Surrey University Stag


Module code: MANM280

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

JACs code: G300

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

Overall student workload

Workshop Hours: 10

Independent Learning Hours: 90

Lecture Hours: 15

Seminar Hours: 10

Guided Learning: 10

Captured Content: 15

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

Assessment type Unit of assessment Weighting
Online Scheduled Summative Class Test CLASS TEST SET DATE AND TIME (50 MIN) 30
Examination Online EXAM SET DATE AND TIME (120 MIN) 70

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

Attributes Developed
001 Describe data using graphical and numerical methods KC
002 Estimate/predict variables of interest using linear/non-linear models with ordinary least square estimators within bivariate/multivariate models KC
003 Test/analyse hypotheses of interest pertaining to bivariate/multivariate models KC
004 Learn to use Excel/Stata softwares to describe data, to estimate, predict linear/non-linear regression models and also to perform various diagnostic tests. PT
005 Use the technical and software skills acquired for writing/evaluating practical assignments including research project, market analysis or policy analysis PT

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 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

Seminar preparation will include

  • 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


Help solving/analysing seminar questions based on different data-sets using Excel/Stata – this repeats the materials covered in seminars

Overall, the learning and teaching strategy is designed to:

  • 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
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.


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.

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Accounting and Finance MSc 1 Compulsory A weighted aggregate mark of 50% is required to pass the module
Economics MA 1 Compulsory A weighted aggregate mark of 50% is required to pass the module
International Corporate Finance MSc 1 Compulsory A weighted aggregate mark of 50% is required to pass the module
International Financial Management MSc 1 Compulsory A weighted aggregate mark of 50% 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.