Applied Data Analysis and Modeling for Energy Engineers and Scientists
Author:   T. Agami Reddy
Publisher:   Springer-Verlag New York Inc.
Edition:   2011 ed.
ISBN:  

9781441996121


Pages:   430
Publication Date:   09 August 2011
Replaced By:   9783031348686
Format:   Hardback
Availability:   In Print   Availability explained
Limited stock is available. It will be ordered for you and shipped pending supplier's limited stock.

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Applied Data Analysis and Modeling for Energy Engineers and Scientists


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Overview

Applied Data Analysis and Modeling for Energy Engineers and Scientists fills an identified gap in engineering and science education and practice for both students and practitioners. It demonstrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability,statistics, experimental design, regression, model building, optimization, risk analysis and decision-making to actual engineering processes and systems. The text provides a formal structure that offers a basic, broad and unified perspective,while imparting the knowledge, skills and confidence to work in data analysis and modeling. This volume uses numerous solved examples, published case studies from the author’s own research, and well-conceived problems in order to enhance comprehension levels among readers and their understanding of the “processes”along with the tools.

Full Product Details

Author:   T. Agami Reddy
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   2011 ed.
Dimensions:   Width: 21.00cm , Height: 3.00cm , Length: 27.90cm
Weight:   1.418kg
ISBN:  

9781441996121


ISBN 10:   1441996125
Pages:   430
Publication Date:   09 August 2011
Audience:   Professional and scholarly ,  Professional & Vocational
Replaced By:   9783031348686
Format:   Hardback
Publisher's Status:   Out of Print
Availability:   In Print   Availability explained
Limited stock is available. It will be ordered for you and shipped pending supplier's limited stock.

Table of Contents

Models, data analysis and decision making.- Probability concepts and probability distributions.- Data collection and preliminary data analysis.- Making statistical inferences from samples.- Estimation of linear model parameters using least squares.- Designed experiments and analysis of non-intrusive data.- Time series models.- Topics in optimization, parameter estimation and clustering methods.- Inverse problems and illustrative examples.- Decision analysis and risk modeling.

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