Undergraduate Course: Mathematics for Chem Eng 4 (MATH08020)
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 8 (Year 2 Undergraduate) |
Credits | 10 |
| Home subject area | Mathematics |
Other subject area | Mathematics for Physical Science & Engineering |
| Course website |
https://info.maths.ed.ac.uk/teaching.html |
Taught in Gaelic? | No |
| Course description | 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. |
Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
|
| Delivery period: 2011/12 Semester 2, Available to all students (SV1)
|
WebCT enabled: Yes |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| King's Buildings | Lecture | Th C, JCMB | 1-11 | | 10:00 - 10:50 | | | | | King's Buildings | Lecture | Th C, JCMB | 1-11 | | | | 10:00 - 10:50 | |
| First Class |
Week 1, Tuesday, 10:00 - 10:50, Zone: King's Buildings. Th C, JCMB |
| Additional information |
Tutorials: W at 0900 hrs and 1000hrs
JCMB, rooms as advised |
| Exam Information |
| Exam Diet |
Paper Name |
Hours:Minutes |
|
|
| Main Exam Diet S2 (April/May) | | 1:30 | | | | Resit Exam Diet (August) | | 1:30 | | |
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. |
Assessment Information
| Coursework: 15%; Degree Examination: 85% |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Not entered |
| Transferable skills |
Not entered |
| Reading list |
Not entered |
| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords | mc4 |
Contacts
| Course organiser | Dr Tom Mackay
Tel: (0131 6)50 5058
Email: T.Mackay@ed.ac.uk |
Course secretary | Mrs Gillian Law
Tel: (0131 6)50 5085
Email: G.Law@ed.ac.uk |
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© Copyright 2011 The University of Edinburgh - 16 January 2012 6:23 am
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