Statistics and Probability Commons^™

Math And Democracy, Kimberly A. Roth, Erika L. Ward

Journal of Humanistic Mathematics

Math and Democracy is a math class containing topics such as voting theory, weighted voting, apportionment, and gerrymandering. It was first designed by Erika Ward for math master’s students, mostly educators, but then adapted separately by both Erika Ward and Kim Roth for a general audience of undergraduates. The course contains materials that can be explored in mathematics classes from those for non-majors through graduate students. As such, it serves students from all majors and allows for discussion of fairness, racial justice, and politics while exploring mathematics that non-major students might not otherwise encounter. This article serves as a guide …

Counting The Impossible: Sampling And Modeling To Achieve A Large State Homeless Count, Jennifer L. Priestley, Jane Massey

Jennifer L. Priestley

Objective: Using inferential statistics, we develop estimates of the homeless population of a geographically large and economically diverse state -- Georgia.

Methods: Multiple independent data sources (2000 U.S. Census, the 2006 Georgia County Guide, Georgia Chamber of Commerce) were used to develop Clusters of the 150 Georgia Counties. These clusters were used as "strata" to then execute traified sampling. Homeless counts were conducted within the sample counties, allowing for multiple regression models to be developed to generate predictions of homeless persons by county.

Results: In response to a mandate from the US Department of Housing and Urban Development, the State …

Counting The Impossible: Sampling And Modeling To Achieve A Large State Homeless Count, Jennifer L. Priestley, Jane Massey

Faculty Articles

Objective: Using inferential statistics, we develop estimates of the homeless population of a geographically large and economically diverse state -- Georgia.

Methods: Multiple independent data sources (2000 U.S. Census, the 2006 Georgia County Guide, Georgia Chamber of Commerce) were used to develop Clusters of the 150 Georgia Counties. These clusters were used as "strata" to then execute traified sampling. Homeless counts were conducted within the sample counties, allowing for multiple regression models to be developed to generate predictions of homeless persons by county.

Results: In response to a mandate from the US Department of Housing and Urban Development, the State …