Aims The module aims to introduce analytical methods to solve computer science problems, using concepts from probability and statistics. This includes random variables and their distributions, mathematical expectation, point and confidence interval estimation, hypothesis testing, correlation, regression and engineering applications. Learning outcomes Knowledge On completion of this module, the successful student will be able to: • Analyze different statistical problems. (1) • Find problem solution and find confidence intervals. (2) Skills This module will call for the successful student to demonstrate: • Testing statistical hypothesis. (3) • Making statistical decisions. (4) Syllabus • Probability. • Random variables. • Mathematical expressions. • Discrete distribution. • Continuous distribution. • Statistical inference. Learning, Teaching and Assessment Strategy Weekly lectures to introduce the basic concepts of the course subjects. Weekly tutorials to discuss the solution of the weekly homework assignments. Assessment Scheme • Unseen Examinations 60 % • - Coursework 40% Learning materials • M. Lind, Statistical Techniques in Business and Economics with Global Data Set, 13th edition, New York, Mc-Graw Hill, 2008.