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This online, interactive lesson on random samples provides examples, exercises, and applets concerning sample mean, law of large numbers, sample variance, partial sums, central limit theorem, special properties of normal samples, order statistics, and sample covariance and correlation.

This Java applet tutorial prompts the user to input the components of a hypothesis test for the mean. Hints are provided whenever the user enters an incorrect value. Once the steps are completed and the user has chosen the correct conclusion for accepting or rejecting the null hypothesis, a statement summarizing the conclusion is displayed. The applet is supported by an explanation of the steps in hypothesis testing and a description of one-tailed and two-tailed tests.

This pdf file gives definitions for average, standard deviation, and relative standard deviation, and works through a short problem as an example.

This is an exercise in interpreting data that is generated by a phenomenon that causes the data to become biased. You are presented with the end product of this series of events. The craters occur in size classes that are color-coded. After generating the series of impacts, it becomes your assigned task to figure out how many impact craters correspond to each of the size class categories.

This lesson deals with the statistics of political polls and ideas like sampling, bias, graphing, and measures of location. As quoted on the site, "Upon completing this lesson, students will be able to identify and differentiate between types of political samples, as well as select and use statistical and visual representations to describe a list of data. Furthermore, students will be able to identify sources of bias in samples and find ways of reducing and eliminating sampling bias." A link to a related worksheet is included.

This site is a glossary of statisical terms searchable by topic or in alphabetical order. Topics include: Basic Definitions, Presenting Data, Sampling, Probability, Confidence Intervals, Hypothesis Testing, Paired Data, Correlation and Regression, Design of Experiments and ANOVA, Categorical Data, Non-parametric Methods, and Time Series Data.

This site gives an explanation, a definition of, and an example using comparison of two means. Topics include confidence intervals and significance tests, z and t statistics, and pooled t procedures.

This site gives an explanation, a definition of and an example using experimental design. Topics include experimentation, control, randomization, and replication.

This site gives an explanation, a definition and an example of multiple linear regression. Topics include confidence intervals, tests of significance, and squared multiple correlation.

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).