QUANTITATIVE METHODS - 2023/4

Module Overview

This module lays the statistical and econometric foundations for data analyses and modelling, covering fundamental topics of estimation and inferences of linear and non-linear econometric models using Excel/Stata software. The quantitative, analytical and software skills acquired from this module will directly enable students to conduct independent quantitative analysis of data using estimation as well as hypotheses testing. As such, the module aims to help students to learn research methods, an integral part of any research project at post-graduate level, market analysis or policy analysis in government and non-government organisations. 

Module provider

Surrey Business School

Module Leader

PAL Sarmistha (SBS)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

Basic knowledge of secondary level Mathematics including linear equations, natural logarithms, laws of exponents and simple differentiation is assumed in constructing this module. We run Mathematics/Statistics primer course in week 1 of the semester for students to review these essential background materials to prepare for the module materials.

Module content

Indicative content includes, but not exhausted to:
 

• Bivariate regression model and inferences


• Multivariate regression models and inferences


• Functional forms and estimation of non-linear models


• Deviations from classical regression models

Assessment pattern

Alternative Assessment

Not applicable

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate their engagement/efforts as well as knowledge/understanding/communications of theoretical and empirical issues of the subject covered in the module. Lectures/seminars/workshops are used to prepare students for the two assessments.

Summative assessment:

• Class test (30%)


• Examination (70%)Students will need to answer theoretical and numerical questions using the materials taught in lectures. This will test their ability to explain key theoretical concepts and solve and analyse empirical problems they learnt in lectures and seminars.

• Students will receive verbal feedback as well as written answers to seminar questions


• Students will receive verbal/written feedback on the class test paper


• Lecturer will go through last year’s exam paper for preparation of January examination; written answers on last year’s exam paper will also be available online

Module aims

• This module is an introduction to data analysis, model specification, estimation and testing of linear and non-linear regression models in Finance/Economics using real data with the help of statistical softwares Excel/Stata.
• To describe univariate/bivariate data with the help of simple statistics, charts, graphs
• To estimate/predict/test bivariate and multivariate linear/non-linear models
• To detect and redress deviations from classical linear regression models

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to include the following:

• Maths/Stats Primer lectures held in week 1


• Module lectures


• Seminars 


• Workshops (optional) to provide further support to seminars


• Regular attendance in lectures/seminars is essential – lecture slides and video recording are not substitutes for attending lectures/seminars/workshops

• Attending lectures


• Listening to captured videos


• Reading Powerpoint slides and relevant textbook chapters


• Preparing answers to seminar activities


• Responding to questions in seminars and receiving feedback from the lecturer


• Attending workshops will clarify if difficulties are faced in seminars


• Formative feedback will be provided in seminars/workshops and also after the class test

• Enable students to develop the knowledge, skills, and critical thinking to be able to engage with quantitative methods in different contexts.


• Engage students with applications of the methods introduced in seminars/workshops using real life data with the help of Excel/Stata.


• Develop students’ digital literacy and quantitative skills to apply the methods learned objectively for research/market/policy analyses, thus helping them develop communication skills, employability as well as professionalism.

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: MANM280

Other information

Digital Capabilities:

Students will develop digital capabilities focusing on various quantitative methods graphical, numerical as well as model building using various data-sets with the help of statistical softwares Excel/Stata.

Employability

Both disciplinary knowledge and digital capabilities enable students to develop essential transferable skills that can be carried forward to their professional life at the end of the university education.

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.