My Academic Background

Explored fundamental techniques for causal inference, including DiD, instrumental variable, and regression discontinuity designs.

Introduced alternative study designs and their practical applicability in the field of political behavior.

Examined simple linear regression and multiple linear regression models. Inference about model parameters and predictions, diagnostic, and remedial measures about the model, independent variable selection, multicollinearity, autocorrelation, and nonlinear regression. SAS implementation of the above topics.

Introduced the theory and method of contemporary political analysis. Emphasizes alternative analytical frameworks, concepts, research design, variables, measurement, and quantitative analysis of political data.

Developed a deeper understanding of description and inference using proportions and odds ratios, multi-way contingency tables, logistic regression and other generalized linear models, log-linear models applications.

Learned the fundamentals of experimental design for fixed, random, and mixed effect models.

Surveyed the building blocks of statistical computing and programming with data. Topics include basic programming in R; data types and data structures in R; importing and cleaning data; specifying statistical models in R; statistical graphics; statistical simulation using pseudo-random numbers; reproducible research and the documentation of statistical analyses.

Examined the fundamentals of GIS and their applications with an emphasis on the concepts needed to effectively manipulate, query, analyze, and visualize spatial-based data