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Concepts and Applications Workbook

  • Date Published: August 1994
  • availability: Unavailable - out of print
  • format: Hardback
  • isbn: 9780521445542


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About the Authors
  • Statistics: Concepts and Applications is a 'classical' general statistics text written in modern voice. The authors bring mathematical, theoretical and conceptual integrity to a body of topics and techniques that is appropriate to a first course in statistics and do so in a way that is accessible to students whose mathematical preparation does not go beyond the standard curriculum for college algebra. The informal, conversational prose delivers conceptual richness and advances a quiet subtext of mathematics instruction that achieves a high level of mathematical rigour. The text presents a thorough, step-by-step development of fundamental principles. Statistics: Concepts and Applications is backed by a package of ancillary materials: an instructor's manual with full solutions to exercises, rather than just answers, and an inexpensive supplementary workbook and tutorial ('User-Friendly') with remarkably powerful and easy-to-use DOS-compatible computer software package (ASP).

    • Undergraduate statistics text book
    • Rigorous mathematics presented in informal, conversational prose
    • ASP software, workbook tutorial and instructor's manual also available
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    Product details

    • Date Published: August 1994
    • format: Hardback
    • isbn: 9780521445542
    • length: 896 pages
    • dimensions: 261 x 187 x 49 mm
    • weight: 1.707kg
    • contains: 104 b/w illus.
    • availability: Unavailable - out of print
  • Table of Contents

    1. The organization of data
    2. Describing distributions
    3. Describing individuals in distributions
    4. Describing joint distributions of data
    5. Introduction to probability
    6. Discrete probability distributions
    7. Continuous probability distributions
    8. Sampling distributions and estimation
    9. Hypothesis testing
    10. Testing hypotheses about population means
    11. Testing hypotheses about population variances
    12. Testing hypotheses about several population means μ1, μ2,..., μj: analysis of variance
    13. More complex analysis of variance
    14. Testing hypotheses about correlation and regression
    15. Testing hypotheses about entire distributions: Pearson's chi-square.

  • Authors

    Harry Frank, University of Michigan, Flint

    Steven C. Althoen, University of Michigan, Flint

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