[Graphical methods in data analysis (author's transl)].

Data analysis is concerned with attentive description and communication of the information contents of a body of data. Background information, conceptual insight and especially graphical methods play a key role in data analysis for developing a feeling for the data both by formal procedures to be applied in the light of specified models and even more by informal inference or methods that are suggestive and conctructive. This paper reviews graphical methods useful for description, screening, analysis, cross-examining, selection, reduction, presentation and summary of data: for uncovering distributional peculiarities and understanding the structure underlying experimental and survey data. Moreover scatter plots, probability plots and residual plots provide insight into the possible inappropriateness of certain assumptions of the statistical model. Some techniques are illustrated by examples: four-dimensional data may be reprented as scatter plot on ordinary graph paper by using a combination of 2 different sets of symbols for at most 7 different levels of the third variable (formula: see text) and of the fourth variable (formula: see text). Comments on the use of tables and graphical methods, a small overview of the latter and of the scope of applications endeavour to pave the way such that structures may be better understandable and unanticipated characteristics may be spotted.