Resource Library

The datasets in this collection are in text format, but are also compatible with Arc software from "Regression Graphics." Each set has a title, description, and data table. The software is available in the relation link below.

This page contains applets and data files that supplement the text "Investigating Statistical Concepts, Applications, And Methods." The applets and files are organized according to chapter; each data file is available in Minitab or text format.

This resource explains Multiple Regression and concepts associated with it. Key Words: Predicted values; Residuals; Dummy Variables; Interaction Effects; T-Test; Regression Coefficients; Correlation; Partial Correlation; R-Squared; Adjusted R-Squared; Multicollinearity; Variance-Inflation Factors; Transformation; Cook's Distance; Validity; Durbin-Watson Coefficient.

This page explains simple linear regression with an example on muscle strength versus lean body mass.

This online textbook covers the following probability and statistics topics: Independence; Venn Diagrams; Bayes's Theorem; Counting; Binomial Expansion; Binomial Distribution; Continuous Distributions; Infinitesimals in Probability; Averaging; Variance; Gaussian Distribution; Random Walks; Correlation; Causation; Linear Regression; Unbiased Estimators; Hypothesis Testing; Shape of the Distribution; Variance of Mean Differences.

This pdf document contains 7 pages of random number tables and 1 page of instructions for use.

This day may possibly be my last: but the laws of probability, so true in general, so fallacious in particular, still allow about fifteen years. A quote of English historian Edward Gibbon (1737 - 1794). The quote was written in 1787 and was published after his death in "Miscellaneous works of Edward Gibbon, with memoirs of his life and writings composed by himself" edited by Lord John Seffield, 1796

This resource explains the t-distribution and hypothesis testing (informally) using an example on laptop quality.

This resource discusses sample sizes and how they are chosen.

This resource explains margin of error using an example on presidential popularity polls.