Humboldt-Universität zu Berlin - Statistics

Datenanalyse I (VL+UE)


The lecture covers classical statistical topics and we will work half of the time with R & RStudio in the PC-Pool. Note: The lecture will be held in german, but the course material will be in english.

Bitte registrieren Sie sich im zugehörigen Moodle-Kurs.

Data analysis I:

  • Survey design
    • Operationalization
    • Validity 
  • Short repetition of Statistics I+II
    • Sampling
    • Scaling
    • Test theory
    • Estimation theory
  • Univariate statistics
    • Graphical representation (stem-and-leaf, strip plot, kernel density, violin plot)
    • Coeffcients (quantile, entropy, higher moments, hinges and spreads)
    • Tests (t-Tests, Mann-Withney-U, Median, Wilcoxon, Kruskal-Wallis, ANOVA, Friedman)
    • Transformations (Power, Box-Cox)
  • Outliers
    • Identification of ouitliers
    • obust coefficients for location and dispersion (L- and M-estimators for location)
  • Missing values
    • Types (MAR, MCAR, MNAR)
    • Imputation methods (single, multiple)
  • Bivariate statistics
    • Graphical representation (sunflower plot, mosaic plot, trellis display)
    • Subgroup analysis

Data analysis II:

  • Bivariate statistics
    • Coeffcients and tests (association and PRE- measure, Cohen's kappa, relative risk, odds ratio)
  • Multivariate statistics
    • Principal component analysis
    • Exploratory factor analysis (reliability for sum scores)
    • Cluster analysis
  • Regression methods
    • Simple linear regression
    • Multiple linear regression
    • Generalized linear regression
    • Non- and Semiparametric Regression
    • Classification and regression trees
    • Neural networks


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  • Mann, P. S. (1992), Introductory Statistics, John Wiley, New York at al.
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