Daphna Harel

Frequentist Inference

This is a course in the intermediate and advanced foundations of statistical inference in the context of applied research. Assuming some prior exposure to probability and statistics, this course will first cover topics such as the principles of estimation and hypothesis testing, and the general and generalized linear models, including scientific computation. This course thoroughly explores the frequentist approach to inference. The student will be expected to understand the mathematical theory, implement related statistical algorithms in statistical programming language such as R, and interpret models and parameters in the context of applied statistical analysis of real data.

Practicum in Applied Statistics: Applied Probability

This is a course in the foundations of statistical inference techniques. Assuming some prior exposure to foundational and intermediate statistical methods, this course will first cover topics such as Kolgomorov's axioms of probabilities, basics of set theory, discrete combinatorial probability, Bayes' theorem, probability distributions and their properties and assumptions of dependence and independence. These topics are followed by the foundational topics of statistics: sampling distributions, the law of large numbers and the central limit theorem. This course will mix theoretical approaches with simulation-based illustrations of these main topics. The student will be expected to understand the mathematical theory and apply the topics covered to problem solving via analytical and simulation based methods in statistical programming language such as R.

Statistical Mysteries and How to Solve Them

An introductory quantitative and statistical reasoning course designed to help students acquire statistical literacy and competency to survive in a data-rich world. The course introduces students to basic concepts in probability, research design, descriptive statistics, and simple predictive models to help them to become more savvy consumers of the information they will routinely be exposed to in their personal, academic and professional lives. Course material will be conveyed through video clips, case studies, puzzle solving, predictive competitions, and group discussions.
Liberal Arts Core/CORE Equivalent - satisfies the requirement for Quantitative Reasoning for certain programs; students should check with their Academic Advisor for confirmation.

Survey Research Methods

The survey is the leading mechanism for collecting information on a wide array of topics in our data-driven world. This course is designed to introduce students to the fundamental aspects of the survey and ways for evaluating this form of data collection. Principal topics include: survey design; coverage, sampling, and non-response; modes of data collection; questionnaire construction and evaluation. Throughout this course, students will be given opportunities to engage in actual survey research activities.