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Statistics and Mathematics for Business Analytics

Module description

This module will cover a range of mathematical methods that are used in business analytics, including key principles in statistics, econometrics, probability and algebra. These will form the foundation of analytical methods that you will explore and apply in later modules.

Full module specification

Module title:Statistics and Mathematics for Business Analytics
Module code:BEMM460
Module level:M
Academic year:2023/4
Module lecturers:
  • Professor Stephen Disney - Lecturer
Module credit:15
ECTS value:



This module is closed to MSc Business Analytics only

Duration of module: Duration (weeks) - term 1:


Duration (weeks) - term 2:


Duration (weeks) - term 3:


Module aims

The module aims to enhance your ability to understand the math and statistics behind analysing a business problem. The student will be able to observe and interpret mathematical concepts in business and economics literature as well as to prepare a business/consulting report with the appropriate mathematical and statistical techniques.

ILO: Module-specific skills

  • 1. P1: Demonstrate knowledge and understanding of fundamental, and domain-specific, analytics methods and tools.
  • 2. P5: Create, manage, interrogate, interpret and visualise data from a wide range of different sources, types and including structured and unstructured forms.

ILO: Discipline-specific skills

  • 3. P6: Critically analyse the use of data within a business context, identifying strengths and limitations.
  • 4. P7: Critically analyse and interpret relevant academic, technical and industry literature.

ILO: Personal and key skills

  • 5. P14: Technological and digital literacy: Our graduates are able to use technologies to source, process and communicate information.

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled learning and teaching activities11Lectures (1 hour per lecture)
Scheduled learning and teaching activities92 hours tutorial at week 2,4,7,9, 1 hour tutorial at week 11
Pre-Independent Study52Preparatory reading prior to workshops and lectures
Post-Independent Study78Practice use of software and concepts from additional exercises and examples

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
In Class ExercisesDuring class hours1-3Verbal

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Examination1002 hours duration1-5Electronic, written comments

Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Examination Examination Resit (2 hours) (100%)1-5Summer re-assessment period

Re-assessment notes

Re-assessment will be in nature to the original assessment, but the topic, data, and materials must be new.


Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a reassessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to re-take some or all parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%.

Syllabus plan

The following content will be covered during the course:


Lecture 1: Introduction to Statistics and R

Lecture 2: Probability, Venn Diagrams, Bays theorem

Lecture 3: Discrete Probability Distribution

Lecture  4: Continuous Probability Distribution

Lecture  5: Point Estimation and Sampling Distribution

Lecture  6: Confidence Interval Estimation

Lecture  7: Hypothesis Testing

Lecture  8: Linear Regression

Lecture  9: Forecasting methods

Indicative learning resources - Basic reading

The following content may be useful references for this course:


  • Anderson, D, et al. (2020). Statistics for Business and Economics, 5th Edition, Cengage.
  • Stinerock, R. (2018). Statistics with R: A beginner's guide. Sage.

Module has an active ELE page?


Indicative learning resources - Web based and electronic resources

The following websites may also be useful:

Origin date


Last revision date