### PSCI 205 Data Analysis II

- Spring 2025
This course builds on PSCI 200, Data Analysis I, taking the linear regression model as its starting point. We will explore various statistical techniques for analyzing a world of data that is relevant to political science in particular, and to the social sciences more broadly. In addition to the classical linear regression model, we will examine models for binary data, durations, counts, censoring and truncation, self-selection, and discrete choice, among others. These models will be applied to topics such as international conflict, civil war onset, parliamentary cabinet survival, international sanctions, campaign contributions, and voting. Students will be taught how to (1) frame research hypotheses, (2) analyze data using the appropriate statistical model, and (3) interpret and present their results. Statistical analysis will be conducted using R and RStudio.

Prerequisite: Students must have taken at least one course in statistics that (1) covers probability, confidence intervals, hypothesis tests, and linear regression; and (2) uses R for data analysis -- e.g., ECON 230, PSCI 200, or STAT 212/213/214. Prior courses in calculus or linear algebra are not required.

Note: Students will need to bring a laptop computer to class with R and RStudio installed. Most tablets will not suffice. - Spring 2024
This course builds on PSCI 200, Data Analysis I, taking the linear regression model as its starting point. We will explore various statistical techniques for analyzing a world of data that is relevant to political science in particular, and to the social sciences more broadly. In addition to the classical linear regression model, we will examine models for binary data, durations, counts, censoring and truncation, self-selection, and discrete choice, among others. These models will be applied to topics such as international conflict, civil war onset, parliamentary cabinet survival, international sanctions, campaign contributions, and voting. Students will be taught how to (1) frame research hypotheses, (2) analyze data using the appropriate statistical model, and (3) interpret and present their results. Statistical analysis will be conducted using R and RStudio.

Prerequisite: Students must have taken at least one course in statistics that (1) covers probability, confidence intervals, hypothesis tests, and linear regression; and (2) uses R for data analysis -- e.g., ECON 230, PSCI 200, or STAT 212/213/214. Prior courses in calculus or linear algebra are not required.

Note: Students will need to bring a laptop computer to class with R and RStudio installed. Most tablets will not suffice. - Spring 2023
This course builds on PSCI 200, Data Analysis I, taking the linear regression model as its starting point. We will explore various statistical techniques for analyzing a world of data that is relevant to political science in particular, and to the social sciences more broadly. In addition to the classical linear regression model, we will examine models for binary data, durations, counts, censoring and truncation, self-selection, and discrete choice, among others. These models will be applied to topics such as international conflict, civil war onset, parliamentary cabinet survival, international sanctions, campaign contributions, and voting. Students will be taught how to (1) frame research hypotheses, (2) analyze data using the appropriate statistical model, and (3) interpret and present their results. Statistical analysis will be conducted using R and RStudio.

Prerequisite: Students must have taken at least one course in statistics that (1) covers probability, confidence intervals, hypothesis tests, and linear regression; and (2) uses R for data analysis -- e.g., ECON 230, PSCI 200, or STAT 212/213/214. Prior courses in calculus or linear algebra are not required.

Note: Students will need to bring a laptop computer to class with R and RStudio installed. Most tablets will not suffice. - Spring 2022

Prerequisite: Students must have taken at least one course in statistics that (1) covers probability, confidence intervals, hypothesis tests, and linear regression; and (2) uses R for data analysis -- e.g., ECON 230, PSCI 200, or STAT 212/213/214. Prior courses in calculus or linear algebra are not required.

Note: Students will need to bring a laptop computer to class with R and RStudio installed. Most tablets will not suffice. - Spring 2021
This course builds on PSC 200, Data Analysis I, taking the linear regression model as its starting point. We will explore various statistical techniques for analyzing a world of data that is relevant to political science in particular, and to the social sciences more broadly. We will examine models for binary data, durations, counts, censoring and truncation, self-selection, and strategic choice, among others. These models will be applied to topics such as international conflict, civil war onset, parliamentary cabinet survival, international sanctions, campaign contributions, and voting. Students will be taught how to (1) frame research hypotheses, (2) analyze data using the appropriate statistical model, and (3) interpret and present their results. Statistical analysis will be conducted using R. Prerequisites: Students should have taken a course (such as PSC 200, ECO 230, STT 211, STT 212, STT 213, or STT 214) that introduces them to hypothesis tests, confidence intervals, and linear regression. Students who have not used R in a previous course should familiarize themselves with it prior to the first class. Specifically, students should be able to load a data set, print summary statistics, create a scatterplot, and conduct linear regression.

- Spring 2020
This course builds on PSC 200, Data Analysis I, taking the linear regression model as its starting point. We will explore various statistical techniques for analyzing a world of data that is relevant to political science in particular, and to the social sciences more broadly. We will examine models for binary data, durations, counts, censoring and truncation, self-selection, and strategic choice, among others. These models will be applied to topics such as international conflict, civil war onset, parliamentary cabinet survival, international sanctions, campaign contributions, and voting. Students will be taught how to (1) frame research hypotheses, (2) analyze data using the appropriate statistical model, and (3) interpret and present their results. Statistical analysis will be conducted using R. Prerequisites: Students should have taken a course (such as PSC 200, ECO 230, STT 211, STT 212, STT 213, or STT 214) that introduces them to hypothesis tests, confidence intervals, and linear regression.

- Spring 2019

Prerequisite: Students must have taken at least one course in statistics that (1) covers probability, confidence intervals, hypothesis tests, and linear regression; and (2) uses R for data analysis -- e.g., ECON 230, PSCI 200, or STAT 212/213/214. Prior courses in calculus or linear algebra are not required.

Note: Students will need to bring a laptop computer to class with R and RStudio installed. Most tablets will not suffice. - Spring 2018
This course builds on PSC 200, Data Analysis I, taking the linear regression model as its starting point. We will explore various statistical techniques for analyzing a world of data that is relevant to political science in particular, and to the social sciences more broadly. We will examine models for binary data, durations, counts, censoring and truncation, self-selection, and strategic choice, among others. These models will be applied to topics such as international conflict, civil war onset, parliamentary cabinet survival, international sanctions, campaign contributions, and voting. Students will be taught how to (1) frame research hypotheses, (2) analyze data using the appropriate statistical model, and (3) interpret and present their results. Statistical analysis will be conducted using R. Prerequisites: Students should have taken a course (such as PSC 200, ECO 230, STT 211, STT 212, STT 213, or STT 214) that introduces them to hypothesis tests, confidence intervals, and linear regression. Students who have not used R in a previous course should familiarize themselves with it prior to the first class. Specifically, students should be able to load a data set, print summary statistics, create a scatterplot, and conduct linear regression.