Mathematical statistics - Data processing.
Overview
| Works: | 141 works in 34 publications in 34 languages | |
|---|---|---|
Titles
SAS?for Monte Carlo studie = a guide for quantitative researchers /
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Excel 2007 for educational and psychological statistics = a guide to solving practical problems /
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Statistical analysis and data display = an intermediate course with examples in R /
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Comparative approaches to using R and Python for statistical data analysis
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Advanced R statistical programming and data models = analysis, machine learning, and visualization /
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Applications in statistical computing = from music data analysis to industrial quality improvement /
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Using MATLAB to solve statistical problems = a practical guide to the book "Statistics for chemical and process engineers" /
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Intermediate statistical methods and applications : = a computer package approach /
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Computer intensive statistical methods : = validation model selection and bootstrap /
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SAS/STAT technical report : = spatial prediction using the SAS system.
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Statistical computing : = an introduction to data analysis using S-Plus /
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JMP for basic univariate and multivariate statistics = a step-by-step guide /
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Guide to intelligent data analysis = how to intelligently make sense of real data /
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Computing statistics under interval and fuzzy uncertainty = applications to computer science and engineering /
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Statistics and data analysis for microarrays using R and Bioconductor /
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Advances in complex data modeling and computational methods in statistics
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An introduction to statistics with Python = with applications in the life sciences /
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Statistical disclosure control for microdata = methods and applications in R /
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Reliability and statistical computing = modeling, methods and applications /
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Guide to intelligent data science = how to intelligently make use of real data /
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Artificial intelligence, big data and data science in statistics = challenges and solutions in environmetrics, the natural sciences and technology /
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An introduction to statistics with Python = with applications in the life sciences /
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Core concepts in data analysis = summarization, correlation and visualization /
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Problem solving and data analysis using Minitab = a clear and easy guide to six sigma methodology /
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Using R and RStudio for data management, statistical analysis, and graphics /
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Computer age statistical inference = algorithms, evidence, and data science /
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