Registry

Module Specifications

Current Academic Year 2012 - 2013
Please note that this information is subject to change.

Module Title	Quantitative Analysis for Business Decisions
Module Code	CA200
School	School of Computing
Online Module Resources
Module Co-ordinator	Semester 1: Heather Ruskin Semester 2: Heather Ruskin Autumn: Heather Ruskin
Module Teacher	Heather Ruskin
NFQ level	8	Credit Rating	5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

Description

This module introduces students to the basic tools of statistical and OR quantitative techniques. Students are introduced to different data types and will be able to identify the methods of analysis for these data. In addition, concepts of probability are introduced enabling students to analyse problems of decision making under risk. Linear regression applications are introduced by way of problem formulation and solution using graphical methods and analysis. Elementary methods of analysis statistical inference in management are also included. Introduction to linear programming and to some other OR areas such as queue theory, inventory control or PERT/CPM.

Learning Outcomes

1. Calculate probabilities of events and calculate expected values
2. Identify different data types
3. Summarise and present data sets
4. Identify optimal strategies under risk
5. Apply Binomial, geometric and normal distributions in straightforward situations.
6. Formulate simple linear regression applications.
7. Obtain point and interval estimates of population parameters
8. Use "SPSS" or "R" in description and analysis of business data.
9. Formulate simple linear programming problems and solve them graphically; be able to apply a tool such as "ampl" to set up and solve linear programming problems.
10. Apply to simple problems some OR methods and results from such areas as queue theory, inventory control and PERT/CPM.

Workload	Full-time hours per semester
*Type*	*Hours*	*Description*
Lecture	24	2 hours a week
Tutorial	12	1 hour a week
Laboratory	12	1 hour a week
Independent learning	48	2 hours for each lecture
Independent learning	30	project work
Total Workload: 126

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

None

Assessment Breakdown
Continuous Assessment	25%	Examination Weight	75%

Course Work Breakdown
Type	Description	% of total	Assessment Date
Assignment	Laboratory sessions are scheduled for this module and some of these will be used to test proficiency in software tool use (e.g. SPSS or R, ampl). More extended project work will also be assigned, including preparation of report(s) and class presentation(s).	25%	n/a

Reassessment Requirement
Resit arrangements are explained by the following categories; 1 = A resit is available for all components of the module 2 = No resit is available for 100% continuous assessment module 3 = No resit is available for the continuous assessment component
This module is category 3

Indicative Reading List

Jane M Horgan: 2009, Probability with "R" an Introduction with Computer Science applications, First, All, Wiley, New York,
Mark Berenson and David Levine: 1996, Basic Business Statistics, Prentice hall,
Peter Delgaard: 2008, Introductory Statistics with "R", Second, All, Springer,
Terry Lucey: 2002, Quantitative techniques, Sixth, Continuum, London, 0-8264-5854-8
Robert Fourer, David M. Gay, Brian W. Kernighan: 2003, AMPL, First few chapters, Thomson/Brooks/Cole, Pacific Grove, CA, 0534388094
Hamdy A. Taha: 1997, Operations research, Prentice Hall, Upper Saddle River, N.J., 0132729156

Other Resources

None

Array

Programme or List of Programmes

BSSA

Study Abroad (DCU Business School)

BSc in Enterprise Computing

SHSA

Study Abroad (Science & Health)

Timetable this semester: Timetable for CA200

Date of Last Revision

20-JAN-12