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Descriptive Statistics for Scientists and Engineers: Applications in R (Synthesis Lectures on Mathematics & Statistics) - Tapa blanda

Libro 53 de 72: Synthesis Lectures on Mathematics & Statistics

Chattamvelli, Rajan; Shanmugam, Ramalingam

 
9783031323324: Descriptive Statistics for Scientists and Engineers: Applications in R (Synthesis Lectures on Mathematics & Statistics)
Tapa blanda
ISBN 10: 3031323327 ISBN 13: 9783031323324
Editorial: Springer, 2024

Sinopsis

This book introduces descriptive statistics and covers a broad range of topics of interest to students and researchers in various applied science disciplines. This includes measures of location, spread, skewness, and kurtosis; absolute and relative measures; and classification of spread, skewness, and kurtosis measures, L-moment based measures, van Zwet ordering of kurtosis, and multivariate kurtosis. Several novel topics are discussed including the recursive algorithm for sample variance; simplification of complicated summation expressions; updating formulas for sample geometric, harmonic and weighted means; divide-and-conquer algorithms for sample variance and covariance; L-skewness; spectral kurtosis, etc. A large number of exercises are included in each chapter that are drawn from various engineering fields along with examples that are illustrated using the R programming language. Basic concepts are introduced before moving on to computational aspects.  Some applicationsin bioinformatics, finance, metallurgy, pharmacokinetics (PK), solid mechanics, and signal processing are briefly discussed. Every analyst who works with numeric data will find the discussion very illuminating and easy to follow.

"Sinopsis" puede pertenecer a otra edición de este libro.

Acerca del autor

Rajan Chattamvelli, PhD, is a Professor in the School of Computer Science and Engineering at Amrita University, Amaravati. He has published more than 20 research articles in international journals of repute and at various conferences. His research interests are in computational statistics, design of algorithms, parallel computing, cryptography, data mining, machine learning, combinatorics, and big data analytics. His prior assignments include Denver Public Health, Colorado; Metromail Corporation, Lincoln, Nebraska; Frederick University, Cyprus; Indian Institute of Management; Periyar Maniammai University, Thanjavur; Presidency University, Bangalore, and VIT University, Vellore.

Ramalingam Shanmugam, Ph.D., is an Honorary Professor in the School of Health Administration at Texas State University, San Marcos. He is the Editor-in-Chief of four journals including Advances in Life Sciences; Global Journal of Research and Review; Journal of Obesity and Metabolism; and the International Journal of Research in Medical Sciences. He has published more than 200 researcharticles and 120 conference papers. Dr. Shanmugam's research interests include theoretical and computational statistics, number theory, operations research, biostatistics, decision making, and epidemiology.

De la contraportada

This book introduces descriptive statistics and covers a broad range of topics of interest to students and researchers in various applied science disciplines. This includes measures of location, spread, skewness, and kurtosis; absolute and relative measures; and classification of spread, skewness, and kurtosis measures, L-moment based measures, van Zwet ordering of kurtosis, and multivariate kurtosis. Several novel topics are discussed including the recursive algorithm for sample variance; simplification of complicated summation expressions; updating formulas for sample geometric, harmonic and weighted means; divide-and-conquer algorithms for sample variance and covariance; L-skewness; spectral kurtosis, etc. A large number of exercises are included in each chapter that are drawn from various engineering fields along with examples that are illustrated using the R programming language. Basic concepts are introduced before moving on to computational aspects.  Some applications in bioinformatics, finance, metallurgy, pharmacokinetics (PK), solid mechanics, and signal processing are briefly discussed. Every analyst who works with numeric data will find the discussion very illuminating and easy to follow.

In addition, this book:

  • Provides exercises throughout that are illustrated via the R programming language
  • Assists readers to do various numeric data transformations, normality testing, etc.
  • Aids readers to build, analyze, and interpret various descriptive statistical models
  • Presents numerous examples from various engineering fields

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2024
Idioma
Inglés
ISBN 10 
3031323327
ISBN 13 
9783031323324
Encuadernación
Tapa blanda
Número de edición
2
Número de páginas
144
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