Module Code: |
H7BIS |
Long Title
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Business Intelligence & Statistics
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Title
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Business Intelligence & Statistics
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Module Level: |
LEVEL 7 |
EQF Level: |
6 |
EHEA Level: |
First Cycle |
Module Coordinator: |
MICHAEL BANE |
Module Author: |
CORINA SHEERIN |
Departments: |
School of Business
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Specifications of the qualifications and experience required of staff |
No special specifications. Programme level specifications apply.
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Learning Outcomes |
On successful completion of this module the learner will be able to: |
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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. |
Dependencies |
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).
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No recommendations listed |
Co-requisite Modules
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No Co-requisite modules listed |
Module Content & Assessment
Indicative Content |
Introduction (Week 1)
• Definition and role of statistics
• Descriptive vs. Inferential Statistics
• Types of data and scales of measurement
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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
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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
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Correlation & Regression (Week 4-5)
• Correlation Coefficient
• Calculating the covariance and correlation between two variables
• Coefficient of Determination
• Introduction to Regression Analysis
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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)
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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
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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
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Assessment Breakdown | % |
Coursework | 100.00% |
AssessmentsFull Time
Coursework |
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. |
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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. |
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No End of Module Assessment |
Reassessment Requirement |
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
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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 |
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Lind D.A., Marchal W.G., and Wathen S.A... (2021), Statistical Techniques in Business and Economics, 18th ed.. McGraw Hill.
| Supplementary Book Resources |
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Luiz Moutinho, Fiona Davies and Mark Goode.. (2020), Quantitative analysis in marketing management, John Wiley & Sons, Chichester.
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Sonia Taylor. (2007), Business statistics for non-mathematicians, Basingstoke, Hampshire ; Palgrave Macmillan.
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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 |
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This module does not have any other resources |
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