<p><span>Now in a thoroughly revised and expanded second edition, this classroom-tested text demonstrates and illustrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability, statistics, experimental design, regression, optimization, parameter es
Applied Data Analysis and Modeling for Energy Engineers and Scientists
β Scribed by T. Agami Reddy (auth.)
- Publisher
- Springer US
- Year
- 2011
- Tongue
- English
- Leaves
- 453
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
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 authorβs own research, and well-conceived problems in order to enhance comprehension levels among readers and their understanding of the βprocessesβalong with the tools.
Applied Data Analysis and Modeling for Energy Engineers and Scientists is an ideal volume for researchers, practitioners, and senior level or graduate students working in energy engineering, mathematical modeling and other related areas.
β¦ Table of Contents
Front Matter....Pages i-xxi
Mathematical Models and Data Analysis....Pages 1-25
Probability Concepts and Probability Distributions....Pages 27-60
Data Collection and Preliminary Data Analysis....Pages 61-101
Making Statistical Inferences from Samples....Pages 103-140
Estimation of Linear Model Parameters Using Least Squares....Pages 141-182
Design of Experiments....Pages 183-205
Optimization Methods....Pages 207-230
Classification and Clustering Methods....Pages 231-251
Analysis of Time Series Data....Pages 253-288
Parameter Estimation Methods....Pages 289-325
Inverse Methods....Pages 327-357
Risk Analysis and Decision-Making....Pages 359-396
ERRATUM....Pages E1-E4
Back Matter....Pages 397-430
β¦ Subjects
Energy Efficiency (incl. Buildings); Engineering Thermodynamics, Heat and Mass Transfer; Probability Theory and Stochastic Processes; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
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