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The majority of modern instruments are computerised and provide incredible amounts of data. Methods that take advantage of the flood of data are now available; importantly they do not emulate 'graph paper analyses' on the computer. Modern computational methods are able to give us insights into data, but analysis or data fitting in chemistry requires the quantitative understanding of chemical processes. The results of this analysis allows the modelling and prediction of processes under new conditions, therefore saving on extensive experimentation. Practical Data Analysis in Chemistry exe

Analysis of variance. --- Chemistry --- Statistical methods. --- ANOVA (Analysis of variance) --- Variance analysis --- Mathematical statistics --- Experimental design

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Analysis of variance (ANOVA) is a core technique for analysing data in the Life Sciences. This reference book bridges the gap between statistical theory and practical data analysis by presenting a comprehensive set of tables for all standard models of analysis of variance and covariance with up to three treatment factors. The book will serve as a tool to help post-graduates and professionals define their hypotheses, design appropriate experiments, translate them into a statistical model, validate the output from statistics packages and verify results. The systematic layout makes it easy for readers to identify which types of model best fit the themes they are investigating, and to evaluate the strengths and weaknesses of alternative experimental designs. In addition, a concise introduction to the principles of analysis of variance and covariance is provided, alongside worked examples illustrating issues and decisions faced by analysts.

Biometry. --- Analysis of variance. --- Analysis of variance --- Biometry --- Biological statistics --- Biology --- Biometrics (Biology) --- Biostatistics --- Biomathematics --- Statistics --- ANOVA (Analysis of variance) --- Variance analysis --- Mathematical statistics --- Experimental design --- Statistical methods

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Designed as a self-contained text, this book covers a wide spectrum of topics on portfolio theory. It covers both the classical-mean-variance portfolio theory as well as non-mean-variance portfolio theory. The book covers topics such as optimal portfolio strategies, bond portfolio optimization and risk management of portfolios. In order to ensure that the book is self-contained and not dependent on any pre-requisites, the book includes three chapters on basics of financial markets, probability theory and asset pricing models, which have resulted in a holistic narrative of the topic. Retaining the spirit of the classical works of stalwarts like Markowitz, Black, Sharpe, etc., this book includes various other aspects of portfolio theory, such as discrete and continuous time optimal portfolios, bond portfolios and risk management. The increase in volume and diversity of banking activities has resulted in a concurrent enhanced importance of portfolio theory, both in terms of management perspective (including risk management) and the resulting mathematical sophistication required. Most books on portfolio theory are written either from the management perspective, or are aimed at advanced graduate students and academicians. This book bridges the gap between these two levels of learning. With many useful solved examples and exercises with solutions as well as a rigorous mathematical approach of portfolio theory, the book is useful to undergraduate students of mathematical finance, business and financial management.

Analysis of variance. --- Portfolio management --- Mathematical models. --- ANOVA (Analysis of variance) --- Variance analysis --- Mathematical statistics --- Experimental design --- Anàlisi de variància --- Gestió de cartera --- Models matemàtics

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Analysis of variance is the backbone of experimental research. This book is a clear and straightforward guide to how to do the analyses, with an emphasis on how to interpret statistical results and translate them into prose that will clearly tell the audience what the data are saying.

Analysis of variance. --- Experimental design. --- Design of experiments --- Statistical design --- Mathematical optimization --- Research --- Science --- Statistical decision --- Statistics --- Analysis of means --- Analysis of variance --- ANOVA (Analysis of variance) --- Variance analysis --- Mathematical statistics --- Experimental design --- Experiments --- Methodology

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« Comme toutes les sciences, les sciences cognitives sont confrontées à la variabilité des phénomènes qu'elles étudient, et cherchent à dégager de cette variabilité un ensemble de régularités, d'invariants, sur lesquels ancrer les connaissances. Cette quête d'invariance implique des choix quant aux formes de variabilité à prendre en considération. Certaines, jugées pertinentes pour l'objet d'étude, sont utilisées ou manipulées pour en extraire des invariants, tandis que d'autres, jugées sans importance, sont négligées ou neutralisées. Concernant ces choix, les opinions et les pratiques sont changeantes selon les époques, l'état d'avancement des disciplines ou les courants théoriques au sein d'une même discipline. Des formes de variabilité ignorées à une époque peuvent devenir intéressantes un peu plus tard. Il semble précisément que nous soyons à une époque où le regard porté sur la variabilité évolue, notamment dans les sciences cognitives. La recherche d'universaux a souvent conduit à centrer l'analyse sur les tendances moyennes et à attribuer la variabilité observée autour de ces tendances aux erreurs de mesure ou à des bruits parasites sans grande importance. Or, dans beaucoup de disciplines concernées par la cognition, le rôle reconnu à la variabilité dans les mécanismes adaptatifs et, plus particulièrement, dans les processus d'auto-organisation, conduit à reconsidérer son statut. Cette évolution des idées suscite un regain d'intérêt pour l'étude des différentes formes de variabilité - intra-individuelle, interindividuelle, intergroupes, inter-langues, interculturelles, etc. - et conduit souvent à questionner, repenser, les invariants dans le domaine de la cognition. La recherche de nouvelles formes d'articulation entre les variabilités et les invariants apparaît donc comme un des thèmes émergents autour desquels peuvent se nouer - entre les sciences cognitives - des échanges fructueux aux plans épistémologique, théorique et méthodologique. »

Mental Processes --- Brain --- Models, Psychological --- Cognitive science --- Variability (Psychometrics) --- Analysis of variance --- physiology --- Statistical methods --- Analysis of variance. --- Statistical methods. --- ANOVA (Analysis of variance) --- Variance analysis --- Mathematical statistics --- Experimental design --- Difference (Psychology) --- Psychometrics --- Science --- Philosophy of mind --- Mental Processes - congresses --- Brain - physiology - congresses --- Models, Psychological - congresses --- Cognitive science - Statistical methods --- sciences cognitives --- sciences du langage --- variabilités --- sociologie