Descriptive Statistics for Scientists and Engineers

This book offers a thorough introduction to descriptive statistics, covering essential topics for students and researchers in various scientific fields.

This book serves as a comprehensive introduction to descriptive statistics, tailored for students and researchers across various applied science fields. Descriptive Statistics for Scientists and Engineers delves into essential topics such as measures of location, spread, skewness, and kurtosis. It also explores absolute and relative measures, alongside classification techniques for spread and kurtosis, including L-moment based measures and van Zwet ordering of kurtosis. This foundational knowledge is crucial for anyone working with data in scientific disciplines.

In addition to traditional topics, Descriptive Statistics for Scientists and Engineers presents several innovative concepts. These include a recursive algorithm for sample variance, simplifications of complex summation expressions, and updated formulas for geometric, harmonic, and weighted means. The book also introduces divide-and-conquer algorithms for calculating sample variance and covariance, as well as L-skewness and spectral kurtosis. Each chapter is accompanied by a variety of exercises drawn from engineering fields, providing readers with practical applications of the concepts discussed.

The book emphasizes computational aspects while ensuring that basic concepts are thoroughly understood. Applications in bioinformatics, finance, metallurgy, pharmacokinetics, solid mechanics, and signal processing are briefly examined, offering insights into how descriptive statistics can be applied in real-world scenarios. Analysts working with numeric data will find the discussions in Descriptive Statistics for Scientists and Engineers both illuminating and accessible.