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Module ASB-2110:
Statistical Methods

Module Facts

Run by Bangor Business School

10 Credits or 5 ECTS Credits

Semester 1

Organiser: Dr Alan Thomas

Overall aims and purpose

AIMS: The module will introduce students to the ideas of probability and statistical inference, and demonstrate the application of those ideas. The overall aim is to provide a good foundation in statistical methods and techniques.

Course content

Hypothesis tests; Type I and Type II errors; Level of significance; Correlation and causality; Linear Regression model; Ordinary Least Squares estimation; Testing the significance of a regression.

Assessment Criteria


A basic knowledge of course material.


In addition to the above, an ability to write analytically on specific issues.


In addition to the above, the ability to illustrate and enhance arguments and analyses through the use of relevant supporting evidence drawn from the established literature.

Learning outcomes

  1. Demonstrate an understanding of basic statistical concepts.

  2. Understand basic ideas of sampling and statistical inference.

  3. Formulate and test hypotheses.

  4. Carry out investigations using summary statistics, graphs, techniques and exploratory data analysis, correlation and regression.

  5. Analyse data, and intepret the results.

Assessment Methods

Type Name Description Weight
CLASS TEST Online Test

A series of three online tests, with best score recorded

EXAM Exam S1 2hrs 75

Teaching and Learning Strategy


One 2-hour lecture per week;


One 1-hour tutorial per fornight.

Private study 76

Transferable skills

  • Numeracy - Proficiency in using numbers at appropriate levels of accuracy
  • Self-Management - Able to work unsupervised in an efficient, punctual and structured manner. To examine the outcomes of tasks and events, and judge levels of quality and importance
  • Critical analysis & Problem Solving - Able to deconstruct and analyse problems or complex situations. To find solutions to problems through analyses and exploration of all possibilities using appropriate methods, rescources and creativity.
  • Self-awareness & Reflectivity - Having an awareness of your own strengths, weaknesses, aims and objectives. Able to regularly review, evaluate and reflect upon the performance of yourself and others

Subject specific skills

  • Analysis, deduction and induction. Economic reasoning is highly deductive, and logical analysis is applied to assumption-based models. However, inductive reasoning is also important. The development of such analytical skills enhances students' problem-solving and decision-making ability.
  • Quantification and design. Data, and their effective organisation, presentation and analysis, are important in economics. The typical student will have some familiarity with the principal sources of economic information and data relevant to industry, commerce, society and government, and have had practice in organising it and presenting it informatively. This skill is important at all stages in the decision-making process.
  • Problem solving and critical analysis: analysing facts and circumstances to determine the cause of a problem and identifying and selecting appropriate solutions.
  • Numeracy: the use of quantitative skills to manipulate data, evaluate, estimate and model business problems, functions and phenomena.
  • Articulating and effectively explaining information.
  • Self-management: a readiness to accept responsibility and flexibility, to be resilient, self-starting and appropriately assertive, to plan, organise and manage time.


Talis Reading list

Reading list

Newbold, P., Carlson, W. and Thorne, B. (2012). Statistics for Business and Economics, 8th ed. Pearson.

Pre- and Co-requisite Modules

Courses including this module

Compulsory in courses:

Optional in courses: