Resource Library

The dataset described in this article contains data on retired Major League Baseball players, eligible for the MLB Hall of Fame. The data can be used to illustrate descriptive statistical methods (numerical, graphical, and tabular) or inferential statistics (hypothesis testing, confidence intervals, etc.). The data is in .dat format.

This article describes a dataset containing Major League Baseball data from seasons 1969 through 2000 and illustrates how this data can be used as a course long project covering basic data management, the use of exploratory data analysis to "clean" data, and construction of regression models. The data is in .dat format.

This article describes a dataset on the 2000 National Football League (NFL) regular season and the exploratory data analyses performed. Key Words: Multivariate analysis; Summary ranking measures

This PowerPoint presentation evaluates type I errors in civil trials compared to criminal trials as well as provides an example of a hypothesis test and its components. The original presenation is available for download.

This PowerPoint lecture presentation discusses comparing the means of two dependent populations using the paired T-test and defines the concepts of this hypothesis test. The original presentation is available for downloading.

This applet performs a hypothesis test for the mean of a single normal population, variance known. Users set the hypothesized mean, true mean, variance, and appropriate alternative hypothesis. The applet plots a representative distribution under the given values with power shaded in blue and significance level shaded in red.

The 29-item attitudinal scale consists of two subscales: attitude toward the field of statistics (20 items) and attitude toward the course (9 items). Students are asked to respond to how they currently feel about a statement (i.e., "I feel that statistics will be useful to me in my profession") using a 1 (strongly disagree) to 5 (strongly agree) response scale.

This Compendium describes distributions appropriate for the modeling of random data. The number of distributions (56) is large, including: 1. Continuous distributions (30), (Symmetric (11) and Skewed (19)) 2. Continuous binary mixtures(17), 3. Discrete distributions (5), 4. Discrete binary mixtures (4), All formulas are shown in their fully-parametrized form, not the standard form. Many of the formulas given are seldom described. Random variate generation is included where feasible.

This site discusses types of data, stem and leaf plots, mean and median, histograms, and barcharts. Exercises are also provided, as well as their corresponding answers.

This site provides applets, lessons, and objectives for learning about conditional probability. The applet activity introduces multiple-outcomes events and computing probabilities.