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,
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.