Applicable Mathematics 4 (Phys Sci) (U01685)

? Credit Points : 10 ? SCQF Level : 8 ? Acronym : MAT-2-am4

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 : Prior attendance at MAT-2-am3

? Prohibited combinations : MAT-2-SVC, MAT-2-MAM, MAT-mc4, MAT-me4, MAT-2-mi4

Subject Areas

Home subject area

Mathematics for Physical Science & Engineering, (School of Mathematics, Schedule P)

Delivery Information

? Normal year taken : 2nd year

? Delivery Period : Semester 2 (Blocks 3-4)

? Contact Teaching Time : 2 hour(s) 30 minutes per week for 11 weeks

First Class Information

Date	Start	End	Room	Area	Additional Information
08/01/2008	10:00	11:00	Lecture Theatre 6301, JCMB	KB

All of the following classes

Type	Day	Start	End	Area
Lecture	Tuesday	10:00	10:50	KB
Lecture	Thursday	10:00	10:50	KB

? Additional Class Information : Tutorials: M at 1110 and 1210 (shared with MAT-2-mm4)

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.