By Chen Sh.
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LUPAC, Commission on spectrochemical and other optical procedures for analysis. Pure Appl. , 45 (1976) 99. Eurachem Workshop draft version 5, Quantifying uncertainty in analytical measurement (1994). D. McCormick and A. Roach, Measurement, Statistics and Computation, Analytical Chemistry by Open Learning. John Wiley, Chichester, 1987. R. G. B. P. A. M. M. Hodson, Development of data sets for the validation of analytical instrumentation. J. Assoc. Off. Anal. Chem. 77(1994)777-781. J. Duncan, Quality Control and Industrial Statistics, 5th edn.
E. the difference between the two replicate values for theyth sample and k the number of pairs. 4. This useful result can be derived as follows. Let us first consider the standard deviation computed for a single pair of results, x\ and X2, with mean x. v using paired data. The data concern moisture in American Cheese measured on 20 successive days (adapted from ref. ). 0218. 148. Using eq. 7) for pooling the variances of the k pairs, we then obtain eq. 8). Since this is a pooled standard deviation it is subject to the same assumptions, namely that the variance is the same for all samples analyzed.
The answer of course remains the same. 3. Because of the symmetry of the normal distribution, these two tables give /7-values only for positive z-values. 2 gives the areas between two boundaries. One boundary is z = 0 and the table gives the area between this value of z and the chosen value (see Fig. 3b). Example: a large number of determinations was carried out on the same sample and the results are known to be normally distributed with |i = 215 and a = 35. What percentage of determinations will fall between the boundaries 200 and 250?