Libros de estadística y probabilidad, página 2 de 116

Un viaje privilegiado que revela cómo los maestros del riesgo moldean el mundo moderno «Una lectura cautivadora y reveladora que analiza la capacidad de asumir riesgos y cómo esta se aplica a las inv

Desde las antiguas interpretaciones al analizar vísceras de animales sacrificados hasta la más cercana experiencia de la lotería nacional, la humanidad siempre ha dependido del azar. en nuestro mundo moderno, la vida misma puede llegar a estar en juego cuando las probabilidades hacen acto de presencia: en la probabilidad de un falso negativo en un análisis médico, en la fiabilidad de una prueba de adn como prueba pericial legal, o en la probabilidad de sufrir una enfermedad mortal congénita.

A complete introduction to partial differential equations, this textbook provides a rigorous yet accessible guide to students in mathematics, physics and engineering. The presentation is lively and up to date, paying particular emphasis to developing an appreciation of underlying mathematical theory. Beginning with basic definitions, properties and derivations of some basic equations of mathematical physics from basic principles, the book studies first order equations, classification of second order equations, and the one-dimensional wave equation. Two chapters are devoted to the separation of variables, whilst others concentrate on a wide range of topics including elliptic theory, Green's functions, variational and numerical methods. A rich collection of worked examples and exercises accompany the text, along with a large number of illustrations and graphs to provide insight into the numerical examples. Solutions to selected exercises are included for students whilst extended solution sets are available to lecturers from solutions@cambridge.org.

In an age of big data, data journalism and with a wealth of quantitative information around us, it is not enough for students to be taught only 100 year old statistical methods using 'out of the box' software. They need to have 21st-century analytical skills too. This is an excellent and student-friendly text from two of the world leaders in the teaching and development of spatial analysis. It shows clearly why the open source software R is not just an alternative to commercial GIS, it may actually be the better choice for mapping, analysis and for replicable research. Providing practical tips as well as fully working code, this is a practical 'how to' guide ideal for undergraduates as well as those using R for the first time. It will be required reading on my own courses." - Richard Harris, Professor of Quantitative Social Science, University of Bristol R is a powerful open source computing tool that supports geographical analysis and mapping for the many geography and `non-geography' students and researchers interested in spatial analysis and mapping. This book provides an introduction to the use of R for spatial statistical analysis, geocomputation and the analysis of geographical information for researchers collecting and using data with location attached, largely through increased GPS functionality. Brunsdon and Comber take readers from `zero to hero' in spatial analysis and mapping through functions they have developed and compiled into R packages. This enables practical R applications in GIS, spatial analyses, spatial statistics, mapping, and web-scraping. Each chapter includes: Example data and commands for exploring itScripts and coding to exemplify specific functionalityAdvice for developing greater understanding - through functions such as locator(), View(), and alternative coding to achieve the same ends Self-contained exercises for students to work throughEmbedded code within the descriptive text. This is a definitive 'how to' that takes students - of any discipline - from coding to actual applications and uses of R.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

This book provides a lively and accessible introduction to the numerical solution of stochastic differential equations with the aim of making this subject available to the widest possible readership. It presents an outline of the underlying convergence and stability theory while avoiding technical details. Key ideas are illustrated with numerous computational examples and computer code is listed at the end of each chapter.The authors include 150 exercises, with solutions available online, and 40 programming tasks. Although introductory, the book covers a range of modern research topics, including Ito versus Stratonovich calculus, implicit methods, stability theory, nonconvergence on nonlinear problems, multilevel Monte Carlo, approximation of double stochastic integrals, and tau leaping for chemical and biochemical reaction networks. An Introduction to the Numerical Simulation of Stochastic Differential Equations is appropriate for undergraduates and postgraduates in mathematics, engineering, physics, chemistry, finance, and related disciplines, as well as researchers in these areas.The material assumes only a competence in algebra and calculus at the level reached by a typical first-year undergraduate mathematics class, and prerequisites are kept to a minimum. Some familiarity with basic concepts from numerical analysis and probability is also desirable but not necessary.

Aquest manual recull uns apunts preparats per fer l'assignatura "Anàlisi de dades i introducció a la probabilitat", del Grau de Matemàtiques de la Universitat de Barcelona..Es tracta d'una assignatur

El presente texto ofrece un completo panorama del estado en que se encuentra hoy en día la metodología conjunta. Desde las posiciones ya clásicas del Análisis Conjunto tradicional hasta los modernos enfoques, preponderantes en la actualidad, del Analisis de Eleccion Experimental (dos enfoques tan distintos que algunos autores los consideran sin relacion alguna entre ellos). Los dos ejemplos completos incluidos, ejecutados sobre algunos de los programas informaticos mas comunes hoy en dia (SPSS y SMRT de Sawtooth Software), facilitan la comprension de la metodologia y de sus potencialidades de aplicacion. Reducida al minimo la formulacion matematica, nos encontramos ante un manual accesible y ameno, indispensable para todo aquel lector que desee conocer en que consiste el Analisis Conjunto y como aplicarlo a sus investigaciones.

Dentro de esta colección, el análisis de correspondencias se enmarca entre las técnicas multivariantes de interdependencia, siendo su característica básica la ponderación de la distancia entre los elementos, lo que da origen a la distancia chi-cuadrado. Se aborda el analisis de correspondencias a partir de un ejemplo desarrollado intuitiva y manualmente. La exposicion teorica se realiza bajo un enfoque geometrico. Se explican tambien aspectos relativos a la codificacion difusa de variables cuantitativas agrupadas en intervalos, asi como a las particularidades del analisis de correspondencias con ellas. Los resultados de los ejemplos se obtienen por medio de los paquetes informaticos de analisis de datos SPAD version 3.21 y SPSS version 9.0 ambas para Windows. Dado que el uso de SPAD no esta demasiado generalizado, se dedican unas paginas a su funcionamiento general asi como a las caracteristicas del programa para analisis de correspondencias simples. Tambien se desarrolla lo concerniente a este tipo de analisis en SPSS, puesto que en la version 9.0 se han ampliado considerablemente las opciones disponibles. Se desarrolla con detalle el ejemplo de analisis de correspondencias simples, utilizando el de correspondencias multiples con el fin de explicar aspectos tecnicos. Se realiza tambien una comparacion entre los resultados de los dos paquetes de programas.