語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Introduction to Nonparametric Statis...
~
Yates, Jan M.
Introduction to Nonparametric Statistics for the Biological Sciences Using R
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Introduction to Nonparametric Statistics for the Biological Sciences Using R/ by Thomas W. MacFarland, Jan M. Yates.
作者:
MacFarland, Thomas W.
其他作者:
Yates, Jan M.
面頁冊數:
XV, 329 p. 65 illus., 64 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Statistics . -
電子資源:
https://doi.org/10.1007/978-3-319-30634-6
ISBN:
9783319306346
Introduction to Nonparametric Statistics for the Biological Sciences Using R
MacFarland, Thomas W.
Introduction to Nonparametric Statistics for the Biological Sciences Using R
[electronic resource] /by Thomas W. MacFarland, Jan M. Yates. - 1st ed. 2016. - XV, 329 p. 65 illus., 64 illus. in color.online resource.
Chapter 1 Nonparametric Statistics for the Biological Sciences -- Chapter 2 Sign Test -- Chapter 3 Chi-Square -- Chapter 4 Mann-Whitney U Test -- Chapter 5 Wilcoxon Matched-Pairs Signed-Ranks Test -- Chapter 6 Kruskal-Wallis H-Test for Oneway Analysis of Variance (ANOVA) by Ranks -- Chapter 7 Friedman Twoway Analysis of Variance (ANOVA) by Ranks -- Chapter 8 Spearman's Rank-Difference Coefficient of Correlation -- Chapter 9 Other Nonparametric Tests for the Biological Sciences.
This book contains a rich set of tools for nonparametric analyses, and the purpose of this supplemental text is to provide guidance to students and professional researchers on how R is used for nonparametric data analysis in the biological sciences: To introduce when nonparametric approaches to data analysis are appropriate To introduce the leading nonparametric tests commonly used in biostatistics and how R is used to generate appropriate statistics for each test To introduce common figures typically associated with nonparametric data analysis and how R is used to generate appropriate figures in support of each data set The book focuses on how R is used to distinguish between data that could be classified as nonparametric as opposed to data that could be classified as parametric, with both approaches to data classification covered extensively. Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses and tests using R to broadly compare differences between data sets and statistical approach. This supplemental text is intended for: Upper-level undergraduate and graduate students majoring in the biological sciences, specifically those in agriculture, biology, and health science - both students in lecture-type courses and also those engaged in research projects, such as a master's thesis or a doctoral dissertation And biological researchers at the professional level without a nonparametric statistics background but who regularly work with data more suitable to a nonparametric approach to data analysis.
ISBN: 9783319306346
Standard No.: 10.1007/978-3-319-30634-6doiSubjects--Topical Terms:
1253516
Statistics .
LC Class. No.: QA276-280
Dewey Class. No.: 519.5
Introduction to Nonparametric Statistics for the Biological Sciences Using R
LDR
:03546nam a22004095i 4500
001
979428
003
DE-He213
005
20200629220435.0
007
cr nn 008mamaa
008
201211s2016 gw | s |||| 0|eng d
020
$a
9783319306346
$9
978-3-319-30634-6
024
7
$a
10.1007/978-3-319-30634-6
$2
doi
035
$a
978-3-319-30634-6
050
4
$a
QA276-280
072
7
$a
PBT
$2
bicssc
072
7
$a
MED090000
$2
bisacsh
072
7
$a
PBT
$2
thema
072
7
$a
MBNS
$2
thema
082
0 4
$a
519.5
$2
23
100
1
$a
MacFarland, Thomas W.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
889346
245
1 0
$a
Introduction to Nonparametric Statistics for the Biological Sciences Using R
$h
[electronic resource] /
$c
by Thomas W. MacFarland, Jan M. Yates.
250
$a
1st ed. 2016.
264
1
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2016.
300
$a
XV, 329 p. 65 illus., 64 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
505
0
$a
Chapter 1 Nonparametric Statistics for the Biological Sciences -- Chapter 2 Sign Test -- Chapter 3 Chi-Square -- Chapter 4 Mann-Whitney U Test -- Chapter 5 Wilcoxon Matched-Pairs Signed-Ranks Test -- Chapter 6 Kruskal-Wallis H-Test for Oneway Analysis of Variance (ANOVA) by Ranks -- Chapter 7 Friedman Twoway Analysis of Variance (ANOVA) by Ranks -- Chapter 8 Spearman's Rank-Difference Coefficient of Correlation -- Chapter 9 Other Nonparametric Tests for the Biological Sciences.
520
$a
This book contains a rich set of tools for nonparametric analyses, and the purpose of this supplemental text is to provide guidance to students and professional researchers on how R is used for nonparametric data analysis in the biological sciences: To introduce when nonparametric approaches to data analysis are appropriate To introduce the leading nonparametric tests commonly used in biostatistics and how R is used to generate appropriate statistics for each test To introduce common figures typically associated with nonparametric data analysis and how R is used to generate appropriate figures in support of each data set The book focuses on how R is used to distinguish between data that could be classified as nonparametric as opposed to data that could be classified as parametric, with both approaches to data classification covered extensively. Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses and tests using R to broadly compare differences between data sets and statistical approach. This supplemental text is intended for: Upper-level undergraduate and graduate students majoring in the biological sciences, specifically those in agriculture, biology, and health science - both students in lecture-type courses and also those engaged in research projects, such as a master's thesis or a doctoral dissertation And biological researchers at the professional level without a nonparametric statistics background but who regularly work with data more suitable to a nonparametric approach to data analysis.
650
0
$a
Statistics .
$3
1253516
650
0
$a
Biostatistics.
$3
783654
650
0
$a
Agriculture.
$3
660421
650
1 4
$a
Statistics for Life Sciences, Medicine, Health Sciences.
$3
670172
650
2 4
$a
Statistics and Computing/Statistics Programs.
$3
669775
650
2 4
$a
Statistical Theory and Methods.
$3
671396
700
1
$a
Yates, Jan M.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
584153
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783319306339
776
0 8
$i
Printed edition:
$z
9783319306353
776
0 8
$i
Printed edition:
$z
9783319808567
856
4 0
$u
https://doi.org/10.1007/978-3-319-30634-6
912
$a
ZDB-2-SMA
912
$a
ZDB-2-SXMS
950
$a
Mathematics and Statistics (SpringerNature-11649)
950
$a
Mathematics and Statistics (R0) (SpringerNature-43713)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入