Statistical Planning and Inference
eBook - PDF

Statistical Planning and Inference

Concepts and Applications

  1. 236 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Statistical Planning and Inference

Concepts and Applications

About this book

Explore the foundations of, and cutting-edge developments in, statistics

Statistical Planning and Inference: Concepts and Applications delivers a robust introduction to statistical planning and inference, including classical and computer age developments in statistical science. The book examines the challenges faced in statistical planning and inference, exploring the optimum methods identifying limitations and commonly encountered pitfalls.

It addresses linear and non-linear statistical inference and discusses noise-effect reduction, error rates, balanced and unbalanced data, model selection, discrimination and classification, truncated and censored data, and experimental designs.

Each chapter offers readers problems and solutions and illustrative examples to introduce the concepts and methods discussed within.

The book offers:

  • Analysis of both classical theory and modern developments in the field of statistical inference and planning
  • Expansive discussions of linear and non-linear statistical inference
  • Statistical problems and solutions to test the reader's progress through and retention of the material contained within

Aimed at practitioners and researchers in the field of statistics, Statistical Planning and Inference: Concepts and Applications is also a must-read resource for graduate students, professors, and researchers in the life sciences, agriculture, psychology, education and measurement, sociology, computer and engineering sciences, and all other fields that rely on statistical concepts.

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Yes, you can access Statistical Planning and Inference by Subir Ghosh in PDF and/or ePUB format, as well as other popular books in Biological Sciences & Science General. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Contents
  5. Preface
  6. Chapter 1 Foundation of Experiments
  7. Chapter 2 Completely Randomized Design
  8. Chapter 3 Randomized Complete Block Design
  9. Chapter 4 Randomized Incomplete Block Design
  10. Chapter 5 Error Rates
  11. Chapter 6 Nutrition Experiment
  12. Chapter 7 The Pearson Dependence
  13. Chapter 8 The Multivariate Dependence
  14. Chapter 9 The Conditional Mean Dependence
  15. Chapter 10 More Parameters Than Observations
  16. Chapter 11 Eigenvalues, Eigenvectors, and Applications
  17. Chapter 12 Covariance Estimation
  18. Chapter 13 Discriminant Analysis
  19. Chapter 14 Optimizing the Variance–Bias Trade‐Off
  20. Chapter 15 Specification, Discrimination, Robustness, and Sensitivity
  21. Data Index
  22. Subject Index
  23. EULA