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.

Defines what Out of Range Values are and how to account for them in data collection and statistical analysis.Competencies/Skills that HIBB Addresses: Understand how to minimize out of range values when creating new data sets.

In this computer-based exercise, students analyze realistically simulated X-ray spectra of a supernova remnant and determine the abundances of various elements in them. In the end, they will find that the elements necessary for life on Earth -- the iron in their blood, the calcium in their bones -- are created in these distant explosions. The product consists of both downloadable software (PC only) and a user's manual, with sections for both teachers and students.

This lab was designed to promote the integration of a freshman biology lab and an Applied Statistics course. Students collect data sets to be used in statistics for examples and projects, and then present their findings in biology lab. Students learn how to determine the actual size of cells using calipers for chicken eggs, microscopes with ocular micrometers for onion root cells, microscopes with digital cameras/measuring software for red and white blood cells, and rulers for measurements of organelles and bacterial cells on electron micrographs. Students are also introduced to SPSS software to be used in both classes.

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_ocl
Password: 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."

Defines what Out of Range Values are and how to account for them in data collection and statistical analysis.Competencies/Skills that HIBB Addresses: Understand how to minimize out of range values when creating new data sets.

Out of Range Values Detailed Specifications - Defines what Out of Range Values are and how to account for them in data collection and statistical analysis. Competencies/Skills that HIBB Addresses: Understand how to minimize out of range values when creating new data sets.

Defines what Out of Range Values are and how to account for them in data collection and statistical analysis.Competencies/Skills that HIBB Addresses: Understand how to minimize out of range values when creating new data sets.

Defines what Out of Range Values are and how to account for them in data collection and statistical analysis. Competencies/Skills that HIBB Addresses: Understand how to minimize out of range values when creating new data sets.

Defines what Out of Range Values are and how to account for them in data collection and statistical analysis.Competencies/Skills that HIBB Addresses: Understand how to minimize out of range values when creating new data sets.

This course is designed to provide an introduction to a variety of empirical research methods used by political scientists. The primary aims of the course are to make you a more sophisticated consumer of diverse empirical research and to allow you to conduct advanced independent work in your junior and senior years. This is not a course in data analysis. Rather, it is a course on how to approach political science research.

Detection and measurement of radio and optical signals encountered in communications, astronomy, remote sensing, instrumentation, and radar. Statistical analysis of signal processing systems, including radiometers, spectrometers, interferometers, and digital correlation systems. Matched filters and ambiguity functions. Communications channel performance. Measurement of random electromagnetic fields. Angular filtering properties of antennas, interferometers, and aperture synthesis systems. Radiative transfer and parameter estimation.

The purpose of this module is to assist histogram design. The module presents an integral in terms of the Riemann sum equation for finding the area of a data-set. It builds on an associative technique (from a discrete to continuous function) suggested by the sub-interval in the delta-x and bin number in the histogram. The resulting area spectrum contains possible areas and an actual area for a data-set. The module concludes with statements on the possible optimization of the integral by calculating the actual area. Building a histogram in this way may imply compression qualities for variance in the data-set frequency distribution.