The aim of the module is to provide non-specialist mathematicians with an introduction and grounding on mathematical and statistical tools necessary for quantitative analysis within business, economic and accounting environments.

A    Knowledge and Understanding:

• display a sound understanding of certain mathematical and statistical methods to enable a broad range of data analysis approaches.
• appreciate the value and completeness of these mathematical and statistical methods in helping to understand varying business/economic activity.
• display the progression of intellectual development beyond analysis to synthesis and evaluation.

B    Intellectual Skills: 

• acquire the necessary grounding in the theory, concepts, assumptions and techniques of the varying methods.

C    Discipline Specific Skills: 

• display the ability to appreciate and assess empirical work, and therefore learn to employ the correct technique for specific problems. 

D    Transferable Skills: 

• display transferable subject-specific and core skills.
• understanding the progression from problem formulation to solution identification. 

The formal timetabled components of the module comprise 34 hours of lectures and 8 hours of tutorials. The lecture programme is supported by detailed handouts, which strive to challenge and empower students to attempt tutorial questions, which are closely integrated to the lecture material. Students are supported throughout the module through the provision of regular surgeries (18-20 hours per year), advertised weekly student-lecturer private consultation, revision sessions and e-mail student-lecturer contact.

Indicative study hours:   200

The examinations contain a range of questions designed to cover the learning outcomes for the module and to test skill development. Questions set are not only designed to test students’ basic knowledge and comprehension of the syllabus, but also to assess their ability to apply such knowledge in particular contexts. Questions require a combination of numerical and written answers, which test students’ development of intellectual, communication, numeric and reasoning skills, as well as subject-specific knowledge.

A varied range of tutorial, examination-standard, questions are designed to stimulate independent learning and to provide an ongoing mechanism for assessing the extent to which students are meeting the module’s learning outcomes. Students are encouraged throughout the module to submit queries, etc., to the module co-ordinator for discussion and where appropriate, marking.

Functions, first and second derivatives. Applications including elasticity of demand. Partial differentiation, method of Lagrange multipliers. Series, including application within Investment Appraisal. Matrices, including applications to solving simultaneous equations and Lieontief input-output analysis. Linear programming, including shadow prices and sensitivity of the solution. Summarising information, including percentage frequency and cumulative frequency tables. Graphical presentation of data including Bar chart, Histogram. Summary Statistics for raw and grouped data including measures of central tendency. Probability, including binomial, Poisson and normal distributions. Central Limit Theorem, including the sampling distribution of percentages, point and confidence interval estimation. Hypothesis testing. Correlation and Regression including significance of rank correlation.

Anderson, Sweeney, Williams, Freeman, and Shoesmith (2007) Statistics for Business and Economics, West, Saint Paul, MN.

Curwin, J. and Slater, R. (2001) Quantitative Methods for Business Decisions, Thomson Learning.

Waters, D. (2001) Quantitative Methods for Business, Financial Times/Prentice Hall.