Postgraduate Course: Decision Analytics (BUST11221)

Course Outline
School	Business School	College	College of Humanities and Social Science
Credit level (Normal year taken)	SCQF Level 11 (Postgraduate)	Availability	Not available to visiting students
SCQF Credits	10	ECTS Credits	5
Summary	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.
Course description	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.

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.

Course Delivery Information
Not being delivered

Learning Outcomes
A. Knowledge and understanding of ż a range of simple but powerful quantitative techniques that can be used to analyse data to support decision making; ż the underlying assumptions, uses and limitations of these techniques; ż 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: ż recognise quantitative techniques suitable for data analysis in particular management situations; ż critically review the collection, presentation and analysis of data to support decision making; ż interpret the results of data analysis to provide insight on the underlying management situation. C. Professional/subject specific/practical skills Students will gain: ż experience of applying statistical and modelling techniques to data sets; ż experience of interpreting the results of data analysis to support decision making; ż 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: ż the use of techniques to collect, present and analyse data; ż working individually and as part of a team to construct and interpret reports on the analysis on management situations; ż the communication of complex technical issues coherently and precisely.