Within-samples variance

A second way to work with the data in Table 14.7 is to treat the results for each analyst separately. Because the repeatability for any analyst is influenced by indeterminate errors, the variance, s, of the data in each column provides an estimate of O rand- A better estimate is obtained by pooling the individual variances. The result, which is called the within-sample variance (s ), is calculated by summing the squares of the differences between the replicates for each sample and that sample s mean, and dividing by the degrees of freedom. 

Once a significant difference has been demonstrated by an analysis of variance, a modified version of the f-test, known as Fisher s least significant difference, can be used to determine which analyst or analysts are responsible for the difference. The test statistic for comparing the mean values Xj and X2 is the f-test described in Chapter 4, except that Spool is replaced by the square root of the within-sample variance obtained from an analysis of variance. 

This value of fexp is compared with the critical value for f(a, v), where the significance level is the same as that used in the ANOVA calculation, and the degrees of freedom is the same as that for the within-sample variance. Because we are interested in whether the larger of the two means is significantly greater than the other mean, the value of f(a, v) is that for a one-tail significance test. 

Figure 65-1 shows a schematic representation of the F-test for linearity. Note that there are some similarities to the Durbin-Watson test. The key difference between this test and the Durbin-Watson test is that in order to use the F-test as a test for (non) linearity, you must have measured many repeat samples at each value of the analyte. The variabilities of the readings for each sample are pooled, providing an estimate of the within-sample variance. This is indicated by the label Operative difference for denominator . By Analysis of Variance, we know that the total variation of residuals around the calibration line is the sum of the within-sample variance (52within) plus the variance of the means around the calibration line. Now, if the residuals are truly random, unbiased, and in particular the model is linear, then we know that the means for each sample will cluster... 

The one-tailed F test is used to test whether the between-sample variance is significantly greater than the within-sample variance. Applying the F test we obtain ... 

Spinning cup module A device used to rotate the sample during recording of the NIR spectrum. Reduces the within sample variance emanating from heterogeneity in e.g., particle size and chemical composition. 

It is possible to compare the means of two relatively small sets of observations when the variances within the sets can be regarded as the same, as indicated by the F test. One can consider the distribution involving estimates of the true variance. With sj determined from a group of observations and S2 from a second group of N2 observations, the distribution of the ratio of the sample variances is given by the F statistic ... 

However, some undetermined factor produces a negative bias, particularly in samples nos. 3, 6, 9, and 10 this points to either improper handling of the fixed-volume dispenser, or clogging. The reduction of the within-group variance from VVV to VWV is, to a major part, due to the elimination of... 

These are instruments developed to identify specific psychiatric disorders. Most of these scales are based on a structured or semi-structured interview lasting some 60 90 min. Structured interviews contain a mix of open-ended and closed questions. Open-ended questions are essential for the validity of a diagnostic procedure closed questions support the reliability and the standardization of diagnostic tools. The primary goal for the use of diagnostic scales is to reduce the variance within samples caused by diagnostic differences, i.e. to create homogeneous patient populations included in clinical trials. 

Shewhart control charts enable average process performance to be monitored, as reflected by the sample mean. It is also advantageous to monitor process variability. Process variability within a sample of k measurements can be characterized by its range, standard deviation, or sample variance. Consequently, control charts are often used for one of these three statistics. 

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