Factor effects, significance

Both primary factors and lesser secondary factors affect our sense of satisfac tion with the thermal environment. The primaiy factors have significant reproducible effects and directly affect heat transfer and the occupant s thermal state, Secondar factors that may affect one s sense of satisfaction with a space are conditions such as color and ambiance, local climate, age, physical fitness, sound, food, and illness. These secondary factors have smaller to negligible effects on one s thermal state and will not be discussed here, but such information is available. ... 

A related effect has been described for IR spectroscopy - Surface Enhanced Infrared Absorption spectroscopy (SEIRA). However, as the enhancement factors are significantly lower than for SERS and both the required metal particle size and the activation distance between the target molecule and the particle are more than one order of magnitude smaller, no practically applicable SEIRA sensors have been demonstrated up to now. 

Primary prevention through the modification of risk factors should significantly reduce the prevalence of IHD. Secondary intervention is effective in reducing subsequent morbidity and mortality. 

When a time effect or drift is present, the responses are corrected relative to the nominal result obtained before the design experiments. Otherwise possibly wrong decisions on the significance of the factor effects are drawn. These corrected responses, calculated with Equation (2), are then used to estimate the factor effects, which thus are estimated free from the drift effect. The correction of design results is also illustrated in Figure 5. 

After estimation of the factor effects, they usually graphically and/or statistically are interpreted, to determine their significance. 

The conditions with the worst response value can be derived from the estimated effects. The worst-case situation is that combination of factor levels resulting in the worst result, e.g., the lowest resolution. The worst-case conditions are set using only the effects significant at... 

Most frequently, the design results, or more specifically the factor effects, are analyzed graphically and/or statistically, to decide on method robustness. A method is considered robust when no significant effects are found on responses describing the quantitative aspects. When significant effects are found on quantitative responses, non-significance intervals for the significant quantitative factors can be defined, to obtain a robust response. However, no case studies were found in CE where such intervals actually were determined. 

Figure 9.4 Relationships among SS, SS and SS for calculating both the coefficient of multiple determination, R, and the variance-ratio for the significance of the factor effects,...

The statistical test for the effectiveness of the factors asks the question, Has a significant amount of variance in the data set been accounted for by the factors as they appear in the model Another way of asking this is to question whether or not one or more of the parameters associated with the factor effects is significant. For models containing a Pq term, the null hypothesis to be tested is... 

It has been suggested that it is unfair to judge the effectiveness of the factors (i.e., the significance of the regression) on the basis of s /s] one of the components of is and the factor effects should not be asked (or expected) to account for imprecision. An alternative F-test might be s f. Comment. 

The saturated fractional factorial designs are satisfactory for exactly 3, or 7, or 15, or 31, or 63, or 127 factors, but if the number of factors is different from these, so-called dummy factors can be added to bring the number of factors up to the next largest saturated fractional factorial design. A dummy factor doesn t really exist, but the experimental design and data treatment are allowed to think it exists. At the end of the data treatment, dummy factors should have very small factor effects that express the noise in the data. If the dummy factors have big effects, it usually indicates that the assumption of first-order behavior without interactions or curvature was wrong that is, there is significant lack of fit. 

According to the vendor, the estimated price of remediation using a soil slurry-sequencing batch reactor system was 50 to 110/m of waste treated in 1995. Costs are usually 1.5 to 2 times less than excavation and inceration. The quantity of waste and initial contaminant concentration were cited as the most significant factors effecting price (D10036N, p. 15 D15328G, p. 7). 

When an unreplicated experiment is run, the error or residual sum of squares is composed of both experimental error and lack-of-fit of the model. Thus, formal statistical significance testing of the factor effects can lead to erroneous conclusions if there is lack-of-fit of the model. Therefore, it is recommended that the experiment be replicated so that an independent estimate of the experimental error can be calculated and both lack-of-fit and the statistical significance of the factor effects can be formally tested. 

Normal probability plots or half normal probability plots (Bimbaun plots) [24,29] are graphical methods that help to decide which factors are significant. Effects that are normally distributed around zero are effects... 

A possible consequence of using the dummy factor effects or the two-factor interactions is that significant interactions will increase the (SE)g... 

Decrease in fetal body weight Adverse respiratory effects Significant increase in tumor necrosis factor for alveolar macrophages No deaths... 

The value of an effect and its standard deviation are calculated in the same way as for factorial designs. Multiply the responses by their contrast coefficients for a given factor, sum them, and divide this number by half the number of experiments (2n, for 4n experiments) to give the value of the effect. With no high-order interactions available in the design, either an independent estimate of repeatability or the use of dummy variables is essential. For m dummy variables, the effect standard deviation is calculated using equation 3.14, where ) is the measured effect of the Jth dummy factor. The significance of the effect is then determined by a Student s t test described earlier. 

The extent to which intermediate precision should be established depends on the circumstances under which the procedure is intended to be used. The developing analyst should establish the effects of random events on the precision of the analytical procedure and identify which of the above factors contributes significant variability to the final result. The objective of intermediate precision validation is to verify that in the same laboratory the method will provide the same results once the developmental phase is over. 

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