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WiN-Academy: Wissenschaftlicher Nachwuchs an der Universität Bayreuth

Promotion - Postdoc - Habilitation

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R-Workshop (Promovierende, Postdocs, Habilitierende)

14 & 15 July 2021
Online

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

Trainer: Dr. habil Robert Hable
Language: English                                                                                                                                                                   
Qualification AreaScientific Practice; Doctoral Studies
Registration: until 30 June 2021
for doctoral candidates via BayDOChttps://baydoc.uni-bayreuth.de
for postdocs and habilitation candidates 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.  

Qualification Goals                                                                                                                                                               

Being able to analyze data in R 

Participation Requirements

  • 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

Verantwortlich für die Redaktion: Eva Querengässer

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