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Now in its second edition, Text Analysis with R provides a practical introduction to computational text analysis using the open source;
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Master text-taming techniques and build effective text-processing applications with R About This Book * Develop all the relevant;
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modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a;
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without having to plough through theorems and proofs. -Journal of Applied Statistics Statistical Meta-Analysis with R and Stata, Second;
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without having to plough through theorems and proofs. -Journal of Applied Statistics Statistical Meta-Analysis with R and Stata, Second;
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of observation, survey, and experiment, the text pairs each method chapter with a chapter on the analysis of the types of results usually obtained with;
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Researchers in the social sciences and beyond are dealing more and more with massive quantities of text data requiring analysis, from;
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limited quantitative backgrounds. With practical use in mind, the text provides detailed coverage of topics such as how to estimate expected;
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Ontology analysis, pathway analysis, and machine learning techniques. Methods are illustrated with toy examples and real data and the R code for;
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software package for data analysis, statistical programming, and graphics. Integrating R code and examples throughout, the text only requires basic;
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for risk and decision analysis. At the end of each chapter, topics are illustrated with step-by-step instructions for DecisionTools (R). This;
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published for the first time. The text is suitable for a first course in Multivariate Statistical Analysis or a first course in Robust Statistics;
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See How Graphics Reveal Information Graphical Data Analysis with R shows you what information you can gain from graphical displays. The;
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See How Graphics Reveal Information Graphical Data Analysis with R shows you what information you can gain from graphical displays. The;
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analysis, and the use of MATLAB(R), Mathcad(R), or Mathematica(R). The author provides an unequaled combination of the study of conventional;
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analysis, and the use of MATLAB (R), Mathcad (R), or Mathematica (R). The author provides an unequaled combination of the study of conventional;
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Learn How to Properly Analyze Categorical DataAnalysis of Categorical Data with R presents a modern account of categorical data analysis;
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, hypothesis testing, linear regression analysis, and clustering with two appendices on installing and using R and R-Commander. A novel feature of this;
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sciences to students using the R language. Based on the assumption that readers have minimal familiarity with statistical concepts, the author uses;
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" ""In an age of big data, data journalism and with a wealth of quantitative information around us, it;
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methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way;
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that have practical applications. The authors' approach is to integrate MATLAB (R) with numerical analysis in a way which adds clarity to the;
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With the rise in data science development, we now have many remarkable techniques and tools to extend data analysis from numeric and;
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R is the most widely used open-source statistical and programming environment for the analysis and visualization of biological data;
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R is the most widely used open-source statistical and programming environment for the analysis and visualization of biological data;
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, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more. Data;
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