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Force-System Resultants and Equilibrium
Published in Richard C. Dorf, The Engineering Handbook, 2018
In reporting measurements, an indication of the probable error in the result is often stated based on an absolute error prediction (e.g., a temperature of 48.3±0.1, based on a 95% probability) or on a relative error basis (e.g., voltage of 9.0V±2% (based on a 95% probability)]. The choice of probability value corresponding to the error limits is arbitrary, but a value of 95%, corresponding to ±2σ, appears to be widely used.
Introduction
Published in Benjamin D. Shaw, Uncertainty Analysis of Experimental Data with R, 2017
Uncertainty is sometimes characterized as the probable error of a measurement [2]. Another statement for uncertainty is that it is a nonnegative parameter characterizing the dispersion of values attributed to a measurand (the quantity being measured) [3]. Basically, knowing the uncertainty of a measurement will tell us something about an interval within which the true value of the measured variable is likely to be found. We typically will be interested in evaluating uncertainty intervals of the following form: uncertaintyinterval=middlevalue±uncertainty. For example, the uncertainty interval for a temperature measurement might be T=300±2K(95%),where the percentage represents a “confidence level.” The confidence interval can be interpreted as meaning that, if we had performed this same experiment a large number of times, generating a new uncertainty interval for the data from each experiment, then about 95% of the uncertainty intervals we generated would contain the true value of the temperature.
Principles of Surveying
Published in P.K. Jayasree, K Balan, V Rani, Practical Civil Engineering, 2021
P.K. Jayasree, K Balan, V Rani
The most probable error is defined as that quantity which when added to and subtracted from, the most probable value fixes the limits within which it is an even chance the true value of the measured quantity must lie.
Generalized skew-normal distribution model of partition curves based on quartile
Published in International Journal of Coal Preparation and Utilization, 2022
should have a normal distribution form (Jowett 1986). The integral function of the simplest symmetrical normal distribution model of partition curve can be expressed as Equation (4), where is the partition coefficient as a function of δ, t involves the separation density δ50 and the probable error Ep as given in Equation (5).