Modern Statistics : (Record no. 6526)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 05518nam a22002177a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240318b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9783031075650 |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | QA276 |
| Item number | .K33 2022 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Kenett, Ron, |
| 245 ## - TITLE STATEMENT | |
| Title | Modern Statistics : |
| Remainder of title | a computer-based approach with Python / |
| Statement of responsibility, etc | Ron Kenett, Shelemyahu Zacks, Peter Gedeck. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | Cham, Switzerland : |
| Name of publisher, distributor, etc | Birkhäuser, |
| Date of publication, distribution, etc | [2022]. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | (vii, 438 pages); |
| Other physical details | illustrations (some color). |
| Dimensions | 24 cm. |
| 500 ## - GENERAL NOTE | |
| General note | Includes bibliographical references and index. |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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 ## - SUMMARY, ETC. | |
| Summary, etc | 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. 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. 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. 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. 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.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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Statistics |
| General subdivision | Data processing. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Python (Computer program language) |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Zacks, Shelemyahu, |
| Dates associated with a name | 1932- |
| Relator term | author. |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Gedeck, Peter, |
| Relator term | author. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Library of Congress Classification |
| Item type | Books |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Full call number | Barcode | Date last seen | Copy number | Price effective from | Koha item type | Public note |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Library of Congress Classification | General Collection | Rabdan Academy | Rabdan Academy | General Stacks | 03/18/2024 | GOBI | QA276 .K33 2022 | 22970 | 03/18/2024 | C. 1 | 03/18/2024 | Books | USD 136.49 |