Relative standard deviation parameters

A simple, rapid and seleetive eleetroehemieal method is proposed as a novel and powerful analytieal teehnique for the solid phase determination of less than 4% antimony in lead-antimony alloys without any separation and ehemieal pretreatment. The proposed method is based on the surfaee antimony oxidation of Pb/Sb alloy to Sb(III) at the thin oxide layer of PbSOyPbO that is formed by oxidation of Pb and using linear sweep voltammetrie (LSV) teehnique. Determination was earried out in eoneentrate H SO solution. The influenee of reagent eoneentration and variable parameters was studied. The method has deteetion limit of 0.056% and maximum relative standard deviation of 4.26%. This method was applied for the determination of Sb in lead/aeid battery grids satisfaetory. 

The discrete Poisson distribution is only characterized by one parameter, the mean Y. The standard deviation is given by sY = Jy and the relative standard deviation by SyreI = 1 / JY. 

Figure 8.38. Structural parameters of an ensemble of needle-shaped soft domains in a poly(ether ester) as a function of elongation . D (open circles) is the average needle diameter, a/D (filled circles) is the relative standard deviation of the needle-diameter distribution. Square symbols demonstrate the lateral compressibility of the soft needles during elongation... 

Disorder of Nanocomposites and Common Polymers. If one compares the distortion parameters of particular nanocomposites with those of common polymer materials, the relative standard deviations are generally smaller by 1 order of magnitude. More than 30 layers are correlated to each other, whereas the correlation in commercial polymer materials is generally ranging shorter than 4 layers. 

Sole use of the correlation coefficient (r) alone is not recommended as a means to demonstrate linearity. The correlation coefficient describes the relation between two random parameters, and shows no relevance for the analytical calibration [31]. The correlation coefficient does not indicate the linearity or lack thereof, unless r exceeds 0.999 [8, 32, 33]. If the value of r is less than 0.999, other parameters such as Vxo, Xp value, ANOVA linear testing, etc., should be calculated. Ebel [34] described using the transformation of r (i.e., Vu ) for expressing the degree of linearity, where the acceptance value of (1 — r ) should be less than 0.05. Camag (Muttents) described the sdv parameter (i.e., the relative standard deviation of the calibration curve) for expressing the linearity of a calibration curve for TLC/HPTLC in its CATS software, and can be calculated as follows ... 

Figure 5. Relative standard deviation on the fitting of the deposition rate of the unattached daughters (Xun) and on the fitting of the ventilation rate (Xvent)> calculated by means of a Monte- Carlo simulation model. The lower curve is obtained with counting statistics alone. The upper curve includes one hour time fluctuations on the input parameters, with 10% rel. stand, dev. on X, un (15/h), a(.35/h), Vent(.45/h) and radon cone. (50 bq/m ) and 2% on recoil factor (.83), penetration unattached (.78) and flow rate (28 1/min).

Relative standard deviation. One final deviation parameter is the relative standard deviation (RSD). It is calculated by dividing the standard deviation by the mean and then multiplying by 100 or 1000 ... 

Parameter Mean Standard Deviation Relative Standard Deviation, %... 

Abraham and Johnston20 also showed that reaction (15) (R = Et) was considerably accelerated as the solvent was changed from methanol to aqueous methanol rate coefficients and activation parameters are given in Table 8. The relative standard deviation of the rate coefficients was given as not greater than 1 %. From the variation of AG swith the dielectric constant of the solvent, it was deduced that in the transition state for reaction (15) (R = Et) a charge separation of as much as 0.7 units had occurred, viz. 

Precision is the agreement between the measurements of the same property under a given set of conditions. Precision or random error is a quantitative parameter that can be calculated in several different ways as the standard deviation relative standard deviation (RSD) or as relative percent difference (RPD). The first two are common statistical parameters that are used for the evaluation of multiple replicate measurements, whereas RPD is used for measuring precision between two duplicate measurements. Equation 1 in Table 2.2 illustrates the method for calculating RPD as a measure of precision. 

Relative standard deviation is the parameter of choice for expressing precision in analytical sciences. 

The overall quality of the model is excellent, with a coefficient of determination of 0.987 and a relative standard deviation of the error of 14.5 percent. Nonetheless, the values for K, K2, and K3 are jointly confounded with one another and thus represent only one of many families of values for the parameters that would fit the data virtually equally well. This means that inferences that these parameters really represent chemisorption equilibrium constants are unwarranted, but the model is nonetheless useful for its intended purpose. If it had been desirable to do so, additional experiments could have been run to narrow the joint confidence intervals of these parameters. 

For system suitability smdies, five replicate injections of mixed standard solutions were injected and parameters such as relative standard deviation of peak area, column efficiency, resolution, and tailing factors of the peaks were calculated. Results are shown in Table 3. 

The second means of collecting multiple spectra involves collection from numerous locations within the vessel at one or more times. Multiple spectra drawn at one time from various locations in a blender may be compared to themselves or to spectra collected at different times, with similarity indicating content uniformity. Parameters for comparison can again include common statistical factors such as standard deviation, relative standard deviation, variance, a host of indices based on standard deviation, or the results from pattern recognition routines. Of course, spectra collected in this manner can also be compared to a library of spectra from previous homogenous blends. 

The quantitation of enzymes and substrates has long been of critical importance in clinical chemistry, since metabolic levels of a variety of species are known to be associated with certain disease states. Enzymatic methods may be used in complex matrices, such as serum or urine, due to the high selectivity of enzymes for their natural substrates. Because of this selectivity, enzymatic assays are also used in chemical and biochemical research. This chapter considers quantitative experimental methods, the biochemical species that is being measured, how the measurement is made, and how experimental data relate to concentration. This chapter assumes familiarity with the principles of spectroscopic (absorbance, fluorescence, chemi-and bioluminescence, nephelometry, and turbidimetry), electrochemical (poten-tiometry and amperometry), calorimetry, and radiochemical methods. For an excellent coverage of these topics, the student is referred to Daniel C. Harris, Quantitative Chemical Analysis (6th ed.). In addition, statistical terms and methods, such as detection limit, signal-to-noise ratio (S/N), sensitivity, relative standard deviation (RSD), and linear regression are assumed familiar Chapter 16 in this volume discusses statistical parameters. 

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