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Statistical Analyses of Ozone Temporal Trends - An Application of Multivariate Geostatistics

modeling the temporal phenomena. Various statistical methodologies, i.e., linear regression, kriging and stochastic simulation, were performed;

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Multivariate Geostatistics

a brief review of statistical concepts, a detailed introduction to linear geostatistics, and an account of three basic methods;

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Multivariate Geostatistics: An Introduction with Applications

distributed in space or time. The text contains a brief review of statistical concepts, a detailed introduction to linear geostatistics, and an;

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Spatiotemporal Analysis of Extreme Hydrological Events

Spatio-temporal Analysis of Extreme Hydrological Events offers an extensive view of the experiences and applications of the latest;

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Multiple-point Geostatistics

images. Multiple-point geostatistics aims at bridging the gap between physical modelling/realism and spatio-temporal stochastic modelling. The;

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Geostatistics Explained

. Worked examples at the end of each chapter reinforce a clear understanding of the statistical tests and their applications.;

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Geostatistics Explained

. Worked examples at the end of each chapter reinforce a clear understanding of the statistical tests and their applications.;

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Spatiotemporal Design

A state-of-the-art presentation of optimum spatio-temporal sampling design - bridging classic ideas with modern statistical modeling;

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Multivariate Statistical Methods

Multivariate Statistical Methods: A Primer provides an introductory overview of multivariate methods without getting too deep into the;

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Multivariate Statistical Methods

Multivariate Statistical Methods: A Primer provides an introductory overview of multivariate methods without getting too deep into the;

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Using Multivariate Statistics

This text takes a practical approach to multivariate data analysis, with an introduction to the most commonly encountered statistical and;

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Using Multivariate Statistics

This text takes a practical approach to multivariate data analysis, with an introduction to the most commonly encountered statistical and;

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Using Multivariate Statistics

This text takes a practical approach to multivariate data analysis, with an introduction to the most commonly encountered statistical and;

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Statistical and Multivariate Analysis in Material Science

helping them to carry out statistical analyses and to interpret the results. It also focuses on establishing a consistent multivariate workflow;

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Multivariate Statistical Methods

Multivariate statistics refer to an assortment of statistical methods that have been developed to handle situations in which multiple;

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Multivariate Statistical Methods

Multivariate statistics refer to an assortment of statistical methods that have been developed to handle situations in which multiple;

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Multivariate Data Analysis

graduate and postgraduate students undertaking statistical analysis for business degrees, providing an application-oriented introduction to;

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An Introduction to Multivariate Data

statistics courses as well as for professionals who require an understanding of statistical techniques for analyzing their own data sets. Focused on;

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Modern Statistical Methods for Spatial and Multivariate Data

applications such as the use of spatial and spatio-temporal models for rainfall in South Carolina and the multivariate sparse areal mixed model for the;

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Using Multi Stat Pnie

, readers should be able to: Learn to conduct numerous types of multivariate statistical analyses Find the best technique to use;

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Statistical Inference For Models With Multivariate T-Distrib

the development of linear statistical models with applications to engineering, the physical sciences, and mathematics * Contains an up-to-date;

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Multivariate Analysis

This book presents the state-of-the-art information of multivariate analysis and its application in various fields. Currently, statistical;

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Univariate Bivariate & Multivariate Stat

essential statistical analyses and data management tasks in R. Requiring only minimal prior knowledge, the book introduces concepts needed for an;

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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches

Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process;

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Multivariate Humanities

analyses of various types of datasets from a wide range of sub-disciplines, promoting the use of multivariate analysis and illustrating its wide;

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Multivariate Humanities

analyses of various types of datasets from a wide range of sub-disciplines, promoting the use of multivariate analysis and illustrating its wide;

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Epidemics of Plant Diseases

disease epidemics. Topics treated are: methods in multivariate analyses; ordination and classification; modelling of temporal and spatial aspects;

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