| 书目名称 | Statistics is Easy | | 副标题 | Case Studies on Real | | 编辑 | Manpreet Singh Katari,Dennis Shasha,Sudarshini Tya | | 视频video | http://file.papertrans.cn/877/876759/876759.mp4 | | 丛书名称 | Synthesis Lectures on Mathematics & Statistics | | 图书封面 |  | | 描述 | Computational analysis of natural science experiments often confronts noisy data due to natural variability in environment or measurement. Drawing conclusions in the face of such noise entails a statistical analysis. Parametric statistical methods assume that the data is a sample from a population that can be characterized by a specific distribution (e.g., a normal distribution). When the assumption is true, parametric approaches can lead to high confidence predictions. However, in many cases particular distribution assumptions do not hold. In that case, assuming a distribution may yield false conclusions. The companion book Statistics is Easy, gave a (nearly) equation-free introduction to nonparametric (i.e., no distribution assumption) statistical methods. The present book applies data preparation, machine learning, and nonparametric statistics to three quite different life science datasets. We provide the code as applied to each dataset in both R and Python 3. We also include exercises for self-study or classroom use. | | 出版日期 | Book 2021 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-02433-7 | | isbn_softcover | 978-3-031-01305-8 | | isbn_ebook | 978-3-031-02433-7Series ISSN 1938-1743 Series E-ISSN 1938-1751 | | issn_series | 1938-1743 | | copyright | Springer Nature Switzerland AG 2021 |
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