Descriptive Statistics for Scientists and Engineers

Applications in R
2. Auflage 2023
ISBN: 978-3-031-32332-4
Verlag: Springer Nature Switzerland

Buch, Englisch, 130 Seiten, Format (B × H): 168 mm x 240 mm, Gewicht: 255 g

Reihe: Synthesis Lectures on Mathematics & Statistics

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.

Zielgruppe

Upper undergraduate

Autoren/Hrsg.

Shanmugam, Ramalingam

Chattamvelli, Rajan

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Stochastische Prozesse
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure

Inhaltsverzeichnis

Descriptive Statistics.- Measures of Location.- Measures of Spread.- Measures of Skewness and Kurtosis.

Über Autor(innen)

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 and the . 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.