Anny-Claude Joseph
Assistant Professor of Mathematics
Biostatistician interested in topics at the intersection of health and place, statistics pedagogy, and data ethics.
Broadly, my research is focused on the relationship between place-where we live, learn, and work -and health/social outcomes. Much of my ongoing research work involves using Bayesian statistical methods to investigate the relationship between historic redlining and contemporary asthma prevalence in major cities in the United States. My past work has centered on using spatial epidemiological approaches to assess the impact of incorporating residential histories into the analysis of cancer risk.
Over the years I have had the privilege of teaching mathematics and statistics courses at various levels. Regardless of the course, my overarching objective is to develop sophisticated consumers of quantitative research who are well-equipped to use data to investigate self-generated research questions of interest in an ethical and responsible manner.
Outside of the classroom, I am interested in mentoring students who are interested in facilitating K-12 outreach programs and pathway building STEM workshops for minority groups.
Current and upcoming courses
This is a calculus-based introductory statistics course. Topics covered include data collection, data visualization, descriptive statistics, linear regression, sampling schemes, design of experiment, probability, random variables (both discrete and continuous cases), Normal model, statistical tests and inference (e.g. one-sample and two-sample z-tests and t-tests, chi-square test, etc). Statistical language R will be used throughout the course to realize data visualization, linear regression, simulations, and statistical tests and inference.
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Spatial Statistics
STAT219
Spatial data is becoming increasingly available in a wide range of disciplines, including social sciences such as political science and criminology, as well as sciences such as geosciences and ecology. This course will introduce methods for exploring and analyzing spatial data. We will cover methods to describe and analyze three main types of spatial data: areal, point process, and point-referenced (geostatistical) data. We will also introduce tools for working with spatial data in R.