One of the lectures 701-1541-00 (autumn semester) OR 752-2110-00 (spring semester) are highly recommended for students in Environmental Sciences with the Major Environmental systems and Policy.
The course teaches multivariate statistical methods such as linear regression, analysis of variance, cluster analysis, factor analysis and logistic regression.
Learning objective
Upon completion of this course, the student should have acquired: (1) Knowledge on the foundations of several methods of multivariate data analysis, along with the conditions under which their use is appropriate (2) Skill in the estimation, specification and diagnostics of the various models (3) Hands-on experience with those methods through the use of appropriate software and actual data sets in the PC lab
Content
The course will begin with an introduction to multivariate methods such as analysis of variance and multiple linear regression, where a metric dependent variable is "explained" by two or more independent variables. Then two methods for structuring complex data, cluster analysis and factor analysis will be covered. In the last part, procedures for the analysis of relationships involving dichotomous or polytomous dependent variables (e.g., the choice of a mode of transportation) will be discussed.
Literature
Will be announced at the beginning of the course.
Performance assessment
Performance assessment information (valid until the course unit is held again)
The performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examination
written 60 minutes
Additional information on mode of examination
The exercise assignments are considered as a facultative Learning Element and can thus increase the final grade by 0.25. Solving 2/3 of the exercises that are handed out in good quality is the criterion for the successful performance in the exercises. Details are announced in the first lecture.
Die Übungen werden als fakultatives Lernelement berücksichtigt und können die Gesamtnote um 0.25 Notenpunkte verbessern. Eine Lösung von 2/3 der ausgegebenen Übungen in guter Qualität wird als Kriterium für das Bestehen der Übungen vorausgesetzt. Details werden in der ersten Vorlesungsstunde bekanntgegeben.
Performance assessment as a semester course