Statistics - Data processing.
Overview
| Works: | 96 works in 41 publications in 41 languages | |
|---|---|---|
Titles
Intermediate statistical methods and applications :a computer package approach /
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Introduction to business statistics :a Microsoft Excel, integrated approach /
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An introduction to R :notes on R: a programming environment for data analysis and graphics, version 1.4.1 /
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Statistical analysis and data display :an intermediate course with examples in S-plus, R, and SAS /
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C++ programming with applications in administration, finance, and statistics /
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Statistical analysis of clinical data on a pocket calculatorstatistics on a pocket calculator /
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Symmetry studiesan introduction to the analysis of structured data in applications /
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Foundations of statistical algorithms :with reference to R packages /
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Data science in R :a case studies approach to computational reasoning and problem solving /
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Statistical analysis and data displayan intermediate course with examples in R /
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Statistical analysis and data display :an intermediate course with examples in R /
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Using R and RStudio for data management, statistical analysis, and graphics
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Introduction to business statistics :a computer integrated approach /
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Introduction to data science :data analysis and prediction algorithms with R /
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Applications in statistical computingfrom music data analysis to industrial quality improvement /
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A Python data analyst's toolkit :learn Python and Python-based libraries with applications in data analysis and statistics /
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A Python data analyst's toolkitlearn Python and Python-based libraries with applications in data analysis and statistics /
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Leadership in statistics and data scienceplanning for inclusive excellence /
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Open source software for statistical analysis of big dataemerging research and opportunities /
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Statistics with Juliafundamentals for data science, machine learning and artificial intelligence /
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Beginning data science in R 4data analysis, visualization, and modelling for the data scientist /
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R 4 data science quick referencea pocket guide to APIs, libraries, and packages /
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Practical statistics for data scientists :50+ essential concepts using R and Python /
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