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

By David J. Livingstone

ISBN-10: 047068481X

ISBN-13: 9780470684818

ISBN-10: 0470851538

ISBN-13: 9780470851531

Encouraged by means of the author's want for sensible advice within the techniques of knowledge research, a realistic advisor to medical info research has been written as a statistical significant other for the operating scientist.  This instruction manual of knowledge research with labored examples makes a speciality of the appliance of mathematical and statistical recommendations and the translation in their effects.

protecting the most typical statistical equipment for interpreting and exploring relationships in info, the textual content comprises huge examples from numerous clinical disciplines.

The chapters are organised logically, from making plans an scan, via studying and exhibiting the information, to developing quantitative types. each one bankruptcy is meant to face on my own in order that informal clients can check with the part that's fantastic to their challenge.

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

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

Of useful 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

The factorial approach is not only more efficient in terms of the number of experiments required and the identification of interaction effects, it can also be useful in optimization. For example, having estimated the main effects and interaction terms of some experimental factors it may be possible to predict the likely combinations of these factors which will give an optimum response. One drawback to this procedure is that it may not always be possible to establish all possible combinations of treatments, resulting in an unbalanced design.

Why should interdependence between substituent constants be important? There are a number of answers to this question, as discussed further in this book, but for the present it is sufficient to say that independence between parameters is required so that clearer, perhaps mechanistic, conclusions might be drawn from correlations. As part of the investigation Craig plotted various parameters together, for example the plot of σ vs. 2; such plots have since become known as Craig plots. This diagram nicely illustrates the concept of a physicochemical parameter space.

2). 6) This is an example of a simple linear regression equation. Regression equations and the statistics which may be used to describe their ‘goodness of fit’, to a linear or other model, are explained in detail in Chapter 6. 442). 442). 6). These are examples of pattern recognition although regression models would not normally be classed as pattern recognition methods. Pattern recognition and chemometrics are more or less synonymous. Some of the pattern recognition techniques are derived from research into artificial intelligence.

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


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