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Related Concept Videos

Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
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Random and Systematic Errors01:20

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Random and Systematic Errors01:20

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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Bias01:22

Bias

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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
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Types of Errors: Detection and Minimization01:12

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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
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Uncertainty in Measurement: Accuracy and Precision03:37

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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. 
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Systematic Error Modeling and Bias Estimation.

Feihu Zhang1, Alois Knoll2

  • 1Robotics and Embedded Systems, Technische Universität München, 80333 München, Germany. feihu.zhang@tum.de.

Sensors (Basel, Switzerland)
|May 24, 2016
PubMed
Summary
This summary is machine-generated.

This study models systematic error in range and bearing, using a weighted nonlinear least squares method for bias estimation. The approach demonstrates high performance in error modeling and bias calculation.

Keywords:
biasleast square methodsystematic error

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Area of Science:

  • Navigation Systems
  • Geomatics Engineering
  • Error Analysis

Background:

  • Systematic errors in range and bearing measurements can impact navigation system accuracy.
  • Accurate modeling and estimation of these errors are crucial for reliable positioning.

Purpose of the Study:

  • To analyze the statistical properties of systematic errors in range and bearing during transformation processes.
  • To develop and validate a method for bias estimation in navigation systems.

Main Methods:

  • Statistical analysis of systematic error properties.
  • Application of a weighted nonlinear least squares method.
  • Development of proposed error models for bias calculation.

Main Results:

  • The study successfully analyzed the statistical properties of systematic errors.
  • The weighted nonlinear least squares method effectively calculated biases.
  • The proposed approach demonstrated high performance in error modeling and bias estimation.

Conclusions:

  • The developed approach provides an effective solution for systematic error modeling.
  • Accurate bias estimation is achievable using the proposed weighted nonlinear least squares method.
  • This research contributes to improved accuracy in navigation systems.