Registry
Module Specifications
Archived Version 2011 - 2012
Module Title |
Probability & Statistics
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Module Code |
CA266
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School |
School of Computing
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Online Module Resources
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Module Co-ordinator | Dr Jennifer Foster | Office Number | L2.16 |
NFQ level |
8 |
Credit Rating |
5
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Pre-requisite |
None
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Co-requisite |
None
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Compatibles |
None
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Incompatibles |
None
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Description
Summary:
Summarising and displaying statistical data in R;
Introduction to probability: discrete sample spaces; axioms; addition and multiplication laws; conditional probability and independence; reliability of systems; Bayes theorem; •
Discrete Random Variables: Bernouilli, hypergeometric, binomial, geometric and Poisson distributions; expectation;
Sampling Inspection Schemes: Single and double sampling; operating characteristic function; average outgoing quality; consumers's and producer's risks.
Continuous Random Variables: Uniform, exponential and normal
distributions; normal approximation to binomial.
Tchebechev's and Markov's inequalities •
Aims:
• To introduce the basic probability concepts and their applications to computer disciples;
• To provide an understanding of discrete and continuous distributions;
• To cover the essentials of the statistical computing system R.
• To introduce the essentials of statistical analysis using R
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Learning Outcomes
1. At the end of the module the student will:
• have a through understanding of the statistical computing system R;
• understanding the basics of probability;
• recognise problems that may be solved using the standard discrete and continuous statistical models;
• know how to obtain expectations of discrete and continuous random variables;
• have developed a package in R to generate pdfs and cdfs of discrete distributions
• be able to carry out a basic statistical analysis in R, including measures of central tendency and dispersion, and graphical displays such as stem and leaf, and boxplots.
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Workload |
Full-time hours per semester |
Type |
Hours |
Description |
Lecture | 24 | 2 lectures per week | Tutorial | 12 | 1 | Independent Study | 48 | post lecture study | Group work | 30 | project development | Laboratory | 12 | learning R | Total Workload: 126 |
All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml
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Indicative Content and
Learning Activities
Indicative SyllabusSummarising and displaying statistical data in R;
Introduction to probability: discrete sample spaces; axioms; addition and multiplication laws; conditional probability and independence; reliability of systems; Bayes theorem; •
Discrete Random Variables: Bernouilli, hypergeometric, binomial, geometric and Poisson distributions; expectation;
Sampling Inspection Schemes: Single and double sampling; operating characteristic function; average outgoing quality; consumers's and producer's risks.
Continuous Random Variables: Uniform, exponential and normal
distributions; normal approximation to binomial.
Tchebechev's and Markov's inequalities •
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Assessment Breakdown | Continuous Assessment | 20% | Examination Weight | 80% |
Course Work Breakdown |
Type | Description | % of total | Assessment Date |
Reassessment Requirement |
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component |
Unavailable |
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Indicative Reading List
- Jane M. Horgan: 2009, Probability with R, Wiley, Hoboken, N.J., 978-0-470-28073-7
- Dalgaard Peter: 2008, Statistics with R, 2nd,
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Other Resources
None
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Programme or List of Programmes
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Archives: | |