Residuals diagnostic value

Glycosydation AChE and BChE carry 3 and 9, respectively, N-glycosylation consensus sequences attaching carbohydrate residues to the core protein via asparagines. Different molecular forms of the enzymes in various tissues, show different number and composition of carbohydrate residues. N-glycosylation at all sites was shown to be important for effective biosynthesis, secretion and clearance of ChEs from the circulation. Altered patterns of AChE glycosylation have been observed in the brain and cerebrospinal fluid of Alzheimer s disease (AD) patients, with potential diagnostic value. 

Spirometry has its limitations, however. It can measure only ventilated volumes. It cannot measure lung capacities that contain the residual volume. Measurements of TLC, FRC, and RV have diagnostic value in defining lung overdistension or restrictive pulmonary disease the body plethysmograph can determine these absolute lung volumes. 

The remaining diagnostic plots shown in Figure 4.17 are the QQ-plot for checking the assumption of normal distribution of the residuals (upper right), the values of the y-variable (response) versus the fitted y values (lower left), and the residuals versus the fitted y values (lower right). The symbols + for outliers were used for the same objects as in the upper left plot. Thus it can be seen that the... 

PCA Residual and Distance from Boioidary Table (Model and Sample Diagnostic The different contribution to the value in Table 4.20 can be examined to understand why the four class B samples are not classified into class B. 

Statistical Prediction Error vs. Sample Number Plot (Sample Diagnostic) A statistic is available for the predicted values using Equation 5.28. We will refer to this as me statistical prediction error to distinguish it from the observed concentration residuals. 

Values Whereas the sample leverage is based on the score vector of a sample, the value is based on the residual spectrum. The values are a convenient screening diagnostic for identifj ing samples that need closer examination. 

Summary of Prediction Diagnostic Tools for SIMCA From the prediction diagnostics, the conclusion is that unknow.as 1 and 4 do not belong to either of the TEA or MEK classes. Sample 3 is a member of the TEA class and sample 2 is a member of the MEK class. There is considerable reliability in the classifications due to the large values for the excluded samples both in the validation and prediction phases. The residuals and score plots are consistent with the values. 

Measurement Residuals Plot (Sample and Variable Diagnostic) There is nonrandom behavior in the spectral residuals, indicating inadequacies in the model (see Figure 5-31). This is consistent with the statistical prediction errors being an order of magnitude larger than the ideal value. Several preprocessing... 

Summat of Prediction Diagnostic Tools for ICLS, Example 2 Based on the prediction diagnostics, the conclusion is that the predicted values for 98 of 99 prediction samples are reasonable. Based on the range of validation concentration residuals (see Figure 5.56), the errors in the predicted caustic concentrations of the unknovsrns are expected to be within 0.17 wt.% corresponding to an RMSEP of 0.06 wt.%. 

The nonzero spectral residuals in step (d) indicate a problem (no noise was added to the data and therefore zero residuals are expected). Tlie concentration residuals are also larger than the expected errors (again zero noise), further indicating a problem. In summary, the classical approach has failed to produce the correct concentration values, but the diagnostic tools flagged a problem. 

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