Este libro, ahora en versión digital, ha sido escrito pensando en las necesidades que tienen los estudiantes de las diferentes ciencias experimentales, así como los de Ingenierías. Todos ellos necesitan imbuirse de la idea de que vivimos en un mundo de variabilidad, de modo que es necesario acostumbrarse a convivir con ella. Por ello resulta esencial aprender, en primer lugar, a medir y a describir la variabilidad, para así poder llegar a controlarla; éstos son objetivos esenciales de la Estadística Aplicada. Una idea adicional, que es preciso asimilar, es la diferencia esencial entre la naturaleza y los modelos científicos que se usan para describirla. Siguiendo un enfoque moderno de la Estadística Aplicada, el libro se estructura en siete capítulos que abarcan desde Estadística Descriptiva y Modelos de Probabilidad y Variables Aleatorias, hasta Ajuste de Distribuciones, Control Estadístico de Procesos e Inferencia. Los conceptos se ilustran con numerosos ejemplos realistas. Además, se proporcionan más de cien ejercicios resueltos distribuidos entre los diferentes capítulos.

This bestselling professional reference has helped over 100,000 engineers and scientists with the success of their experiments. The new edition includes more software examples taken from the three most dominant programs in the field: Minitab, JMP, and SAS. Additional material has also been added in several chapters, including new developments in robust design and factorial designs. New examples and exercises are also presented to illustrate the use of designed experiments in service and transactional organizations. Engineers will be able to apply this information to improve the quality and efficiency of working systems.

The volume presents a broad spectrum of papers which illustrates a range of current research related to the theory, methods and applications of health related quality of life (HRQoL) as well as the interdisciplinary nature of this work.

Understanding spatial statistics requires tools from applied and mathematical statistics, linear model theory, regression, time series, and stochastic processes. It also requires a mindset that focuses on the unique characteristics of spatial data and the development of specialized analytical tools designed explicitly for spatial data analysis. Statistical Methods for Spatial Data Analysis answers the demand for a text that incorporates all of these factors by presenting a balanced exposition that explores both the theoretical foundations of the field of spatial statistics as well as practical methods for the analysis of spatial data. This book is a comprehensive and illustrative treatment of basic statistical theory and methods for spatial data analysis, employing a model-based and frequentist approach that emphasizes the spatial domain. It introduces essential tools and approaches including: measures of autocorrelation and their role in data analysis; the background and theoretical framework supporting random fields; the analysis of mapped spatial point patterns; estimation and modeling of the covariance function and semivariogram; a comprehensive treatment of spatial analysis in the spectral domain; and spatial prediction and kriging. The volume also delivers a thorough analysis of spatial regression, providing a detailed development of linear models with uncorrelated errors, linear models with spatially-correlated errors and generalized linear mixed models for spatial data. It succinctly discusses Bayesian hierarchical models and concludes with reviews on simulating random fields, non-stationary covariance, and spatio-temporal processes. Additional material on the CRC Press website supplements the content of this book. The site provides data sets used as examples in the text, software code that can be used to implement many of the principal methods described and illustrated, and updates to the text itself.

The first volume defined statistical data editing and presented associated methods and software. This volume, containing some 30 contributions divided into six chapters, addresses how to solve individual data editing tasks, focusing on efficient techniques for data editing operations and for evaluat