STATISTICS FOR ECONOMICS - 2023/4
Module code: ECO1020
Module Overview
This module introduces students to core elements of probability and statistical inference. This enables students to begin undertaking empirical work. An emphasis in this module is on applications. The module also prepares students for further study in econometrics.
Module provider
Economics
Module Leader
PAREY Matthias (Economics)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 4
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 65
Lecture Hours: 20
Tutorial Hours: 5
Laboratory Hours: 5
Guided Learning: 35
Captured Content: 20
Module Availability
Semester 2
Prerequisites / Co-requisites
None
Module content
Indicative content includes:
- Probability – independent and dependent events; Bernoulli, Binomial, Normal, t and Chi-squared distributions; joint, marginal, and conditional distributions; expectation, variance, covariance, corrrelation; Law of Large Numbers; Central Limit Theorem
- Statistical inference – random samples; sampling distributions; point and interval estimation; hypothesis testing with critical values and p-values; confidence intervals
Assessment pattern
Assessment type | Unit of assessment | Weighting |
---|---|---|
Online Scheduled Summative Class Test | ONLINE CLASS TEST 1 (1HR WITHIN A 4HR WINDOW)) | 20 |
Online Scheduled Summative Class Test | ONLINE CLASS TEST 2 (1HR WITHIN A 4HR WINDOW) | 20 |
Coursework | COURSEWORK REPORT | 60 |
Alternative Assessment
If it is not possible to submit the group coursework report during term, then an alternative individual assessment will be given.
Assessment Strategy
The assessment strategy is designed to provide students with the opportunity to demonstrate:
Understanding of probability concepts, confidence intervals, hypothesis tests and the context in which to apply this knowledge. The preparation of the report (group work), allows students to demonstrate the use of these concepts in the context of an empirical application.
Thus, the summative assessment for this module consists of:
- two class tests
- a report (group work)
Formative assessment and feedback
Learning activities provide the students with feedback on their understanding of the material.
Students also receive feedback on the assessments.
The tutorials also provide feedback. For these, students are provided with a set of exercises relating to the lecture material which they solve in advance of the tutorials. In the sessions, they receive feedback on their answers, and guidance on how the answers could be improved.
Module aims
- To introduce students to key concepts in probability and inference.
- To learn how to formulate and implement hypothesis tests.
- To understand the role statistical inference plays in economic analysis.
- To learn how to apply statistical concepts to address economic questions.
Learning outcomes
Attributes Developed | ||
001 | Student will have a good understanding of probability concepts including both discrete and continuous probability distributions | KCPT |
002 | Students will be able to construct and interpret point estimates and confidence intervals | KCPT |
003 | Students will be able to formulate and conduct hypothesis tests | KCPT |
004 | Students will be able to apply the concepts of this module in economic applications | KCPT |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
Using lectures to develop core concepts in probability and statistical inference, as well as applications of the concepts.
Tutorials are designed to help students apply the concepts based on problem sets.
Computer lab sessions are designed to help students apply the concepts using data sets and computer software.
The learning and teaching method include:
Lectures
Tutorials
Computer lab
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Reading list
https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: ECO1020
Other information
The School of Economics is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities particularly in the following areas:
Digital capabilities
The module develops students’ capabilities for data analysis using computer software.
Employability
The module develops the students’ data skills, which are a key skill in the labour market.
Programmes this module appears in
Programme | Semester | Classification | Qualifying conditions |
---|---|---|---|
Economics and Finance BSc (Hons) | 2 | Compulsory | A weighted aggregate mark of 40% is required to pass the module |
Business Economics BSc (Hons) | 2 | Compulsory | A weighted aggregate mark of 40% is required to pass the module |
Economics BSc (Hons) | 2 | Compulsory | A weighted aggregate mark of 40% is required to pass the module |
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.