Resource Library

In this module you will explore some of the impacts of this immigration by examining the characteristics of the foreign-born population, comparing these characteristics to those of the native born population. You will get a chance to explore where immigrants come from, how the composition of the immigrant population has changed, where immigrants settle, and what they do once they get here. Most importantly, you will have the opportunity to test some key hypotheses drawn from the most popular theory used to explain the incorporation of immigrants into the American social and economic mainstream.

The textbook for this course discusses cross-cultural variations in household structure, as well as changes across time in household structure in the United States. The purpose of this exercise is to examine variations in household structure in the United States according to race and historical period. By the end of the exercise students should have a better appreciation of the fact that household structure in the U.S. is very fluid and that changes over time in household structure have not progressed uniformly for all race groups.

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 tutorial provides a basic introduction to many topics in statistics and probability. Topics include: Sets and subsets, Statistical experiments, Counting, Basic probability rules, Bayes' theorem, Probability distributions, Discrete vs. Continuous, Binomial, Negative Binomial, Hypergeometric, Multinomial, Poisson, Normal, Sampling theory, Central tendency, Variability, Sampling distributions, t Distribution, Chi-Square Distribution, F Distribution, Estimation problems, Hypothesis testing, Power, Survey sampling, Simple random samples, Stratified samples, Cluster samples, Sample size.

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 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 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.