Udvidet returret til d. 31. januar 2025

Introduction to Statistics and Data Analysis

Bag om 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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  • Sprog:
  • Engelsk
  • ISBN:
  • 9783031118326
  • Indbinding:
  • Hardback
  • Sideantal:
  • 604
  • Udgivet:
  • 31. januar 2023
  • Udgave:
  • 23002
  • Størrelse:
  • 160x38x241 mm.
  • Vægt:
  • 1062 g.
  • BLACK NOVEMBER
  Gratis fragt
Leveringstid: 8-11 hverdage
Forventet levering: 7. december 2024

Beskrivelse af 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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