This course addresses the challenges of defining a relationship between exposure to environmental chemicals and human disease. Course topics include epidemiological approaches to understanding disease causation; biostatistical methods; evaluation of human exposure to chemicals, and their internal distribution, metabolism, reactions with cellular components, and biological effects; and qualitative and quantitative health risk assessment methods used in the U.S. as bases for regulatory decision-making. Throughout the term, students consider case studies of local and national interest.

How genetics can add to our understanding of cognition, language, emotion, personality, and behavior. Use of gene mapping to estimate risk factors for psychological disorders and variation in behavioral and personality traits. Mendelian genetics, genetic mapping techniques, and statistical analysis of large populations and their application to particular studies in behavioral genetics. Topics also include environmental influence on genetic programs, evolutionary genetics, and the larger scientific, social, ethical, and philosophical implications.

In this data rich world, we need to understand how things are organized on the Earth's surface. Those things are represented by spatial data and necessarily depend upon what surrounds them. Spatial statistics provide insights into explaining processes that create patterns in spatial data. In geographical information analysis, spatial statistics such as point pattern analysis, spatial autocorrelation, and spatial interpolation will analyze the spatial patterns, spatial processes, and spatial association that characterize spatial data. Understanding spatial analysis will help you realize what makes spatial data special and why spatial analysis reveals a truth about spatial data.

The main goal of the course is to highlight the general assumptions and methods that underlie all statistical analysis. The purpose is to get a good understanding of the scope, and the limitations of these methods. We also want to learn as much as possible about the assumptions behind the most common methods, in order to evaluate if they apply with reasonable accuracy to a given situation. Our goal is not so much learning bread and butter techniques: these are pre-programmed in widely available and used software, so much so that a mechanical acquisition of these techniques could be quickly done "on the job". What is more challenging is the evaluation of what the results of a statistical procedure really mean, how reliable they are in given circumstances, and what their limitations are.Login: guest_oclPassword: ocl

" 9.63 teaches principles of experimental methods in human perception and cognition, including design and statistical analysis. The course combines lectures and hands-on experimental exercises and requires an independent experimental project. Some experience in programming is desirable. To foster improved writing and presentation skills in conducting and critiquing research in cognitive science, students are required to provide reports and give oral presentations of three team experiments. A fourth individually conducted experiment includes a proposal with revision, and concluding written and oral reports."

Psychology is designed to meet scope and sequence requirements for the single-semester introduction to psychology course. The book offers a comprehensive treatment of core concepts, grounded in both classic studies and current and emerging research. The text also includes coverage of the DSM-5 in examinations of psychological disorders. Psychology incorporates discussions that reflect the diversity within the discipline, as well as the diversity of cultures and communities across the globe.Senior Contributing AuthorsRose M. Spielman, Formerly of Quinnipiac UniversityContributing AuthorsKathryn Dumper, Bainbridge State CollegeWilliam Jenkins, Mercer UniversityArlene Lacombe, Saint Joseph's UniversityMarilyn Lovett, Livingstone CollegeMarion Perlmutter, University of Michigan

By the end of this section, you will be able to:

Explain what a correlation coefficient tells us about the relationship between variables
Recognize that correlation does not indicate a cause-and-effect relationship between variables
Discuss our tendency to look for relationships between variables that do not really exist
Explain random sampling and assignment of participants into experimental and control groups
Discuss how experimenter or participant bias could affect the results of an experiment
Identify independent and dependent variables

The disciplines of music history and music theory have been slow to embrace the digital revolutions that have transformed other fields' text-based scholarship (history and literature in particular). Computational musicology opens the door to the possibility of understanding - even if at a broad level - trends and norms of behavior of large repertories of music. This class presents the major approaches, results, and challenges of computational musicology through readings in the field, gaining familiarity with datasets, and hands on workshops and assignments on data analysis and "corpus" (i.e., repertory) studies. Class sessions alternate between discussion/lecture and labs on digital tools for studying music. A background in music theory and/or history is required, and experience in computer programming will be extremely helpful. Coursework culminates in an independent research project in quantitative or computational musicology that will be presented to the class as a whole.