
- 236 pages
- English
- PDF
- Available on iOS & Android
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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Information
Table of contents
- Cover
- Title Page
- Copyright
- Contents
- Preface
- Chapter 1 Foundation of Experiments
- Chapter 2 Completely Randomized Design
- Chapter 3 Randomized Complete Block Design
- Chapter 4 Randomized Incomplete Block Design
- Chapter 5 Error Rates
- Chapter 6 Nutrition Experiment
- Chapter 7 The Pearson Dependence
- Chapter 8 The Multivariate Dependence
- Chapter 9 The Conditional Mean Dependence
- Chapter 10 More Parameters Than Observations
- Chapter 11 Eigenvalues, Eigenvectors, and Applications
- Chapter 12 Covariance Estimation
- Chapter 13 Discriminant Analysis
- Chapter 14 Optimizing the VarianceāBias TradeāOff
- Chapter 15 Specification, Discrimination, Robustness, and Sensitivity
- Data Index
- Subject Index
- EULA