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.

Fall, spring. Credit, three hours. This course is designed to introduce students to the style of analytical thinking required for research 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.

Fall, spring. Credit, two hours. This course is an introduction to the R programming language. It will cover the programming basics of R: data types, controlling flow using loops/conditionals, and writing functions. In addition to these basics, this course will emphasize skills that are relevant for data analysis.

TBA. Credit, two hours. Introduction to the Python programming language and SQL without prior programming experience. This course prepares students for upper-level electives in data analysis related courses. It will cover the programming basics of Python which include understanding data types, controlling flow using loops and conditional statements, and writing functions. This course will put emphasis on skills that are relevant for data analysis which include 1) data manipulation such as merging, appending, and reshaping using SQL and Python, and 2) making various plots for descriptive analysis using Python. 

Spring. 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.