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
Modern Statistics = A Computer-Based Approach with Python /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Modern Statistics/ by Ron S. Kenett, Shelemyahu Zacks, Peter Gedeck.
其他題名:
A Computer-Based Approach with Python /
作者:
Kenett, Ron S.
其他作者:
Gedeck, Peter.
面頁冊數:
XXIII, 438 p. 138 illus., 17 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Industrial and Production Engineering. -
電子資源:
https://doi.org/10.1007/978-3-031-07566-7
ISBN:
9783031075667
Modern Statistics = A Computer-Based Approach with Python /
Kenett, Ron S.
Modern Statistics
A Computer-Based Approach with Python /[electronic resource] :by Ron S. Kenett, Shelemyahu Zacks, Peter Gedeck. - 1st ed. 2022. - XXIII, 438 p. 138 illus., 17 illus. in color.online resource. - Statistics for Industry, Technology, and Engineering,2662-5563. - Statistics for Industry, Technology, and Engineering,.
Analyzing Variability: Descriptive Statistics -- Probability Models and Distribution Functions -- Statistical Inference and Bootstrapping -- Variability in Several Dimensions and Regression Models -- Sampling for Estimation of Finite Population Quantities -- Time Series Analysis and Prediction -- Modern analytic methods: Part I -- Modern analytic methods: Part II -- Introduction to Python -- List of Python packages -- Code Repository and Solution Manual -- Bibliography -- Index.
This innovative textbook presents material for a course on modern statistics that incorporates Python as a pedagogical and practical resource. Drawing on many years of teaching and conducting research in various applied and industrial settings, the authors have carefully tailored the text to provide an ideal balance of theory and practical applications. Numerous examples and case studies are incorporated throughout, and comprehensive Python applications are illustrated in detail. A custom Python package is available for download, allowing students to reproduce these examples and explore others. The first chapters of the text focus on analyzing variability, probability models, and distribution functions. Next, the authors introduce statistical inference and bootstrapping, and variability in several dimensions and regression models. The text then goes on to cover sampling for estimation of finite population quantities and time series analysis and prediction, concluding with two chapters on modern data analytic methods. Each chapter includes exercises, data sets, and applications to supplement learning. Modern Statistics: A Computer-Based Approach with Python is intended for a one- or two-semester advanced undergraduate or graduate course. Because of the foundational nature of the text, it can be combined with any program requiring data analysis in its curriculum, such as courses on data science, industrial statistics, physical and social sciences, and engineering. Researchers, practitioners, and data scientists will also find it to be a useful resource with the numerous applications and case studies that are included. A second, closely related textbook is titled Industrial Statistics: A Computer-Based Approach with Python. It covers topics such as statistical process control, including multivariate methods, the design of experiments, including computer experiments and reliability methods, including Bayesian reliability. These texts can be used independently or for consecutive courses The mistat Python package can be accessed at https://gedeck.github.io/mistat-code-solutions/ModernStatistics/ "In this book on Modern Statistics, the last two chapters on modern analytic methods contain what is very popular at the moment, especially in Machine Learning, such as classifiers, clustering methods and text analytics. But I also appreciate the previous chapters since I believe that people using machine learning methods should be aware that they rely heavily on statistical ones. I very much appreciate the many worked out cases, based on the longstanding experience of the authors. They are very useful to better understand, and then apply, the methods presented in the book. The use of Python corresponds to the best programming experience nowadays. For all these reasons, I think the book has also a brilliant and impactful future and I commend the authors for that." Professor Fabrizio Ruggeri Research Director at the National Research Council, Italy President of the International Society for Business and Industrial Statistics (ISBIS) Editor-in-Chief of Applied Stochastic Models in Business and Industry (ASMBI) .
ISBN: 9783031075667
Standard No.: 10.1007/978-3-031-07566-7doiSubjects--Topical Terms:
593943
Industrial and Production Engineering.
LC Class. No.: QA276.4-.45
Dewey Class. No.: 519.5
Modern Statistics = A Computer-Based Approach with Python /
LDR
:05176nam a22004335i 4500
001
1083343
003
DE-He213
005
20220923010756.0
007
cr nn 008mamaa
008
221228s2022 sz | s |||| 0|eng d
020
$a
9783031075667
$9
978-3-031-07566-7
024
7
$a
10.1007/978-3-031-07566-7
$2
doi
035
$a
978-3-031-07566-7
050
4
$a
QA276.4-.45
072
7
$a
PBT
$2
bicssc
072
7
$a
UFM
$2
bicssc
072
7
$a
COM077000
$2
bisacsh
072
7
$a
PBT
$2
thema
072
7
$a
UFM
$2
thema
082
0 4
$a
519.5
$2
23
100
1
$a
Kenett, Ron S.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1059548
245
1 0
$a
Modern Statistics
$h
[electronic resource] :
$b
A Computer-Based Approach with Python /
$c
by Ron S. Kenett, Shelemyahu Zacks, Peter Gedeck.
