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Module Descriptor School of Computer Science and Statistics

Module CodeST1252
Module Short Title
Semester TaughtHilary term
Contact HoursLecture hours: 22Lab hours: 2Tutorial hours: 9Total hours:33 
Module PersonnelLecturer - Arthur White
Learning Outcomes

On successful completion of this module students should:

  • have a strong grasp of the fundamental statistical ideas of significance tests and confidence intervals, which underpin statistical analysis
  • be able to apply simple statistical methods to practical problems
  • be able to explain why statistical methods are so widely applied in both the natural and social sciences, engineering and business
  • have a sound basis for developing their knowledge of more advanced statistical ideas and methods.  
Learning Aims

To introduce students to the elementary ideas of statistical inference and the use of simple statistical methods in practical situations

Module Content
  • Statistical variation;
  • parameter estimation;
  • statistical tests and their properties;
  • design and analysis of simple comparative studies for both binary and continuous variables;
  • introductions to Analysis of Variance (ANOVA), regression and contingency tables.

The theory will be illustrated by examples from biology, engineering, industry, medicine and the social sciences. 

Recommended Reading List

Extensive handouts (amounting to a course text) will be provided, so the following is for supplemental reading.  The book is quite discursive, as it is oriented to a general, rather than a specifically mathematically oriented student readership.  

D.S. Moore and G. P. McCabe, Introduction to the practice of statistics, Freeman, 5th edition, 2006  

Module PrerequisitesST1251
Assessment Details

% Exam: 100% or %Coursework:  20% + 80% Exam.

Description of assessment & assessment regulations. The final mark is either the examination mark or a weighted average of examination and coursework, whichever is higher. 

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