Graduate students

  • This tutorial on the Mann-Whitney test includes its definition, assumptions, characteristics, and hypotheses as well as procedures for graphical comparisons. An example using output from the WINKS software is given, but those without the software can still use the tutorial. An exercise is given at the end that can be done with any statistical software package.
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  • This tutorial explains the theory and use of Student's t-test for matched pairs and demonstrates it with an example on project quality. Data is given as well as SPSS and Minitab code.
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  • This tutorial describes various measures of central tendency, their theory and use, and demonstrates them with an example on final exam scores. Data is given as well as SPSS and Minitab code. Key Words: Mean; Median; Mode; Variance; Standard Deviation.
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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 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 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 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.
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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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