Module Code: H7BSADM
Long Title Business Statistics and Analytics for Decision Making
Title Business Statistics and Analytics for Decision Making
Module Level: LEVEL 7
EQF Level: 6
EHEA Level: First Cycle
Credits: 5
Module Coordinator: COLETTE DARCY
Module Author: Isabela Da Silva
Departments: School of Business
Specifications of the qualifications and experience required of staff  
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Appraise the use of statistics and business analytics in cross functional and holistic decision making within a variety of business and management contexts.
LO2 Critique and apply statistical and analytical techniques in modelling business problems and developing conclusions about populations based on sample results.
LO3 Synthesise data and analyse business problems under conditions of uncertainty, formulate null and alternative hypotheses and exercise judgement in the resolution of business problems using hypothesis testing.
LO4 Evaluate and interpret relationships between variables through the use of correlation and regression analysis.
LO5 Use appropriate software in the application and interpretation of statistical methods and techniques and present findings/output in a professional and technical or nontechnical manner as required.
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).
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

As per programme requirements (outlined in 4.2.2 Minimum requirements for general learning)     
 

Module Content & Assessment

Indicative Content
Introduction
Role and Evolution of Statistics and Analytics in Business Decision Making Sources of Statistical Data ‘Big’ Data and Business Intelligence Population and Sample Data
Using Samples to Make Decisions
Sampling Methods What is a Representative Sample? Introducing and Building a Probability Distribution The Central Limit Theorem Point estimates and confidence intervals for a mean Sampling and Error Sample Application of Content: Using sample data to make estimates about the population. For example, marketing data from loyalty club cards have been obtained. The average spending on product X as well as the variation on product X spending by 100 customers is estimated and inferences drawn concerning the average population spends and variances and how these statistics influence decision making on stock levels, pricing etc.
Probability Distributions
Binomial probability distribution Normal probability distribution Standardisation and probabilities under a normal curve Sample Application of Content: Using data on salary payments in company X to construct an appropriate distribution to represent the data. Assuming the data is normally distributed, demonstrate understanding of the process of standardisation and calculate probabilities using the standard normal distribution.
Hypothesis Testing
Introduction to Hypothesis Testing Hypothesis Testing Procedures One Sample Tests of Hypothesis The Student T Distribution Sample Application of Content: Selecting from a range of hypothesis tests to check the validity of a business statement(s) about a population parameter. For example candidates may be provided with a benchmark figure concerning hospital response rates by doctors in the surgical department and asked to test whether sample data supports the benchmark
Correlation & Regression
Correlation & Covariance Coefficient Measuring and Interpreting the Correlation and Covariance coefficients Introduction to Regression Analysis Principles of Ordinary Least Squares Technique (OLS) Using Regression for Predictions Sample Application of Content: Exploring the relationship between crime and resulting police complaints and hence estimating the strength of the relationship, testing for spurious correlations and using the regression equation in prediction.
SOFTWARE APPLICATION:
The practical lab session will be dedicated to the use of software, for example MS Excel, in order to test for relationships between variables using graphics, correlation and hence regression analysis.
Assessment Breakdown%
Coursework40.00%
End of Module Assessment60.00%

Assessments

Full Time

Coursework
Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
Non-Marked: Yes
Assessment Description:
Formative assessment will be provided to learners through the use of short answer questions. In addition in class problems and discussions will provide an opportunity for formative learning and student feedback to be provided. Provision of individual feedback will be provided individually outside of lecture time or on line through Moodle.
Assessment Type: Project % of total: 40
Assessment Date: n/a Outcome addressed: 1,3,5
Non-Marked: No
Assessment Description:
Learners will be presented with a dataset and/or case study drawn from a business discipline (for example: European Social Survey data). Learners will be expected to work as part of a team. A number of questions will be presented to the learner(s) and they will be expected to evaluate, combine and synthesise the information in order to develop and apply the appropriate inferential statistics. They may be required to present a detailed report of the findings. Learners may be required to undertake a formal presentation defending their findings.
End of Module Assessment
Assessment Type: Terminal Exam % of total: 60
Assessment Date: End-of-Semester Outcome addressed: 1,4
Non-Marked: No
Assessment Description:
The examination will be two hours in duration with learners required to answer two questions, each worth 50 marks. Each question will have multiple parts and will include both calculation and theory elements. All questions will be marked according to clarity and the ability to apply statistical and quantitative techniques to solve business problems. Learners are required to interpret findings and communicate both an understanding of the process undertaken as well as the findings uncovered in a technical and non-technical manner as required.
No Workplace Assessment
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.
Reassessment Description
The repeat strategy for this module is by examination. Learners will be afforded an opportunity to repeat the assessment(s) at specified times.

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 Classroom and demonstrations 24 Per Semester 2.00
Independent Learning Independent learning 101 Per Semester 8.42
Total Weekly Contact Hours 2.00
 

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
  • Camm, J.D., Cochran, J.J., Fry, M.J. and Ohlmann, J.W. (2021), Business Analytics (eTextbook version also available) Cengage Learning.
  • Anderson, D.R., Sweeney, D.J., Williams, T.A., Camm, J.D. and Cochran, J.J. (2020), Modern business statistics with Microsoft Excel, Cengage Learning.
  • Field, A , 2017, Discovering Statistics using IBM SPSS Statistics, SAGE Publications.
  • Salkind, N.J. and Frey, B.B, 2021, Statistics for People who (Think They) Hate Statistics Using Microsoft Excel,Sage publications.
  • Levine, D., Stephan, D.F. and Szabat, K.A, 2021, Statistics for Managers Using MS Excel, Pearson Education.
Supplementary Article/Paper Resources
  • Journal of Applied Quantitative Methods.
  • Computational Statistics & Data Analysis.
  • Journal of Business and Economic Statistics.
  • Journal of Financial and Quantitative Analysis.
  • Review of Economics and Statistics.
  • Oxford Bulletin of Economics and Statistics.
  • Journal of Applied Statistics.
  • Quantitative and Qualitative Analysis in Social Sciences.
  • Quantitative Finance.
  • Journal of Multivariate Analysis.
  • Review of Quantitative Finance and Accounting.
  • Review of Economic Analysis.
  • Decision Analysis.
Other Resources
Discussion Note: