Module Specifications..
Current Academic Year 2023 - 2024
Please note that this information is subject to change.
| |||||||||||||||||||||||||||||||||||||||
None The student will have the option of re submitting project |
|||||||||||||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||||||||||||
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 |
|||||||||||||||||||||||||||||||||||||||
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 • | |||||||||||||||||||||||||||||||||||||||
| |||||||||||||||||||||||||||||||||||||||
Indicative Reading List
| |||||||||||||||||||||||||||||||||||||||
Other Resources None | |||||||||||||||||||||||||||||||||||||||
Programme or List of Programmes
| |||||||||||||||||||||||||||||||||||||||
Archives: |
|