Statistics using R: an integrative approach
Material type:
TextPublication details: UK Cambridge University Press 2021 Description: xviii, 676 pISBN: 9781108719148Subject(s): R (Computer program language) | Statistics--Data processing | Mathematical statistics--Data processing | Statistics--Computer programsDDC classification: 519.5 Summary: Description
Using numerous examples with real data, this textbook closely integrates the learning of statistics with the learning of R. It is suitable for introductory-level learners, allows for curriculum flexibility, and includes, as an online resource, R-code script files for all examples and figures included in each chapter, for students to learn from and adapt and use in their future data analytic work. Other unique features created specifically for this textbook include an online R tutorial that introduces readers to data frames and other basic elements of the R architecture, and a CRAN library of datasets and functions that is used throughout the book. Essential topics often overlooked in other introductory texts, such as data management, are covered. The textbook includes online solutions to all end-of-chapter exercises and PowerPoint slides for all chapters as additional resources, and is suitable for those who do not have a strong background in mathematics.
| Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
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Indian Institute of Management LRC General Stacks | IT & Decisions Sciences | 519.5 WEI (Browse shelf(Opens below)) | 1 | Available | 003870 |
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| 519.2076 SPI Probability and statistics | 519.5 ACZ Complete business statistics | 519.5 BER Guide to intelligent data science: how to intelligently make use of real data | 519.5 WEI Statistics using R: | 519.5 WOL Primer for data analytics and graduate study in statistics | 519.50285 SAI Introduction to data analysis in R: | 519.502855133 GIL A guide to R for social and behavioral science statistics |
Description
Using numerous examples with real data, this textbook closely integrates the learning of statistics with the learning of R. It is suitable for introductory-level learners, allows for curriculum flexibility, and includes, as an online resource, R-code script files for all examples and figures included in each chapter, for students to learn from and adapt and use in their future data analytic work. Other unique features created specifically for this textbook include an online R tutorial that introduces readers to data frames and other basic elements of the R architecture, and a CRAN library of datasets and functions that is used throughout the book. Essential topics often overlooked in other introductory texts, such as data management, are covered. The textbook includes online solutions to all end-of-chapter exercises and PowerPoint slides for all chapters as additional resources, and is suitable for those who do not have a strong background in mathematics.
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