Author: Hans-Michael Kaltenbach
Edition: 2012
Publisher: Springer
Binding: Kindle Edition
ISBN: B007EL3Z7I
A Concise Guide to Statistics (SpringerBriefs in Statistics)
The text gives a concise introduction into fundamental concepts in statistics. Medical books A Concise Guide to 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 Medical books A Concise Guide to Statistics. A Concise Guide to Statistics, ISBN-13: 9783642235016, ISBN-10: 3642235018
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A Concise Guide to Statistics, ISBN-13: 9783642235016, ISBN-10: 3642235018
Springer 9783642235016 A Concise Guide to Statistics (2012 Edition) Description 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 esti
Store Search search Title, ISBN and Author A Concise Guide to Statistics by Hans-Michael Kaltenbach Estimated delivery 3-12 business days Format Paperback Condition Brand New An introduction into fundamental concepts in statistics. It includes chapters on: short exposition of probability theory, using generic examples; estimation in theory and practice, using biologically motivated examples; and, Hypothesis testing with emphasis on concepts, particularly type-I , type-II errors, and interpretin
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Medical Book A Concise Guide to 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.
