| 000 -LEADER |
|---|
| fixed length control field |
a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250306b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9783031013058 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
519.5 |
| Item number |
KAT |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Katari, Manpreet Singh |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Statistics is easy : case studies on real scientific datasets |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Name of publisher, distributor, etc |
Springer, |
| Date of publication, distribution, etc |
2021 |
| Place of publication, distribution, etc |
Cham : |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xi, 62 p. ; |
| Other physical details |
ill., (chiefly col.), |
| Dimensions |
24 cm |
| 365 ## - TRADE PRICE |
|---|
| Price amount |
19.99 |
| Price type code |
€ |
| Unit of pricing |
93.20 |
| 490 ## - SERIES STATEMENT |
|---|
| Series statement |
Synthesis lectures on mathematics and statistics ; |
| Volume number/sequential designation |
39 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references. |
| 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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Nonparametric statistics |
|
|---|
| Topical term or geographic name as entry element |
Weight Change |
|
|---|
| Topical term or geographic name as entry element |
Diet |
|
|---|
| Topical term or geographic name as entry element |
Breast cancer |
|
|---|
| Topical term or geographic name as entry element |
Regression analysis |
|
|---|
| Topical term or geographic name as entry element |
Decision tree |
|
|---|
| Topical term or geographic name as entry element |
Random Forest classifier |
|
|---|
| Topical term or geographic name as entry element |
RNA-seq Set |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Tyagi, Sudarshini |
|
|---|
| Personal name |
Shasha, Dennis Elliott |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Item type |
Books |