Mathematics for Chem Eng 4 (U01688)

? Credit Points : 10 ? SCQF Level : 8 ? Acronym : MAT-2-mc4

Integration in two and three variables. Scalar and vector fields, gradient, divergence and curl, divergence theorem. Diffusion equation in one dimension, separation of variables, error function. Laplace's equation in two dimensions, separation of variables, analytic functions. 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-mc3

? Prohibited combinations : MAT-2-mm3, MAT-2-mm4, MAT-2-am4, MAT-2-SVC, MAT-2-MAM, MAT-2-mi4, MAT-2-me4

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 : 3 hour(s) per week for 11 weeks

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: W at 0900 hrs

Summary of Intended Learning Outcomes

1. An ability to evaluate surface and volume integrals.
2. An ability to apply div, grad and curl.
3. An ability to solve Partial Differential Equations using separation of variables, similarity variables and the complex-variable method.
4. An ability to perform elementary probability calculations, and work with discrete and continuous random variables.
5. An ability to recognise when binomial, Poisson, Normal probability distributions are appropriate models.
6. Understanding what a sampling distribution is.
7. An ability to recognise when large sample approximations (eg Central Limit Theorem) are useful.
8. 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.
9. An ability to construct a least squares fitting of a straight line regression.