Module code: MANM467

Module Overview

This module is an introduction to the methods of specification, estimation and testing of statistical and econometric models in a general. The techniques are applied to real data making use of the statistical computer packages (e.g. Stata).

Module provider

Surrey Business School

Module Leader


Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

JACs code: N200

Module cap (Maximum number of students): 100

Overall student workload

Independent Learning Hours: 117

Lecture Hours: 22

Laboratory Hours: 11

Module Availability

Semester 1

Prerequisites / Co-requisites


Module content

Review of probability and descriptive statistics • Statistical inference (e.g., estimation, hypotheses testing, etc.) • Linear Regression basics, and OLS model • Multivariate regression using cross sectional and panel data • Regression diagnostics (e.g., multicollinarity, serial correlation and heteroskedasticity) • Endogeneity, instrumental variables, and two-stage least squares (2SLS) regression • Supplementary topics (e.g., nonlinear regressions, time series, etc.)

Assessment pattern

Assessment type Unit of assessment Weighting
School-timetabled exam/test A written exam about the probability and statistics and simple regressions 40
Project (Group/Individual/Dissertation) A final report on applying econometric analyses on a real world data set using statistical computer package 60

Alternative Assessment

Coursework exercise which can be completed off-campus.

Assessment Strategy

This module has a technical and a practical component. The latter is, at this level, more important. As such, assessment emphasises work based on applying theoretical knowledge using econometric and statistical package (mainly Stata) in the form of an off-class project, in which students are asked to analyse real economic data. The technical component is assessed via a written examination. Thus, the summative assessment for this module consists of: • 40% In-semester test: written exam about the probability and statistics and simple regressions. • 60% a final written report of applying econometric analyses on a real world dataset using computer package. Formative assessment and feedback is done by a Feedback section in Surreylearn, plus specific, individualised written comments, feedback meetings with students and general feedback in classes.

Module aims

  • Provide the student with the theoretical and practical skills necessary to construct econometric models. The module will equip the student with the ability to undertake, understand, and critically assess empirical work in business, with a view to enable the student to use statistics and econometrics to describe and analyse quantitative data and test various propositions.

Learning outcomes

Attributes Developed
001 Recovering and refreshing the fundamental knowledge of statistics needed for econometrics and data analysis
002 Systematically understand the principles of statistical inference, estimation and hypothesis testing
003 Demonstrate comprehensive knowledge of the properties of different estimators and tests
004 Demonstrate a practical understanding of the application of econometric techniques to actual data using computer packages
005 Be critically aware of the assumptions made in building econometric models
006 Write up the results of a study of an economic problem that includes econometric analysis, demonstrating the ability to communicate clearly their findings and evaluate critically empirical research in that field of business

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: give students the theoretical (and practical) tools they need to analyse real world situations; encourage rigour in their approach to problems; encourage hands-on study of empirical problems; The learning and teaching methods include: readings using lecturers, solving exercises, responding to questions in class, preparing and taking part in the test.

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: MANM467

Other information


Programmes this module appears in

Programme Semester Classification Qualifying conditions
Business Analytics 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 2019/0 academic year.