Module Details

Module Code: STAT6010
Title: Intro. to Probability & Stats
Long Title: Introduction to Probability and Statistics
NFQ Level: Fundamental
Valid From: Semester 1 - 2014/15 ( September 2014 )
Duration: 1 Semester
Credits: 5
Field of Study: 4620 - Statistics
Module Delivered in: 8 programme(s)
Module Description: This module provides an introduction to both probability models and statistical procedures for technology.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Graphically display and numerically summarise data using methods of descriptive statistics.
LO2 Apply the rules of probability and use probability models for data analysis.
LO3 Use mathematical and statistical techniques for fitting curves to data.
LO4 Analyse statistical output from a statistical package.
Dependencies
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 MTU 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
 
Indicative Content
Descriptive statistics
Collection & presentation of data: frequency distributions, histograms, box plots, stem & leaf plots, ogives. Calculation of summary statistics: measures of central tendency and measures of dispersion.
Probability
Classical, relative frequency and subjective probability. Axiomatic definitions. Laws of probability, conditional probability, mutually exclusive events and addition rules, independent events and multiplication rules. Baye's Theorem. Reliability block diagrams.
Probability Distributions
Random variables. Discrete and continuous distributions. Nature of probability density functions & cumulative density functions. Binomial, Poisson, normal, exponential distributions. Use of tables.
Curve Fitting
Finite differences, Newton-Gregory interpolation formula, correlation, least squares regression including non-linear to linear form, Lagrangian interpolation.
Statistical Packages
Analyse statistical output from a statistical package such as Minitab. Demonstrate software package in class.
Module Content & Assessment
Assessment Breakdown%
Coursework40.00%
End of Module Formal Examination60.00%

Assessments

Coursework
Assessment Type Other % of Total Mark 30
Timing Every Second Week Learning Outcomes 1,2,3
Assessment Description
Series of in class assessments based on homework
Assessment Type Other % of Total Mark 10
Timing Week 12 Learning Outcomes 4
Assessment Description
Open book practical lab exam
End of Module Formal Examination
Assessment Type Formal Exam % of Total Mark 60
Timing End-of-Semester Learning Outcomes 1,2,3,4
Assessment Description
End-of-Semester Final Examination
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 University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Formal lecture Every Week 3.00 3
Tutorial Contact Tutorial Every Second Week 0.50 1
Lab Contact Statistical or spreadsheet package lab Every Second Week 0.50 1
Independent & Directed Learning (Non-contact) Non Contact Class Notes & Exercise Sheets Every Week 3.00 3
Total Hours 8.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Formal Every Week 3.00 3
Tutorial Contact Tutorial Every Second Week 0.50 1
Lab Contact Statistical or spreadsheet package lab Every Second Week 0.50 1
Independent & Directed Learning (Non-contact) Non Contact Class Notes & Exercise Sheets Every Week 3.00 3
Total Hours 8.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 
Module Resources
Recommended Book Resources
  • Montgomery, D.C. & Runger G.C.. (2014), Applied Statistics & Probability for Engineers, 6. Wiley, [ISBN: 978-1-118-74412-3].
Supplementary Book Resources
  • Richard A. Johnson. (2011), Applied Probability and Statistics for Engineers, 8. Prentice Hall, Boston, [ISBN: 978-0-32-1640772].
  • Sheldon M. Ross. (2009), Introduction to Probability and Statistics for Engineers and Scientists, 4. Academic Press, [ISBN: 978-0-12-370483-2].
This module does not have any article/paper resources
Other Resources
  • Software, Minitab, Excel.
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_EBENS_8 Bachelor of Engineering (Honours) in Building Energy Systems 4 Mandatory
CR_ESENT_8 Bachelor of Engineering (Honours) in Sustainable Energy Engineering 4 Mandatory
CR_EBIME_7 Bachelor of Engineering in Biomedical Engineering 4 Mandatory
CR_EBSEN_7 Bachelor of Engineering in Building Services Engineering 4 Mandatory
CR_EMECH_7 Bachelor of Engineering in Mechanical Engineering 4 Mandatory
CR_EBIME_6 Higher Certificate in Engineering in Biomedical Engineering 4 Group Elective 1
CR_EBSEN_6 Higher Certificate in Engineering in Building Services Engineering 4 Mandatory
CR_SGMPR_6 Higher Certificate in Science in Good Manufacturing Practice and Technology 2 Mandatory