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Lecture

Quantitative Analysis: Statistical Methods

In course

CH-108(a): Chemistry Laboratory Work I

Familiariser l'étudiant avec le travail au laboratoire. Travailler de façon quantitative et/ou qualitative. TP réalisés en relation avec les cours de chimie de 1ere année et complémentaires avec le contenu des enseignements TP2 dispensés au semestre suiva ...

Description

This lecture covers the statistical methods used in quantitative analysis, focusing on the calculation of mean values, standard deviations, and the relationship between precision, accuracy, and sensitivity. It explains how to determine the true value of a quantity from multiple measurements, the importance of representative samples, and the impact of random and systematic errors on analytical results.

Instructors (2)

Julien Alexandre Andres

Anne-Sophie Chauvin

Web of Science Researcher ID F-4905-2011 Google Scholar page: https://scholar.google.ch/citations?user=O_HhJUEAAAAJ&hl=fr&oi=ao ORCID number is 0000-0001-9222-3866 Anne-Sophie Chauvin studied chemistry and biology at the university Paris V-René Descartes in France where she did a PhD in organic chemistry, working on mimetic complexes of the active site of Nitrile Hydratase, under the supervision of Prof. Jean-Claude Chottard. On 1999 she moved for 20 months to the University of Geneva, for a post-doctoral stay under the supervision of Prof. Alexandre Alexakis, where she focused on the determination of the absolute configuration of chiral alcohols using Organophosphorous Diamine Derivatizing Agents by 31P and 1H NMR Spectroscopy. On 2000 she joined the group of Prof Jean-Claude G. Bünzli and was appointed part-time lecturer in 2001, assuming teaching and research responsibilities. On 2006, she obtained the habilitation to direct research from the University René Descartes (HDR, Paris V, France) and since october 2007 she is Maître d'Enseignement et de Recherche at the EPFL. In 2010, she joigned the Laboratory for Photonics and Interfaces (LPI), headed by Pr. Michaël Graëtzel. Since the end of 2014, with the arrival of Dr Marinella Mazzanti at EPFL, she is back to lanthanide chemistry, dealing with coordination polymers. Her research interests concern supramolecular chemistry with the design of ligands which form water soluble complexes with luminescent lanthanides in view of biological applications. She is also interested in the synthesis of ligands and polymers for the extraction of lanthanide ions with high selectivity, and in the development of invisibke inks. She also developed organic dyes for dyes sensitized solar cells DSSC. She is now focusing on coordination polymers with luminescent properties and catalytic activity. Anne-Sophie Chauvin is involved in the teaching of General and Analytical Chemistry for students enrolled on first year in Pharmacy and Biology (UNIL): ex-cathedra courses (Chimie Générale et analytique I et II, approfondissement en chimie analytique pour pharmaciens) and exercices. She is in charge of practical sessions for students enroled in chemistry, forensic sciences, pharmacy and biology. She is elected at the FSB Faculty Council and was member of the EPFL Assembly (AE) for 6 years, until 2018. She was member of the Management committee of the Cost CM 1006 action entitled Eufen: European F-Element Network. She is Member of the Swiss Chemical Society (SCS) and Fellow of the Royal Society of Chemistry (FRSC).

Official source

Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics

Related concepts (32)

Standard deviation

In statistics, the standard deviation is a measure of the amount of variation or dispersion of a set of values. A low standard deviation indicates that the values tend to be close to the mean (also called the expected value) of the set, while a high standard deviation indicates that the values are spread out over a wider range. Standard deviation may be abbreviated SD, and is most commonly represented in mathematical texts and equations by the lower case Greek letter σ (sigma), for the population standard deviation, or the Latin letter s, for the sample standard deviation.

Average absolute deviation

The average absolute deviation (AAD) of a data set is the average of the absolute deviations from a central point. It is a summary statistic of statistical dispersion or variability. In the general form, the central point can be a mean, median, mode, or the result of any other measure of central tendency or any reference value related to the given data set. AAD includes the mean absolute deviation and the median absolute deviation (both abbreviated as MAD). Several measures of statistical dispersion are defined in terms of the absolute deviation.

Deviation (statistics)

In mathematics and statistics, deviation is a measure of difference between the observed value of a variable and some other value, often that variable's mean. The sign of the deviation reports the direction of that difference (the deviation is positive when the observed value exceeds the reference value). The magnitude of the value indicates the size of the difference. Errors and residuals A deviation that is a difference between an observed value and the true value of a quantity of interest (where true value denotes the Expected Value, such as the population mean) is an error.

Standard error

The standard error (SE) of a statistic (usually an estimate of a parameter) is the standard deviation of its sampling distribution or an estimate of that standard deviation. If the statistic is the sample mean, it is called the standard error of the mean (SEM). The sampling distribution of a mean is generated by repeated sampling from the same population and recording of the sample means obtained. This forms a distribution of different means, and this distribution has its own mean and variance.

Median absolute deviation

In statistics, the median absolute deviation (MAD) is a robust measure of the variability of a univariate sample of quantitative data. It can also refer to the population parameter that is estimated by the MAD calculated from a sample. For a univariate data set X1, X2, ..., Xn, the MAD is defined as the median of the absolute deviations from the data's median : that is, starting with the residuals (deviations) from the data's median, the MAD is the median of their absolute values. Consider the data (1, 1, 2, 2, 4, 6, 9).

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