Statistics

This course examines time series with applications. Fundamental concepts of time series such as trends, stationary process, ARIMA process, model building (including parameter estimation, order determination and diagnostic checking), forecasting and seasonal models, ARCH and GARCH models will be covered. The use of related statistical packages will be demonstrated. 

This course seeks to immerse students in a professional work environment. Students have the opportunity to observe and interact with co-workers, and learn how to recognize and respond to cultural differences. Students compare concepts of teamwork and interpersonal interactions in different cultures as experienced on the job. Seminar work helps students apply academic knowledge in a business setting and identify opportunities to create value within the company. Students research a specific topic related to their work placement and present their findings in a final research report.

This service-learning course combines a structured curriculum and extensive partnership with a local community-based organization to offer tangible community service. Here, student community service includes direct
engagement as well as a research-based action plan addressing a specific challenge or goal identified by a community-based organization. Students begin by exploring key community-based organizations: examining their
mission, vision and goals, and the place of the organization in the local community. Each student then works with an assigned partner organization and invests at least 90 hours partnering with the organization, working with them
and investigating ways to solve a challenge or issue the organization has identified. Student service-learning includes exploring the proximate and ultimate drivers of the organization's chosen challenge, and the organization's
infrastructure, resources, limitations and possibilities for reducing barriers to achieving the organization's self-identified goals. In concert, coursework probes the role of community-based organizations in both local and global
contexts, common challenges of community-based organizations in defining and implementing their goals, the role of service-learning in addressing these issues, and effective ways for students to help them achieve their mission,
vision, and goals. Coursework also guides the student's service-learning experience by helping students develop sound international service ethics, provide tools to investigate solutions to common development issues, aid in
data analysis and presentation, and provide best practices to illustrate findings and deliver approved joint recommendations orally and in writing. Throughout, students use service-learning as a means to expand their global awareness and understanding, explore shared aspirations for social justice, and develop skills to work with others to effect positive change.

This course examines key ideas behind algorithms from a statistical perspective, and provide an in-depth knowledge that will enable students to apply the methods with awareness of their strengths and limitations. The topics covered will include probabilistic and analytic foundations, multivariate statistical analysis and machine learning, with a particular focus on clustering, classification, model selection and high-dimensional statistical analysis. 

This course focuses on applications of basic statistical techniques. In particular, we explore model formulation, model fitting, interpretation and presentation of analysis results for simple and multiple linear regressions, and logistic regression models. Some applications to data from the field of Agriculture, Biology , Economics, Finance etc. will be explain various concepts. Applications using R statistical software is also considered.

This course provides an introduction to the statistical and econometric theory underlying surveys and counterfactual policy evaluations, which have long played a prominent role in democracies' political life. Doing so, it sharpens critical appraisal of the very many surveys and policy evaluations that are to be found in public discourse. This class uses mathematical notation and proofs: students should be motivated to engage with mathematically formalized material.

This course covers conditional distributions based on densities, including conditioning in the Gaussian distribution; hierarchical/mixed-effects models (theoretical and practical aspects); Bayesian analyses and computations, such as prior and posterior distributions, credible intervals, MCMC sampling; and software for mixed-effects models and Bayesian computations. This is an advanced course in statistics; it is not an introductory course. Prerequisites include probability distributions with densities, linear normal models, logistic and Poisson regression, R usage. 

This class is designed to equip students with the fundamental principles of statistics commonly used in communication research. This course is the first in a sequence of graduate methodology classes required of all students enrolled in the M.A. or Ph.D. program in Communication. Students acquire working familiarity with the basic principles and theory behind descriptive and inferential statistics. By the end of the semester students understand the difference between descriptive and inferential statistics, understand the logic of null hypothesis significance testing, and be able to conduct basic statistical analyses (including t-tests, a single-factor ANOVA, correlation, regression, and chi-square) using commonly used statistical software such as R. Students who complete this course are able to read and understand empirical research, analyze data from their research projects, and report results in accordance with the APA standards. Topics include Basic Concepts and Vocabulary, Introduction to R, Probability, Independence, and the Normal Distribution, Hypothesis Testing Concepts and Applications, Factorial ANOVA, Correlation & Chi-square, and Regression fundamentals.

CIEE supports qualified students who wish to pursue an academically rigorous independent research project while abroad. In order to enroll, students must submit a research proposal including a clearly defined research topic,
explanation of research plans, description of preparation in the planned area of study, list of resources, tentative outline of a final paper, and suggested schedule of progress. Students complete a total of 100-120 hours of
research and meet regularly with an advisor to complete an academically rigorous, ethically sound, and culturally appropriate research project and final research paper. Approval for participation in Directed Independent Research
must be obtained from CIEE and the student's home institution prior to arrival on the program.

This is an independent research course with research arranged between the student and faculty member. The specific research topics vary each term and are described on a special project form for each student. A substantial paper is required. The number of units varies with the student’s project, contact hours, and method of assessment, as defined on the student’s special study project form.