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Business Statistics: Communicating with Numbers ISE

Bag om Business Statistics: Communicating with Numbers ISE

Business Statistics: Communicating with Numbers takes a comprehensive and contemporary approach that aims to bridge the gap between how statistics is taught and applied in the business world. This title not only prepares students in basic statistics but also gets them excited about further exploration of data analytics. The authors emphasize communicating with numbers rather than number crunching, through relatable case studies with relevant statistical methods and takeaways. This title incorporates timely examples from various fields, reinforces core features from previous editions, uses Excel and R to analyze data and solve problems, and includes new improvements, such as a revised chapter on data visualization, an exclusive chapter on logistic regression, and digital enhancements such as a big data capstone project with algorithmic exercises that span across multiple chapters.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9781266802508
  • Indbinding:
  • Paperback
  • Sideantal:
  • 784
  • Udgivet:
  • 20. Februar 2024
  • Udgave:
  • 5
  • Størrelse:
  • 277x217x29 mm.
  • Vægt:
  • 1424 g.
  • Ukendt - mangler pt..

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Beskrivelse af Business Statistics: Communicating with Numbers ISE

Business Statistics: Communicating with Numbers takes a comprehensive and contemporary approach that aims to bridge the gap between how statistics is taught and applied in the business world. This title not only prepares students in basic statistics but also gets them excited about further exploration of data analytics. The authors emphasize communicating with numbers rather than number crunching, through relatable case studies with relevant statistical methods and takeaways. This title incorporates timely examples from various fields, reinforces core features from previous editions, uses Excel and R to analyze data and solve problems, and includes new improvements, such as a revised chapter on data visualization, an exclusive chapter on logistic regression, and digital enhancements such as a big data capstone project with algorithmic exercises that span across multiple chapters.

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