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DRPS : Course Catalogue : School of Mathematics : Mathematics

Undergraduate Course: Applicable Mathematics 4 (Phys Sci) (MATH08017)

Course Outline
School	School of Mathematics	College	College of Science and Engineering
Course type	Standard	Availability	Available to all students
Credit level (Normal year taken)	SCQF Level 08 (Year 2 Undergraduate)	Credits	10
Home subject area	Mathematics	Other subject area	Mathematics for Physical Science & Engineering
Course website	http://student.maths.ed.ac.uk
Course description	Vectors, curves and their properties. Vector fields, divergence and curl. Potential and line integrals. Surfaces, normal vectors, area. Spherical coordinates. Surface integrals. Integral theorems. Revision of basic probability and discrete and continuous random variables. Sampling distributions, in particular in large samples. Hypothesis testing on one and two Normal expectations, including matches pairs design, and goodness-of-fit tests on tables of frequency counts. Simple linear regression calculations.

Entry Requirements
Pre-requisites		Co-requisites
Prohibited Combinations	Students MUST NOT also be taking Several Variable Calculus (MATH08006) OR Methods of Applied Mathematics (MATH08035) OR Mathematics for Chem Eng 4 (MATH08020) OR Mathematics for Elec/Mech Eng 4 (MATH08034) OR Mathematics for Informatics 4a (MATH08044) OR Mathematics for Informatics 4b (MATH08045)	Other requirements	None
Additional Costs	None

Information for Visiting Students
Pre-requisites	None
Prospectus website	http://www.ed.ac.uk/studying/visiting-exchange/courses

Summary of Intended Learning Outcomes
1. The ability to formulate some problems arising in physics and engineering in terms of notions of vector calculus 2. The ability to solve elementary problems in vector calculus 3. An ability to perform elementary probability calculations, and work with discrete and continuous random variables. 4. An ability to recognise when binomial, Poisson, Normal probability distributions are appropriate models. 5. Understanding what a sampling distribution is. 6. An ability to recognise when large sample approximations (eg Central Limit Theorem) are useful. 7. An ability to carry out simple hypothesis tests on binomials, Poissons, and Normals - this includes distinguishing between a two-sample problem and a matched pairs design - and chi-squared goodness-of-fit tests on tables of frequency counts. 8. An ability to construct a least squares fitting of a straight line regression.

Assessment Information
Coursework: 15%; Degree Examination: 85%; at least 40% must be achieved in each component.
Please see Visiting Student Prospectus website for Visiting Student Assessment information

Special Arrangements
Not entered

Contacts
Course organiser	Dr Joan Simon Soler Tel: (0131 6)50 8571 Email: J.Simon@ed.ac.uk	Course secretary	Mrs Karen Downie Tel: (0131 6)50 5793 Email: K.Downie@ed.ac.uk

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