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Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title Quantitive Analysis for Business Decisions
Module Code CA3000
School School of Computing
Module Co-ordinatorSemester 1: Claudia Mazo
Semester 2: Claudia Mazo
Autumn: Claudia Mazo
Module TeachersMartin Crane
Claudia Mazo
NFQ level 8 Credit Rating 5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None

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 is included as well.

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.

Workload Full-time hours per semester
Type Hours Description
Lecture242 hours a week
Laboratory121 hour a week
Independent Study59study of lecture material and exam preparation
Independent Study30project work
Total Workload: 125

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

Assessment Breakdown
Continuous Assessment25% Examination Weight75%
Course Work Breakdown
TypeDescription% of totalAssessment Date
AssignmentLaboratory 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 Type
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

  • Terence Lucey: 2002, Quantitative Techniques, Burns & Oates, 0-8264-5854-8
  • Jane M Horgan: 2009, Probability with "R" an Introduction with Computer Science applications, First Ed., All, Wiley New York,
  • Mark Berenson and David Levine: 1996, Basic Business Statistics, Prentice hall,
  • Robert Fourer,David M. Gay,Brian W. Kernighan: 2003, AMPL, Duxbury Press, 0534388094
  • Hamdy A. Taha,Turkey: 0, Loi relative à la Banque centrale de la République de Turquie, 0132729156
Other Resources

Programme or List of Programmes
COMBUSBSc in Computing for Business
ECBSc in Enterprise Computing
ECSAStudy Abroad (Engineering & Computing)
ECSAOStudy Abroad (Engineering & Computing)

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