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Related Concept Videos

Instrument Calibration01:12

Instrument Calibration

Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and...
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
Accuracy and Precision01:52

Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.  Highly accurate measurements...
Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...

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A Tactile Automated Passive-Finger Stimulator (TAPS)
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A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

Calibration and measurement control based on Bayes statistics.

K M Hangos1, L Leisztner, M Kárný

  • 1Computer and Automation Institute Hungarian Academy of Sciences PO Box 63 Budapest H1502 Hungary.

The Journal of Automatic Chemistry
|January 1, 1989
PubMed
Summary

This study introduces a Bayesian methodology for selecting plausible calibration curves using data, expert knowledge, and models. It highlights the importance of modeling measurement errors, demonstrated with head-space gas chromatography.

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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

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A Tactile Automated Passive-Finger Stimulator (TAPS)
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Area of Science:

  • Analytical Chemistry
  • Statistical Modeling

Background:

  • Calibration curves are essential in analytical chemistry for quantifying substances.
  • Traditional calibration methods may not fully account for measurement errors and prior knowledge.
  • Integrating theoretical models and expert knowledge can improve calibration accuracy.

Purpose of the Study:

  • To present a Bayesian methodology for selecting calibration curves.
  • To demonstrate the application of Bayesian calibration using real-world data.
  • To emphasize the significance of modeling measurement errors in calibration.

Main Methods:

  • Review of basic Bayesian calibration steps.
  • Application of Bayesian methods to head-space gas chromatographic data.
  • Modeling linear calibration with log-normal distributed measurement errors.

Main Results:

  • The Bayesian approach effectively selects plausible calibration curves.
  • Demonstrated successful application in head-space gas chromatography.
  • Log-normal error modeling enhances the treatment of random noise.

Conclusions:

  • Bayesian methodology offers a robust framework for calibration.
  • Accurate modeling of measurement errors is crucial for reliable results.
  • The proposed method integrates data, expert knowledge, and theory for improved calibration.