STATISTICS FOR ECONOMICS - 2023/4
Module code: ECO1020
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.
PAREY Matthias (Economics)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 4
JACs code: G300
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
Prerequisites / Co-requisites
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 type||Unit of assessment||Weighting|
|School-timetabled exam/test||CLASS TEST 1 (1 HR)||20|
|School-timetabled exam/test||CLASS TEST 2 (1 HR)||20|
If it is not possible to submit the group coursework report during term, then an alternative individual assessment will be given.
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, each worth 20% of the final grade
- a report (group work), worth 60% of the final grade
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.
- 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.
|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|
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:
2 hour lectures per week x 11 weeks
1 hour tutorials x 5 weeks
1 hour computer lab x 5 weeks
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.
Upon accessing the reading list, please search for the module using the module code: ECO1020
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:
The module develops students’ capabilities for data analysis using computer software.
The module develops the students’ data skills, which are a key skill in the labour market.
Programmes this module appears in
|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.