Topics in Applied Statistics

• Part 1: Correspondence Analysis
  Correspondence Analysis is a method of analyzing and visualizing categorical data. The course covers the theory and application of simple, multiple and joint correspondence analysis. Further topics include subset analyses and aspects of stability and inference.
  The statistical software package R will be used in the computer lab sessions in which participants will be expected to apply the methods in a variety of case studies.


• Part 2: Bootstrap
  The bootstrap method is a computer-based method for assigning measures of accuracy to sample estimates. This technique allows estimation of the sample distribution of almost any statistic using only very simple but computationally intensive methods.
  The course will cover the theory and application of the bootstrap for the estimation of the bias and standard error of estimators, and for the computation of confidence intervals. Topics covered will include double bootstrap methods, the bootstrap-t method, the percentile method and the bias-corrected-accelerated method.
  The statistical software package R will be used in the computer lab sessions in which participants will be expected to apply the methods in a variety of case studies.

• M. Greenacre (2007): "Correspondence Analysis in Practice", 2nd Edition, Chapmann & Hall/CRC, Boca Raton.
• M. Greenacre (1984): "Theory and Applications of Correspondence Analysis", Academic Press, London.
• M. Greenacre & J. Blasius (eds., 2006): "Multiple Correspondence Analysis and Related Methods", Chapmann & Hall / CRC, Boca Raton.

• Lecture notes "Bootstrap"
• A.C. Davidson & D.V. Hinkley (1997): "Bootstrap Methods and Their Applications", Cambridge University Press.
• B. Efron & R.J. Tibshirani (1993): "An Introduction to the Bootstrap", Chapman & Hall.

• Course page on UniVZ