Statistics is Easy : Case Studies on Real Scientific Datasets
Katari, Manpreet Singh
Statistics is Easy : Case Studies on Real Scientific Datasets by Manpreet Singh Katari, Sudarshini Tyagi, Dennis Shasha - 1st edition 2021 - 1 recurso en línea (XI, 62 páginas) - Synthesis Lectures on Mathematics & Statistics 1938-1751 .
Acknowledgments -- Introduction -- Chick Weight and Diet -- Breast Cancer Classification -- RNA-seq Data Set/ Summary and Perspectives -- Bibliography -- Authors' Biographies.
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.
9783031024337
10.1007/978-3-031-02433-7 doi
Estadística no paramétrica
Aprendizaje automático
QA278.8 / 2021 EB
Statistics is Easy : Case Studies on Real Scientific Datasets by Manpreet Singh Katari, Sudarshini Tyagi, Dennis Shasha - 1st edition 2021 - 1 recurso en línea (XI, 62 páginas) - Synthesis Lectures on Mathematics & Statistics 1938-1751 .
Acknowledgments -- Introduction -- Chick Weight and Diet -- Breast Cancer Classification -- RNA-seq Data Set/ Summary and Perspectives -- Bibliography -- Authors' Biographies.
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.
9783031024337
10.1007/978-3-031-02433-7 doi
Estadística no paramétrica
Aprendizaje automático
QA278.8 / 2021 EB