PSC 505 Maximum Likelihood Estimation

Political Science Field: Techniques of Analysis
Typically offered every year

Curtis S. Signorino
Fall 2014 — TR 10:30-12:00

Course Syllabus

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

Course Syllabus

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

Course Syllabus

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 2011 — TR 10:30-12:00

Course Syllabus

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 2010 — TR 10:30-12:00

Course Syllabus

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 2009 — R 12:30-15:15

Course Syllabus

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.