rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Applied Data Analysis and Modeling for Energy Engineers and Scientists
  name:
    name part: Reddy, T. Agami.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2011
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXI, 430 p. online resource.
  abstract: 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. 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. .
  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.
  note: by T. Agami Reddy.
  subject:
    topic: Energy
  subject:
    topic: Energy efficiency
  subject:
    topic: Probabilities
  subject:
    topic: Statistics
  subject:
    topic: Thermodynamics
  subject:
    topic: Heat engineering
  subject:
    topic: Heat transfer
  subject:
    topic: Mass transfer
  subject:
    topic: Energy
  subject:
    topic: Energy Efficiency (incl. Buildings)
  subject:
    topic: Engineering Thermodynamics, Heat and Mass Transfer
  subject:
    topic: Probability Theory and Stochastic Processes
  subject:
    topic: Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
  classification: T58.8
  classification: 658.26
  related item:
    title info:
      title: Springer eBooks
  identifier: 9781441996138
  identifier: http://dx.doi.org/10.1007/978-1-4419-9613-8
  record info:
    record creation date: 110729
    record change date: 20151204164244.0
    record identifier: 978-1-4419-9613-8