Statistics and Mathematics for Business Analytics
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|
This module is closed to MSc Business Analytics only
|Duration of module:||
Duration (weeks) - term 1: |
11Duration (weeks) - term 2:
0Duration (weeks) - term 3:
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 Activities||Guided independent study||Placement / study abroad|
Details of learning activities and teaching methods
|Category||Hours of study time||Description|
|Scheduled learning and teaching activities||11||Lectures (1 hour per lecture)|
|Scheduled learning and teaching activities||9||2 hours tutorial at week 2,4,7,9, 1 hour tutorial at week 11|
|Pre-Independent Study||52||Preparatory reading prior to workshops and lectures|
|Post-Independent Study||78||Practice use of software and concepts from additional exercises and examples|
|Form of assessment||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|In Class Exercises||During class hours||1-3||Verbal|
Summative assessment (% of credit)
|Coursework||Written exams||Practical exams|
Details of summative assessment
|Form of assessment||% of credit||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|Examination||100||2 hours duration||1-5||Electronic, written comments|
Details of re-assessment (where required by referral or deferral)
|Original form of assessment||Form of re-assessment||ILOs re-assessed||Timescale for re-assessment|
|Examination||Examination Resit (2 hours) (100%)||1-5||Summer re-assessment period|
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%.
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:
- Official page for R software: http://www.r-project.org
- Download page: http://www.cran.r-project.org
- Official page for R studio: http://rstudio.com
- Download page for RStudio Desktop: https://rstudio.com/products/rstudio/download/#download
Last revision date