Resource Library

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 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 explains the theory and use of Pearson's Product Moment Coefficient of Correlation and demonstrates it with an example on GPA and test scores. Data is given as well as SPSS and Minitab code.

Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write! Quote from the presidential address in 1951 of mathematical statistician Samuel S. Wilks (1906 - 1964) to the American Statistical Association found in "JASA",Vol. 46, No. 253., pp. 1-18. Wilks was paraphrasing Herbert G. Wells (1866 - 1946) from his book "Mankind in the Making". The full H.G. Wells quote reads: "The great body of physical science, a great deal of the essential fact of financial science, and endless social and political problems are only accessible and only thinkable to those who have had a sound training in mathematical analysis, and the time may not be very remote when it will be understood that for complete initiation as an efficient citizen of one of the new great complex worldwide States that are now developing, it is as necessary to be able to compute, to think in averages and maxima and minima, as it is now to be able to read and write."

A quick pun about "autocorrelation" by Bruce White.

This activity uses a computer program to explore probability concepts such as sample space, independent events, law of large numbers, and reliability. An outline of the activity and the computer program are provided.

This article discusses teaching causality without being discipline specific. It explains the causal differences between description, prediction and explanation.

This article describes a dataset containing information on economic class of passengers and mortality rates from the sinking of the Titanic. The dataset can be used to foster statistical thinking by giving students the data and asking them to determine the source.

This article describes a dataset on life expectancies, densities of people per television set, and densities of people per physician in various countries of the world. The example addresses correlation versus causation and data transformations. Key Word: Prediction.

This Flash applet provides an introduction to simple linear regression for introductory statistics students. It combines a brief narrated animation with an interactive scatterplot function. Students are able to place points on the scatterplot by clicking with a mouse or typing X-Y coordinates. Students use these points to learn about the best fit line by placing a guess on the plot and comparing it with the least squares line. Students also learn about the value of the correlation coefficent and points that would be considered outliers. Students may also specify a value of x (within the range of the data) and obtain the resulting predicted value.