Statistical Inference & Techniques

  • This tutorial introduces various statistics used to analyze and summarize data. The tutorial covers both the arithmetic and geometric means, median, mode, standard deviation, coefficient of variation, skewness, kurtosis, quadrants, and histrogram analysis. The application is flow cytometry, but others may use this tutorial as well.
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  • This course website provides materials for teaching and learning path analysis. Materials include Regression Review, Introduction to Path Notation, Standardized Path Models, Unstandardized Path Models, Matrix Algebra, and many SAS programs.
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  • 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.
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  • 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.
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  • 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.
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  • This page will calculate the factorial of any number.
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  • 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.
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  • This glossary gives definitions for numerous statistical terms, concepts, methods, and rules.
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  • 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.
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  • CAST contains three complete introductory statistics courses, one advanced statistical methods course, and additional modules. Each introductory course presents the same topics, but with different applications. The first is a general version, the second is a biometric version with examples relating to biological, agricultural and health sciences, and the third is a business version. Each course comes in a student version and a lecture version. The additional modules cover Multiple and Nonlinear Regression, Quality Control, and Simulation. Registration is required, but free. Individuals or classes can register.
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