Mathematics for Social Scientists, Part 3
Dr Dan Brawn, University of Essex
10 -21 August (two week course / 15 hrs)
Dan Brawn is a Lecturer in the Department of Mathematical Sciences.
- This component of the course focuses on solving systems of linear equations by Gaussian elimination; inverse matrices and singularity; vector spaces and subspaces; linear dependence, dimension, and rank; matrix eigenvalues and eigenvectors. It also considers the application of these topics to the linear regression problem and to the principal components problem.
- To provide participants with the essentials of linear algebra required for the study of multivariate analysis. Emphasis is placed on the relationship between the algebra and geometry of vector spaces.
- For participants who have not taken the second half of Mathematics for Social Scientists, Part 2, some familiarity with matrix arithmetic is required.
- For a review of the concepts listed in the prerequisites we recommend the Matrices and Vectors quick reference leaflets which can be found by following the leaflets link from http://www.mathscentre.ac.uk/students.php. Note that the site also has learning resources available for these and other basic mathematical topics.
Representative Backround Reading
- Haeussler, E.F., Paul, R.S., and, Wood, R. 2004. Introductory Mathematical Analysis for Business, Economics, and the Life and Social Sciences. Prentice Hall.