Termine
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ANKÜNDIGUNG: R-Workshop (Promovierende, Postdocs, Juniorprofessor*innen mit und ohne Tenure Track)
14. July, 9:00-17:00 / 15. July 9:00-13:00
online
Time: 9:00am - 5:00pm , 9:00 - 1:00am
Registration: until 30. June
for doctoral candidates via BayDOC: https://baydoc.uni-bayreuth.de
for postdocs, habilitation candidates and junior professors (with and without Tenure Track) via WiN-UBT Portal
- 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.
Qualifikationsziele
Being able to analyze data in R
Teilnahmevoraussetzungen
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.
Trainer
- 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
- 2014-2019: Head of „Big Data Analytics“ at Technologiecampus Grafenau at TH Deggendorf; statistical consultant
- Since 2019: Professor for Artificial Intelligence at TH Deggendorf