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Module Title LC Probability & Statistics
Department Mathematics
Module Code 06 25663
Module Lead Dr Henning Sulzbach
Level Certificate Level
Credits 10
Semester Semester 2
Co-requisites LC Vectors, Geometry & Linear Algebra - (06 25664)
LC Real Analysis & the Calculus - (06 25660)
Restrictions None
Contact Hours Lecture-23 hours
Seminar-0 hours
Tutorial-0 hours
Project supervision-0 hours
Demonstration-0 hours
Practical Classes and workshops-0 hours
Supervised time in studio/workshop-0 hours
Fieldwork-0 hours
External Visits-0 hours
Work based learning-0 hours
Guided independent study-77 hours
Placement-0 hours
Year Abroad-0 hours
Description Statistics, often regarded as distinct science rather than a branch of mathematics, is the study of data and uncertainty. Statistical techniques allow us to make conclusions, such as whether or not living near electricity pylons is dangerous, from sets of data. Statistics is also used in the design of effective experiments and in determining what data should be collected. For example, statistical techniques might be used to determine the frequency with which aircraft components should be tested for safety. Underlying these techniques is the assumption that these data are samples of a random variable that follows a probability distribution describing their behaviour. This module provides an introduction to probability and statistics. Axiomatic probability theory, including Bayes’ Theorem, is discussed briefly. Key discrete and continuous probability modules (such as the binomial, Poisson and normal distributions) are introduced. Properties of expectation and variance are discussed. The Weak Law of Large Numbers and the Central Limit Theorem are covered before basic statistical ideas, such as statistical inference and hypothesis testing are introduced. Real world data are used to illustrate the theory.
Learning Outcomes By the end of the module students should be able to:
  • Calculate probabilities and conditional probabilities and apply Bayes’ Theorem in standard situations.
  • Know and use the standard discrete and continuous probability models in appropriate situations.
  • Know the properties of expectation and variance and apply them to in standard situations.
  • Appreciate the significance of the Weak Law of Large Numbers and the Central Limit Theorem.
  • Understand and apply basic statistical techniques such as inference, point estimation, confidence intervals, hypothesis testing.
Assessment 25663-01 : Raw Module Mark : Coursework (0%)
25663-02 : Final Module Mark : Coursework (100%)
25663-04 : Formative : Coursework (0%)
Assessment Methods & Exceptions 1.5 hour examination (80%), work done during semester (20%)
Reading List