This chapter focuses on the general theory of random errors. It discusses some simple methods that can be used to isolate blunders in sets of data. For any group of normally distributed measurements, the probability of the normal random variable can be computed by analyzing the integration of the distribution function. For any group of observations, the 50% probable error establishes the limits within which 50% of the errors should fall. In other words, any measurement has the same chance of coming within these limits as it has of falling outside them. Its value can be obtained by multiplying the observations' standard deviation by the appropriate t value. Standard errors and errors of other percent probabilities are commonly used to evaluate measurements for acceptance. Project specifications and contracts often require that acceptable errors be within specified limits such as the 90% and 95% errors.

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Random error effect.

Seungsoo Hahn (175873), Dongsup Kim (50568) · Figshare · 2016

Pearson's correlation coefficients of off-diagonal elements between contact frequency maps from TH data are plotted. In the off-diagonal elements, cis- and trans-contacts are separately considered. Mean values of experimentally-determined ULCs are plotted as thick lines. Theoretically-determined ULCs are plotted as solid circles with error bars. (A) The segment size effect on ULC was evaluated at the data size of 5,000,000 points. (B) The data size effect on ULC was evaluated based on the fixed segment size of 200 kb. ULCs for larger data sizes were predicted from the data size of 1,000,000 and plotted as thin lines with error bars. (C) ULCs for normalized contact frequency maps were evaluated at the data size of 5,000,000 points. Here error bars denote the standard deviation obtained from the analysis of 10 data sets.

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Random Error and Bias

Steven Piantadosi · Wiley series in probability and statistics · 2005

This chapter contains sections titled: Introduction Random Error Clinical Biases Statistical Bias Summary Questions for Discussion

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Sources of Random Error

2020

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Percent error Random 1.0

B. T. Arnberg · 2016

On p. 3 of the preprint the authors do the error analysis as follows: 1. Linear sum of random (experimental) error and bias (possi-ble systematic error due to lack of calibration of flow measure-ment method).

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BIAS, OR RANDOM ERROR

Nigel Harvey · UCL Discovery (University College London) · 1995

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Random error propagation challenge

Juris Meija · Analytical and Bioanalytical Chemistry · 2009

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- Random Errors Random Error- Precision of an analysis:

Ila Pillalamarri · 2012

Precision of an analytical measurement means – repeatability or reproducibility of the results of an experiment performed several times under the same conditions. The variation of random error in an analysis usually given by a normal distribution. Precision is measured by the standard deviation of replicate analysis.

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Clinical trial and random error.

Peter Urbach · PubMed · 1987

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Error analysis of random switching

Corriette, Irene Camilla · ThinkTech (Texas Tech University) · 2012

This thesis looks at the numerical solution of the Riccati Equation y0 = 1 - y2, which has a known analytical solution. This equation is converted into a two-point boundary value problem before being analyzed as an initial value problem. First- order Ordinary Differential Equations (ODE) solvers, that minimize inherent error, are tailored to the Ricatti equation. We focus on finding the value of theta that would minimize the inherent error of the Theta Method. Random switching is then done between implicit and explicit numerical methods and error analysis is conducted on this switching method. Here we are interested in whether or not random switching produces more accurate results (smallest inherent error) than the individual methods.

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Random Error in Prepayment Projections

Lakhbir S. Hayre · The Journal of Fixed Income · 1997

Background and Purpose. Young children with severe motor impairments, such as spinal muscular atrophy, are often unable to move around their environment independently; therefore, they may be at risk for delays in areas of development not directly related to their motor limitations. Power mobility is an intervention that provides young children a means of independent movement and enables them to independently explore their environment. Case Description. The participant was a 20-month-old girl with type II spinal muscular atrophy. Intervention. The authors provided the child with a power wheelchair and gave her mother and physical therapist general guidelines to encourage her use of the power wheelchair. Outcomes. Within 6 weeks after receiving the power wheelchair, the child operated the wheelchair independently. She showed developmental gains in all domains of the Battelle Developmental Inventory and the Pediatric Evaluation of Disability Inventory over 6 months. The authors conducted an interview with the child9s mother before and after intervention. She reported that the child was more independent after receiving the power wheelchair. Discussion. The power wheelchair may have been associated with the changes in the child9s mobility and her developmental changes over 6 months.

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