Module Code: H7BIS
Long Title Business Intelligence & Statistics
Title Business Intelligence & Statistics
Module Level: LEVEL 7
EQF Level: 6
EHEA Level: First Cycle
Credits: 10
Module Coordinator: MICHAEL BANE
Departments: School of Business
Specifications of the qualifications and experience required of staff

No special specifications. Programme level specifications apply. 

Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Recognise different types of data and hence summarise and present information in a useful and informative manner using appropriate graphics and statistical measures.
LO2 Synthesise, evaluate and interpret relationships between two variables through the use of correlation and regression analysis and apply this knowledge within marketing and business contexts.
LO3 Be proficient in the principles and application of statistical inference and apply this knowledge in developing conclusions about populations based on sample results.
LO4 Select and apply probability distributions to utilise within various scenarios and compute probabilities based on practical situations and problems.
LO5 Demonstrate a comprehensive understanding of statistical principles, theories and methods and appreciate how they apply in a range of marketing and business decision making situations.
LO6 Summarise and communicate statistical findings in both a technical and non-technical manner as appropriate to the business scenario.
LO7 Work independently and/or as part of a multidisiplinary team in order to select appropriate quantitative tools and hence utilise statistical findings in an integrative manner.
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).

No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements  

Module Content & Assessment

Indicative Content
Introduction (Week 1)
• Definition and role of statistics • Descriptive vs. Inferential Statistics • Types of data and scales of measurement
Graphical Presentation of Data (Week 2-3)
• Frequency tables and frequency distributions • Graphical presentation of qualitative data • Graphical presentation of quantitative data • Relationship between two variables: contingency tables and scatter diagrams
Numerical Summary of Data (Week 3-4)
• Measures of central tendency – mean, median, mode, geometric mean • Measures of dispersion – range, mean deviation, population and sample variance and standard deviation • Interpretation and uses of the standard deviation • Skewedness
Correlation & Regression (Week 4-5)
• Correlation Coefficient • Calculating the covariance and correlation between two variables • Coefficient of Determination • Introduction to Regression Analysis
Probability (Week 6-8)
• The concept and language of probability • The role of probability in statistics • Approaches to assigning probabilities • Rules of addition and multiplication for computing probability • Conditional probability • Principles of counting (permutation and combination formulas)
Probability Distributions (Week 8-10)
• The concept of probability distribution • Random variables • Mean, variance and standard deviation of a probability distribution, the concept of expected value • Binomial probability distribution • Normal probability distribution • Standardisation and probabilities under a normal curve
An Introduction to Statistical Inference (Week 10-12)
• Sampling methods • Sampling distribution of the sample mean • Central Limit Theorem • Point estimates and confidence intervals for a mean
Assessment Breakdown%


Full Time

Assessment Type: Project % of total: 50
Assessment Date: n/a Outcome addressed: 1,2,3,4,5,6,7
Non-Marked: No
Assessment Description:
Learners will be presented with a data set and/or case study which is set within a marketing context. This module assessment is integrated with the Market Research module in order to highlight the cross over and integrative nature of marketing research and statistical tools and techniques. Learners will be expected to summarise the data graphically and statistically and must undertake a number of prescribed tests on the data. A number of questions will be presented to the learner and they will be expected to evaluate, combine and synthesise the information and develop and present a detailed report of the findings.
Assessment Type: Continuous Assessment % of total: 50
Assessment Date: n/a Outcome addressed: 1,2,3,4,5,6
Non-Marked: No
Assessment Description:
Learners will be given two in class assessments/problem sets which address four key aspects of the module curriculum: graphical representation of data plus correlation and regression and probability plus probability distributions,. The in class assessments/problem sets may include a mix of: short answer questions, multiple choice, vignettes and or problem based questions. All questions presented to students will be within a marketing context.
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

NCIRL reserves the right to alter the nature and timings of assessment


Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture No Description 36 Per Semester 3.00
Tutorial Mentoring and small-group tutoring 12 Per Semester 1.00
Independent Learning No Description 202 Per Semester 16.83
Total Weekly Contact Hours 4.00

Module Resources

Recommended Book Resources
  • Lind D.A., Marchal W.G., and Wathen S.A... (2021), Statistical Techniques in Business and Economics, 18th ed.. McGraw Hill.
Supplementary Book Resources
  • Luiz Moutinho, Fiona Davies and Mark Goode.. (2020), Quantitative analysis in marketing management, John Wiley & Sons, Chichester.
  • Sonia Taylor. (2007), Business statistics for non-mathematicians, Basingstoke, Hampshire ; Palgrave Macmillan.
  • Jason S. Wrench... [et al.].. (2008), Quantitative research methods for communication, Oxford University Press, New York.
This module does not have any article/paper resources
This module does not have any other resources
Discussion Note: