Undergraduate Course: Engineering Mathematics 2B (SCEE08010)
Course Outline
School  School of Engineering 
College  College of Science and Engineering 
Credit level (Normal year taken)  SCQF Level 8 (Year 2 Undergraduate) 
Availability  Available to all students 
SCQF Credits  10 
ECTS Credits  5 
Summary  The course consists of two main themes:
Theme 1: Vector calculus and integration of in two parts, taught in the first half of the term in weeks 15, and
Theme 2: Introduction to probability and statistics, at the second half in weeks 610.
In the first 10 lectures on theme 1 I will introduce the concepts of scalar and vector fields in 2 and 3 dimensions and give realworld examples of such fields in engineering systems. We will cover differentiation of these fields as well as line, double, triple and surface integration focusing on work and flux integrals. For the second theme we also have a total of 10 lectures, where 2D integration of scalar fields is fundamental, we will introduce the concepts of random events and variables, as well as the axioms of probability, with emphasis on joint and conditional probabilities, independence, Bayes theorem, and the central limit theorem. In the second half of theme 2, we switch from probability to statistics to learn about point estimators from data, their bias and variance, and then interval estimators and how to conduct hypothesis tests using data samples, before we close with an introduction in linear regression and the least squares method which is ubiquitous in engineering analysis.
The course has 1 handwritten coursework assessments with 10% of the credit each, one on each theme, and a final exam on both themes for the remaining 80% of the credit. Each coursework is scheduled for a 10 hour load including preparation reading. There will also be 4 online quizzes, two on each theme that the students are encouraged to do for formative feedback and selfassessment. In every aspect of the delivery and assessment, i.e., lectures, tutorials, coursework, exam questions, themes 1 and 2 carry equal merit. 
Course description 
Theme 1: Vector calculus and integration
Lecture 1: Scalar and vector fields, the gradient
Lecture 2: Conservative fields, divergence and curl
Lecture 3: Harmonic fields, vector calculus laws
Lecture 4: Line integration, the work integral
Lecture 5: Flux integrals, scalar line integrals
Lecture 6: Work and flux integrals in polar coordinates
Lecture 7: Double integration, changing the order
Lecture 8: Variable transformations and double integrals in polar
Lecture 9: Green's theorems for work and flux
Lecture 10: Triple integrals, cylindrical coordinates
Theme 2: Applied probability and statistics
Lecture 11: Probability axioms and laws
Lecture 12: Conditional probability, Bayes theorem
Lecture 13: Continuous and discrete random variables
Lecture 14: Bernoulli, binomial, the uniform and normal
Lecture 15: Joint random variables and independence central limit theorem, sums of random variables
Lecture 16: Sum of random variables, central limit theorem
Lecture 17: Maximum likelihood estimators
Lecture 18: Confidence intervals
Lecture 19: Z and T hypothesis tests, Type I & II errors
Lecture 20: Linear regression, least squares
Both themes are supported by tutorial classes every week from week 2 to 11.
Lecture slides and instructor notes which also include solved examples and narrated exercises as well as selfassessment questions and answers will be provided for every lecture's material. Unless specified explicitly in the lectures, all material presented in lecture slides and exercises is examinable, unless otherwise explicitly stated.

Information for Visiting Students
Prerequisites  Mathematics units passed equivalent to Mathematics for Science and Engineering 1a and Mathematics for Science and Engineering 1b. 
High Demand Course? 
Yes 
Course Delivery Information

Academic year 2024/25, Available to all students (SV1)

Quota: None 
Course Start 
Semester 2 
Timetable 
Timetable 
Learning and Teaching activities (Further Info) 
Total Hours:
100
(
Lecture Hours 20,
Seminar/Tutorial Hours 5,
Supervised Practical/Workshop/Studio Hours 5,
Online Activities 10,
Formative Assessment Hours 2,
Summative Assessment Hours 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
46 )

Assessment (Further Info) 
Written Exam
80 %,
Coursework
20 %,
Practical Exam
0 %

Additional Information (Assessment) 
Students must pass the exam and the course overall. If you fail a course you will be required to resit it. You are only required to resit components which have been failed. 
Feedback 
Not entered 
Exam Information 
Exam Diet 
Paper Name 
Hours & Minutes 

Main Exam Diet S2 (April/May)  Engineering Mathematics 2B  1:120   Resit Exam Diet (August)  Engineering Mathematics 2B Resit  1:120  
Learning Outcomes
On completion of this course, the student will be able to:
 Understanding of scalar and vector fields, differential operators for gradient, divergence and curl, line integrals for work and flux, Green's theorems on the plane and their implications on conservative and solenoidal fields
 Ability to use the basic vector differential identities and to calculate integrals over simple 2D and 3D geometries.
 Understanding the concepts of random events and variables, common discrete and continuous probability distributions, joint and independent random variables.
 Ability to compute point and interval estimators from data and quantify their error,
 Ability to perform statistical hypotheses tests and linear regression analysis

Reading List
Students are expected to access a copy of :
1. Advanced Modern Engineering Mathematics by Glyn James, Prentice Hall, ISBN 9780273719236
Students are recommended to download a copy of the free, open source, R statistics package from www.rproject.org
Additional reading list
1. Michael Corral, Vector Calculus (electronic copy free to use from the library)
1. Blitzstein, Joseph K ; Hwang, Jessica, Introduction to Probability (electronic copy free to use from the library. Covers the material of the first half of theme 2)
2. Sarah Stowell. Using R for Statistics. Apress, 2014. ISBN 9781484201404.
3. William Navidi, Statistics for Engineers and Scientists, McGrawHill, 2014. ISBN 9781259251603

Additional Information
Graduate Attributes and Skills 
Not entered 
Keywords  Vector calculus,Multiple integrals,Statistical method,Regression,Probability 
Contacts
Course organiser  Dr Nicholas Polydorides
Tel: (0131 6)50 2769
Email: N.Polydorides@ed.ac.uk 
Course secretary  Miss Mhairi Sime
Tel: (0131 6)50 5687
Email: msime2@ed.ac.uk 

