A Concise Guide to Statistics

1. Front Matter

   Pages i-xiii

   PDF

2. Basics of Probability Theory

   • Hans-Michael Kaltenbach

   Pages 1-27

3. Estimation

   • Hans-Michael Kaltenbach

   Pages 29-51

4. Hypothesis Testing

   • Hans-Michael Kaltenbach

   Pages 53-75

5. Regression

   • Hans-Michael Kaltenbach

   Pages 77-108

6. Back Matter

   Pages 109-111

   PDF

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.

• Estimation
• Hypothesis testing
• Multiple Testing
• Probability theory
• Regression

From the reviews:

“This slender volume … covers the basics of probability, estimation, hypothesis testing, and regression. The book would be a good refresher for someone with a basic background in statistics … . Overall, this is a very nice presentation of fundamental statistical methods for basic research. The writing is clear, and the mathematical presentation is careful and accurate. … Kaltenbach’s book will … be a good addition to the bookshelves of statistics students. Summing Up: Recommended. Upper-division undergraduates.” (R. L. Pour, Choice, Vol. 49 (11), July, 2012)

• Basel, Switzerland

  Hans-Michael Kaltenbach

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.

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