Statistics is Easy (Record no. 85502)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03290nam a22005415i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-02433-7 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730164247.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220601s2021 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031024337 |
| -- | 978-3-031-02433-7 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 510 |
| 100 1# - AUTHOR NAME | |
| Author | Katari, Manpreet Singh. |
| 245 10 - TITLE STATEMENT | |
| Title | Statistics is Easy |
| Sub Title | Case Studies on Real Scientific Datasets / |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2021. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XI, 62 p. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Synthesis Lectures on Mathematics & Statistics, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Acknowledgments -- Introduction -- Chick Weight and Diet -- Breast Cancer Classification -- RNA-seq Data Set/ Summary and Perspectives -- Bibliography -- Authors' Biographies. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Tyagi, Sudarshini. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Shasha, Dennis. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-02433-7 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2021. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Mathematics. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Statistics . |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Engineering mathematics. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Mathematics. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Statistics. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Engineering Mathematics. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 1938-1751 |
| 912 ## - | |
| -- | ZDB-2-SXSC |
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