Registry

Module Specifications

Current Academic Year 2012 - 2013
Please note that this information is subject to change.

Module Title	Probability & Statistics
Module Code	CA266
School	School of Computing
Online Module Resources
Module Co-ordinator	Semester 1: Liam Tuohey Semester 2: Liam Tuohey Autumn: Liam Tuohey
Module Teacher	Liam Tuohey
NFQ level	8	Credit Rating	5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

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 normaldistributions; 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

Workload	Full-time hours per semester
*Type*	*Hours*	*Description*
Lecture	24	2 lectures per week
Tutorial	12	1
Independent learning time	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

Indicative Content and Learning Activities

Indicative Syllabus.
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 normaldistributions; normal approximation to binomial.Tchebechev's and Markov's inequalities •.

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
This module is category 3

Indicative Reading List