Applied Statistics and Forecasting
Essex Business School
Postgraduate: Level 7
Monday 13 January 2020
Friday 20 March 2020
02 January 2020
Requisites for this module
MSC N11112 Business Analytics,
MSC N21612 International Logistics and Supply Chain Management
The importance of data driven decision making cannot be overstated in today's big data era. Decision makers including private equity investors, venture capitalists, analysts, entrepreneurs, management consultants, and business managers are increasingly facing a very complex business environment and have to make decisions which potentially could have a huge impact on not only the business unit in question, but also on employees, stakeholders and society at large.
While the problems have become more complex, decision makers have also at their fingertips, access to vast amounts of data as well as tools which can help them analyse the data. The ability to understand the data and use them to support their decisions is increasingly proving to be a necessary skill in a decision maker's portfolio.
This module provides students with a number of key skills and techniques, which will enable them to make quicker and smarter decisions in the face of increasing uncertainty and complexity. The module will focus on organising, visualising, computing, and analysing data to provide insights which can then act as inputs into the decision making process. The approach will use both theoretical and practical concepts. It will concentrate on the applicability of a range of statistical and computational methods in data driven decision making.
No information available.
On successful completion of this module students should be able to:
1. Obtain a critical understanding of principal-driven and data-driven approaches in statistical computing and modelling which can be used to analyse data for answering real-life questions.
2. Develop key analytical skills of analysing data using modern software tools and techniques from an application point of view.
3. Gain overall perspective on the importance of data analysis and statistics in both strategic and tactical decision making faced by decision makers in the modern business world.
4. Critically differentiate between the questions which can be tackled using qualitative methods and those which require statistical analytical techniques.
No additional information available.
The lectures will be developed around the key theoretical concepts of modern statistical computing and modelling, and how they are generally utilised in analysis and answer real business oriented questions. The lecture material provides an overall view of how to use multiple methods in statistical computing and methods, based on the nature of the question posed as well as nature and source of available data.
The seminars will focus on practical aspects of using the material taught in lectures for solving real life problems. They will use freely available datasets to learn and practise the use of statistical methods taught in the lectures in a practical context. They will give the students hands on practice of the freely available statistics software SPSS, which has a whole range of functionalities from basic to advance along with excellent graphing qualities.
- Hair, Joseph F.; Black, William C.; Babin, Barry J.; Anderson, Rolph E. (2019) Multivariate data analysis.
- IBM SPSS Advanced Statistics 21 Guide- SPSS_Advanced_Statistics_21.pdf, http://www.sussex.ac.uk/its/pdfs/SPSS_Advanced_Statistics_21
- Landau, Sabine. (no date) A handbook of statistical analyses using SPSS / Sabine Landau and Brian S. Everitt..
The above list is indicative of the essential reading for the course. The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students. Further reading can be obtained from this module's reading list.
Assessment items, weightings and deadlines
|Coursework / exam
||1440 minutes during Summer (Main Period) (Main)
Module supervisor and teaching staff
Dr Juan Fernandez De Arroyabe Fernandez, email: firstname.lastname@example.org.
Dr Juan Carlos Fernandez de Arroyabe
Dr Ping Zheng
Canterbury Christ Church University
Available via Moodle
Of 22 hours, 20 (90.9%) hours available to students:
2 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
Disclaimer: The University makes every effort to ensure that this information on its Module Directory is accurate and up-to-date. Exceptionally it can
be necessary to make changes, for example to programmes, modules, facilities or fees. Examples of such reasons might include a change of law or regulatory requirements,
industrial action, lack of demand, departure of key personnel, change in government policy, or withdrawal/reduction of funding. Changes to modules may for example consist
of variations to the content and method of delivery or assessment of modules and other services, to discontinue modules and other services and to merge or combine modules.
The University will endeavour to keep such changes to a minimum, and will also keep students informed appropriately by updating our programme specifications and module directory.
The full Procedures, Rules and Regulations of the University governing how it operates are set out in the Charter, Statutes and Ordinances and in the University Regulations, Policy and Procedures.