Lecture

Quantitative Analysis: Statistical Methods

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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)

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).

Julien Alexandre Andres

Official source

Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics

Related concepts (31)

Mean

There are several kinds of mean in mathematics, especially in statistics. Each mean serves to summarize a given group of data, often to better understand the overall value (magnitude and sign) of a given data set. For a data set, the arithmetic mean, also known as "arithmetic average", is a measure of central tendency of a finite set of numbers: specifically, the sum of the values divided by the number of values. The arithmetic mean of a set of numbers x1, x2, ..., xn is typically denoted using an overhead bar, .

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.

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).

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.

Geometric mean

In mathematics, the geometric mean is a mean or average which indicates a central tendency of a finite set of real numbers by using the product of their values (as opposed to the arithmetic mean which uses their sum). The geometric mean is defined as the nth root of the product of n numbers, i.e., for a set of numbers a1, a2, ..., an, the geometric mean is defined as or, equivalently, as the arithmetic mean in logscale: Most commonly the numbers are restricted to being non-negative, to avoid complications related to negative numbers not having real roots, and frequently they are restricted to being positive, to enable the use of logarithms.

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