Discovering Econometrics with R

label Diverse autorenew 2025-09-29, 17:01 history_edu Ioana Dinescu
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). The aim of this course is 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. The course is a spin off from the MARBLE (Maastricht Research Based Learning for excellence) programme. This programme aims to provide excellent students with extra research training.

Period
04-08-2014 - 15-08-2014 (2 weeks)

Target group
Bachelor's and master's students with - knowledge of the basics of algebra and statistics- Fluency in English- Literacy in computer usage and in standard software (e.g., Excel spread sheets and a text editor).

Course aim
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



Credits
3.0 ECTS credits

Course fee
EUR 750[Convert to USD]Course fee including software

Course leader
Denis de Crombrugghe and Richard Bluhm

Scholarships
None

Maastricht University + Maastricht School of Management Student Service Centre
Address: Bonnefantenstraat 2 P.O. Box 616
Postal code: 6211 KL Maastricht
City: Maastricht
Country: Netherlands
Website: http://www.maastrichtuniversity.nl/summerschool
E-mail: summerschool@maastrichtuniversity.nl
Phone: +31433885295