Module Specifications..
Current Academic Year 2023  2024
Please note that this information is subject to change.
 
Repeat examination Array 

Description The aim of this module is to give a thorough grounding in probability, statistics and calculus of several variables as required for the successful understanding and solution of problems in science. Students will learn how mathematics can be used as a tool for solving scientific problems and a language for communicating information. This is a knowhow and skills module. Students will participate in the following learning activities: Lectures: Students will attend two onehour lectures per week. These lectures are designed to introduce learners to the mathematical principles and problem solving techniques that underpin this module. Tutorials: Each student will attend one onehour tutorial per week. Problem sheets based on lecture content are distributed to the students and they are strongly advised to attempt all tutorial questions in advance of the tutorial.Reading: Students are expected to fully utilise the textbooks recommended.  
Learning Outcomes 1. apply the rules of probability and assign probabilities to events. 2. know how to obtain expectations of discrete and continuous random variables. 3. use the normal and student tdistributions to test statistical hypotheses and to compute confidence intervals. 4. perform the calculations that arise when the calculus of several variables is used to solve problems.  
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
Sets Definitions, set operations; set identities. Russell's paradox. Probability Random experiments; axioms of probability; independent events; conditional probability; Bayes' theorem Random variables and probability densities Discrete and continuous random variables; characteristics of random variables; probability distributions and densities. Some important probability densities Basic combinatorics, the binomial, Poisson, Pascal and normal distributions. Statistical inference Point estimates and confidence intervals; the central limit theorem; hypothesis tests. Vector calculus and functions of several variables Vectors; scalar and cross product; applications. Scalar and vector fields; partial derivatives; div, grad and curl; surfaces; optimization problems.  
 
Indicative Reading List
 
Other Resources None  
Programme or List of Programmes  
Date of Last Revision  26SEP07  
Archives: 
