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Analysis of covariance --- Analysis of covariance. --- Statistique mathématique --- Mathematical statistics --- Analyse de covariance
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Analysis of covariance --- Multivariate analysis --- Statistics
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A complete guide to cutting-edge techniques and best practices for applying covariance analysis methods The Second Edition of Analysis of Covariance and Alternatives sheds new light on its topic, offering in-depth discussions of underlying assumptions, comprehensive interpretations of results, and comparisons of distinct approaches. The book has been extensively revised and updated to feature an in-depth review of prerequisites and the latest developments in the field. The author begins with a discussion of essential topics relating to experimental design and analysis
Mathematical statistics --- Analysis of covariance --- Analysis of covariance. --- Covariance analysis --- Statistical methods --- Quasi-experiments --- Single-case studies --- Regression analysis --- Regression analysis.
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The aim of this book is to provide computationally feasible and statistically efficient methods for estimating sparse and large covariance matrices of high-dimensional data Focusing on methodology and computation more than on theorems and proofs, this book provides computationally feasible and statistically efficient methods for estimating sparse and large covariance matrices of high-dimensional data. Extensive in breadth and scope, it features ample applications to a number of applied areas, including business and economics, computer science, engineering, and financial mathematics; recognizes the important and significant contributions of longitudinal and spatial data; and includes various computer codes in R throughout the text and on an author-maintained web site
Mathematical statistics --- Analysis of covariance --- Multivariate analysis --- Mathematics --- Probability --- Statistics
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This book is about the coordinate-free, or geometric, approach to the theory of linear models; more precisely, Model I ANOVA and linear regression models with non-random predictors in a finite-dimensional setting. This approach is more insightful, more elegant, more direct, and simpler than the more common matrix approach to linear regression, analysis of variance, and analysis of covariance models in statistics. The book discusses the intuition behind and optimal properties of various methods of estimating and testing hypotheses about unknown parameters in the models. Topics covered range from linear algebra, such as inner product spaces, orthogonal projections, book orthogonal spaces, Tjur experimental designs, basic distribution theory, the geometric version of the Gauss-Markov theorem, optimal and non-optimal properties of Gauss-Markov, Bayes, and shrinkage estimators under assumption of normality, the optimal properties of F-test, and the analysis of covariance and missing observations.
Linear models (Statistics) --- Analysis of variance --- Regression Analysis --- Analysis of covariance --- Analysis of variance. --- Regression analysis. --- Analysis of covariance. --- Covariance analysis --- Regression analysis --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- ANOVA (Analysis of variance) --- Variance analysis --- Mathematical statistics --- Experimental design --- Models, Linear (Statistics) --- Mathematical models --- Statistics --- Mathematical Sciences --- Probability
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Structural equation modeling. --- Analysis of covariance. --- Linear models (Statistics) --- Multilevel models (Statistics) --- Hierarchical linear models (Statistics) --- Mixed effects models (Statistics) --- Random coefficient models (Statistics) --- Variance component models (Statistics) --- Mathematical models --- Regression analysis --- Models, Linear (Statistics) --- Mathematical statistics --- Statistics --- Covariance analysis --- SEM (Structural equation modeling) --- Multivariate analysis --- Factor analysis --- Path analysis (Statistics) --- Structural equation modeling --- Analysis of covariance
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VARIANCE[ANALYSIS OF] --- REGRESSION AND CORRELATION --- COVARIANCE[ANALYSIS OF] --- STATISTICAL METHODS IN AGRONOMY --- STATISTICAL METHODS IN BIOLOGY --- SAMPLING THEORY --- Statistics --- Agriculture --- Biology
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La statistique - considérée comme l'ensemble des méthodes qui ont pour but de recueillir et d'analyser des données relatives à des groupes d'individus ou d'objets - joue un rôle essentiel dans de très nombreuses disciplines. Tel est le cas, entre autres, pour les sciences du vivant : biologie, agronomie, écologie, etc. Les deux tomes de Statistique théorique et appliquée ont précisément pour objectif de permettre aux scientifiques de disciplines très variées, en particulier les sciences du vivant, d'utiliser au mieux les méthodes statistiques classiques, sans en négliger ni les fondements ni les limites. L'objet du tome 1 est la présentation des notions de base de statistique descriptive (à une et à deux dimensions), de statistique théorique (à une et à deux dimensions également), et d'inférence statistique (distributions d'échantillonnage, problèmes d'estimation et tests d'hypothèses). Cet ouvrage est conçu de manière à être à la fois un manuel et un livre de référence. A cette fin, il comporte une documentation détaillée, dont plus de 350 références bibliographiques, des tables, et divers index (index bibliographique, index des traductions anglaises, index des matières et index des symboles). Son utilisation comme manuel est facilitée par la définition de différents plans de lecture, clairement indiqués tout au long du texte, et par la présence de nombreux exemples et exercices, accompagnés de leurs solutions. Des informations complémentaires sont présentées dans un site web. Quatrième de couverture.
Mathematische statistiek --- Statistique mathématique --- Mathematical statistics --- Méthode statistique --- Statistical methods --- Statistique mathématique --- Statistique mathématique. --- Analyse de variance. --- Analysis of variance --- Analyse de covariance. --- Analysis of covariance --- Statistics as Topic --- Statistics as Topic. --- Probabilités. --- Statistics --- data collection --- Mathematical statistics. --- Statistique --- Distribution (théorie des probabilités) --- Probability --- Probabilities
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1.Introduction générale et collecte des données - 2.La statistique descriptive - 3.La probabilité mathématique et les distributions théoriques - 4.Les principes de l'inférence statistique - 5.Annexes
Statistics as Topic --- Probabiliteit--Theorie --- Probabiliteitstheorie --- Probabilité [Théorie de la ] --- Waarschijnlijkheid--Theorie --- Waarschijnlijkheidstheorie --- Analyse de covariance --- Mathematical statistics --- Statistics as Topic. --- Statistique mathématique --- Probability --- data collection --- Statistical methods --- Statistique mathématique --- Analyse de variance --- Statistics --- Probabilities --- Statistique mathématique. --- Analyse de variance. --- Analyse de covariance. --- Probabilités. --- Mathematical statistics. --- Statistique --- Distribution (théorie des probabilités) --- Statistics as Topic - textbooks --- Probability - textbooks
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