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 8 (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
|
Taught in Gaelic? |
No |
| 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. |
Information for Visiting Students
| Pre-requisites |
None |
| Displayed in Visiting Students Prospectus? |
No |
Course Delivery Information
|
| Delivery period: 2011/12 Semester 2, Not available to visiting students (SS1)
|
WebCT enabled: No |
Quota: 0 |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| No Classes have been defined for this Course |
| First Class |
First class information not currently available |
| Additional information |
Tutorials: M at 1000, 1110 and 1210 (shared with MAT-2-mm4) |
| 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. 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. |
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 |
am4 |
Contacts
| Course organiser |
Dr Joan Simon Soler
Tel: (0131 6)50 8571
Email: |
Course secretary |
Mrs Karen Downie
Tel: (0131 6)50 5793
Email: |
|
© Copyright 2011 The University of Edinburgh - 18 November 2011 6:08 am
|
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