Resource Library

The dataset presented in this article referes to game-by-game information for Mark McGwire and Sammy Sosa during summer of 1998. This data can be used to demonstrate graphical displays, categorical data analysis, analysis of variance, logistic regression, and smoothing methods for Poisson and binomial data.

This article presents data for examining the ability of individuals to choose numbers randomly. Three datasets of six-tuples selected by a lottery game, generated by S-Plus, and chosen by college students can be compared using descriptive statistics and goodness of fit tests to explore bias and randomness. Key Words: Boxplots; Chi-squared tests; Minimum gap; QQ plots.

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 PowerPoint presentation dicusses general concepts of confidence intervals and interprets confidence intervals for a mean, difference in two means, and the relative risk. The original presenation is available for download.

This PowerPoint lecture presenation explains confidence intervals for a mean when using a small sample. It discusses the t-distribution, compares the t-statistic to the z-statistic, and provides an example of a small sample confidence interval. The original presentation is available for download.

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.