Postgraduate Course: Decision Analytics (BUST11221)
||College||College of Humanities and Social Science
||Availability||Not available to visiting students
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
|Home subject area||Business Studies
||Other subject area||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)
||Other requirements|| None
|Additional Costs|| None
Course Delivery Information
|Delivery period: 2011/12 Semester 2, Not available to visiting students (SS1)
||WebCT enabled: Yes
|No Classes have been defined for this Course|
||First class information not currently available|
|No Exam Information
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 of the course will be through an exam (weighted 70%) and an assignment (weighted 30%). The degree exam will be in the MBA March diet of exams.|
||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.
|Course organiser||Dr Anthony Kinder
Tel: (0131 6)51 3858
|Course secretary||Mr Stuart Mallen
Tel: (0131 6)50 8071
© Copyright 2011 The University of Edinburgh - 16 January 2012 5:43 am