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: Empirical Modeling and Data Analysis for Engineers and Applied Scientists
  name:
    name part: Pardo, Scott A.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2016
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XV, 247 p. 101 illus., 61 illus. in color. online resource.
  abstract: This textbook teaches advanced undergraduate and first-year graduate students in Engineering and Applied Sciences to gather and analyze empirical observations (data) in order to aid in making design decisions. While science is about discovery, the primary paradigm of engineering and "applied science" is design. Scientists are in the discovery business and want, in general, to understand the natural world rather than to alter it. In contrast, engineers and applied scientists design products, processes, and solutions to problems. That said, statistics, as a discipline, is mostly oriented toward the discovery paradigm. Young engineers come out of their degree programs having taken courses such as "Statistics for Engineers and Scientists" without any clear idea as to how they can use statistical methods to help them design products or processes. Many seem to think that statistics is only useful for demonstrating that a device or process actually does what it was designed to do. Statistics courses emphasize creating predictive or classification models - predicting nature or classifying individuals, and statistics is often used to prove or disprove phenomena as opposed to aiding in the design of a product or process. In industry however, Chemical Engineers use designed experiments to optimize petroleum extraction; Manufacturing Engineers use experimental data to optimize machine operation; Industrial Engineers might use data to determine the optimal number of operators required in a manual assembly process. This text teaches engineering and applied science students to incorporate empirical investigation into such design processes. Much of the discussion in this book is about models, not whether the models truly represent reality but whether they adequately represent reality with respect to the problems at hand; many ideas focus on how to gather data in the most efficient way possible to construct adequate models. Includes chapters on subjects not often seen together in a …
  note: by Scott A. Pardo.
  subject:
    topic: Statistics
  subject:
    topic: Biomedical engineering
  subject:
    topic: Biochemical engineering
  subject:
    topic: Chemical engineering
  subject:
    topic: Environmental sciences
  subject:
    topic: Statistics
  subject:
    topic: Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
  subject:
    topic: Statistical Theory and Methods
  subject:
    topic: Biomedical Engineering/Biotechnology
  subject:
    topic: Biochemical Engineering
  subject:
    topic: Industrial Chemistry/Chemical Engineering
  subject:
    topic: Environmental Science and Engineering
  classification: QA276-280
  classification: 519.5
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783319327686
  identifier: http://dx.doi.org/10.1007/978-3-319-32768-6
  record info:
    record creation date: 160719
    record change date: 20180102142319.0
    record identifier: 978-3-319-32768-6