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This is a text encompassing all of the standard topics in introductory probability theory, together with a significant amount of optional material of emerging importance. The emphasis is on a lucid and accessible writing style, mixed with a large number of interesting examples of a diverse nature. The text will prepare students extremely well for courses in more advanced probability and in statistical theory and for the actuary exam. The book covers combinatorial probability, all the standard univariate discrete and continuous distributions, joint and conditional distributions in the bivariate and the multivariate case, the bivariate normal distribution, moment generating functions, various probability inequalities, the central limit theorem and the laws of large numbers, and the distribution theory of order statistics. In addition, the book gives a complete and accessible treatment of finite Markov chains, and a treatment of modern urn models and statistical genetics. It includes 303 worked out examples and 810 exercises, including a large compendium of supplementary exercises for exam preparation and additional homework. Each chapter has a detailed chapter summary. The appendix includes the important formulas for the distributions in common use and important formulas from calculus, algebra, trigonometry, and geometry. Anirban DasGupta is Professor of Statistics at Purdue University, USA. He has been the main editor of the Lecture Notes and Monographs series, as well as the Collections series of the Institute of Mathematical Statistics, and is currently the Co-editor of the Selected Works in Statistics and Probability series, published by Springer. He has been an associate editor of the Annals of Statistics, Journal of the American Statistical Association, Journal of Statistical Planning and Inference, International Statistical Review, Sankhya, and Metrika. He is the author of Asymptotic Theory of Statistics and Probability, 2008, and of 70 refereed articles on probability and statistics. He is a Fellow of the Institute of Mathematical Statistics.

Mathematics. --- Probability Theory and Stochastic Processes. --- Distribution (Probability theory). --- Mathématiques --- Distribution (Théorie des probabilités) --- Probabilities --- 519.2 --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Mathematical statistics --- Risk

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Mathematical statistics. --- Statistiek --- Mathematical statistics --- 519.2 --- biologie --- milieu --- statistiek --- statistieken --- wiskunde --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- statistiek (wiskunde) --- Statistical methods --- Statistique mathématique --- Statistique mathématique --- Biométrie

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Increased attention is being paid to the need for statistically educated citizens: statistics is now included in the K-12 mathematics curriculum, increasing numbers of students are taking courses in high school, and introductory statistics courses are required in college. However, increasing the amount of instruction is not sufficient to prepare statistically literate citizens. A major change is needed in how statistics is taught. To bring about this change, three dimensions of teacher knowledge need to be addressed: their knowledge of statistical content, their pedagogical knowledge, and their statistical-pedagogical knowledge, i.e., their specific knowledge about how to teach statistics. This book is written for mathematics and statistics educators and researchers. It summarizes the research and highlights the important concepts for teachers to emphasize, and shows the interrelationships among concepts. It makes specific suggestions regarding how to build classroom activities, integrate technological tools, and assess students' learning. This is a unique book. While providing a wealth of examples through lessons and data sets, it is also the best attempt by members of our profession to integrate suggestions from research findings with statistics concepts and pedagogy. The book's message about the importance of listening to research is loud and clear, as is its message about alternative ways of teaching statistics. This book will impact instructors, giving them pause to consider: "Is what I'm doing now really the best thing for my students? What could I do better?" J. Michael Shaughnessy, Professor, Dept of Mathematical Sciences, Portland State University, USA This is a much-needed text for linking research and practice in teaching statistics. The authors have provided a comprehensive overview of the current state-of-the-art in statistics education research. The insights they have gleaned from the literature should be tremendously helpful for those involved in teaching and researching introductory courses. Randall E. Groth, Assistant Professor of Mathematics Education, Salisbury University, USA

Cognitive psychology --- Statistical science --- Didactics --- Didactics of mathematics --- Higher education --- HO (hoger onderwijs) --- didactiek --- cognitieve psychologie --- statistiek --- wiskunde --- Mathematical statistics --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Study and teaching --- Statistical methods

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During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

Statistiekwetenschap --- Wiskundige statistiek --- Statistische fysica --- Moleculaire biologie --- Biologie --- Ingenieurswetenschappen. Technologie --- Programmering --- Informatiesystemen --- Artificiële intelligentie. Robotica. Simulatie. Graphics --- Computer. Informatica. Automatisering --- statistische kwaliteitscontrole --- industriële statistieken --- biologie --- informatica --- database management --- robots --- moleculaire biologie --- statistisch onderzoek --- Bioinformatics. --- Computational intelligence. --- Data mining. --- Forecasting. --- Inference. --- Machine learning. --- Statistics --- Supervised learning (Machine learning). --- Computerintelligentie. --- Statistiek --- Methodology. --- Methodologie. --- MACHINE LEARNING -- 516 --- STATISTICAL LEARNING -- 516 --- SUPERVISED LEARNING -- 516 --- Bioinformatics --- Data mining --- Forecasting --- Inference --- Machine learning --- 519.23 --- 519.2 --- 681.3*I26 --- Learning, Machine --- Artificial intelligence --- Machine theory --- Ampliative induction --- Induction, Ampliative --- Inference (Logic) --- Reasoning --- Forecasts --- Futurology --- Prediction --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Intelligence, Computational --- Soft computing --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- 519.23 Statistical analysis. Inference methods --- Statistical analysis. Inference methods --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- 681.3*I26 Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- Methodology --- Data processing --- Machine Learning --- Computational intelligence --- Statistical methods --- Supervised learning (Machine learning) --- Apprentissage supervisé (Intelligence artificielle) --- EPUB-LIV-FT LIVMATHE LIVSTATI SPRINGER-B --- Mathematical statistics --- Artificial intelligence. Robotics. Simulation. Graphics --- Statistique mathématique --- Artificial intelligence. --- Probabilities. --- Statistics . --- Bioinformatics . --- Computational biology . --- Artificial Intelligence. --- Data Mining and Knowledge Discovery. --- Probability Theory and Stochastic Processes. --- Statistical Theory and Methods. --- Computational Biology/Bioinformatics. --- Computer Appl. in Life Sciences. --- Statistical analysis --- Statistical data --- Statistical science --- Mathematics --- Econometrics --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Risk --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Mathematical statistics. --- Statistique mathématique --- Statistical decision. --- Statistics - Methodology --- Statistics. --- Computational biology.

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John Chambers has been the principal designer of the S language since its beginning, and in 1999 received the ACM System Software award for S, the only statistical software to receive this award. He is author or coauthor of the landmark books on S. Now he turns to R, the enormously successful open-source system based on the S language. R's international support and the thousands of packages and other contributions have made it the standard for statistical computing in research and teaching. This book guides the reader through programming with R, beginning with simple interactive use and progressing by gradual stages, starting with simple functions. More advanced programming techniques can be added as needed, allowing users to grow into software contributors, benefiting their careers and the community. R packages provide a powerful mechanism for contributions to be organized and communicated. The techniques covered include such modern programming enhancements as classes and methods, namespaces, and interfaces to spreadsheets or data bases, as well as computations for data visualization, numerical methods, and the use of text data.

Mathematical statistics --- Programming --- gegevensanalyse --- Numerical analysis --- R (Computer program language) --- Data processing --- Computer software. --- Mathematical statistics. --- Mathematical Software. --- Statistics and Computing/Statistics Programs. --- Data analysis --- Statistical methods --- Data processing. --- Software, Computer --- Computer systems --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics --- GNU-S (Computer program language) --- Domain-specific programming languages --- Numerical analysis - Data processing