Module Details for the Academic Year 2018/2019

POL50050 Quantitative Methods II

Fundamentals of multiple regression analysis, including issues such as heteroscedasticity, autocorrelation, specification. In the second half, attention will be paid to estimating and presenting limited dependent variable models and multilevel and panel data. Roughly covers the curriculum of an introductory econometrics course, but with emphasis on limited dependent variable models rather than time series analysis for the more advanced components.

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- Good understanding of linear regression, its underlying assumptions, and basic diagnostics
- Good understanding of maximum likelihood estimation
- Good practical understanding of regression models for limited dependent variable
- Good practical understanding of using R for statistical analysis
- Basic understanding of time series and panel data methods
- Basic understanding of causal inference techniques
- Ability to present and interpret statistical results for academic publications 
Item Workload


Computer Aided Lab


Autonomous Student Learning




Description % of Final Grade Timing
Assignment: Homework 1


Week 3
Assignment: Homework 2


Week 6
Assignment: Homework 3


Week 9
Assignment: Homework 4


Week 12
Essay: Course paper


Coursework (End of Trimester)


This module is not passable by compensation

Resit Opportunities

In-semester assessment


If you fail this module and the module is on offer the following semester, you must repeat the module. You should register for repeating the module at the start of the following semester.If the module is NOT running in the following semester then there will be a resit available in the form of an ‘in semester’ assessment. You should register for this ‘in semester assessment’ at the start of the following semester. Note that it is YOUR responsibility to contact the Module Coordinator to find out what the ‘in semester assessment’ will be and when it will take place.

Module Requisites and Incompatibles

Equivalent Modules

Prior Learning

This course assumes prior training in basic statistics, including:
- hypothesis tests, p-values, sampling distribution
- correlation, covariance, linear regression
- basic data file management
Curricular information is subject to change