Termine

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

• 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 

• 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