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Product Details 基本信息 ISBN-13 书号 9780198798187 Author 作者 Hector Format 版本 平装-胶订 Pages Number 页数 288页 Publisher 出版社 Oxford University Press Publication Date 出版日期 2021-06-17 Product Dimensions 商品尺寸 240x168mm Shipping Weight 商品重量 546g Language 语种 其它(含多语) Book Contents 内容简介 A proven textbook based on materials developed over the last decade to teach linear, generalized, and mixed model analysis to students of ecology, evolution, and environmental studies. While R is used throughout, the focus is firmly on statistical analysis.
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Product Details 基本信息 ISBN-13 书号 9781484241998 Author 作者 Eric Goh Ming Hui Format 版本 平装-胶订 Pages Number 页数 243页 Publisher 出版社 Apress Publication Date 出版日期 2018-12-01 Language 语种 英语 Book Contents 内容简介 Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. This book covers topics ranging from R syntax basics, de*ive statistics, and data visualizations to inferential statistics and regressions. After learning R’s syntax, you will work through data visualizations such as histograms and boxplot charting, de*ive statistics, and inferential statistics such as t-test, chi-square test, ANOVA, non-parametric test, and linear regressions. Learn R for Applied Statistics is a timely skills-migration book that equips you with the R programming fundamentals and introduces you to applie
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Product Details 基本信息 ISBN-13 书号 9783030185473 Author 作者 Unpingco Format 版本 平装-胶订 Pages Number 页数 384页 Publisher 出版社 Springer Berlin Heidelberg Publication Date 出版日期 2020-08-14 Language 语种 英语 Book Contents 内容简介 This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine lear
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