250
$a
1st ed. 2022.
264
1
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Birkhäuser,
$c
2022.
300
$a
XXIII, 438 p. 138 illus., 17 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
490
1
$a
Statistics for Industry, Technology, and Engineering,
$x
2662-5563
505
0
$a
Analyzing Variability: Descriptive Statistics -- Probability Models and Distribution Functions -- Statistical Inference and Bootstrapping -- Variability in Several Dimensions and Regression Models -- Sampling for Estimation of Finite Population Quantities -- Time Series Analysis and Prediction -- Modern analytic methods: Part I -- Modern analytic methods: Part II -- Introduction to Python -- List of Python packages -- Code Repository and Solution Manual -- Bibliography -- Index.
520
$a
This innovative textbook presents material for a course on modern statistics that incorporates Python as a pedagogical and practical resource. Drawing on many years of teaching and conducting research in various applied and industrial settings, the authors have carefully tailored the text to provide an ideal balance of theory and practical applications. Numerous examples and case studies are incorporated throughout, and comprehensive Python applications are illustrated in detail. A custom Python package is available for download, allowing students to reproduce these examples and explore others. The first chapters of the text focus on analyzing variability, probability models, and distribution functions. Next, the authors introduce statistical inference and bootstrapping, and variability in several dimensions and regression models. The text then goes on to cover sampling for estimation of finite population quantities and time series analysis and prediction, concluding with two chapters on modern data analytic methods. Each chapter includes exercises, data sets, and applications to supplement learning. Modern Statistics: A Computer-Based Approach with Python is intended for a one- or two-semester advanced undergraduate or graduate course. Because of the foundational nature of the text, it can be combined with any program requiring data analysis in its curriculum, such as courses on data science, industrial statistics, physical and social sciences, and engineering. Researchers, practitioners, and data scientists will also find it to be a useful resource with the numerous applications and case studies that are included. A second, closely related textbook is titled Industrial Statistics: A Computer-Based Approach with Python. It covers topics such as statistical process control, including multivariate methods, the design of experiments, including computer experiments and reliability methods, including Bayesian reliability. These texts can be used independently or for consecutive courses The mistat Python package can be accessed at https://gedeck.github.io/mistat-code-solutions/ModernStatistics/ "In this book on Modern Statistics, the last two chapters on modern analytic methods contain what is very popular at the moment, especially in Machine Learning, such as classifiers, clustering methods and text analytics. But I also appreciate the previous chapters since I believe that people using machine learning methods should be aware that they rely heavily on statistical ones. I very much appreciate the many worked out cases, based on the longstanding experience of the authors. They are very useful to better understand, and then apply, the methods presented in the book. The use of Python corresponds to the best programming experience nowadays. For all these reasons, I think the book has also a brilliant and impactful future and I commend the authors for that." Professor Fabrizio Ruggeri Research Director at the National Research Council, Italy President of the International Society for Business and Industrial Statistics (ISBIS) Editor-in-Chief of Applied Stochastic Models in Business and Industry (ASMBI) .
650
2 4
$a
Industrial and Production Engineering.
$3
593943
650
2 4
$a
Data Science.
$3
1174436
650
2 4
$a
Statistical Theory and Methods.
$3
671396
650
1 4
$a
Statistics and Computing.
$3
1366004
650
0
$a
Production engineering.
$3
566269
650
0
$a
Industrial engineering.
$3
679492
650
0
$a
Artificial intelligence—Data processing.
$3
1366684
650
0
$a
Statistics .
$3
1253516
650
0
$a
Mathematical statistics—Data processing.
$3
1366001
700
1
$a
Gedeck, Peter.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1389297
700
1
$a
Zacks, Shelemyahu.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1172388
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783031075650
776
0 8
$i
Printed edition:
$z
9783031075674
776
0 8
$i
Printed edition:
$z
9783031075681
830
0
$a
Statistics for Industry, Technology, and Engineering,
$x
2662-5555
$3
1307621
856
4 0
$u
https://doi.org/10.1007/978-3-031-07566-7
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