Big data doesn’t necessarily mean great insights. Thorough data analysis is at the core of many innovations in business, economics and the sciences in today’s world. Econometrics can be described as the art of gaining new insights from complex data sets with model-driven thinking (meaning you confront data with ideas about how they are actually generated).
Key aspects:
This course aims to provide an accessible, practical and hands-on introduction to econometrics and its workhorse, regression analysis. To achieve this, we will (1) appeal to intuition more than rigour, (2) explore and analyse real-world data sets, and (3) use the non-commercial software R so you will be able to continue using it later on. R is an object-oriented statistical programming language with a huge potential for the future. It is freely downloadable and widely used in the social sciences, statistics, medicine, etc. (Visit www.r-project.org).
If you do not tremble as soon as you see a number with decimals or an algebraic equation, if you understand there may be an interesting difference between a mean and a median, if you wonder whether it is true that you can prove anything with statistics and want to understand how, then this course is for you.
You should be computer-literate, know basic algebra and statistics, and have an investigative mind. You will learn and apply core concepts such as: sample data, population assumptions, regression models, estimated effects, sampling distributions, standard errors, statistical and practical significance, hypothesis tests, Monte-Carlo simulation…
Teachers/coordinators:
Denis de Crombrugghe, SBE
Richard Bluhm, UNU-MERIT
ECTS:
3
Number of participants:
Minimum: 16, maximum: 40
Tuition fee:
€ 800 (software is free).
Course schedule:
– 8 theory sessions + 8 practice sessions,
– Daily from Monday to Thursday, 3-13 August.
– First day: 10:00-12:30 + 13:30-17:00
– Following days: 9:30-12:00 + 13:00-16:00
Teaching location:
Maastricht University,
School of Business and Economics,
Tongersestraat 53, 6211 LM Maastricht
Preferred pre-knowledge and equipment:
– Good basics in algebra and statistics
– Literacy in computer usage and in standard software (e.g., Excel spread sheets, a text editor, a word processor).
– Fluency in English
Although this is not an absolute requirement, we expect students to bring their own personal computers (laptops). The software we use, R, is in the public domain and available online for Windows, Mac OS X, and Linux.
PLEASE APPLY HERE.
MEDIA CREDIT
Flickr / NASA Goddard Space Flight Center