Students can present observations in a way that shows their fundamental structures. This is an important basis for the evaluation of data. Students can select and calculate relevant statistical measures and methods to characterize empirical data. They know the most essential concepts of data visualization and can carry out basic (exploratory) analyzes using statistical software. In this introductory lecture, data analysis is carried out by means of computer-aided processes, i.e. EXCEL and SPSS.

None

The course Descriptive Statistics introduces students to basic terminology, such as randomness, properties, and frequency. Students become familiar with graphic and algebraic methods of describing a variable, e.g. histogram, empirical distribution function, measures of central tendency and variability, box plots, and proportional figures. Furthermore, methods of analyzing two variables, such as contingency tables, scatter plots, measures of correlation (contingency and correlation coefficient) and simple regression are discussed.

• Bühl A. (2016): SPSS 23: Einführung in die moderne Datenanalyze. 15th rev. ed. Pearson Studium.
  
• Bortz J./ Schuster C. (2010): Statistik für Human- und Sozialwissenschaftler: Limitierte Sonderausgabe. 7th ed. Springer.

Lecture, tutorial

Module exam (see module description for details).