Measurements vs. predictions

Figure 2 Measured vs predicted efficiencies for radionuclides decaying by low energy beta emission...

Fig. 16. Measured vs predicted values of molten glass level in the PLS model with 3 LVs fitted from the Slurry-Fed Ceramic Melter (SFCM) data set in (32).

Finally, in Figure 25 the plot of measured vs predicted values of -LOGEC50 in the model resulting from the exploration is shown. No outliers are identified, though the four... 

Figure 8.4 Plot of measured vs. predicted responses (with confidence intervals) to check for model adequacy with respect to variation in predictor values. Here, the model appears to agree with the data.

Figure 4 Measured vs. predicted data for notched impact strength of composites containing 30wt% of sisal fibre...

Figure 45. Comparison of heat transfer coefficient measured in 20 MW bubbling bed combustor vs prediction from MIT cold test. (From Glicksman et al, 1987)...

Sheiner and Beal (1981) have pointed out the errors involved in using the correlation coefficient to assess the goodness of fit (GOF) in pharmacokinetic models. Pearson s correlation coefficient overestimates the predictability of the model because it represents the best linear line between two variables. A more appropriate estimator would be a measure of the deviation from the line of unity because if a model perfectly predicts the observed data then all the predicted values should be equal to all the observed values and a scatter plot of observed vs. predicted values should form a straight line whose origin is at the point (0,0) and whose slope is equal to a 45° line. Any deviation from this line represents both random and systemic error. 

Equations 15.38 and 15.39 were obtained using simple linear regression A vs. MTD the values of A and MTD are from Table 15.8. In these equations r represents correlation coefficient, s is standard deviation, F is the Fisher statistic indices and (cross-validatirm coefficient) measures the predictive powers of the linear equations. One may observe that we have obtained fairly good statistical results, especially for ICTH interactions. 

The measurements and predictions of friction factor vs. Re>Tiolds number with 100, 50 and 0 ppm solutions are shown in Fig. 1. The calculated results show good agreements with the measured values in the fully developed flow region. For different concentrations and different slopes, the same trend exists. No significant effect of the Froude number was apparent. 

Figure 20.4a compares the measured and predicted lateral load vs. top flexural displacement responses for the rectangular wall Specimen RW2. The analytical model captures the measured response with reasonable accuracy. The lateral load capacity and the lateral stiffness of the wall are well-represented for most of the lateral drift levels. Cyclic properties of the response, including stiffness degradation, hysteretic shape, plastic (residual) displacements, and pinching behavior are all well represented in the analytical results. 

F. 20.12 Comparison of measured and predicted shear stress vs. shear strain responses for Specimcai SEIO... 

Although difficult, it is possible to measure stress vs. strain curves of PSAs. Examples of such work include that of Christenson et al. [.3J and Piau et al. [23J. One can do this at various elongation rates and temperatures and create a material response function. Of course, it is much easier to obtain rheological data at small strains than to obtain tensile stress-strain data. One can assume a shape of the stress vs. strain function (i.e. a constitutive relationship) and then use the small strain data to assign values to the parameters in such a function. In order for a predictive model of peel to be useful, one should be able to use readily obtained rheological parameters like those obtained from linear viscoelastic master curve measurements and predict peel force master curves. 

Figure 15 Measured vs. calculated (predicted) molar masses of polyesters obtained by (a) polymerization of s-caprolactone initiated with R 2AiOR (THF, 25 °C) and (b) polymerization of (L,t)-lactide initiated with Sn(0Bu)2 (°, THF, 80 °C) and ( , bulk, 120...

When water activity is low, foods behave more like mbbery polymers than crystalline stmctures having defined domains of carbohydrates, Hpids, or proteins. Water may be trapped in these mbbery stmctures and be more or less active than predicted from equiUbrium measurements. As foods change temperature the mobiUty of the water may change. A plot of chemical activity vs temperature yields a curve having distinct discontinuities indicating phase... 

The sohd line in Figure 3 represents the potential vs the measured (or the appHed) current density. Measured or appHed current is the current actually measured in an external circuit ie, the amount of external current that must be appHed to the electrode in order to move the potential to each desired point. The corrosion potential and corrosion current density can also be deterrnined from the potential vs measured current behavior, which is referred to as polarization curve rather than an Evans diagram, by extrapolation of either or both the anodic or cathodic portion of the curve. This latter procedure does not require specific knowledge of the equiHbrium potentials, exchange current densities, and Tafel slope values of the specific reactions involved. Thus Evans diagrams, constmcted from information contained in the Hterature, and polarization curves, generated by experimentation, can be used to predict and analyze uniform and other forms of corrosion. Further treatment of these subjects can be found elsewhere (1—3,6,18). 

---