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Several topics relevant to modern biology (multiple testing, minimax estimation) usually not found in similar texts
Concise first introduction or repetition of fundamental concepts in statistics - Pointers to relevant R functions and datatypes
Robust and nonparametric counterparts of classical estimators and tests given throughout the text
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.
Content Level »Upper undergraduate
Keywords »Estimation - Hypothesis testing - Multiple Testing - Probability theory - Regression