Resource Library

This site provides definitions and examples for the following topics: Graphical displays (stemplots, histograms, boxplots, scatterplots), Numerical Summaries (mean, median, quantiles, variance, standard deviation), Normal Distributions (assessing normality, normal probability plots), Categorical Data (two-way tables, bar graphs, segmented bar graphs), Linear regression (least-squares, residuals, outliers and influential observations, extrapolation), Correlation (correlation coefficient, rŒ_), Inference in Linear Regression (confidence intervals for intercept and slope, significance tests, mean response and prediction intervals), Multiple Linear Regression (confidence intervals, tests of significance, squared multiple correlation), ANOVA for Regression (analysis of variance calculations for simple and multiple regression, F statistics), Experimental Design (experimentation, control, randomization, replication), Sampling (simple, stratified, and multistage random sampling), Sampling in Statistical Inference (sampling distributions, bias, variability), Probability Models (components of probability models, basic rules of probability), Conditional Probability (probabilities of intersections of events, Bayes' formula), Random variables (discrete, continuous, density function), Mean and Variance of Random Variables (definitions, properties), Binomial Distributions (counts, proportions, normal approximation), Sample Means (mean, variance, distribution, Central Limit Theorem), Confidence Intervals (inference about population mean, z and t critical values), Tests of Significance (null and alternative hypotheses for population mean, one-sided and two-sided z and t tests, levels of significance, matched pairs analysis), Comparison of Two Means (confidence intervals and significance tests, z and t statistics, pooled t procedures), Inference for Categorical Data (confidence intervals and significance tests for a single proportion, comparison of two proportions), Chi-square Goodness of Fit Test (chi-square test statistics, tests for discrete and continuous distributions), Two-Way tables and the Chi-Square test (categorical data analysis for two variables, tests of association).

This online, interactive lesson on distributions provides examples, exercises, and applets which explore the basic types of probability distributions and the ways distributions can be defined using density functions, distribution functions, and quantile functions.

This site gives a definition and an example of stem and leaf plots.

This online, interactive lesson on expected value provides examples, exercises, and applets in which students will explore relationships between the expected value of real-valued random variables and the center of the distribution. Students will also examine how expected values can be used to measure spread and correlation.

This website provides lesson plans, activities, a problem bank, and links to references that meet NCTM standards for probability.

This section of the Engineering Statistics Handbook describes in detail the process of choosing an experimental design to obtain the results you need. The basic designs an engineer needs to know about are described in detail.

The GAISE project was funded by a Strategic Initiative Grant from ASA in 2003 to develop ASA-endorsed guidelines for assessment and instruction in statistics in the K-12 curriculum and for the introductory college statistics course.

A collection of links to video workshops for students in mathematics. Includes many topics from statistics to math and science to algebra.

A good resource for problems in statistics in engineering. Contains some applets, and good textual examples related to engineering. Some topics include Monte Carlo method, Central Limit Theorem, Risk, Logistic Regression, Generalized Linear .Models, and Confidence.

This site briefly defines several different types of sampling methods, contrasts probability and nonprobability sampling, and discusses target population. Part of a tutorial on questionnaire and survey design.