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EXTENDED SYMMETRIC POISSON-BOLTZMANN THEORY ab 48.99 € als Taschenbuch: EXTENSION OF THE SYMMETRIC POISSON-BOLTZMANN EQUATION UPTO A SIX-COMPONENT MIXTURE OF ELECTROLYTES AND NEUTRAL PARTICLES. Aus dem Bereich: Bücher, Wissenschaft, Chemie,

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R is an Open Source implementation of the S language. It works on multiple computing platforms and can be freely downloaded. R is now in widespread use for teaching at many levels as well as for practical data analysis and methodological development. This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. A supplementary R package can be downloaded and contains the data sets. The statistical methodology includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one- and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last six chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, survival analysis, Poisson regression, and nonlinear regression. In the second edition, the text and code have been updated to R version 2.6.2. The last two methodological chapters are new, as is a chapter on advanced data handling. The introductory chapter has been extended and reorganized as two chapters. Exercises have been revised and answers are now provided in an Appendix. Peter Dalgaard is associate professor at the Department of Biostatistics at the University of Copenhagen and has extensive experience in teaching within the PhD curriculum at the Faculty of Health Sciences. He has been a member of the R Core Team since 1997.

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This book is the revised and extended second edition of Statistics for Linguistics with R. The volume is an introduction to statistics for linguists using the open source software R. It is aimed at students and instructors/professors with little or no statistical background and is written in a non-technical and reader-friendly/accessible style. It first introduces in detail the overall logic underlying quantitative studies: exploration, hypothesis formulation and operationalization, and the notion and meaning of significance tests. It then introduces some basics of the software R relevant to statistical data analysis. A chapter on descriptive statistics explains how summary statistics for frequencies, averages, and correlations are generated with R and how they are graphically represented best. A chapter on analytical statistics explains how statistical tests are performed in R on the basis of many different linguistic case studies: For nearly every single example, it is explained what the structure of the test looks like, how hypotheses are formulated, explored, and tested for statistical significance, how the results are graphically represented, and how one would summarize them in a paper/article. A chapter on selected multifactorial methods introduces how more complex research designs can be studied: methods for the study of multifactorial frequency data, correlations, tests for means, and binary response data are discussed and exemplified step-by-step. Also, the exploratory approach of hierarchical cluster analysis is illustrated in detail. The book comes with many exercises, boxes with short think breaks and warnings, recommendations for further study, and answer keys as well as a statistics for linguists newsgroup on the companion website.Just like the first edition, it is aimed at students, faculty, and researchers with little or no statistical background in statistics or the open source programming language R. It avoids mathematical jargon and discusses the logic and structure of quantitative studies and introduces descriptive statistics as well as a range of monofactorial statistical tests for frequencies, distributions, means, dispersions, and correlations. The comprehensive revision includes new small sections on programming topics that facilitate statistical analysis, the addition of a variety of statistical functions readers can apply to their own data, a revision of overview sections on statistical tests and regression modeling, a complete rewrite of the chapter on multifactorial approaches, which now contains sections on linear regression, binary and ordinal logistic regression, multinomial and Poisson regression, and repeated-measures ANOVA, and a new visual tool to identify the right statistical test for a given problem and data set. The amount of code available from the companion website has doubled in size, providing much supplementary material on statistical tests and advanced plotting.

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The presence of Arsenic, a toxic element, ingroundwater poses a threat to human health andenvironment. Arsenic is a serious health hazard notonly in Nepal, which is the study site for thisresearch, but in almost all the countries in SouthEast Asia. Even though mitigation plans for arsenicis extended almost everywhere, the risk of arsenicseems to an omnipresent problem. A mitigationstrategy for arsenic requires the quantification ofthis risk through a series of statistical modelingapproaches that describe the possible relationship ofarsenic with measureable physical quantities and withthe water chemistry in subsurface water. This bookprovides a variety of prediction models to achievethis end. Among the models considered, generalizedquasi-Poisson is comparatively better and quantilemodel is good in modeling extreme cases. Furthermore,the models with the entire dataset are not veryefficient compared to models that use datasub-settings. All in all, the end result of thesemodels equate to prediction of arsenic at unmeasuredlocations, quantitative assessment of health risk,optimum remediation strategies, and cost benefitanalysis for safe drinking water.

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To study the structural and thermodynamic properties of electrolyte solutions the Symmetric Poisson-Boltzmann theory has been extended up to six components and the resulting coupled equations for the mean electrostatic potentials have been solved numerically using a quasi-linearization iterative procedure. The exclusion volume term approximated by PY hard sphere RDF has been calculated using Perram's method along with Verlet and Weis corrections. The RDFs have been computed for four-component systems comprising a single electrolyte with two other neutral components for various cases. Excellent agreement has been found for each case when compared with MC data. The theory predicts the experimental trends when applied to measure the second virial coefficient in a colloidal system of silicotungstate, in a solution of HCl, LiCl or NaCl. The extended theory and the numerical solution techniques can be utilized to study the structural and thermodynamic properties of multicomponent electrolytes which are of great interest because of their frequent presence in industrial processes and in branches of sciences like colloid and surface science, polymer science, biophysics and chemistry.

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This book has discussed some stochastic models on cancer cell growth with Bi-variate stochastic processes. The pathophysiology of cancer growth was modeled with postulates of Poisson processes. In the first phase, the mathematical relations for various statistical measures like expected number, variances of both normal and mutant cells were derived for normal and mutant cells. The second phase deals with extension of proposed model to study the tumor behaviour during drug administration and during drug vacation as a part of cancer treatment with chemotherapy. Sensitivity analysis of the model is carried out with suitable simulated numerical data. Stochastic model for cancer growth as a result of spontaneous mutation and proliferation of cells during drug vacation and during chemotherapy were developed. The model is extended for two stage mutant cell growth. The third phase of the study has formulated two stochastic programming problems for optimal drug administration in drug presence and absence, for calculating the drug effectiveness during chemotherapy. Health care industry may make use of these studies for optimal management of disease

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Graphs with Potential Theory This book is intended for very broad group of graduate students who wish to have a systematic introduction into the theory of nonlinear potential theory applied to graphs for future work. These objects are similar in many ways to Riemannian manifolds. The author focuses on topics such as p- Laplacian, p-harmonicity, p-Dirichlet spaces, p- capacity, extended divergence formula, p-Harnack inequality, p-hyperbolicity, and p-Poisson equations, while always using variational approach on discrete settings of graphs. The aim is to introduce the basic concepts and results coherently, and show how they are interconnected and interplayed. The treatment presupposes an introductory course on real analysis, and the knowledge of basic facts from potential theory. In the introduction, the author includes viable information on basics facts from graph theory, and the construction of spaces of functions on graphs.

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