Module Details

Module Code: BUSS C1405
Module Title: Quantitative Techniques 2 - Business Mathematics
Title: Quantitative Techniques 2
Module Level:: 6
Credits:: 5
Module Coordinator: Martin Meagher
Module Author:: Damien Raftery
Module Description: The aim of this module is to develop students’ mathematical and statistical reasoning and skills, including to explore business scenarios using mathematics. Students will be introduced to the areas of investment mathematics, mathematical modelling, probability, normal distribution, and confidence intervals. The module's emphasis on both the conceptual and practical will assist students to confidently and fluently use mathematical and statistical thinking and techniques to enquire using data, solve problems and make better business decisions.
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Solve well-formed problems in investment mathematics by identifying variables, selecting the appropriate formula, applying appropriate mathematical techniques, and presenting the answer in a business context
LO2 Use mathematical functions and equations to represent and explore business problems, and use differentiation to find optimum solutions
LO3 In business scenarios, calculate and interpret probabilities (including involving the normal distribution) and confidence intervals
Module Recommendations

This is prior learning (or a practical skill) that is recommended before enrolment in this module.

No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Additional Requisite Information
No Co Requisites listed
Indicative Content
Investment Mathematics (40%)
Calculate compound interest; Calculate payments and lump sums for sinking funds, annuities and loans; Evaluate investments using Net Present Value and Internal Rate of Return approaches; Depreciate an asset using the straight line and reducing balance methods; Appreciate the role of information technology in investment mathematics
Mathematical Modelling and Differentiation (20%)
Describe mathematical models and functions; Differentiate simple functions; Find maximum and minimum points for functions using differentiation; Create simple business models using functions; Differentiate simple business models to find maximum revenue and profit and minimum cost; Appreciate the role of information technology in graphing and manipulating functions and models
Probability, Normal Distribution and Confidence Intervals (40%)
Recognise and explain randomness; Use the addition and multiplication laws of probability; Interpret contingency tables; Calculate conditional probability; Calculate expected values; Describe a normal distribution, calculate Z scores and find areas above, below or between given values, and determine Z scores from given probabilities, apply the normal distribution to business problems; Describe the sampling distribution; Calculate confidence intervals
Module Content & Assessment
Assessment Breakdown%
Continuous Assessment40.00%
End of Module Formal Examination60.00%


Full Time

Continuous Assessment
Assessment Type Examination % of Total Mark 10
Timing Week 7 Learning Outcomes 1,2
Non-marked No
Assessment Description
Mid-term test
Assessment Type Other % of Total Mark 30
Timing n/a Learning Outcomes 1,2,3
Non-marked No
Assessment Description
Online quizzes
No Project
No Practical
End of Module Formal Examination
Assessment Type Formal Exam % of Total Mark 60
Timing End-of-Semester Learning Outcomes 1,2,3
Non-marked No
Assessment Description
Final Examination
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

SETU Carlow Campus reserves the right to alter the nature and timings of assessment


Module Workload

Workload: Full Time
Workload Type Workload Category Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact No Description Every Week 3.00 3
Independent Learning Non Contact No Description Every Week 6.00 6
Total Weekly Contact Hours 3.00
Workload: Part Time
Workload Type Workload Category Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact No Description Every Week 1.50 1.5
Independent Learning Time Non Contact No Description Every Week 7.50 7.5
Total Weekly Contact Hours 1.50
Module Resources
Recommended Book Resources
  • Burdess, N.. (2010), Starting Statistics, eBook.
Supplementary Book Resources
  • Oakshott, L.. (2020), Essential Quantitative Methods for Business, Management and Finance, 7th/6th. Book/eBook.
  • Luca, M. and Bazerman, M. H.. (2020), The Power of Experiments: Decision Making in a Data-Driven World, eBook.
  • Rumsey, D. J.. (2015), U Can: Statistics For Dummies, eBook.
  • Swift, L. & Piff, S.. (2014), Quantitative Methods: for Business, Management and Finance, 4.
  • Moore D. and Notz W.. (2019), Statistics: Concepts and Controversies, 10th. eBook.
  • Shield, M.. (2020), Statistical Literacy for Decision Makers.
  • Jacques, I.. (2018), Mathematics for Economics and Business, 9th. eBook.
  • MacInnes, J.. (2019), See numbers in data.
  • Kara, H.. (2019), Write a questionnaire.
  • Nussbaumer Knaflic, C.. (2015), Storytelling with Data: A Data Visualization Guide for Business Professionals, eBook.
  • Nussbaumer Knaflic, C.. (2019), Storytelling with Data: Let's Practice!, eBook.
  • Best, J.. (2013), Stat-Spotting : A Field Guide to Identifying Dubious Data, eBook.
  • Spiegelhalter, D.. (2019), The art of statistics: learning from data.
  • Rosling, H.. (2018), Factfulness: Ten Reasons We're Wrong About The World - And Why Things Are Better Than You Think.
  • Kahneman, D.. (2012), Thinking, fast and slow.
  • Blastland, M. & Wilnot, A.. (2007), The Tiger that Isn’t London.
  • Paulos, J. A.. (1996), A mathematician reads the newspaper.
This module does not have any article/paper resources
Other Resources
Discussion Note: