Quantitative Theory and Methods

Fall, Spring. Credit, four hours. This course provides an introduction to descriptive and inferential statistics. It is designed as a gateway course, with emphasis on practice and implementation. The course introduces probability, sampling distributions, interval estimation, hypothesis testing, ANOVA, and regression. The class consists of lectures and a weekly lab session. The lectures introduce statistical concepts and theory and the lab session applies those lessons using the statistical software. The following departments require Quantitative Theory and Methods 100 as a part of their major coursework: neuroscience and behavioral biology, psychology, anthropology, educational studies, human health, and sociology.

TBA. Credit, three hours. This course is designed to introduce students to the style of analytical thinking required for research in the sciences and the concepts and procedures used in the conduct of empirical research. Students will be introduced to the basic toolkit of researchers which includes sampling, hypothesis testing, Bayesian inference, regression, experiments, instrumental variables, differences in differences, and regression discontinuity. More importantly, students will learn the principles of critical thinking essential for careful and credible research.

TBA. Credit, one hour. This course provides an introduction to statistical computational tools for analyzing data. The material is selected to enable you to become proficient enough to actively implement the methods and tools in your scientific research. By the end of the course, students should be able to 1) deal with complex and messy real data, 2) use graphics to explore and understand data, 3) gain familiarity with basic data collections, storage, and manipulation, and 4) fluently reshape data into the most convenient form for analysis or reporting.

TBA. Credit, One hour. Prerequisite: Quantitative Theory and Methods 150. This course provides a practicum of skills for data science and an introduction to how to do data science with R. The material is selected to enable you to get data into the most useful structure, transform it, visualize it, and model it. By the end of the course, students should be able to 1) deal with complex and messy real data, 2) use graphics to explore and understand data, 3) gain familiarity with basic data manipulation, 4) fluently reshape data into the most convenient form for analysis, and 5) automate cleaning and analysis.

TBA. Credit, four hours. Prerequisite: Mathematics 210. This course covers the structure of probability theory and provides many examples of the use of probabilistic reasoning. We discuss the most commonly encountered probability distributions, both discrete and continuous. The course considers random sampling from a population and the distributions of some sample statistics. We encounter the problem of estimation: the process of using data to learn about the value of unknown parameters of a model. Finally, we discuss hypothesis testing: the use of data to confirm or reject hypotheses formed about the results.