POL40950 Introduction to Statistics

Academic Year 2020/2021

Introduction to the use of data for statistical analysis in political science and related disciplines (sociology, public policy, international relations, etc.). The module will introduce concepts such as measurement, variables, statistical data, and provide an introduction to basic descriptive statistics summarizing numerical data, both graphically and numerically. The core of the module will be an introduction to applied multiple regression analysis, discussing the purpose, implementation, and interpretation of standard regression models, for both continuous and dichotomous variables. It will introduce the basics of statistical inference, drawing conclusions about populations on the basis of sample data, and apply this to the regression context. Foundational knowledge of frequentist statistical inference will be provided and the end result will be basic ability to perform, interpret, and report on multiple regression analysis.

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Curricular information is subject to change

Learning Outcomes:

- basic understanding of working with R and RStudio
- being able to wrangle, summarise, describe, and visualise statistical data
- basic understanding of (frequentist) statistical inference
- basic understanding of executing and interpreting multiple regression
- preliminary understanding of logistic regression

Indicative Module Content:

Accessing and visualising data
Simple regression
Descriptive statistics
Multiple regression
Categorical independent variables
Writing up regression results
Interaction models
Sampling distribution & Central Limit Theorem
Hypothesis tests & confidence intervals in regression
Model specification and fit / statistical vs causal inference
Logistic regression

Student Effort Hours: 
Student Effort Type Hours
Lectures

12

Computer Aided Lab

12

Autonomous Student Learning

200

Total

224

Approaches to Teaching and Learning:
The sessions consist of lectures and labs each week. Some of the lectures and labs will be online. The lectures focus on the fundamental aspects of statistical inference as well as the interpretation of these methods and examples. The lectures will make use of small group exercises to allow students to work directly with example material.

In the online lab, students will be provided with clear instructions and solve problems related to data wrangling, visualisation and statistical methods using the statistical programming language R. The homework assignments are structured so that they gradually lead up to a comprehensive regression analysis and associated social science paper, putting the technical material of the class in practice. 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: Homework assignment Week 6 n/a Graded No

25

Essay: Course paper Coursework (End of Trimester) n/a Graded No

50

Continuous Assessment: Homework assignment Week 3 n/a Graded No

25


Carry forward of passed components
Yes
 
Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Feedback will be provided within 20 days from submission, as per university guidelines. Feedback on Homework 3 in particular will also count as formative assessment in preparation of the course paper.

– Chester Ismay and Albert Y. Kim. 2020: Statistical Inference via Data Science: A ModernDive into R and the tidyverse. CRC Press: Boca Raton. URL: https://moderndive.com

– Hadley Wickham and Garrett Grolemund. 2017. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. Sebastopol: O’Reilly.URL: https://r4ds.had.co.nz

– Kieran Healy. 2019. Data Visualization: A Practical Introduction. Princeton: Princeton University Press. URL: https://socviz.co

– Kosuke Imai. 2017. Quantitative Social Science: An Introduction. Princeton: Princeton University Press.
Name Role
Ms Tamanna Adhikari Tutor
Arya Pillai Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.

 
Autumn
       
Lecture Offering 1 Week(s) - 1, 4, 7, 9, 11 Mon 13:00 - 13:50 Online
Lecture Offering 1 Week(s) - 2, 3, 5, 6, 8, 10 Mon 13:00 - 13:50 Online
Practical Offering 1 Week(s) - 1, 4, 7, 9, 11 Mon 14:00 - 14:50 Online
Practical Offering 1 Week(s) - 2, 3, 5, 6, 8, 10 Mon 14:00 - 14:50 Online
Autumn