Module Code: H7IBSA
Long Title Introduction to Business Statistics and Analytics
Title Introduction to Business Statistics and Analytics
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 Demonstrate an understanding of statistical principles, theories and methods and appreciate how they apply in a range of business and management decision making situations.
LO2 Recognise different types of data and associated statistical measures and their appropriateness in a range of scenarios
LO3 Utilise a range of descriptive statistics in order to evaluate and present information and data associated with univariate analyses and appreciate how they contribute in business intelligence.
LO4 Demonstrate proficiency in the principles and application of probability theory
LO5 Use appropriate software in the presentation and organisation of statistical data and hence select and apply appropriate statistical methods and techniques.
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
Introduction to Data Role of Statistics and Analytics in Business Descriptive vs. Inferential Statistics Data and Data Sources Use and Misuse of Analytical Tools and Statistics Measurement Scales Sample Application of Content: Differentiating between qualitative and quantitative variables and identifying what scales of measurement are appropriate in a variety of business contexts.
Describing Data: Frequency Tables & Graphics
Frequency Data & Frequency Tables Graphical Representation of Data: Bar Charts Pie Charts Histograms Scatter Plots & Linear Representation Sample Application of Content: Using a variety of business data sets containing raw data, both discrete and continuous.
Describing Data: Measures of Central Tendency
Mean Mode Median Sample Application of Content: Compare and contrast the main measures of central tendency and hence using both raw and frequency data from business contexts, identify a suitable measure of central tendency and hence calculate and interpret as appropriate.
Describing Data: Measures of Dispersion
Range & Mean Absolute Deviation Variance & Standard Deviation (Population and Sample) Symmetric Distributions and Skewness Kurtosis Sample Application of Content: Develop a frequency distribution and hence calculate the mean and standard deviation. Graphically present the distribution and discuss the symmetry of the distribution and the implications of same.
Basics of Probability
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 Sample Application of Content: Using probability trees to model business problems and hence calculate conditional probabilities. For example, in the case of finance, modelling an investment problem using a probability tree and hence calculation of conditional probabilities and expected values.
SOFTWARE APPLICATION:
The practical lab session(s) will be dedicated to the use of software, for example MS Excel, in order to develop appropriate tables and graphics to summarise data and to calculate measures of centre and dispersion for both grouped and ungrouped data. Learners will have access to the virtual desktop in order to access MS Excel or other software deemed appropriate.
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: 1,5
Non-Marked: Yes
Assessment Description:
Formative assessment will be provided to learners using 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 online through Moodle.
Assessment Type: In class assessment % of total: 40
Assessment Date: n/a Outcome addressed: 1,2,3,5
Non-Marked: No
Assessment Description:
Learners will be given a time-constrained in class assessment. The time-constrained assessment will take place in class and is worth 40% of the module grade. It may include a mix of short answer questions, multiple-choice choice, vignettes and/or problem based.
End of Module Assessment
Assessment Type: Terminal Exam % of total: 60
Assessment Date: End-of-Semester Outcome addressed: 1,4,5
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.
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.
This module does not have any article/paper resources
Other Resources
Discussion Note: