Correlation in Engineering and the Applied Sciences Applications in R
Produktdetaljer
Biografisk notat
Rajan Chattamvelli, Ph.D., is a Professor in the School of Computer Science and Engineering at Amrita University, India. He has published more than 20 research articles in international journals, and his research interests include computational statistics, design of algorithms, parallel computing, data mining, machine learning, blockchain, combinatorics, and big data analytics.
This book focuses on correlation coefficients and its applications in applied science fields. The book begins by describing the historical development and various types of correlations. Rank correlation methods including Pearson’s, Spearman’s, and Kendall’s correlation are discussed at length. The book also discusses sampling distribution of correlation coefficients and applications of correlations in various fields. The book presents novel topics such as (i) a quick analytical method to approximate Pearson's correlation, (ii) single-variable correlation, (iii) fractional co-skewness and co-kurtosis, and (iv) the fallacy on correlation between the sample mean and sample variance. This book is ideal for courses on mathematical statistics, engineering statistics, and exploratory data analysis and is primarily aimed at upper-undergraduate and graduate level students. The book is also useful for researchers and professionals in various fields who are interested in data analysis.
Measures of Association.- Pearson’s Correlation.- Rank Correlation.- Distribution of Correlation.- Applications of Correlation.
This book focuses on correlation coefficients and its applications in applied science fields. The book begins by describing the historical development and various types of correlations. Rank correlation methods including Pearson’s, Spearman’s, and Kendall’s correlation are discussed at length. The book also discusses sampling distribution of correlation coefficients and applications of correlations in various fields. The book presents novel topics such as (i) a quick analytical method to approximate Pearson's correlation, (ii) single-variable correlation, (iii) fractional co-skewness and co-kurtosis, and (iv) the fallacy on correlation between the sample mean and sample variance. This book is ideal for courses on mathematical statistics, engineering statistics, and exploratory data analysis and is primarily aimed at upper-undergraduate and graduate level students. The book is also useful for researchers and professionals in various fields who are interested in data analysis.
In addition, this book:
- Combines theory with numerical examples and includes the latest developments in the field
- Presents computer code in R software and features plentiful exercises throughout
- Features discussions on measures of association, rank correlation, and the distribution
Produktdetaljer
Biografisk notat
Rajan Chattamvelli, Ph.D., is a Professor in the School of Computer Science and Engineering at Amrita University, India. He has published more than 20 research articles in international journals, and his research interests include computational statistics, design of algorithms, parallel computing, data mining, machine learning, blockchain, combinatorics, and big data analytics.