FOUNDATIONS OF STATISTICS AND ECONOMETRICS - 2023/4
Module code: MANM467
This module is an introduction to the methods of specification, estimation and testing of statistical and econometric models. The techniques are applied to business data making use of the statistical computer packages (e.g. Stata).
Surrey Business School
TAVALAEI Mahdi (SBS)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: N200
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 97
Tutorial Hours: 11
Laboratory Hours: 11
Guided Learning: 20
Captured Content: 11
Prerequisites / Co-requisites
Review of descriptive statistics and statistical inference (e.g., estimation, hypotheses testing, etc.); Analysis of variance; Linear Regression basics, and OLS model; Multiple regression using cross sectional and panel data; Regression diagnostics (e.g., multicollinarity, serial correlation and heteroskedasticity); Endogeneity, causal inference, instrumental variables, and two-stage least squares (2SLS) regression; Supplementary topics (e.g., difference in difference method, nonlinear regressions, etc.)
|Assessment type||Unit of assessment||Weighting|
|Coursework||A FINAL REPORT ON APPLYING ECONOMETRIC ANALYSES ON A REAL WORLD DATA SET USING STATISTICAL COMPUTER PACKAGE||100|
Coursework exercise which can be completed off-campus.
The assessment emphasises work based on implementing theoretical knowledge of econometrics and statistics in the form of an off-class project. The summative assessment for this module consists of a final written report applying econometric analyses on a real-world business dataset using statistical analysis software.Formative assessment and feedback are done via Surreylearn, individualised written feedback, meetings with students if required, and general comments in classes.
- Provide knowledge of fundamental concepts and models widely used in statistical and econometric analyses
- Apply theoretical concepts and use their understanding and insight gained throughout the unit to implement and interpret econometric models
- Use various types of quantitative data to make decisions in the business world
|001||Display a fundamental understanding of the statistical and econometric analysis (such as assumptions, inference, and diagnostics),||KC|
|002||Be able to build econometric models and analyse various types of quantitative data, and interpret the results,||KPT|
|003||Be able to evaluate empirical research in the field of business critically, and propose and apply robustness tests, and||KCPT|
|004||Be able to write up a full-fledged econometric analysis report on real-world data, and to communicate clearly and evaluate the findings critically.||KCPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy are designed to give students the theoretical knowledge and practical tools they need for building and implementing empirical analysis; encourage rigour in their approach to real-world quantitative problems, and encourage critical evaluation of quantitative models. The learning and teaching methods include reading (textbooks, articles, and reports), watching videos (in forms lectures and additional content), participating in computer labs, off-class exercises, and discussion in class.
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: MANM467
This module has a capped number and may not be available to ERASMUS and other international exchange students. Please check with the International Engagement Office email: firstname.lastname@example.org
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.