This course webpage supports an introductory module on quantitative economics as taught by Fowad Murtaza and Domenico Tabasso at the University of Essex in 2009/10. It introduces students to the methods of quantitative economics, i.e. to how data are used in economics. Beginning from an elementary level (assuming no background in statistics), the course shows how economic data can be described and analysed. The elements of probability and random variables are introduced in the context of economic applications. The probability theory enables an introduction to elementary statistical inference: parameter estimation, confidence intervals and hypothesis tests. With these foundations, students are then introduced to the linear regression model that forms a starting point for econometrics. It includes a course outline / handbook, lecture presentations, lecture notes, coursework assignments, problem sets with solutions and statistical data.

# Curricula and Syllabi in Maths for Economists

This course webpage supports a module on methods of economic analysis as taught by Melvyn Coles at the University of Essex in 2009/10. It teaches the necessary mathematical techniques required for a modern degree in Economics. It starts at a fairly basic level and so is ideal for students with a weak background in mathematics. Whenever possible the module considers economics examples so that students not only learn important mathematical skills but also learn how to apply those skills to problems of economic interest. It includes a course outline, lecture notes, problem sets with solutions and assignments.

Lecture notes presented here in PDF, deal with topics including differentiation and integration, utility functions and dynamic models, as part of a webpage supporting a Basic Mathematics for Economists course at the LSE in 2003/4. It also includes a course syllabus, class exercises and solutions. The link goes to Archive.org's copy of the site, which is no longer on the LSE server.

Module page for a course on Mathematical Economics as taught by Martin Sefton and Vincent Anesi of the University of Nottingham. It gives an outline of the aims, content and objectives of the module, with links to supporting learning materials - although some of these are password protected.