Description

Book Synopsis

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.



Table of Contents

Elements of Probability Theory.- Descriptive and Inferential Statistics.- Linear Regression and Correlation.

Introduction to Probabilistic and Statistical Methods with Examples in R

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    A Paperback by Katarzyna Stapor

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      View other formats and editions of Introduction to Probabilistic and Statistical Methods with Examples in R by Katarzyna Stapor

      Publisher: Springer Nature Switzerland AG
      Publication Date: 23/05/2021
      ISBN13: 9783030458010, 978-3030458010
      ISBN10: 3030458016
      Also in:
      Computing Databases / Data management Mathematics Probability and statistics Maths for engineers

      Description

      Book Synopsis

      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.



      Table of Contents

      Elements of Probability Theory.- Descriptive and Inferential Statistics.- Linear Regression and Correlation.

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