The Eurachem reading list

6. Statistics

Web resources

NIST/SEMATECH Engineering Statistics Handbook (www.itl.nist.gov/div898/handbook/)
Data Science Textbook (https://docs.tibco.com/data-science/textbook)

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, 2^nd 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 113-2023, Avoiding some common mistakes in straight line regression. Part 1, https://doi.org/10.1039/D3AY90134C
- AMC TB 100-2021, Multivariate statistics in the analytical laboratory (1): an introduction, https://doi.org/10.1039/D0AY90154G
- AMC TB 95-2020, Experimental design and optimisation (5): an introduction to optimisation https://doi.org/10.1039/D0AY90037K
- AMC TB 93-2020, To p or not to p: the use of p-values in analytical science, https://doi.org/10.1039/C9AY90196E
- AMC TB 87-2019, The correlation between regression coefficients: combined significance testing for calibration and quantitation of bias, https://doi.org/10.1039/C9AY90041A
- AMC TB 82-2017, Are my data normal?, https://doi.org/10.1039/C7AY90126G
- AMC TB 72-2016, AMC Datasets – a resource for analytical scientists, https://doi.org/10.1039/C6AY90016J
- AMC TB 69-2015, Using the Grubbs and Cochran tests to identify outliers, https://doi.org/10.1039/C5AY90053K
- AMC TB 57-2013, An introduction to non-parametric statistics, https://doi.org/10.1039/C3AY90070C
- AMC TB 55-2013, Experimental design and optimisation (4): Plackett-Burman designs, https://doi.org/10.1039/C3AY90020G
- AMC TB 52-2013, Bayesian statistics in action, https://doi.org/10.1039/C2AY90023H
- AMC TB 50-2012, Robust regression: An introduction, https://doi.org/10.1039/C2AY90005J
Under Statistical Analysis tab
- AMC TB 39-2009, Rogues and suspects: How to tackle outliers
- AMC TB 38-2009, Significance, importance and power
- 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
Under Analytical Methods tab
- AMC TB 37-2009, Standard additions: myth and reality

Software

AMC Statistical Software (https://www.rsc.org/membership-and-community/connect-with-others/join-scientific-networks/subject-communities/analytical-science-community/amc/software/)

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Details: Category: Reading list; Last Updated: Tuesday, 14 May 2024 21:03