• Gain insights in the most important multivariate statistical techniques;
• Obtain skills in implementing these techniques using R;
• Being able to select an appropriate multivariate technique, apply it sensibly to empirical data, and write a short report about it.
In this course, students learn to apply several statistical multivariate analysis techniques and their application in business and economics. Among the techniques to be treated are multiple regression, analysis of variance, principal components analysis, cluster analysis, (multinomial) logistic regression, and multidimensional scaling.
Emphasis in this course lies on understanding what statistical technique to use, when to use it, and how to use it given a practical research question. Students are encouraged to bring their own data sets and apply the techniques to these data. Through assignments on empirical data sets (either provided by the student or by the teachers) and by using R software students are trained in using the techniques. It is assumed that the students have followed a basic course in statistics.
During this course, each week a group assignments need to be made. To pass this course, an individual final assignment should be made.
Selected chapters of Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning. Springer-Verlag, New York, ISBN 978-1-4614-7137-0, doi: 10.1007/978-1-4614-7138-7,978-1-4614-7138-7, (freely downloadable at www-bcf.usc.edu/~gareth/ISL/) and selected readings that will provided during the course.
More information and detailed timetables can be found here.
ERIM PhD candidates and Research Master students can register for this course via SIN Online.
External (non-ERIM) participants are welcome to this course. To register, please fill in the registration form and e-mail it to firstname.lastname@example.org by four weeks prior to the start of the course. Please note that the number of places for this course is limited. For external participants, the course fee is 260 euro per ECTS credit.