Dr David Williams
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Weighing the Odds, Hardcover
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Weighing the Odds, Hardcover
David Williams convinces Mathematics students of the intrinsic interest of Statistics and Probability, and Statistics students that the language of Mathematics can bring real insight and clarity to their subject.
Weighing the Odds, Hardcover
Item #: 170185930

Weighing the Odds, Hardcover

Item #: 170185930
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David Williams convinces Mathematics students of the intrinsic interest of Statistics and Probability, and Statistics students that the language of Mathematics can bring real insight and clarity to their subject.
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Product Details

Shop Weighing the Odds, Hardcover online at a best price in Australia. 52845416
Book formatHardcover
Fiction/nonfictionNon-Fiction
GenreTextbooks
Pub date2001-08-02
Pages566
SubgenreProbability & Statistics
Series titleNo Series
Number in series1
Edition1
PublisherCambridge University Press
Original languagesEnglish
LanguageEnglish
Edu focusMathematics
Educational levelCollege
Is collectibleN
Binding typeCase Binding
Retail packagingSingle Piece
Assembled product dimensions (l x w x h)7.00 x 1.20 x 9.90 Inches (17.8 x 3 x 25.1 cm)
Assembled product weight2.9 lb (1.32 kg)
Bisac subject headingMathematics

Product Description

Weighing the Odds, Hardcover

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Customer Questions & Answers

  • Question: What topics does this book cover?

    Answer: It covers Statistics, Probability, Linear Regression, ANOVA, and Bayesian methods.
  • Question: Is this book suitable for beginners?

    Answer: Yes, it helps build intuition and is designed for both Mathematics and Statistics students.
  • Question: Does it include practical coding examples?

    Answer: Yes, it provides C or WinBUGS code for computational examples and simulations.

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Features & Benefits

  • Engaging presentation of Statistics and Probability.
  • Integrates practical applications with theoretical concepts.
  • Focuses on Confidence Intervals over Hypothesis Testing.
  • Explores both Frequentist and Bayesian methods.
  • Includes Gibbs-sampling techniques for Bayesian implementations.
  • Offers C or WinBUGS code for computational examples.

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