Resource Library

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 contains lessons which include steps, examples, and a calculator, on standard deviation, Pearson's r, t-test, one-way ANOVA, and Tukey's Post Hoc Test.

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 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 online, interactive lesson on point estimation provides examples, exercises, and applets concerning estimators, method of moments, maximum likelihood, Bayes estimators, best unbiased estimators, and sufficient, complete and ancillary statistics.

This online, interactive lesson on Markov chains provides examples, exercises, and applets that cover recurrence, transience, periodicity, time reversal, as well as invariant and limiting distributions.

This Electronic Statistics Textbook offers training in the understanding and application of statistics ... and covers a wide variety of applications, including laboratory research (biomedical, agricultural, etc.), business statistics and forecasting, social science statistics and survey research, data mining, engineering and quality control applications, and many others. Quoted from the index page of the text.

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 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 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.