The text gives a concise introduction into fundamental concepts in statistics. Chapter 1: Short exposition of probability theory, using generic examples. Chapter 2: Estimation in theory and practice, using biologically motivated examples. Maximum-likelihood estimation in covered, including Fisher information and power computations. Methods for calculating confidence intervals and robust alternatives to standard estimators are given. Chapter 3: Hypothesis testing with emphasis on concepts, particularly type-I , type-II errors, and interpreting test results. Several examples are provided. T-tests are used throughout, followed important other tests and robust/nonparametric alternatives. Multiple testing is discussed in more depth, and combination of independent tests is explained. Chapter 4: Linear regression, with computations solely based on R. Multiple group comparisons with ANOVA are covered together with linear contrasts, again using R for computations.

Autorentext
Hans-Michael Kaltenbach received his Diploma in Mathematics from the University of Hannover, Germany in 2003 with a thesis on the stochastic runtime analysis of algorithms. Then he joined the International Graduate School for Bioinformatics and Genome Research at the University of Bielefeld, where he completed his PhD in 2007 with a thesis on efficient algorithms and statistics for protein identification using mass spectrometry. In 2007/2008 he became a postdoctoral fellow at the Institut Pasteur in Paris, France, working with Benno Schwikowski on algorithms for mass spectrometry and algorithms and statistics for the analysis of biological networks. Since 2008, he has been working as a postdoctoral fellow with Joerg Stelling in the computational systems biology group at the ETH Zurich, Switzerland.

Inhalt

Basics of Probability Theory.- Estimation.- Hypothesis Testing.- Regression.- References.- Index.