Statistics & Econometrics - ECO00094M
Module summary
This module introduces basic statistical and probability concepts and various econometric methods commonly used in quantitative analysis.
Module will run
| Occurrence | Teaching period |
|---|---|
| A | Semester 1 2026-27 |
Module aims
- Introduce the core principles of probability, statistical inference and econometric modelling used in empirical data analysis.
- Develop understanding of regression-based methods for analysing relationships between variables in economic and social data.
- Introduce key empirical challenges encountered in econometric analysis anddiscuss selected approaches used to address them.
- Provide students with practical experience in applying statistical methods using statistical software to analyse real-world datasets.
Module learning outcomes
On successful completion of the module, students will be able to:
- Demonstrate understanding of fundamental statistical and econometricconcepts used in empirical research.
- Select appropriate econometric models for different types of data and researchquestions.
- Estimate econometric models and conduct statistical inference using statisticalsoftware.
- Interpret, evaluate and communicate empirical results clearly to both technical and non-technical audiences.
Module content
This module introduces the foundations of statistical and econometric
analysis used inempirical economic research. It begins with core
concepts in probability theory and
sampling distributions, which
provide the basis for statistical estimation and hypothesistesting.
The module then develops the theory and application of
regression analysis. Studentsare introduced to linear regression
models and methods for estimating relationships
between
variables. The module also covers statistical inference in regression
modelsand the interpretation of empirical results.
Building on these foundations, the module considers extensions
to regression analysiscommonly used in applied work. Topics may
include models with multiple explanatory
variables, interaction
terms, non-linear transformations and binary regressors. Themodule
also addresses potential challenges in empirical modelling, including
situations
in which key modelling assumptions may be violated and
the implications this has forinterpreting empirical relationships.
Practical applications using statistical software are integrated
throughout the module.Students develop skills in model estimation,
statistical testing, and the interpretation of
empirical results
using real-world datasets.
Indicative assessment
| Task | % of module mark |
|---|---|
| Closed/in-person Exam (Centrally scheduled) | 100.0 |
Special assessment rules
None
Indicative reassessment
| Task | % of module mark |
|---|---|
| Closed/in-person Exam (Centrally scheduled) | 100.0 |
Module feedback
Feedback will be provided in line with University policy
Indicative reading
Wooldridge, J., Introductory Econometrics: A Modern Approach, South Western.