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** Data analysis experimental error and uncertainties **

** Experimental errors and uncertainties **

Parratt, Lyman G., Probability and Experimental Errors in Science, John Wiley Sons, New York (1961). [Pg.409]

Although experimental errors can account for different E scales, it is likely that the transition states in equations 1 and 82 are indeed different enough to account for them. Both reasons were used to justify averaging the Eg values in order to "reduce small specific effects and experimental errors" (91). [Pg.51]

Estimates for Variance Components for Lines (M2I), Testers (M2t), Lines H Testers (M2lt), and Experimental Error (M2e) [Pg.161]

As expected, the effect of differences between numbers is statistically unimportant and is of the same size as the experimental error. Hence we may join sums of squares for the factor between numbers and experimental error into a new combined experimental error, with 42 degrees of freedom. Variations between columns [Pg.250]

Why is it that many computational people, when they use experimental results for comparison, don t seem to care about the origin, physical meaning, and experimental error of the data they use [Pg.83]

Where, A is obtained from accelarated stability tests. The testing result (Y) will include random components representing a lot-to-lot variability and experimental error. Once the estimates of a and S are obtained, stability time is calculated in a similar fashion as in real-time stability testing. Shelf life is the lower confidence limit of the estimated time. [Pg.304]

Note that the residual deviations (denominator in GOF) both arise from modeling and experimental errors. It would, therefore, be appropriate to perform an analysis of variance to test both sources of error. [Pg.62]

Hartmann-Hahn transfer functions for specific multiple-pulse sequences have been used to study the effects of offset and experimental errors (Remerowski et al., 1989 Eaton et al., 1990 Listerud et al., 1993). Hartmann-Hahn transfer in ROE experiments was simulated numerically by Bazzo et al. (1990a). [Pg.123]

Although this solution appears to be straightforward, it is well documented in the lilerature [6,8-10] that small errors in p, (i.e., quadrature and experimental errors) result in large errors in p. The amplification of errors occurs independently of the fact that the inverse of (A A) can be calculated exactly, and it is a direct consequence of the near singularity of the matrix A (if m=n), or more generally (if m>n) of its near incomplete rank. [Pg.272]

The exact ratio of the hydrocarbons formed from the reaction of Cp2Fe2(C0K and LAH in toluene varied with time as shown in Table 1. Ethylene was the predominant product initially (up to 36 hr.) and then decreased dramatically with time. Propylene also decreased with time, although not as dramatically. Butene did not show this change with time but the amounts were small and experimental error could be a factor. [Pg.266]

Here, we want to emphasize that one is able to calculate the fraction of the experimental error only if replicate measurements (at least at one point x ) have been taken. It is then possible to compare model and experimental errors and to test the sources of residual errors. Then, in addition to the GOF test one can perform the test of lack of fit, LOF, and the test of adequacy, ADE, (commonly used in experimental design). In the lack of fit test the model error is tested against the experimental error and in the adequacy test the residual error is compared with the experimental error. [Pg.62]

In general, the number of components N is selected at the point where the addition of a new component does not give relevant additional information within the context of the studied problem or, in other words, when this component explains experimental noise only. Those components explaining proportions of small variance are not investigated, and they are assumed to be mainly related to small background contributions or to noise and experimental error. The selected number [Pg.340]

It should be noted that the number of measurement replications in the matrix of design of completely randomized blocks is marked by K. A distinction should also be made between mean squares for measurement error + experimental error and measurement error. Often this sum of measurement and experimental errors is just called experimental error, and measurement error sampling error. To check significance of the factor effect, the mean square of joint error or experimental error MSCR is used. [Pg.230]

A good rule of thumb is to keep the width of the pulse less than about half the smallest time constant of interest. If the dynamics of the process are completely unknown, it takes a few trials to establish a reasonable pulse width. If the width of the pulse is too small, for a given pulse height, the system is disturbed very little, and it becomes dilTicult to separate the real output signal from noise and experimental error. The height of the pulse can be increased to kick" the process more, but there is a limit here also. [Pg.516]

Fig. 24. Inverse recombination probability of iodine atoms formed by photodissociation (at 485.8 nm) of iodine molecules in various hydrocarbon solvents, , carbon tetrachloride, A and hexachlorobuta-1,3-diene, v, as a graph against inverse viscosity. The temperature was 25 C and experimental errors are quoted to be 10% on the survival probability measurements. After Booth and Noyes [291] and Lampe and Noyes [292], |

** Data analysis experimental error and uncertainties **

** Experimental errors and uncertainties **

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