4. 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
- J. N. Miller, J. C. Miller, R. D. Miller, Statistics and chemometrics for analytical chemistry, 7th Pearson Education, 2018, ISBN 1292186712
- J. V. Stone, Bayes' Rule: A Tutorial Introduction to Bayesian Analysis, Sebtel Press, 2013, ISBN 0956372848
- D. C. Montgomery, E. A. Peck, G. G. Vining, Introduction to linear regression analysis, 5th edition, Wiley, 2012, ISBN 978-0-470-54281-1
- 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 978-1848166172
- 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 671 3
Leaflets
- AMC Technical Briefs, RSC, (www.rsc.org/Membership/Networking/InterestGroups/Analytical/AMC/TechnicalBriefs.asp):
- AMC TB 94-2020, 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 2016), Representing data distributions with kernel density estimates
Articles and reports
- J. Kragten, Calculating standard deviations and confidence intervals with a universally applicable spreadsheet technique, Analyst, 1994, 119, 2161-2165, https://doi.org/10.1039/AN9941902161
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Last Updated: Tuesday, 01 March 2022 16:45
