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Mathematical statistics --- Multiple comparisons (Statistics) --- Multivariate analysis --- #PBIB:2003.3 --- #SBIB:052.AANKOOP --- #SBIB:303H520 --- 519.237 --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Multivariate statistical methods --- 519.237 Multivariate statistical methods --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Matrices --- Comparisons, Multiple (Statistics) --- Correlation (Statistics) --- Regression analysis
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Mathematical statistics --- Multivariate analysis --- 519.2 --- 519.237 --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Matrices --- Probability. Mathematical statistics --- Multivariate statistical methods --- Multivariate statistische analyse --- Statistiek --- Multivariate statistische analyse. --- Statistiek. --- 519.237 Multivariate statistical methods --- 519.2 Probability. Mathematical statistics
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Quantitative methods in social research --- Mathematical statistics --- Multivariate analysis. --- 519.237 --- 519.219 --- Multivariate analysis --- 519.535 --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Matrices --- Multivariate statistical methods --- Random functions of several variables --- #A9610KW --- Multivariate statistische analyse --- 519.219 Random functions of several variables --- 519.237 Multivariate statistical methods --- Multivariate statistische analyse.
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This book is primarily written for ecologists needing to analyse data resulting from field observations and experiments. It will be particularly useful for students and researchers dealing with complex ecological problems, such as the variation of biotic communities with environmental conditions or the response of biotic communities to experimental manipulation. Following a simple introduction to ordination methods, the text focuses on constrained ordination methods (RDA, CCA) and the use of permutation tests on statistical hypotheses of multivariate data. An overview of classification methods, or modern regression methods (GLM, GAM, loess), is provided and guidance on the correct interpretation of ordination diagrams is given. Seven case studies of varying difficulty help to illustrate the suggested analytical methods, using the Canoco for Windows software. The case studies utilise both the descriptive and manipulative approaches, and they are supported by data sets and project files available from the book website.
Ecology. --- Multivariate analysis. --- Ecology --- Multivariate analysis --- Earth & Environmental Sciences --- Statistical methods --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Mathematical statistics --- Matrices --- 519.237 --- 57.087.1 --- 57.087.1 Biometry. Statistical study and treatment of biological data --- Biometry. Statistical study and treatment of biological data --- 519.237 Multivariate statistical methods --- Multivariate statistical methods --- ecology --- biota --- Data analysis --- computer applications --- Statistical methods. --- Ecologie --- Analyse multivariée --- Méthodes statistiques
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The third edition of this very successful text book provides an introduction to geostatistics stressing the multivariate aspects for scientists, engineers or statisticians. Geostatistics offers a variety of models, methods and techniques for the analysis, estimation and display of multivariate data distributed in space or time. This book presents a brief review of statistical concepts, a detailed introduction to linear geostatistics, and an account of three methods of multivariate analysis. Moreover, it contains an advanced presentation of linear models for mulitvariate spatial or temporal data, of nonlinear models and methods for selection problems with change of support, as well as an introduction to non-stationary geostatistics with special focus on the external drift method. Applications from very different areas of science as well as exercices with solutions are provided to help convey the general ideas.
Geology. Earth sciences --- Mathematical statistics --- Geology --- Multivariate analysis --- Géologie --- Analyse multivariée --- Statistical methods --- Méthodes statistiques --- 55:519.2 --- 519.237 --- -Multivariate analysis --- anamorfose --- anisotropie --- covariante functie --- gauss --- geostatistiek --- kansrekening --- lineaire regressie --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Matrices --- Geognosy --- Geoscience --- Earth sciences --- Natural history --- Earth sciences. Geology. Meteorology etc.-:-Probability. Mathematical statistics --- Multivariate statistical methods --- Multivariate analysis. --- Statistical methods. --- 519.237 Multivariate statistical methods --- 55:519.2 Earth sciences. Geology. Meteorology etc.-:-Probability. Mathematical statistics --- Geology. --- Earth sciences. --- Statistics . --- Earth Sciences, general. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Statistical analysis --- Statistical data --- Statistical science --- Mathematics --- Econometrics --- Geosciences --- Environmental sciences --- Physical sciences
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Nonlinear theories --- Linear models (Statistics) --- Parameter estimation --- Regression Analysis --- 519.237 --- 519.246 --- Regression analysis --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Estimation theory --- Stochastic systems --- Nonlinear problems --- Nonlinearity (Mathematics) --- Calculus --- Mathematical analysis --- Mathematical physics --- Models, Linear (Statistics) --- Mathematical models --- Mathematical statistics --- Statistics --- Multivariate statistical methods --- Statistics of stochastic processes. Estimation of stochastic processes. Hypothesis testing. Statistics of point processes. Time series analysis. Auto-correlation. Regression --- 519.246 Statistics of stochastic processes. Estimation of stochastic processes. Hypothesis testing. Statistics of point processes. Time series analysis. Auto-correlation. Regression --- 519.237 Multivariate statistical methods
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Most of the observable phenomena in the empirical sciences are of multivariate nature. This book presents the tools and concepts of multivariate data analysis with a strong focus on applications. The text is devided into three parts. The first part is devoted to graphical techniques describing the distributions of the involved variables. The second part deals with multivariate random variables and presents from a theoretical point of view distributions, estimators and tests for various practical situations. The last part covers multivariate techniques and introduces the reader into the wide basket of tools for multivariate data analysis. The text presents a wide range of examples and 228 exercises.
Multivariate analysis --- Multivariate analysis. --- Mathematical statistics --- 519.2 --- 519.237 --- 519.535 --- 519.237 Multivariate statistical methods --- Multivariate statistical methods --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Matrices --- Probabilities. --- Statistics . --- Economic theory. --- Probability Theory and Stochastic Processes. --- Statistical Theory and Methods. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Economic Theory/Quantitative Economics/Mathematical Methods. --- Economic theory --- Political economy --- Social sciences --- Economic man --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Risk
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