Postgraduate Course: Decision Analytics (BUST11221)
Course Outline
| School | Business School |
College | College of Humanities and Social Science |
| Course type | Standard |
Availability | Not available to visiting students |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Credits | 10 |
| Home subject area | Business Studies |
Other subject area | None |
| Course website |
None |
Taught in Gaelic? | No |
| Course description | Managers must be able to collect, present and analyse data of different types in order to make well-informed, effective decisions. Managers must also be able to cope with situations involving uncertainty or incomplete information. This course will introduce a series of quantitative techniques for data analysis and explain how these can be used to support decisions. The aim of the course is to provide an understanding of the underlying principles of each technique rather than a simple recipe for their application or a thorough grounding in the relevant mathematical theory. The course will enable students to recognise which technique is appropriate for a particular situation, to interpret the results of data analysis properly, and to challenge the assumptions underpinning a data analysis report. |
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | For Business School PG students only, or by special permission of the School. Please contact the course secretary. |
| Additional Costs | None |
Course Delivery Information
|
| Delivery period: 2013/14 Semester 2, Not available to visiting students (SS1)
|
Learn enabled: Yes |
Quota: None |
Web Timetable |
Web Timetable |
| Course Start Date |
13/01/2014 |
| Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 20,
Seminar/Tutorial Hours 8,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
68 )
|
| Additional Notes |
|
| Breakdown of Assessment Methods (Further Info) |
Written Exam
70 %,
Coursework
30 %,
Practical Exam
0 %
|
| Exam Information |
| Exam Diet |
Paper Name |
Hours:Minutes |
|
|
| Outwith Standard Exam Diets April | | 2:00 | | |
Summary of Intended Learning Outcomes
A. Knowledge and understanding of
¿ a range of simple but powerful quantitative techniques that can be used to analyse data to support decision making;
¿ the underlying assumptions, uses and limitations of these techniques;
¿ the practical use of statistical and modelling techniques in management decision making.
B. Intellectual skills
On completion of the course students should be able to:
¿ recognise quantitative techniques suitable for data analysis in particular management situations;
¿ critically review the collection, presentation and analysis of data to support decision making;
¿ interpret the results of data analysis to provide insight on the underlying management situation.
C. Professional/subject specific/practical skills
Students will gain:
¿ experience of applying statistical and modelling techniques to data sets;
¿ experience of interpreting the results of data analysis to support decision making;
¿ experience of using widely available computer software packages such as spreadsheets for data analysis.
D. Transferable skills
During the course students will develop skills in:
¿ the use of techniques to collect, present and analyse data;
¿ working individually and as part of a team to construct and interpret reports on the analysis on management situations;
¿ the communication of complex technical issues coherently and precisely.
|
Assessment Information
| Assessment of the course will be through an open book exam (weighted 70%) and an individual assignment (weighted 30%). The degree exam will be in the MBA March diet of exams. |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
1. Data availability, collection and summary
Sources and types of data, descriptive statistics and methods of displaying data.
2. Probability and distributions
Basic probability concepts, discrete and continuous probability
distributions, examples of probability distributions.
3. Sampling and estimation
Sampling methods, sampling distributions, estimators and confidence intervals.
4. Hypothesis testing
Concepts of hypothesis testing, formulation of hypotheses, tests for means and proportions.
5. Further hypothesis testing
Analysis of variance (ANOVA) and chi-square test for independence.
6. Correlation and simple linear regression
Understanding and measuring correlation, concepts of regression, simple linear regression, interpreting regression analysis.
7. Decisions, uncertainty and risk
Decision modelling, decision making under uncertainty and risk, selection of decision criterion, measuring and managing risk (including value-at-risk (VaR)).
8. Regression modelling
Multiple linear regression, interpreting results from multiple linear regression, building regression models.
9. Simple forecasting models
Judgemental methods, moving average and smoothing models, forecast accuracy, discussion of practical issues.
10. Monte-Carlo simulation
Concepts of a simulation model, sampling from probability distributions, Monte-Carlo simulation.
|
| Transferable skills |
Not entered |
| Reading list |
Not entered |
| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords | MBA DecisionA |
Contacts
| Course organiser | Dr Maurizio Tomasella
Tel:
Email: Maurizio.Tomasella@ed.ac.uk |
Course secretary | Miss Kate Ainsworth
Tel: (0131 6)51 3854
Email: Kate.Ainsworth@ed.ac.uk |
|
© Copyright 2013 The University of Edinburgh - 10 October 2013 3:46 am
|
University Homepage