Module Code: H6QTM
Long Title Quantitative Methods
Title Quantitative Methods
Module Level: LEVEL 6
EQF Level: 5
EHEA Level: Short Cycle
Credits: 5
Module Coordinator: MICHELE KEHOE
Module Author: CORINA SHEERIN
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 a basic understanding of statistical principles, theories and methods and appreciate how they apply in a range of business decision making situations.
LO2 Recognise and evaluate different types of data and associated statistical measures and their appropriateness in a range of scenarios.
LO3 Tabulate, summarise and present information in a useful and informative manner and hence identify and defend appropriate measures of central tendency and dispersion in order to describe a data set.
LO4 Demonstrate proficiency in the principles and application of statistical inference and apply this knowledge in developing conclusions about populations based on sample results.
LO5 Use software in the presentation and organisation of statistical data and hence select, apply appropriate statistical methods and techniques
LO6 Communicate and interpret statistical findings/output in a technical and non-technical 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).
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 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 (Week 2 & 3)
Frequency Data & Frequency Tables Graphical Representation of Data: Bar Charts, Pie Charts, Stem and Leaf Plots, Histograms, Scatter Plots & Linear Representation Software Application: Using Microsoft Excel to develop tables, charts and graphics. Sample Application of Content: Using a variety of business data sets containing raw data, both discrete and continuous, using the excel PivotTables to develop appropriate frequency tables and hence select appropriate graphics and present data in a suitable format and hence interpret presentation of data.
Describing Data: Measures of Central Tendency (Week 4)
· Mean: Arithmetic versus Geometric · Mode · Median Software Application: Using Microsoft excel data analysis to calculate descriptive statistics relating to measures of central tendency and hence interpret statistical output. 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 (Week 5 & 6)
· Range & Mean Absolute Deviation · Variance & Standard Deviation (Population and Sample) · Symmetric Distributions and Skewness Software Application: Using Microsoft excel data analysis toolpak to calculate descriptive statistics and interpret statistical output. 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.
Probability (Week 7 & 8)
· The concepts 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.
Probability Distributions (Week 9 & 10)
· The concept of probability distributions · Binomial probability distribution · Normal probability distribution · Standardisation and probabilities under a normal curve Software Application: Using Microsoft excel to calculate z scores and associated probabilities for population data. 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.
An Introduction to Statistical Inference (Week 11 & 12)
· Sampling methods · Sampling distribution of the sample mean · Central Limit Theorem · Point estimates and confidence intervals for a mean Software Application: Using Microsoft excel to calculate z-scores and calculate associated probabilities for sample data Sample Application of Content: Using the properties of the central limit theorem compute probabilities using the standard normal distribution. Construct confidence intervals for estimates of population parameters.
Assessment Breakdown%
Coursework50.00%
End of Module Assessment50.00%

Assessments

Full Time

Coursework
Assessment Type: Continuous Assessment % of total: 50
Assessment Date: n/a Outcome addressed: 1,2,3,5
Non-Marked: No
Assessment Description:
Candidates are required to complete an assessment.
End of Module Assessment
Assessment Type: Terminal Exam % of total: 50
Assessment Date: End-of-Semester Outcome addressed: 1,2,3,4,5,6
Non-Marked: No
Assessment Description:
Final Examination.
No Workplace Assessment
Reassessment Requirement
Repeat failed items
The student must repeat any item failed
Reassessment Description
Candidates will attempt the repeat assessment for the module, if they do not successfully pass the module. Learners are required to attempt all assessments attaching to a module. For those modules where all learning outcomes are assessable with a final examination, the learner does not have to re-sit failed individual CA components.

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
Tutorial Mentoring and small-group tutoring 12 Per Semester 1.00
Independent Learning Independent learning 89 Per Semester 7.42
Total Weekly Contact Hours 3.00
 

Module Resources

Recommended Book Resources
  • Lind D.A., Marchal W.G., and Wathen S.A.. (2020), Statistical Techniques in Business and Economics, 18th Ed. McGraw Hill.
Supplementary Book Resources
  • Davies, G. and Pecar, B.. (2013), Business Statistics using Excel, 2nd. Oxford University Press.
  • Berenson, M., Levine, Szabat, K.A.. (2015), Basic Business Statistics, Global Edition, 13th. Pearson Education.
  • Field, A. (2020), Discovering Statistics Using R, 2nd. Sage Publications.
  • Taylor, S. (2007), Business Statistics for Non-Mathematicians (Paperback or Ebook version), 2nd. Palgrave Macmillan.
This module does not have any article/paper resources
This module does not have any other resources
Discussion Note: