Lecture

Significant Figures, Error Estimation, Notation

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In course

PHYS-101(en): General physics : mechanics (English)

Students will learn the principles of mechanics to enable a better understanding of physical phenomena, such as the kinematics and dyamics of point masses and solid bodies. Students will acquire the capacity to quantitatively analyze these effects with the ...

Description

This lecture covers the concept of significant figures to quantify measurement uncertainty, rules for working with significant figures, the notation for derivatives in single and multi-variable functions, and error estimation using standard deviation and standard error.

Instructors (3)

Marcelo Baquero Ruiz

Teaching & PhD PhD Students Michael Goddijn, Cyrille Sepulchre Past EPFL PhD Students as codirector Kadi Lyes Roland Courses Fusion and industrial plasma technologies PHYS-632 The course provides an overview of the technologies that are essential for fusion developments and for industrial plasmaapplications, highlighting the synergies between the two fields. The aim is to provide a combined theoretical andexperimental background to the various topics. General physics : mechanics (English

Justin Richard Ball

Dr. Justin Ball is a staff scientist studying theoretical plasma physics and nuclear fusion energy. His research primarily focuses on the effect of plasma shaping on turbulence in tokamaks. He is the principle investigator of the EUROfusion Theory, Simulation, Verification, and Validation project on negative triangularity. Additionally, he is an avid fusion science communicator via magazine articles, lectures, and podcasts. In 2019, he and Jason Parisi published a popular science book titled The Future of F

Suliana Manley

From 2016 Associate professor, EPFL, Lausanne, Switzerland 2009-2016 Tenure-track assistant professor, EPFL, Lausanne, Switzerland 2006-2009 Post-Doctoral fellow, National Institutes of Health, Bethesda, MD, USA 2004-2006 Post-Doctoral fellow, Massachusetts Institute of Technology, Cambridge, MA, USA 1999-2004 PhD (Physics) Awarded 06/2004, Harvard University, Cambridge, MA, USA 1993-1997 Bachelors (Cum Laude) Physics & Mathematics, Rice University, Houston, TX, USA

Official source

Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics

Related concepts (29)

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.

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.

Measurement uncertainty

In metrology, measurement uncertainty is the expression of the statistical dispersion of the values attributed to a measured quantity. All measurements are subject to uncertainty and a measurement result is complete only when it is accompanied by a statement of the associated uncertainty, such as the standard deviation. By international agreement, this uncertainty has a probabilistic basis and reflects incomplete knowledge of the quantity value. It is a non-negative parameter.

Notation for differentiation

In differential calculus, there is no single uniform notation for differentiation. Instead, various notations for the derivative of a function or variable have been proposed by various mathematicians. The usefulness of each notation varies with the context, and it is sometimes advantageous to use more than one notation in a given context. The most common notations for differentiation (and its opposite operation, the antidifferentiation or indefinite integration) are listed below.

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.

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