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Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title
Module Code
School
Module Co-ordinatorSemester 1: Graham Healy
Semester 2: Graham Healy
Autumn: Graham Healy
Module TeachersGraham Healy
NFQ level 8 Credit Rating
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
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.



Workload Full-time hours per semester
Type Hours Description
Lecture242 lectures per week
Tutorial121
Independent Study48post lecture study
Group work30project development
Laboratory12learning 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 normal distributions; normal approximation to binomial. Tchebechev's and Markov's inequalities •

Assessment Breakdown
Continuous Assessment% Examination Weight%
Course Work Breakdown
TypeDescription% of totalAssessment Date
Reassessment Requirement Type
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
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,
Other Resources

None
Programme or List of Programmes
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