Introduction to Probabilistic and Statistical Methods with Examples in R: 176 1st ed. 2020 Edition

Introduction to Probabilistic and Statistical Methods with Examples in R: 176 1st ed. 2020 Edition book cover

Introduction to Probabilistic and Statistical Methods with Examples in R: 176 1st ed. 2020 Edition

Author(s): Katarzyna Stapor (Author)

  • Publisher: Springer
  • Publication Date: 23 May 2021
  • Edition: 1st ed. 2020
  • Language: English
  • Print length: 165 pages
  • ISBN-10: 3030458016
  • ISBN-13: 9783030458010

Book Description

This book strikes a healthy balance between theory and applications, ensuring that it doesn’t offer a set of tools with no mathematical roots. It is intended as a comprehensive and largely self-contained introduction to probability and statistics for university students from various faculties, with accompanying implementations of some rudimentary statistical techniques in the language R.

The content is divided into three basic parts: the first includes elements of probability theory, the second introduces readers to the basics of descriptive and inferential statistics (estimation, hypothesis testing), and the third presents the elements of correlation and linear regression analysis. Thanks to examples showing how to approach real-world problems using statistics, readers will acquire stronger analytical thinking skills, which are essential for analysts and data scientists alike.

Editorial Reviews

From the Back Cover

This book strikes a healthy balance between theory and applications, ensuring that it doesn’t offer a set of tools with no mathematical roots. It is intended as a comprehensive and largely self-contained introduction to probability and statistics for university students from various faculties, with accompanying implementations of some rudimentary statistical techniques in the language R.

The content is divided into three basic parts: the first includes elements of probability theory, the second introduces readers to the basics of descriptive and inferential statistics (estimation, hypothesis testing), and the third presents the elements of correlation and linear regression analysis. Thanks to examples showing how to approach real-world problems using statistics, readers will acquire stronger analytical thinking skills, which are essential for analysts and data scientists alike.

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