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Introduction to Statistics and Data Analysis

About Introduction to Statistics and Data Analysis

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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  • Language:
  • English
  • ISBN:
  • 9783031118326
  • Binding:
  • Hardback
  • Pages:
  • 604
  • Published:
  • January 30, 2023
  • Edition:
  • 23002
  • Dimensions:
  • 160x38x241 mm.
  • Weight:
  • 1062 g.
  In stock
Delivery: 3-5 business days
Expected delivery: December 26, 2024
Extended return policy to January 30, 2025
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Description of Introduction to Statistics and Data Analysis

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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