STATISTICS FOR ECONOMICS - 2022/3

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

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

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, 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.

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

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.

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

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 2022/3 academic year.