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A volumetric pipette, bulb pipette, or belly pipette [1] allows extremely accurate measurement (to four significant figures) of the volume of a solution. [2] It is calibrated to deliver accurately a fixed volume of liquid.
Uncertainty may be implied by the last significant figure if it is not explicitly expressed. [1] The implied uncertainty is ± the half of the minimum scale at the last significant figure position. For example, if the mass of an object is reported as 3.78 kg without mentioning uncertainty, then ± 0.005 kg measurement uncertainty may be implied.
That g-PDF is plotted with the histogram (black line) and the agreement with the data is very good. Also shown in Figure 2 is a g-PDF curve (red dashed line) for the biased values of T that were used in the previous discussion of bias. Thus the mean of the biased-T g-PDF is at 9.800 − 0.266 m/s 2 (see Table 1).
Calculating the measurement uncertainty of individual measurement devices and/or measurement systems Common tools and techniques of measurement system analysis include: calibration studies, fixed effect ANOVA, components of variance, attribute gage study, gage R&R, [ 1 ] ANOVA gage R&R , and destructive testing analysis.
The difference between the calibration mark of Serological pipette (top) and Mohr (bottom) A graduated pipette is a pipette with its volume, in increments, marked along the tube.
Relative uncertainty is the measurement uncertainty relative to the magnitude of a particular single choice for the value for the measured quantity, when this choice is nonzero. This particular single choice is usually called the measured value, which may be optimal in some well-defined sense (e.g., a mean, median, or mode). Thus, the relative ...
The uncertainty on the output is described via uncertainty analysis (represented pdf on the output) and their relative importance is quantified via sensitivity analysis (represented by pie charts showing the proportion that each source of uncertainty contributes to the total uncertainty of the output).
Uncertainty quantification (UQ) is the science of quantitative characterization and estimation of uncertainties in both computational and real world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known.