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WiN-Academy: Wissenschaftlicher Nachwuchs an der Universität Bayreuth

Promotion - Postdoc - Habilitation

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26-27 March 2020
S 56 (RW I)

Time: 9:00am - 5:00pm, 9:00am - 1:00pm

Trainer: Dr. habil Robert Hable
Language: English
Registration: until 12 March 2020
for doctoral candidates via BayDOC: https://baydoc.uni-bayreuth.de
for Postdocs and Habilitierende via https://baydoc.uni-bayreuth.de/ubt/de/intern/postdoc/

This is a basic course on R and it does not assume any preknowledge on R.

The course assumes at least a basic knowledge on statistics: mean, standard deviation, median, quantile, confidence interval, hypothesis testing, p value, significance, linear regression.

An optimal prerequisite would be knowledge of (nearly) all of the statistical methods covered in the workshop. Though all of these methods are quickly recapitulated in the workshop, learning R is easier if the statistical methods are already known.

Workshop content                                                                                                                                                                

  • Short  Introduction  into  the  User  Interface. 
  • Import Data from Excel and Handle Data in R
  • Descriptive Statistics: Mean, Median, Quantiles, Standard Deviation, Correlations (Pearson, Spearman), Scatterplots (and Graphical Parameters), Boxplots, Histograms, Bar Plots
  • R Scripts, R Packages, R Help and Books
  • Comparing Means (and Medians): Confidence Intervals, Popular Tests for
  • Comparisons, (t-Test, Mann-Whitney/Wilcoxon, Kruskal-Wallis, . . . )
  • Check  of  Assumptions:  Testing  Normality,  qq-Plots,  Homoscedasticity:  Variance Homogeneity (Levene)
  • Principal Components Analysis
  • Linear  Regression  and  Analysis  of  Variance:  Introduction  into  Linear  Regression with R, ANOVA, MANOVA, Post-Hoc-Tests, Generalized Linear Regression (logistic, probit)
  • Repeated Measurements (Longitudinal Studies)

This is a hands-on workshop which includes exercises in which participants analyze data in R on the computer.  

Qualification objective

Being able to analyze data in R


  • Diploma in Mathematics (2001-2006)
  • Ph.D. Thesis at the Department of Statistics at LMU Munich (2006-2009)
  • Akademischer Rat a. Zt. At the University of Bayreuth (2009-2014)
  • Habilitation on machine learning at the University of Bayreuth (2012)
  • Associate Editor for the journal Statistics & Probability Letters
  • Author of numerous articles on statistics and machine learning, author of a textbook on stochastic theory
  • Since 2014: Head of „Big Data Analytics“ at Technologiecampus Grafenau at TH Deggendorf; statistical consultant

Verantwortlich für die Redaktion: Eva Querengässer

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