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The target audience for this book is college students who are required to learn statistics, students with little background in mathematics and often no motivation to learn more. It is assumed that the students do have basic skills in using computers and have access to one. Moreover, it is assumed that the students are willing to actively follow the discussion in the text, to practice, and more importantly, to think. Teaching statistics is a challenge. Teaching it to students who are required to learn the subject as part of their curriculum, is an art mastered by few. In the past I have tried to master this art and failed. In desperation, I wrote this book. This book uses the basic structure of generic introduction to statistics course. However, in some ways I have chosen to diverge from the traditional approach. One divergence is the introduction of R as part of the learning process. Many have used statistical packages or spreadsheets as tools for teaching statistics. Others have used R in advanced courses. I am not aware of attempts to use R in introductory level courses. Indeed, mastering R requires much investment of time and energy that may be distracting and counterproductive for learning more fundamental issues. Yet, I believe that if one restricts the application of R to a limited number of commands, the benefits that R provides outweigh the difficulties that R engenders. Another departure from the standard approach is the treatment of probability as part of the course. In this book I do not attempt to teach probability as a subject matter, but only specific elements of it which I feel are essential for understanding statistics. Hence, Kolmogorov’s Axioms are out as well as attempts to prove basic theorems and a Balls and Urns type of discussion. On the other hand, emphasis is given to the notion of a random variable and, in that context, the sample space.

Mathematics --- Applied mathematics --- Computer Science

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Gene mapping is used in experimental genetics to improve the hardiness or productivity of animals or plants of agricultural value, to explore basic mechanisms of inheritance, or to study animal models of human inheritance. In human populations it is used as a first step to identify genes associated with human health and disease. This book presents a unified discussion of the statistical concepts applied in gene mapping, first in the experimental context of crosses of inbred lines and then in outbred populations, primarily humans. The development involves elementary principles of probability and statistics, which are implemented by computational tools based on the R programming language to simulate genetic experiments and evaluate statistical analyses. The viewpoint reflects the modern approach of using anonymous DNA markers distributed throughout the genome to identify regions likely to contain genes of interest. The reader is assumed to have some familiarity with probability/statistics and with elementary genetics. Important topics are reviewed in the first three chapters. The R programming language is developed in the text. Each chapter contains exercises, both theoretical and computational, some routine and others that are more challenging. The book is suitable for upper level undergraduate students or graduate students of genetics or statistics. David Siegmund is the John D. and Sigrid Banks Professor in the Department of Statistics,Stanford University. He has been a visitor at The Hebrew University, the University of Zurich, the University of Heidelberg, the National University of Singapore, and the Free University of Amsterdam. He is a member of the National Academy of Sciences (USA) and the American Academy of Arts and Sciences. Benjamin Yakir is Associate Professor of Statistics at The Hebrew University of Jerusalem and has been a visiting professor at Stanford University, the University of Pennsylvania, and the National University of Singapore.

Gene mapping --- Cartes chromosomiques --- Statistical methods. --- Méthodes statistiques --- EPUB-LIV-FT LIVBIOMO LIVMEDEC SPRINGER-B --- Human genetics. --- Bioinformatics. --- Statistics. --- Computer simulation. --- Animal genetics. --- Genetics --- Human Genetics. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Simulation and Modeling. --- Animal Genetics and Genomics. --- Genetics and Population Dynamics. --- Mathematics. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Biology --- Embryology --- Mendel's law --- Adaptation (Biology) --- Breeding --- Chromosomes --- Heredity --- Mutation (Biology) --- Variation (Biology) --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- Bio-informatics --- Biological informatics --- Information science --- Computational biology --- Systems biology --- Heredity, Human --- Human biology --- Physical anthropology --- Data processing --- Statistics . --- Biomathematics.

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Gene mapping is used in experimental genetics to improve the hardiness or productivity of animals or plants of agricultural value, to explore basic mechanisms of inheritance, or to study animal models of human inheritance. In human populations it is used as a first step to identify genes associated with human health and disease. This book presents a unified discussion of the statistical concepts applied in gene mapping, first in the experimental context of crosses of inbred lines and then in outbred populations, primarily humans. The development involves elementary principles of probability and statistics, which are implemented by computational tools based on the R programming language to simulate genetic experiments and evaluate statistical analyses. The viewpoint reflects the modern approach of using anonymous DNA markers distributed throughout the genome to identify regions likely to contain genes of interest. The reader is assumed to have some familiarity with probability/statistics and with elementary genetics. Important topics are reviewed in the first three chapters. The R programming language is developed in the text. Each chapter contains exercises, both theoretical and computational, some routine and others that are more challenging. The book is suitable for upper level undergraduate students or graduate students of genetics or statistics. David Siegmund is the John D. and Sigrid Banks Professor in the Department of Statistics,Stanford University. He has been a visitor at The Hebrew University, the University of Zurich, the University of Heidelberg, the National University of Singapore, and the Free University of Amsterdam. He is a member of the National Academy of Sciences (USA) and the American Academy of Arts and Sciences. Benjamin Yakir is Associate Professor of Statistics at The Hebrew University of Jerusalem and has been a visiting professor at Stanford University, the University of Pennsylvania, and the National University of Singapore.

Gene mapping --- Cartes chromosomiques --- Statistical methods. --- Méthodes statistiques --- EPUB-LIV-FT LIVBIOMO LIVMEDEC SPRINGER-B