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

By David J. Livingstone

Encouraged by way of the author's want for functional tips within the procedures of knowledge research, a pragmatic advisor to medical facts research has been written as a statistical spouse for the operating scientist.  This guide of knowledge research with labored examples specializes in the applying of mathematical and statistical recommendations and the translation in their effects.

protecting the commonest statistical tools for studying and exploring relationships in info, the textual content contains broad examples from various medical disciplines.

The chapters are organised logically, from making plans an scan, via studying and showing the knowledge, 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 by means of a hugely certified and the world over revered writer this article:

  • Presents information for the non-statistician
  • Explains quite a few tips on how to extract details from information
  • Describes the appliance of statistical how you can the layout of “performance chemical substances”
  • Emphasises the appliance of statistical strategies and the translation in their effects

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

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Additional info for A Practical Guide to Scientific Data Analysis

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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. We can ‘borrow’ some useful jargon from this field which is related to the concept of ‘training’ an algorithm or device to carry out a particular task. Suppose that we have a set of data which describes a collection of compounds which can be classified as active or inactive in some biological test.

The dependent variable, in this case membership of the active or inactive class, is used in the learning or training process. Unsupervised learning, on the other hand, does not make use of a dependent variable. 22 INTRODUCTION An example of unsupervised learning for this data set might be to plot the values of two of the descriptor variables against one another. Class membership for the compounds could then be marked on the plot and a pattern may be seen to emerge from the data. 5 and melting point >250 ◦ C.

Once again, balance is important, it may be better to examine the effect of one factor in a block of experiments using a larger number of replicates. This is the way that block designs are usually employed, examining the effect of one experimental factor while holding other factors constant. This does introduce the added complication of possible differences between the blocks. In a blocked design, the effect of a factor is of interest, not normally the effect of the blocks, so the solution is to ensure good randomization within the block and/or to repeat the block of experimental treatments.

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