Teaching

Harvard University, GOV 52: Models

Semester
Spring
Year offered
2021

This course covers statistical models useful for investigating political and social phenomena. We will address the theory and principles behind these methods, their applications, and their limitations. The course will be useful for those undertaking a quantitative thesis or wanting to have data science modeling skills for other applications. Specific models to be studied include the standard linear regression model in detail, and Generalized Linear Model forms such as logit, probit, log-linear (Poisson), Gamma, ordered choice, multinomial, tobit, and more. In addition Bayesian estimation, Monte Carlo simulation, multilevel, and nonparametric models will be covered.

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American University: Statistics 618/GOV 618 (Every FALL): Bayesian Statistics for Social and Biomedical Sciences

Semester
Fall
Year offered
2020

Course Description
Principles and applications of modern statistical decision theory, with a special focus on Bayesian modeling, data analysis, inference, and optimal decision making. Prior and posterior; comparison of Bayesian and frequentist approaches, including minimax decision making and elementary game theory. Bayesian estimation, hypothesis testing, credible sets, and Bayesian prediction. Introduction to Bayesian computing software and applications to diverse fields. Grading: A-F only. Prerequisite: STAT-514 or permission of instructor... 

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Introduction to Survey Research

Semester
Spring
Year offered
2020

This is an introduction to survey research and polling. Surveys, generally speaking, address questions that are of interest to political researchers, political actors, corporations, government, and...

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Survival Models

Year offered
2019

Survival models for the social and biomedical sciences. An occasional course.

Concordia University Workshops

January 2o2o Description of Generalized Linear Models for the Social Sciences:
This Workshop covers basic GLMs with theory and applications. The following are the slide sets in order of presentation.

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