Resource Library

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.

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

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

This short article discusses the difference between "important" and "statistically significant." The data used come from a study comparing male faculty salaries to female faculty salaries.

This page provides survey data on the sexual activity of male and female subjects and discusses choosing appropriate statistics to describe the data as well as reporting bias. It also links to a Chance article about the same study.

Chance News reviews current issues in the news that use probability or statistical concepts. Its aim is to give the general public a better understanding of chance news as reported by the media and to allow teachers of probability and statistics courses to liven up their courses with current chance news.

This page was written as instructions for a SAS lab assignment, but the example can be used with other programs. The study compares three treatments for rape victims against each other and a control group to see which treatment is most effective at reducing Post Traumatic Stress Disorder symptoms.

This example is designed to test whether religiosity is correlated with optimism. The page describes the study, has a link to the data set, and describes the method of analysis. Analysis includes ANOVA and regression.

This site offers a list of sample questions that can be used when teaching basic probability concepts, probability distributions, data collection methods, inferential statistics, hypothesis testing, analysis of variance, regression analysis, or problem sensing related to descriptive statistics. Links to the answers are also provided. Application is not limited to business.

The datasets on this page are classified by analysis technique (ANOVA, Linear Regression, Markov Chain Monte Carlo, Nonlinear Regression, and Univariate Summary Statistics) and by level of difficulty (lower, average, higher). They were originally intended to test statistical software.