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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.
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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