Resource Library

This module provides an activity were students will attempt to explain how each of the following variables is related to child poverty within the United States: Race, Age, Family Type, Family Size, and Immigrant Status.

As discussed, the murder rates for Blacks in the United States are substantially higher than those for Whites, with Latino murder rates falling in the middle. These differences have existed throughout the 20th and into the 21st century and, with few exceptions, are found in different sections of the United States. Although biological and genetic explanations for racial differences in crime rates, including murder, have been discredited and are no longer accepted by most criminologists, both cultural and structural theories are widespread in the literature on crime and violence. It is also important to remember that Latino is an ethnic rather than a racial classification. The point of this exercise is to examine differences in selected structural positions of Blacks, Whites and Latinos in the United States that may help explain long-standing differences in their murder rates.

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 lesson introduces simple linear regression with several Excel spreadsheet examples such as temperature versus cricket chirps, height versus shoe size, and laziness versus amount of TV watched. These activities require class participation.

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

This page provides a table for selecting an appropriate statistical method based on type of data and what information is desired from the data. It also compares parametric and nonparametric tests, one-sided and two-sided p-values, paired and unpaired tests, Fisher's test and the Chi-square test, and regression and correlation. It comes from Chapter 37 of the textbook, "Intuitive Biostatistics".

This applet allows students to explore three methods for measuring "goodness of fit" of a linear model. Users can manipulate both the data and the regression line to see changes in the square error, the absolute error, and the shortest distance from the data point to the regression line.

Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write! Quote from the presidential address in 1951 of mathematical statistician Samuel S. Wilks (1906 - 1964) to the American Statistical Association found in "JASA",Vol. 46, No. 253., pp. 1-18. Wilks was paraphrasing Herbert G. Wells (1866 - 1946) from his book "Mankind in the Making". The full H.G. Wells quote reads: "The great body of physical science, a great deal of the essential fact of financial science, and endless social and political problems are only accessible and only thinkable to those who have had a sound training in mathematical analysis, and the time may not be very remote when it will be understood that for complete initiation as an efficient citizen of one of the new great complex worldwide States that are now developing, it is as necessary to be able to compute, to think in averages and maxima and minima, as it is now to be able to read and write."

Forecasting is very difficult, especially about the future. A quote of business economist Edgar R. Fiedler (1929 - 2003) found in "Across the Board", the magazine of The Conference Board, Inc. (June, 1977). The quote also appears in "Statistically Speaking: A dictionary of quotations" compiled by Carl Gaither and Alma Cavazos-Gaither.

A quick pun about "autocorrelation" by Bruce White.