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Module Descriptor School of Computer Science and Statistics

Module CodeST1251
Module Short Title
Semester TaughtMichaelmas term
Contact HoursLecture hours: 22Lab hours: 2Tutorial hours: 9Total hours: 33 
Module PersonnelLecturing staff: Arthur White
Learning Outcomes

At the end of this module students should be able to:

  • explain the elementary ideas underlying probability models
  • distinguish between discrete and continuous random variables
  • apply a number of simple probability models to practical problems in a range of disciplines
  • demonstrate an understanding of the elementary properties of random variables (expectation, variance and covariance)  
Learning Aims

To introduce students to the elementary ideas of probability and the use of simple probability models

Module Content

Elementary probability ideas;discrete probability models, including binomial, hypergeometric, geometric, Poisson; continuous distributions including Normal, uniform and exponential; expectations and variances of random variables; combining random variables; sampling distributions.  

Recommended Reading List
  1. Ross, S.M., "Introduction to Probability and Statistics for Engineers and Scientists", Harcourt/Academic, 2000 [also Wiley, 1987].
  2. Chatfield, C., "Statistics for Technologists", 3rd edition, Chapman and Hall, London, 1983.  
Module Prerequisites
Assessment Details

% Exam: 100% or % Coursework: 10% + 90% 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.  

Module Website
Academic Year of Data2014/15