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MATH6033 - Quantitative Techniques

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Title:Quantitative Techniques
Long Title:Quantitative Techniques
Module Code:MATH6033
 
Duration:1 Semester
Credits: 5
NFQ Level:Fundamental
Field of Study: Mathematics
Valid From: Semester 1 - 2016/17 ( September 2016 )
Module Delivered in 4 programme(s)
Next Review Date: November 2020
Module Coordinator: David Goulding
Module Author: AINE NI SHE
Module Description: An introduction to Quantitative Techniques for Management.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Apply the methods of descriptive statistics to organise, summarise, present and analyse data manually and electronically.
LO2 Compute index numbers and apply forecasting techniques for time series data.
LO3 Compute regression and correlation estimates.
LO4 Compute probabilites for compound events.
LO5 Construct the network representation for a projects and identify critical and non-critical activites.
Pre-requisite learning
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named CIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.

No requirements listed
 

Module Content & Assessment

Indicative Content
Descriptive Statistics
Nature of data collection, tabulation and presentation of data. Graphical presentation of data. Measures of location to include mean, median and mode. Measures of dispersion to include range, interquartile range, mean and standard deviation. Measures of skewness.
Index Numbers & Time Series
Price relatives. Construction of indices using both aggregate and average of relative methods. Laspeyres and Paasche weighting. Chainging the base period. Deflation. Charting of time series. Additive and multiplicative models. Decomposition using moving average method. Trend identification and forecasting. First order exponential smoothing.
Probability
Probability and relative frequency. Events, compound events, language and laws of probability. Expected values and decision trees.
Critical Path Analysis
Projects, activities, Network represetnation of a project. Critical path. Floats.
Regression
Least squares, Scattergraph and Line of best fit. Coefficients of determination and correlation. Rank Correlation.
Assessment Breakdown%
Course Work30.00%
End of Module Formal Examination70.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Short Answer Questions Series of class tests based on homework and tutorial sheets 1,2,3,4,5 30.0 Every Second Week
End of Module Formal Examination
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Formal Exam End-of-Semester Final Examination 1,2,3,4,5 70.0 End-of-Semester
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.

The institute reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Theory 3.0 Every Week 3.00
Tutorial Revision based on exercise sheets 1.0 Every Second Week 0.50
Lecture Theory 1.0 Every Second Week 0.50
Independent & Directed Learning (Non-contact) Work based on texts and class materials. 3.0 Every Week 3.00
Total Hours 8.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Theory 2.0 Every Week 2.00
Lecture Theory 1.0 Every Second Week 0.50
Tutorial Revision based on exercise sheets 1.0 Every Second Week 0.50
Independent & Directed Learning (Non-contact) Review of lecture material, completion of exercise sheets 4.0 Every Week 4.00
Total Hours 8.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
 

Module Resources

Recommended Book Resources
  • G.Burton, G. Carrol & S.Wall 2002, Quantitative Methods for Business and Economics, Second Ed., Prentice Hall [ISBN: 0-273-65570-1]
  • Jon Curwin 2013, Quantitative Methods for Business Decisions, 7th Ed., Cengage Learning [ISBN: 978-140806498]
Supplementary Book Resources
  • D.F. Groebner, P.W.Shannon and P.C. Fry 2013, Business Statistics - A Decision-Making Approach, 9th Ed., Pearson [ISBN: 9780133373097]
  • S. Taylor 2007, Business Statistics for Non-Mathematicians, 2nd Ed., Palgrave Macmillan [ISBN: 9780230506466]
  • L. Oakshott 2012, Essential Quantitative Methods for Business, Management and Finance, 5th Ed., Palgrave Macmillan [ISBN: 9780230302661]
This module does not have any article/paper resources
Other Resources
 

Module Delivered in

Programme Code Programme Semester Delivery
CR_BACCT_8 Bachelor of Business (Honours) in Accounting 2 Mandatory
CR_BACCT_7 Bachelor of Business in Accounting 2 Mandatory
CR_BACCT_6 Higher Certificate in Business in Accounting 2 Mandatory
CR_BACCT_6 Higher Certificate in Business in Accounting 3 Mandatory

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Email: help@cit.edu.ie