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Mathematical statistics --- Analysis of variance --- ANOVA (Analysis of variance) --- Variance analysis --- Experimental design

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Mixed modeling is one of the most promising and exciting areas of statistical analysis, enabling the analysis of nontraditional, clustered data that may come in the form of shapes or images. This book provides in-depth mathematical coverage of mixed models' statistical properties and numerical algorithms, as well as applications such as the analysis of tumor regrowth, shape, and image. The new edition includes significant updating, over 300 exercises, stimulating chapter projects and model simulations, inclusion of R subroutines, and a revised text format. The target audience continues to be graduate students and researchers. An author-maintained web site is available with solutions to exercises and a compendium of relevant data sets

Analysis of variance --- Mathematics --- ANOVA (Analysis of variance) --- Variance analysis --- Mathematical statistics --- Experimental design

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The analysis of variance (ANOYA) models have become one of the most widely used tools of modern statistics for analyzing multifactor data. The ANOYA models provide versatile statistical tools for studying the relationship between a dependent variable and one or more independent variables. The ANOYA mod­ els are employed to determine whether different variables interact and which factors or factor combinations are most important. They are appealing because they provide a conceptually simple technique for investigating statistical rela­ tionships among different independent variables known as factors. Currently there are several texts and monographs available on the sub­ ject. However, some of them such as those of Scheffe (1959) and Fisher and McDonald (1978), are written for mathematically advanced readers, requiring a good background in calculus, matrix algebra, and statistical theory; whereas others such as Guenther (1964), Huitson (1971), and Dunn and Clark (1987), although they assume only a background in elementary algebra and statistics, treat the subject somewhat scantily and provide only a superficial discussion of the random and mixed effects analysis of variance.

Analysis of variance --- 519.233.4 --- #ABIB:astp --- ANOVA (Analysis of variance) --- Variance analysis --- Mathematical statistics --- Experimental design --- Variance analysis. Covariance analysis --- 519.233.4 Variance analysis. Covariance analysis --- Applied mathematics. --- Engineering mathematics. --- Probabilities. --- Statistics . --- Mathematical analysis. --- Analysis (Mathematics). --- Applications of Mathematics. --- Probability Theory and Stochastic Processes. --- Statistical Theory and Methods. --- Analysis. --- 517.1 Mathematical analysis --- Mathematical analysis --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Risk --- Engineering --- Engineering analysis

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The analysis of variance is presented as an exploratory component of data analysis, while retaining the customary least squares fitting methods. Balanced data layouts are used to reveal key ideas and techniques for exploration. The approach emphasizes both the individual observations and the separate parts that the analysis produces. Most chapters include exercises and the appendices give selected percentage points of the Gaussian, t, F chi-squared and studentized range distributions.

Mathematical statistics --- Analyse de variance --- Analysis of variance --- Variantie-analyse --- ANOVA (Analysis of variance) --- Variance analysis --- Experimental design --- Analysis of variance.

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Multivariate analysis --- Analysis of variance --- ANOVA (Analysis of variance) --- Variance analysis --- Mathematical statistics --- Experimental design --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Matrices