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Module Specifications

Archived Version 2015 - 2016

Module Title
Module Code
School

Online Module Resources

NFQ level 9 Credit Rating 5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
Description

Aims to review and complement foundation statistical knowledge and to establish the context for a range of methods, used in the analysis of simple and complex systems. Reasonable proficiency in algebra and the ability to grasp concepts of probability and its importance are predominantly required. The emphasis is on an intuitive understanding of the principles and a practical ability to apply these to data examples drawn from diverse systems, rather than mathematical sophistication.

Learning Outcomes

1. 'demonstrate' that they understand different levels of measurement and data types
2. 'demonstrate' that they understand and apply underlying probability principles and distribution examples
3. 'demonstrate' that they can distinguish between descriptive and inferential statistical quantities in the theory and practice of statistics and in data analytics
4. 'demonstrate' that they appreciate the scope and robustness of common analytical methods for one to many samples
5. use a range of analytical statistical techniques and interpret outcomes
6. select appropriate statistical software, having been exposed to several examples; (options for practical work)
7. 'demonstrate' that they can apply techniques to a range of illustrative examples and case studies in complex bio- and other real-world systems



Workload Full-time hours per semester
Type Hours Description
Lecture36principles and methods
Tutorial12examples
Assignment Completion56practical application/formal analysis
Independent Study83.5reading, understanding, applying conceots and reviewing examples
Total Workload: 187.5

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

Review
Basic Probability and nature of Statistical Investigations

Probability distributions/Bayes and data handling
Discrete and Continuous distributions and examples Conditional, joint Probability, data and Bayes

Sampling distributions and Statistical Inference
generation, interpretation, use and examples for data types

Estimation and Hypothesis Testing
One, two, many samples and principal analytical methods

More on many samples
Analytical methods for counts/proportions, means/variances and role of regression

Principles of Non-parametrics
less rigorous assumptions and distributional requirements

Advanced methods
experimental design and Multivariate - an outline

Complex Systems Models & Analysis
Problem-solving : blueprint/approach for real-world data analytics Illustrative Examples/Case Studies from Biology,Env. Sci., Business & Finance

Assessment Breakdown
Continuous Assessment% Examination Weight%
Course Work Breakdown
TypeDescription% of totalAssessment 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
Indicative Reading List

  • Michael J. Crawley: 2005, An Introduction Using R, J. Wiley, Chichester, West Sussex, England, 0470022981
  • C. Philip Wheater and Penny A. Cook; illustrated by Jo Wright: 0, Using statistics to understand the environment, London ; Routledge, 2000., 0415198887
  • Moore P.: 1997, Introductory Statistics for Environmentalists, (Environmental Management, Science & Technology), Ellis Horwood, 013128077
  • Nicholas J. Gotelli, Aaron M. Ellison: 2004, A primer of ecological statistics, Sinauer Associates Publishers, Sunderland, MA, 0878932690
  • Thomas H. Davenport and Jeanne G. Harris: 2007, Competing on analytics, Harvard Business School Press, Boston, Mass., 1422103323
  • Thomas H. Davenport, Jeanne G. Harris, Robert Morison: 0, Analytics at Work, Harvard Business Press, 1422177696
  • David Vose: 0, Risk analysis, Chichester, England ; Wiley, c2008., 0470512849
  • Sam L. Savage: 2009, The flaw of averages, Wiley, Hoboken, N.J., 0471381977
  • Steve McKillup: 0, Statistics Explained, Cambridge University Press, 0521183286
  • Claus Thorn Ekstrom, Helle Sorensen: 0, Introduction to Statistical Data Analysis for the Life Sciences, CRC Press, 1439825556
  • Sorin Draghici: 0, An Introduction to Statistics and Data Analysis for Bioinformatics using R, Chapman and Hall/CRC, 1439892369
Other Resources

None
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