Description

Now in its second edition, this introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. This revised and extended edition features new chapters on logistic regression, simple random sampling, including bootstrapping, and causal inference.

The text is primarily intended for undergraduate students in disciplines such as business administration, the social sciences, medicine, politics, and macroeconomics. It features a wealth of examples, exercises and solutions with computer code in the statistical programming language R, as well as supplementary material that will enable the reader to quickly adapt the methods to their own applications.

Introduction to Statistics and Data Analysis: With Exercises, Solutions and Applications in R

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£74.99

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Hardback by Christian Heumann , Michael Schomaker

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Now in its second edition, this introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture... Read more

    Publisher: Springer International Publishing AG
    Publication Date: 31/01/2023
    ISBN13: 9783031118326, 978-3031118326
    ISBN10: 3031118324

    Number of Pages: 584

    Non Fiction , Mathematics & Science , Education

    Description

    Now in its second edition, this introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. This revised and extended edition features new chapters on logistic regression, simple random sampling, including bootstrapping, and causal inference.

    The text is primarily intended for undergraduate students in disciplines such as business administration, the social sciences, medicine, politics, and macroeconomics. It features a wealth of examples, exercises and solutions with computer code in the statistical programming language R, as well as supplementary material that will enable the reader to quickly adapt the methods to their own applications.

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