Regression in Engineering and the Applied Sciences
Applications in R
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Beschreibung
This book presents a broad range of regression models including count regression models, constrained and penalised regression models such as ridge, LASSO, and elasticnet regression that are used in various applied science fields. The author describes the historical development of the least squares principle, simple linear regression, and Polynomial regression. In addition, logistic regression and multiple linear regression are discussed at length. A novel method to estimate the slope of linear regression is presented along with an emphasis on the importance of numeric problems.
In addition, this book:
- Combines theory with numerical examples and presents a large number of exercises chosen from various fields
- Utilizes R computer code to further illustrate the applications
- Discusses more than 40 different regression models and is ideal for advanced undergraduate and graduate level students
About the Author
Rajan Chattamvelli, Ph.D., is a Professor in the School of Advanced Sciences 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 presents a broad range of regression models including count regression models, constrained and penalised regression models such as ridge, LASSO, and elasticnet regression that are used in various applied science fields. The author describes the historical development of the least squares principle, simple linear regression, and Polynomial regression. In addition, logistic regression and multiple linear regression are discussed at length. A novel method to estimate the slope of linear regression is presented along with an emphasis on the importance of numeric problems.
Combines theory with numerical examples and presents a large number of exercises chosen from various fields Utilizes R computer code to further illustrate the applications Discusses more than 40 different regression models and is ideal for advanced undergraduate and graduate level students
Autor*in
Rajan Chattamvelli
Themen in »Regression in Engineering and the Applied Sciences«
Applications of Regression Least Squares Ridge Regression Multiple Regression Logistic Regression Quantile Regression