AERD30200
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Academic Year 2025/2026
Statistics and Econometrics (AERD30200)
Subject: : Agribus Extension & Rural Dev
College: : Health & Agricultural Sciences
School: : Agriculture & Food Science
Level: : 3 (Degree)
Credits: : 5
Module Coordinator: : Dr Rania Tremma
Trimester: : Autumn
Mode of Delivery: : Blended
Internship Module: : No
How will I be graded? : Letter grades
Curricular information is subject to change.
The aim of this module is to introduce students to the analysis of economic data using statistics and econometrics. The module builds and extends on prior statistics knowledge. Statistical and econometric methods are applied to learn from economic data about relationships, causes and effects. The module starts with adopting standard statistical concepts and continues with different econometric regression models, discusses common problems when estimating such models and explains how to interpret the estimates from the models. The course has an applied focus and students will get hands-on experience with analysing actual survey and economic data by applying statistical and econometric methods.
About this Module
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What will I learn?
Learning Outcomes:
On successful completion of this module, you should be able to apply statistical concepts and econometric models and interpret the results. Specifically, you should be able to:\ \ • select the appropriate among a set of statistical approaches, apply it to a data set and interpret its results\ • explain the meaning and purpose of econometric analysis \ • understand concepts and assumptions of the linear regression model \ • critically evaluate linear and non-linear regression models and discuss potential problems \ • estimate and interpret statistics using the computer software SPSS\
Indicative Module Content:
• Surveys, samples and data\ • Descriptive statistics and visualizations for single variables and relationships between variables\ • Inferential statistics including hypothesis test and confidence intervals\ • Linear regression with one variable\ • Linear regression with multiple variables\ • Non-linear regression (polynomial, logarithmic, interaction effects)\
How will I learn?
Student Effort Hours:
| Student Effort Type | Hours |
|---|---|
| Lectures | 18 |
| Computer Aided Lab | 18 |
| Specified Learning Activities | 12 |
| Autonomous Student Learning | 60 |
| --- | --- |
| Total | 108 |
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Approaches to Teaching and Learning:
The approach to teaching and learning is based on lectures, active learning and hands-on, computer-based work.
Am I eligible to take this module?
Requirements, Exclusions and Recommendations
Learning Requirements:\
This module will build on the material covered in an “Introduction to Statistics” course, such as Applied Biostatistics (FOR20100).
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Module Requisites and Incompatibles
Not applicable to this module. \ \
How will I be assessed?
Assessment Strategy
| Description | Timing | Component Scale | Must Pass Component | % of Final Grade | In Module Component Repeat Offered |
|---|---|---|---|---|---|
| Exam (In-person): Final term examination - 2 hour End of Trimester Exam | End of trimester Duration: 2 hr(s) | Graded | No | 60 | No |
| Group Work Assignment: Group Project Report & Presentation with secondary data answering different research questions | Week 10 | Graded | No | 20 | No |
| Quizzes/Short Exercises: Quizzes and exercises | Week 9 | Graded | No | 20 | No |
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Carry forward of passed components
No
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What happens if I fail?
| Resit In | Terminal Exam |
|---|---|
| Spring | No |
Please see Student Jargon Buster for more information about remediation types and timing. \
Assessment feedback
Feedback Strategy/Strategies
• Group/class feedback, post-assessment\
How will my Feedback be Delivered?
Solutions to assessments will be discussed interactively in class after the assessment.
Reading List
Stock, J. and Watson. M (2014). "Introduction to Econometrics", updated 3rd edition, Pearson Education.\ Frost, J. (2019) "Introduction to Statistics", State College, Pennsylvania, U.S.A.
When is this module offered?
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) - Autumn: All Weeks | Wed 09:00 - 10:50 |
| Autumn | Computer Aided Lab | Offering 1 | Week(s) - Autumn: All Weeks | Wed 15:00 - 16:50 |