Module: Quantitive Analysis for Business Decisions

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Quantitive Analysis for Business Decisions
Module Code	CA3000 (ITS) / CSC1041 (Banner)
Faculty	Engineering & Computing	School	Computing
Module Co-ordinator	Claudia Mazo
Module Teachers	Martin Crane
NFQ level	8	Credit Rating	5
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

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 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.

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	24	2 hours a week
Laboratory	12	1 hour a week
Independent Study	59	study of lecture material and exam preparation
Independent Study	30	project 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 Assessment	25%	Examination Weight	75%

Type	Description	% of total	Assessment Date
Course Work Breakdown
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 Type
Resit arrangements are explained by the following categories: Resit category 1: A resit is available for both* components of the module. Resit category 2: No resit is available for a 100% continuous assessment module. Resit category 3: No resit is available for the continuous assessment component where there is a continuous assessment and examination element.
* ‘Both’ is used in the context of the module having a Continuous Assessment/Examination split; where the module is 100% continuous assessment, there will also be a resit of the assessment
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

None