Resource Library

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 exercise includes a discussion on comparing data with very different sample sizes and nonhomogeneity of variance. The data comes from a study on the behavior of pregnant women with regard to cigarette smoking.

This lesson describes bootstrapping in the context of a statistics class for psychology students.

This excerpt from Engineering Statistics Handbook gives a definition for and examples of outliers. A sub-page also discusses Grubbs' Test for Outliers

A short discussion of what outliers are and their helpfulness in analyzing data.

This text document lists detailed learning objectives for introductory statistics courses. Learning objectives are brief, clear statements of what learners will be able to perform at the end of a course. These objectives were developed for a one semester general education introductory statistics course. The objectives cover the broad categories of Graphics, Summary Statistics, The Normal Distribution, Correlation and Scatterplots, Introduction to Regression, Two way Tables, Data Collection and Surveys, Basic Probability, Sampling Distributions, Confidence Intervals, Tests of Hypothesis, and T-distributions.

This webpage uses the criminal trials in the US Justice system to illustrate hypothesis testing, type I error, and type II error. An applet allows the user to examine the probability of type I errors and type II errors under various conditions. An applet allows users to visualize p-values and the power of a test. Keywords: type I error, type II error, type one error, type two error, type 1 error, type 2 error

This is an example of "growing" a decision tree to analyze two possible outcomes. The tree's branches examine the two possible conditions of employee drug use with corresponding probabilities. This example looks at the final outcome probabilities of being correctly and incorrectly identified versus testing accuracy.

This page explores Benford's Law: For naturally occurring data, the digits 1 through 9 do not have equal probability of being the first significant digit in a number; the digit 1 has greater odds of being the first significant digit than the others. This law can be used to catch tax fraud because truly random numbers used by embezzlers do not meet this condition.

This page explores Benford's law and the Pareto Principle (or 80/20 rule). Benford's law may also have a wider meaning if the digits it evaluates are considered ranks or places. The digit's probability of occurring could be considered the relative share of total winnings for each place (1st through 9th). In other words, 1st place would win 30.1%, 2nd place 17.6%, 3rd 12.5%,... 9th place 4.6% of the available rewards. The normalized Benford curve could be used as a model for ranked data such as the wealth of individuals in a country. To determine if the Benford model gives results similar to those of the Pareto principle we use the normalized Benford equation in a computer program.