BUU11530 Quantitative Methods for Business Assignment Brief 2026 | TCD BUU11530 Quantitative Methods for
BUU11530 Quantitative Methods for Business Assignment Brief 2026 | TCD
BUU11530 Quantitative Methods for Business
| Module code | BUU11530 |
| Module title | Quantitative Methods for Business |
| Academic year | 2026/27 |
| ECTS | 10 |
| Lecturer(s) | Dr. Diego Pérez Guisande |
| TBC | |
| Office hours | TBC |
Module Description
This module introduces the mathematical and statistical analysis that supports evidence-based business decision-making. Students learn how to analyse data, make statistical inferences, and use quantitative evidence to inform managerial choices.
The course combines conceptual foundations with practical application. Exercises, case material, and real-world data are used to connect quantitative methods with business problems in finance, economics, management, and related professional contexts.
Across the two semesters, students move between theory and hands-on analysis. Excel is the main software platform for the module, with occasional discussion of other relevant statistical tools and workflows.
Learning and Teaching Approach
The module is delivered through one 2-hour lecture block and one 1-hour tutorial each week.
Tutorials support the lecture material through problem practice, applied exercises, and revision of mathematical foundations needed for the module and the wider programme.
BUU11530 Learning Outcomes
The module prepares students for business and management contexts where data analysis is central to decision-making. After successfully completing this module, students should be able to:
- Explain the role of data-driven insight in real-world business situations.
- Clean, organise, summarise and analyse structured business datasets using descriptive and exploratory techniques
- Apply relevant mathematical concepts to business scenarios.
- Use Excel to generate insight from real-world datasets.
- Analyse and apply key statistical concepts in business settings.
- Identify challenges and limitations in data-driven decision-making.
Workload
| Content | Indicative hours |
| Lecturing hours | 40 |
| Tutorials | 20 |
| Preparation for lectures and tutorials | 50 |
| Reading of assigned materials and active reflection on lecture and course content and linkage to personal experiences | 40 |
| In-class exercises preparation | 50 |
| End-term exam preparation | 50 |
| Total | 250 |
Recommended Texts and Required Readings
I recommend the following core textbooks for the course, which will be broadly followed throughout the course.
- Levine, Stephan and Szabat. Statistics for Managers Using Microsoft Excel. Global edition. Pearson.
- Jacques, Ian. Mathematics for Economics and Business. Pearson Education, 2006.
General Supplemental Readings
Students may consult additional foundational statistics texts through the library and other appropriate sources. Supplemental material may also be provided through Blackboard during the year.
- Oakshott, Les. Essential Quantitative Methods. Red Globe Press/Macmillan International
- Lee and Peters. Business Statistics Using Excel and SPSS. Sage, with STATLAB, 1st ed., 2016.
- Veal. Business Research Methods: A Managerial Approach. Pearson. Multiple copies are available in the TCD Library.
- Gujarati, Damodar N., and Dawn C. Porter. Basic Econometrics. New York: McGraw-Hill Irwin, 2009.
Student Preparation for the Module
- Attendance Policy
- Students are expected to attend all lectures and tutorials. Medical absences should be communicated to the instructors as early as possible.
Preparation - Students should complete assigned readings before class and allocate sufficient time outside class for revision, preparation, and assessment work, consistent with the workload guidance above. You will be given practice problems to solve during tutorials and in your own time. These problems will resemble exam questions. The solutions will be posted and you can discuss the problems during the office hours of the lecturer and/or tutor.
Course Communication
Course-related email communication must be sent from official TCD email addresses.
Assessment
The module will be assessed in three parts.
| Component | Weight | Description |
| In-class exercises | 40% | Four individual in-class exercises, each worth 10%. These may take the form of quizzes or problem-solving exercises and are completed and submitted in class. |
| End of semester 1 final exam | 30% | A 2-hour exam with subjective, conceptual, and methodological questions. Students apply techniques from class and justify methodological choices using the statistical theory covered. |
| End of semester 2 final exam | 30% | A 2-hour end-term assessment with subjective, conceptual, and methodological questions based on business data. Students apply second-semester techniques, derive insights, and justify their choices. |
Your final grade will be calculated as follows: four in-class exercises worth 10% each, the Semester 1 exam worth 30%, and the Semester 2 exam worth 30%.
Reassesment
Students who fail the module must sit a supplemental examination during the supplemental examination period. Students who have failed only one semester will be required to sit the examination for that semester only. Students who have failed both semesters will be required to sit both examinations. Each supplemental examination will count for 100% of the grade for the relevant semester.
Additional information about exam protocol will be provided closer to the relevant due dates.
Assessment Schedule
| Assessment |
Timing Weight Notes |
|||||
| In-class exercise 1 |
During Week 5 – Semester 1 Lecture |
10% | Laptop Required | |||
| In-class exercise 2 |
During Week 10 – Semester 1 Lecture |
10% | Laptop Required | |||
| In-class exercise 3 |
During Week 4 – Semester 2 Lecture |
10% | Laptop Required | |||
| In-class exercise 4 |
During Week 11 – Semester 2 Lecture |
10% | Laptop Required | |||
| Semester 1 final exam | End of semester 1 | 30% | 2 hours | |||
| Semester 2 final exam | End of semester 2 | 30% | 2 hours | |||
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Teaching Schedule Semester 1 – Michaelmas Term |
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Session |
Date |
Lecture topic |
Tutorial / preparation |
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1 |
TBD |
Introduction & Linear Equations |
TBD |
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2 |
TBD |
Linear Equations & Non-Linear Equations |
TBD |
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3 |
TBD |
Non-Linear Equations & Logs, Growth and Compounding |
TBD |
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4 |
TBD |
Systems of Equations and Matrices |
TBD |
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|
5 |
TBD |
In-Class Exercise I |
TBD |
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6 |
Reading Week |
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7 |
TBD |
Data Description and Summarization |
TBD |
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8 |
TBD |
Measures of central tendency & Dispersion |
TBD |
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|
9 |
TBD |
Introduction to Probability |
TBD |
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10 |
TBD |
In-Class Exercise II |
TBD |
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|
11 |
TBD |
Revision |
TBD |
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Semester 2 – Hilary Term
| Session | Date | Lecture topic | Tutorial / preparation |
| 1 |
Probability Recap & Discrete Probability Distributions |
TBD | |
| 2 | TBD | Continuous Probability Distributions | TBD |
| 3 | TBD | Sampling Distributions |
TBD |
| 4 | TBD | In-Class Exercise III | TBD |
| 5 | TBD |
Confidence Intervals |
TBD |
| 6 | TBD | Hypothesis Testing – One Sample Test | TBD |
| 7 | Reading Week |
|
|
| 8 | TBD | Hypothesis Testing – Two Sample Test | TBD |
| 9 | TBD | Correlations and the Linear Regression |
TBD |
| 10 | TBD | Simple & Multiple Linear Regression | TBD |
| 11 | TBD | In-Class Exercise IV | TBD |
| 12 | TBD | Regression Diagnostics & Revision | TBD |
Biographical Note
Diego Pérez Guisande is an Assistant Professor in Finance at Trinity Business School. He worked before as a Lecturer in Accounting & Finance at the University of Sussex and holds a PhD in Banking and Finance from University College Dublin (UCD). His research focuses on firm reputation, shareholder engagement, climate transition valuation, NGO activism and corporate lobbying. His research is quantitative and contributes to the fields of institutional investors, stakeholders and data providers in sustainable finance. Diego has published in the Journal of Business Ethics and Organization Studies and has presented his research at international academic conferences such as the European Finance Association
