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Practicing R for statistical computing

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Practicing R for statistical computingby Muhammad Aslam, Muhammad Imdad Ullah.
作者:	Aslam, Muhammad.
其他作者:	Imdad Ullah, Muhammad.
出版者:	Singapore :Springer Nature Singapore :2023.
面頁冊數:	xvii, 292 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	StatisticsComputer programs.
電子資源:	https://doi.org/10.1007/978-981-99-2886-6
ISBN:	9789819928866$q(electronic bk.)

Practicing R for statistical computing
Aslam, Muhammad.

Practicing R for statistical computing[electronic resource] /by Muhammad Aslam, Muhammad Imdad Ullah. - Singapore :Springer Nature Singapore :2023. - xvii, 292 p. :ill., digital ;24 cm.

Chapter 1. R Language: Introduction -- Chapter 2. Obtaining and Installing R Language -- Chapter 3. Using R as a Calculator -- Chapter 4. Data Mode and Data Structure -- Chapter 5. Working with Data -- Chapter 6. Descriptive Statistics -- Chapter 7. Probability and Probability Distributions -- Chapter 8. Confidence Intervals and Comparison Tests -- Chapter 9. Correlation & Regression Analysis -- Chapter 10. Graphing in R -- Chapter 11. Control Flow: election and Iteration -- Chapter 12. Functions and R Resources -- Chapter 13. Common Errors and Mistakes -- Chapter 14. Functions for Better Programming -- Chapter 15. Some Useful Functions -- Chapter 16. Important Packages.

This book is designed to provide a comprehensive introduction to R programming for data analysis, manipulation and presentation. It covers fundamental data structures such as vectors, matrices, arrays and lists, along with techniques for exploratory data analysis, data transformation and manipulation. The book explains basic statistical concepts and demonstrates their implementation using R, including descriptive statistics, graphical representation of data, probability, popular probability distributions and hypothesis testing. It also explores linear and non-linear modeling, model selection and diagnostic tools in R. The book also covers flow control and conditional calculations by using ''if'' conditions and loops and discusses useful functions and resources for further learning. It provides an extensive list of functions grouped according to statistics classification, which can be helpful for both statisticians and R programmers. The use of different graphic devices, high-level and low-level graphical functions and adjustment of parameters are also explained. Throughout the book, R commands, functions and objects are printed in a different font for easy identification. Common errors, warnings and mistakes in R are also discussed and classified with explanations on how to prevent them.

ISBN: 9789819928866$q(electronic bk.)

Standard No.: 10.1007/978-981-99-2886-6doiSubjects--Topical Terms:

216836
Statistics
--Computer programs.

LC Class. No.: HA32

Dewey Class. No.: 519.50285536

Practicing R for statistical computing
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505 0 $a Chapter 1. R Language: Introduction -- Chapter 2. Obtaining and Installing R Language -- Chapter 3. Using R as a Calculator -- Chapter 4. Data Mode and Data Structure -- Chapter 5. Working with Data -- Chapter 6. Descriptive Statistics -- Chapter 7. Probability and Probability Distributions -- Chapter 8. Confidence Intervals and Comparison Tests -- Chapter 9. Correlation & Regression Analysis -- Chapter 10. Graphing in R -- Chapter 11. Control Flow: election and Iteration -- Chapter 12. Functions and R Resources -- Chapter 13. Common Errors and Mistakes -- Chapter 14. Functions for Better Programming -- Chapter 15. Some Useful Functions -- Chapter 16. Important Packages.
520 $a This book is designed to provide a comprehensive introduction to R programming for data analysis, manipulation and presentation. It covers fundamental data structures such as vectors, matrices, arrays and lists, along with techniques for exploratory data analysis, data transformation and manipulation. The book explains basic statistical concepts and demonstrates their implementation using R, including descriptive statistics, graphical representation of data, probability, popular probability distributions and hypothesis testing. It also explores linear and non-linear modeling, model selection and diagnostic tools in R. The book also covers flow control and conditional calculations by using ''if'' conditions and loops and discusses useful functions and resources for further learning. It provides an extensive list of functions grouped according to statistics classification, which can be helpful for both statisticians and R programmers. The use of different graphic devices, high-level and low-level graphical functions and adjustment of parameters are also explained. Throughout the book, R commands, functions and objects are printed in a different font for easy identification. Common errors, warnings and mistakes in R are also discussed and classified with explanations on how to prevent them.
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