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Download A Practical Guide to Scientific Data Analysis by David J. Livingstone PDF

By David J. Livingstone

ISBN-10: 0470851538

ISBN-13: 9780470851531

Encouraged through the author's desire for useful assistance within the methods of information research, a realistic consultant to clinical info research has been written as a statistical better half for the operating scientist.  This guide of information research with labored examples specializes in the appliance of mathematical and statistical concepts and the translation in their effects.

overlaying the commonest statistical tools for studying and exploring relationships in info, the textual content contains vast examples from various clinical disciplines.

The chapters are organised logically, from making plans an scan, via reading and showing the information, to developing quantitative types. each one bankruptcy is meant to face by myself in order that informal clients can check with the part that's wonderful to their challenge.

Written via a hugely certified and across the world revered writer this article:

  • Presents data for the non-statistician
  • Explains various ways to extract info from info
  • Describes the appliance of statistical ways to the layout of “performance chemical compounds”
  • Emphasises the applying of statistical strategies and the translation in their effects

Of functional use to chemists, biochemists, pharmacists, biologists and researchers from many different medical disciplines in either and academia.

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Additional resources for A Practical Guide to Scientific Data Analysis

Sample text

A definition of pattern recognition is that it ‘seeks similarities and regularities present in the data’. Some 4 But, of course, it is restricted to chemical problems. 2 Anaesthetic activity and hydrophobicity of a series of alcohols (reproduced from ref. [5] with permission from American Society for Pharmacology and Experimental Therapeutics (ASPET)). 124 of the display techniques described in Chapter 4 are quite obvious examples of pattern recognition since they result in a visual display of the patterns in data.

It may, for example, be a coincidence that all the active compounds have high melting points; in such a case the rule will not be predictive. This may be misleading, embarrassing, expensive, or all three! Chance effects may also occur with unsupervised learning but are much less likely since unsupervised learning does not seek to generate rules. Chance effects are discussed in more detail in Chapters 6 and 7. The concept of learning may also be used to define some data sets. A set of compounds which have already been tested in some biological system, or which are about to be tested, is known as a learning or training set.

A balanced design is one in which the treatments to be compared are replicated the same number of times, and this is desirable because it maintains orthogonality between factors (an important assumption in the analysis of variance). The factors which have been discussed so far are susceptible to change by the experimentalist and are thus referred to as controlled factors. Other factors may also affect the experimental response and these are referred to as uncontrolled factors. How can experiments be designed EXPERIMENTAL DESIGN TECHNIQUES 29 to detect, and hopefully eliminate, the effects of uncontrolled factors on the response?

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