Re-sampling statistics

The underlying principles of the bootstrap approach are easily understood, and the iterative calculations are simple, for example as a macro written for Minitab (see Bibliography). The most important applications in analytical practice are likely to be more complex situations than the one in the above example. Suggested uses have included the estimation of between-laboratory precision in collaborative trials (see Chapter 4), and in the determination of the best model to use in multivariate calibration (see Chapter 8). 

Robust methods may not be quite so easy to use, in view of the need for iterative computer calculations, but they represent the best way of tackling one of the most common and difficult problems for practising analysts, the occurrence of suspicious or outlying results. 

Overall a great variety of significance tests - parametric, non-parametric, and robust - are available, and often the most difficult task in practice is to decide which method is best suited to a particular problem. The diagram in Appendix 1 is designed to make such choices easier, though inevitably it cannot cover all possible practical situations. 

Analytical Methods Committee. 2001. Robust Statistics A Method of Coping with Outliers, Royal Society of Chemistry, Cambridge. (One of a series of technical briefs which can be downloaded from www.rsc.org. The same site provides a Minitab implementation of the Huber algorithm that can be freely downloaded.) 

Analytical Methods Committee. 2001. The Bootstrap A Simple Approach to Estimating Standard Errors and Confidence Intervals when Theory Fails, also obtainable from www.rsc.org. (Shows how to write a Minitab macro for the bootstrap calculation.) 

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