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Introduction to Probabilistic and Statistical Methods with Examples in R [electronic resource] / by Katarzyna Stapor.

By: Stapor, Katarzyna [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Intelligent Systems Reference Library: 176Publisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020.Description: VIII, 157 p. 33 illus., 24 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030457990.Subject(s): Statistics  | Engineering—Data processing | Engineering mathematics | Applied Statistics | Data Engineering | Engineering MathematicsAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 519 Online resources: Click here to access online
Contents:
Elements of Probability Theory -- Descriptive and Inferential Statistics -- Linear Regression and Correlation.
In: Springer Nature eBookSummary: 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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Elements of Probability Theory -- Descriptive and Inferential Statistics -- Linear Regression and Correlation.

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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