Module Specifications.
Current Academic Year 2024 - 2025
All Module information is indicative, and this portal is an interim interface pending the full upgrade of Coursebuilder and subsequent integration to the new DCU Student Information System (DCU Key).
As such, this is a point in time view of data which will be refreshed periodically. Some fields/data may not yet be available pending the completion of the full Coursebuilder upgrade and integration project. We will post status updates as they become available. Thank you for your patience and understanding.
Date posted: September 2024
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None The student will have the option of re submitting project |
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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. | |||||||||||||||||||||||||||||||||||||||||||
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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Indicative Reading List
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Other Resources None | |||||||||||||||||||||||||||||||||||||||||||