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9 items $119.77
“Numerical Methods for Engineers 6th Edition by Steven Chapra, ISBN-13: 978-0073401065” has been added to your cart. View cart
An Introduction to Mathematical Statistics and Its Applications 6th Edition, ISBN-13: 978-0134114217
Home Mathematics An Introduction to Mathematical Statistics and Its Applications 6th Edition, ISBN-13: 978-0134114217
An Introduction to Statistical Concepts 3rd Edition by Debbie L. Hahs-Vaughn, ISBN-13: 978-0415880053
An Introduction to Statistical Concepts 3rd Edition by Debbie L. Hahs-Vaughn, ISBN-13: 978-0415880053 $50.00 Original price was: $50.00.$24.25Current price is: $24.25.
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All the Mathematics You Missed: But Need to Know for Graduate School, ISBN-13: 978-0521792851
All the Mathematics You Missed: But Need to Know for Graduate School, ISBN-13: 978-0521792851 $50.00 Original price was: $50.00.$20.99Current price is: $20.99.

An Introduction to Mathematical Statistics and Its Applications 6th Edition, ISBN-13: 978-0134114217

$50.00 Original price was: $50.00.$14.98Current price is: $14.98.

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Category: Mathematics Tags: An Introduction to Mathematical Statistics and Its Applications 6th Edition, ISBN-13: 978-0134114217, Morris Marx, Richard Larsen
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An Introduction to Mathematical Statistics and Its Applications 6th Edition, ISBN-13: 978-0134114217

[PDF eBook eTextbook]

 

  • Publisher: Pearson; 6th edition (January 5, 2017)
  • Language: English
  • 752 pages
  • ISBN-10: 0134114213
  • ISBN-13: 978-0134114217

 

For courses in Mathematical Statistics.

Introducing the principles of statistics and data modeling.

Introduction to Mathematical Statistics and Its Applications, 6th Edition is a high-level calculus student’s first exposure to mathematical statistics. This book provides students who have already taken three or more semesters of calculus with the background to apply statistical principles. Meaty enough to guide a two-semester course, the book touches on both statistics and experimental design, which teaches students various ways  to analyze data. It gives computational-minded students a necessary and realistic exposure to identifying data models.

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