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: Practical data analysis in chemistry
  name:
    name part: Maeder, Marcel.
    role:
      role term: creator
  name:
    name part: Neuhold, Yorck-Michael.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: enk
    date issued: 2007
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: remote
    extent: pages cm.
  abstract: The majority of modern instruments are computerised and provide incredible amounts of data. Methods that take advantage of the flood of data are now available; importantly they do not emulate 'graph paper analyses' on the computer. Modern computational methods are able to give us insights into data, but analysis or data fitting in chemistry requires the quantitative understanding of chemical processes. The results of this analysis allows the modelling and prediction of processes under new conditions, therefore saving on extensive experimentation. Practical Data Analysis in Chemistry exemplifies every aspect of theory applicable to data analysis using a short program in a Matlab or Excel spreadsheet, enabling the reader to study the programs, play with them and observe what happens. Suitable data are generated for each example in short routines, this ensuring a clear understanding of the data structure. Chapter 2 includes a brief introduction to matrix algebra and its implementation in Matlab and Excel while Chapter 3 covers the theory required for the modelling of chemical processes. This is followed by an introduction to linear and non-linear least-squares fitting, each demonstrated with typical applications. Finally Chapter 5 comprises a collection of several methods for model-free data analyses. * Includes a solid introduction to the simulation of equilibrium processes and the simulation of complex kinetic processes. * Provides examples of routines that are easily adapted to the processes investigated by the reader * 'Model-based' analysis (linear and non-linear regression) and 'model-free' analysis are covered.
  table of contents: 1. Introduction -- 2. Matrix Algebra -- 2.1 Matrices, Vectors, Scalars -- 2.2 Solving Systems of Linear Equations -- 3. Physical/Chemical Models -- 3.1 Beer-Lambert's Law -- 3.2 Chromatography / Gaussian Curves -- 3.3 Titrations, Equilibria, the Law of Mass Action -- 3.4 Kinetics, Mechanisms, Rate Laws -- 4. Model-based Analyses -- 4.1 Background to Least-Squares Methods -- 4.2 Linear Regression -- 4.3 Non-Linear Regression -- 4.4 General Optimisation -- 5. Model-Free Analyses -- 5.1 Factor Analysis, FA -- 5.2 Target Factor Analyses -- 5.3 Evolving Factor Analyses -- 5.4 Alternating Least-Squares -- 5.5 Resolving Factor Analysis, RFA -- 5.6 Principle Component Regression and Partial Least Squares, PCR and PLS.
  note: Marcel Maeder and Yorck-Michael Neuhold.
  note: Electronic resource.
  note: Electronic reproduction. Amsterdam : Elsevier Science & Technology, 2007. Mode of access: World Wide Web. System requirements: Web browser. Title from title screen (viewed on July 25, 2007). Access may be restricted to users at subscribing institutions.
  note: Includes bibliographical references and index.
  subject:
    topic: Chemistry
    topic: Statistical methods
  classification: QD39.3.S7 M34 2007
  classification: 540.15195
  related item:
    identifier: (OCoLC)137313131
  related item:
    title info:
      title: Data handling in science and technology ; v. 26
  identifier: 9780444530547
  identifier: 0444530541
  identifier: 132136:132243 Elsevier Science & Technology
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: OPELS
    record creation date: 070802
    record change date: 20260609193322.2
    record identifier: in00002429617