Multivariate Categorical Relationships

  • At their best, graphics are instruments for reasoning about quantitative information. is a quote by American statistician and political scientist Edward R. Tufte (1942 - ). The quote appears on page 9 of Tufte's 1983 book "The Visual Display of Quantitative Information".
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  • The same set of statistics can produce opposite conclusions at different levels of aggregation. is a quote useful in teaching about Simpson's Paradox from American Economist Thomas Sowell (1930 - ). The quote may be found on page 102 of his 1996 book "The vision of the Anointed: Self-Congratulation As a Basis for Social Policy". The quote may also be found at the science history website www.todayinsci.com.
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  • The best way to predict the future is to invent it. This is a quote by American computer scientist Alan C. Kay (1940 - ). The quote was said at a 1971 meeting of Xerox Corporation's Palo Alto Research Center.
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  • A cartoon suitable for use in teaching about time series plots and changepoints. The cartoon is number 418 (May, 2008) from the webcomic series at xkcd.com created by Randall Munroe. Free to use in the classroom and on course web sites under a creative commons attribution-non-commercial 2.5 license.

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  • 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.
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  • Everyday we have specific routines we engage in. Many of these routines are tailored to preventing us from becoming victims of crime. We do things like lock our doors, watch where we walk at night, or avoid walking alone. We take these actions because at some level we are afraid of the possibility of being a victim of crime. Although we may not consciously think about it, these routines may be influenced by a variety of factors. What factors might make some individuals more afraid than others?

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  • In this module you will have the opportunity to explore the frequency of different types of residential moves carried out by Americans. You will examine some of the basic determinants of residential mobility by looking at variations in different types of mobility by age, marital status, education, and housing tenure. Finally, you will have an opportunity to test hypotheses, drawn from a popular theoretical perspective, about racial differences in residential mobility.
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  • How are earnings determined? Why do some people earn more than others? Does a better job necessarily mean a better salary? In this module, students will attempt to answer these questions and many others by examining factors such as education and occupation in terms of the role they play in determining earnings. Students will also look at the earnings of whites and compare them to the earnings of blacks, Latinos, and Asians. Another consideration will center on the effect of gender. Finally, students will turn their attention to the age of workers in terms what role it plays in determing earnings. Aside from earnings, students will also take a brief look at poverty with respect to the effect race-ethnicity and family structure has on creating and sustaining it.
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  • This module is designed to illustrate the effects of selection bias on the observed relationship between premarital cohabitation and later divorce. It also serves as a review of key methodological concepts introduced in the first part of the course.
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  • This collection of datasets covers many application areas, but are all for time series analysis. The data are in text format.
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