBF) networks. These networks rely heavily on regression analysis techniques. Topics include Nonparametric Regression, Classification and Time Series Prediction, Linear Models, Least Squares, Model Selection Criteria, Ridge Regression, and Forward Selection.

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.

This tutorial explains the theory and use of the Sign Test and demonstrates it with an example on intervention methods. Data is given as well as SPSS and Minitab code.

This tutorial explains the theory and use of two-way ANOVA and demonstrates it with an example on final exam scores. Data is given as well as SPSS and Minitab code.