The Eurachem reading list

6. Statistics

Web resources

Standards

  • ISO 3534-1:2006. Statistics – Vocabulary and symbols – Part 1: General statistical terms and terms used in probability. (www.iso.org)
  • ISO 3534-2:2006. Statistics – Vocabulary and symbols – Part 2: Applied statistics. (www.iso.org)
  • ISO 3534-3:2013. Statistics – Vocabulary and symbols – Part 3: Design of experiments. (www.iso.org)
  • ISO 3534-4:2014. Statistics – Vocabulary and symbols – Part 4: Survey sampling (www.iso.org)

Books

  • S. Crowder, C. Delker, E. Forrest, N. Martin, Introduction to statistics in metrology. Springer, 2020, ISBN 978-3-030-53328-1
  • J. N. Miller, J. C. Miller, R. D. Miller, Statistics and chemometrics for analytical chemistry, 7th Pearson Education, 2018, ISBN 978-1-292-18671-9
  • J. V. Stone, Bayes' Rule: A Tutorial Introduction to Bayesian Analysis, Sebtel Press, 2013, ISBN 978-0-9563728-4-0
  • D. P. Kroese, T. Taimre, Z. I. Botev, Handbook of Monte Carlo methods, Wiley, 2011, ISBN 978-0-470-17793-8
  • M. Thompson and P. J. Lowthian, Notes on statistics and data quality for analytical chemists, Imperial College Press, 2011, ISBN 978-1-84816-617-2
  • S. L. R. Ellison, V. J. Barwick, T. J. Duguid Farrant, Practical statistics for the analytical scientist: A bench guide, 2nd Edition, RSC, 2009, ISBN 978-0-85404-131-2
  • E. Mullins, Statistics for the quality control chemistry laboratory, RSC, 2003, ISBN 978-0-85404-131-2

Leaflets

  • AMC Technical Briefs, RSC, (https://www.rsc.org/membership-and-community/connect-with-others/join-scientific-networks/subject-communities/analytical-science-community/amc/technical-briefs/):
    • AMC TB 100-2021, Multivariate statistics in the analytical laboratory (1): an introduction
    • AMC TB 95-2020, Experimental design and optimisation (5): an introduction to optimisation
    • AMC TB 93-2020, To p or not to p: the use of p-values in analytical science
    • AMC TB 87-2019, The correlation between regression coefficients: combined significance testing for calibration and quantitation of bias
    • AMC TB 82-2017, Are my data normal?
    • AMC TB 72-2016, AMC Datasets – a resource for analytical scientists
    • AMC TB 69-2015, Using the Grubbs and Cochran tests to identify outliers
    • AMC TB 57-2013, An introduction to non-parametric statistics
    • AMC TB 55-2013, Experimental design and optimisation (4): Plackett-Burman designs
    • AMC TB 52-2013, Bayesian statistics in action
    • AMC TB 50-2012, Robust regression: An introduction
    • AMC TB 39-2009, Rogues and suspects: How to tackle outliers
    • AMC TB 38-2009, Significance, importance and power
    • AMC TB 37-2009, Standard additions: myth and reality
    • AMC TB 36-2009, Experimental design and optimisation (3): some fractional factorial designs
    • AMC TB 30-2008, The standard deviation of the sum of several variables
    • AMC TB 27-2007, Why are we weighting?
    • AMC TB 26-2006, Experimental design and optimisation (2): Handling uncontrolled factors
    • AMC TB 24-2006, Experimental design and optimisation (1): An introduction to some basic concepts
    • AMC TB 23-2006, Mixture models for describing multimodal data
    • AMC TB 14-2003, A glimpse into Bayesian statistics
    • AMC TB 10-2002, Fitting a linear functional relationship to data with error on both variables
    • AMC TB 08-2001, The Bootstrap: A Simple Approach to Estimating Standard Errors and Confidence – Intervals when Theory Fails
    • AMC TB 06-2001, Robust statistics: a method of coping with outliers
    • AMC TB 04-2001 (revised March 2006), Representing data distributions with kernel density estimates

Software

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