This tutorial explains the theory and use of the Kruskal-Wallis test and demonstrates it with an example on exam scores, homework scores, and project scores. Data is given as well as SPSS and Minitab code.
This tutorial explains the theory and use of the Friedman Two Way ANOVA and demonstrates it with an example on exam scores, homework scores, and project scores. Data is given as well as SPSS and Minitab code.
This tutorial explains the theory and use of the Spearman's Rank-Difference Correlation Coefficient and demonstrates it with an example on exam scores, homework scores, and project scores. Data is given as well as SPSS and Minitab code.
This collection of tutorials demonstrates various statistical topics with data and provides SPSS and Minitab code. Topics covered: Measures of Central Tendency; Sign Test; Chi-Square; Mann-Whitney Test; Wilcoxon Matched-Pairs Signed-Ranks Test; Kruskal-Wallis One-Way Analysis of Variance; Friedman Two-Way Analysis of Variance; Spearman Rank Correlation; Pearson Product-Moment Correlation; Multiple Regression; t-Test for Independent Samples; t-Test for Matched Pairs; One-Way ANOVA; Two-Way ANOVA.
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.
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.
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.
