## PSC 505 Maximum Likelihood Estimation

Political Science Field: Techniques of Analysis*Typically offered every year*

Curtis S. Signorino

Fall 2016 — MW

*W 1000-1130 & F 0900-1015.* The classical linear regression model is inappropriate for many of the most interesting problems in political science. This course builds upon the analytical foundations of PSC 404 and 405, taking the latter's emphasis on the classical linear model as its point of departure. Here students will learn methods to analyze models and data for event counts, durations, censoring, truncation, selection, multinomial ordered/unordered categories, strategic choices, spatial voting models, and time series. A major goal of the course will be to teach students how to develop new models and techniques for analyzing issues they encounter in their own research.

Curtis S. Signorino

Fall 2015 — MW 10:30-12:00

The classical linear regression model is inappropriate for many of the most interesting problems in political science. This course builds upon the analytical foundations of PSC 404 and 405, taking the latter's emphasis on the classical linear model as its point of departure. Here students will learn methods to analyze models and data for event counts, durations, censoring, truncation, selection, multinomial ordered/unordered categories, strategic choices, spatial voting models, and time series. A major goal of the course will be to teach students how to develop new models and techniques for analyzing issues they encounter in their own research.

Curtis S. Signorino

Fall 2014 — TR 10:30-12:00

The classical linear regression model is inappropriate for many of the most interesting problems in political science. This course builds upon the analytical foundations of PSC 404 and 405, taking the latter's emphasis on the classical linear model as its point of departure. Here students will learn methods to analyze models and data for event counts, durations, censoring, truncation, selection, multinomial ordered/unordered categories, strategic choices, spatial voting models, and time series. A major goal of the course will be to teach students how to develop new models and techniques for analyzing issues they encounter in their own research.

Curtis S. Signorino

Fall 2013 — TR 10:30-12:00

The classical linear regression model is inappropriate for many of the most interesting problems in political science. This course builds upon the analytical foundations of PSC 404 and 405, taking the latter's emphasis on the classical linear model as its point of departure. Here students will learn methods to analyze models and data for event counts, durations, censoring, truncation, selection, multinomial ordered/unordered categories, strategic choices, spatial voting models, and time series. A major goal of the course will be to teach students how to develop new models and techniques for analyzing issues they encounter in their own research.

Curtis S. Signorino

Fall 2012 — TR 10:30-12:00

Curtis S. Signorino

Fall 2011 — TR 10:30-12:00

Curtis S. Signorino

Fall 2010 — TR 10:30-12:00

Curtis S. Signorino

Fall 2009 — R 12:30-15:15