STATISTICS FOR ECONOMICS - 2021/2
Module code: ECO1020
This module covers probability and statistical inference thus enabling students to undertake empirical work. The emphasis is on applications rather than mathematical rigour.
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
Workshop Hours: 11
Independent Learning Hours: 96
Tutorial Hours: 5
Guided Learning: 22
Captured Content: 16
Prerequisites / Co-requisites
- 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||TIMED CLASS TEST 1||20|
|School-timetabled exam/test||TIMED CLASS TEST 2||20|
|Examination Online||FINAL EXAM||60|
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.
Thus, the summative assessment for this module consists of:
- a final exam, at the end of the Semester.
- two class tests, to take place (respectively) at week 5 and week 10.
Formative assessment and feedback
Learning activities provide the students with feedback on their understanding of the material.
Students receive their class test results and have the opportunity to discuss this with the module supervisor during office hours.
The students have fortnightly feedback sessions. For these, students are being provided with a set of exercises relating to the lecture material which they solve independently or in teams. In the sessions, they receive feedback on their answers, and guidance on how the answers could be improved.
- To further develop good data handling skills
- To understand the role statistical inference plays in economic analysis
|1||Have a good understanding of probability concepts including both discrete and continuous probability distributions;|
|2||Be able to construct and interpret point estimates and confidence intervals;|
|3||Be able to formulate and conduct hypothesis tests for a wide range of applications.|
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:
Develop skills in statistical inference that will enhance and develop students’ skills for independent academic study in economics and form a basis which can be built upon in subsequent years.
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
Programmes this module appears in
|Business Economics BSc (Hons)||2||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Economics and Finance 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 2021/2 academic year.