| Title: | Intro. to Probability & Stats |
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| Long Title: | Introduction to Probability and Statistics |
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| Field of Study: |
Statistics
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| Valid From: |
Semester 1 - 2014/15 ( September 2014 ) |
| Module Coordinator: |
David Goulding |
| Module Author: |
MARETTA BRENNAN |
| 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: |
| 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. |
| 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). |
| No recommendations listed |
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
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| 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.
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| No requirements listed |
Co-requisites
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| No Co Requisites listed |
Module Content & Assessment
| Indicative Content |
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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.
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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.
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Probability Distributions
Random variables. Discrete and continuous distributions. Nature of probability density functions & cumulative density functions. Binomial, Poisson, normal, exponential distributions. Use of tables.
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Curve Fitting
Finite differences, Newton-Gregory interpolation formula, correlation, least squares regression including non-linear to linear form, Lagrangian interpolation.
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Statistical Packages
Analyse statistical output from a statistical package such as Minitab. Demonstrate software package in class.
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| Assessment Breakdown | % |
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| Course Work | 40.00% |
| End of Module Formal Examination | 60.00% |
| Course Work |
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| Assessment Type |
Assessment Description |
Outcome addressed |
% of total |
Assessment Date |
| Other |
Series of in class assessments based on homework |
1,2,3 |
30.0 |
Every Second Week |
| Other |
Open book practical lab exam |
4 |
10.0 |
Week 12 |
| 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 |
60.0 |
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 |
Formal lecture |
3.0 |
Every Week |
3.00 |
| Tutorial |
Tutorial |
1.0 |
Every Second Week |
0.50 |
| Lab |
Statistical or spreadsheet package lab |
1.0 |
Every Second Week |
0.50 |
| Independent & Directed Learning (Non-contact) |
Class Notes & Exercise Sheets |
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 |
Formal |
3.0 |
Every Week |
3.00 |
| Tutorial |
Tutorial |
1.0 |
Every Second Week |
0.50 |
| Lab |
Statistical or spreadsheet package lab |
1.0 |
Every Second Week |
0.50 |
| Independent & Directed Learning (Non-contact) |
Class Notes & Exercise Sheets |
3.0 |
Every Week |
3.00 |
| Total Hours |
8.00 |
| Total Weekly Learner Workload |
7.00 |
| Total Weekly Contact Hours |
4.00 |
Module Resources
| Recommended Book Resources |
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- Montgomery, D.C. & Runger G.C. 2014, Applied Statistics & Probability for Engineers, 6 Ed., Wiley [ISBN: 978-1-118-74412-3]
| | Supplementary Book Resources |
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- Richard A. Johnson 2011, Applied Probability and Statistics for Engineers, 8 Ed., Prentice Hall Boston [ISBN: 978-0-32-1640772]
- Sheldon M. Ross 2009, Introduction to Probability and Statistics for Engineers and Scientists, 4 Ed., Academic Press [ISBN: 978-0-12-370483-2]
| | This module does not have any article/paper resources |
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| Other Resources |
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Module Delivered in
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