Statistics is Easy
Case Studies on Real Scientific Datasets
Seiten
2021
Morgan & Claypool Publishers (Verlag)
978-1-63639-089-5 (ISBN)
Morgan & Claypool Publishers (Verlag)
978-1-63639-089-5 (ISBN)
- Lieferbar
- Versandkostenfrei
- Auch auf Rechnung
- Artikel merken
Applies data preparation, machine learning, and nonparametric statistics to three quite different life science datasets. The authors provide the code as applied to each dataset in both R and Python 3. The book includes exercises for self-study or classroom use.
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.
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.
Acknowledgments
Introduction
Chick Weight and Diet
Breast Cancer Classification
RNA-seq Data Set
Summary and Perspectives
Bibliography
Authors' Biographies
| Erscheinungsdatum | 28.04.2021 |
|---|---|
| Reihe/Serie | Synthesis Lectures on Mathematics and Statistics |
| Verlagsort | San Rafael |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Gewicht | 333 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Algebra |
| Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
| Mathematik / Informatik ► Mathematik ► Statistik | |
| ISBN-10 | 1-63639-089-7 / 1636390897 |
| ISBN-13 | 978-1-63639-089-5 / 9781636390895 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
Mehr entdecken
aus dem Bereich
aus dem Bereich
Mathematik von Studierenden für Studierende erklärt und kommentiert
Buch | Softcover (2024)
Springer Spektrum (Verlag)
CHF 48,95
Begriffe, Sätze und zahlreiche Beispiele in kurzen Lerneinheiten
Buch | Softcover (2022)
Springer Spektrum (Verlag)
CHF 76,95
Buch | Softcover (2022)
Springer Spektrum (Verlag)
CHF 55,95
