Average response

A 2 factorial design with two factors requires four runs, or sets of experimental conditions, for which the uncoded levels, coded levels, and responses are shown in Table 14.4. The terms Po> Po> Pfc> and Pafc in equation 14.4 account for, respectively, the mean effect (which is the average response), first-order effects due to factors A and B, and the interaction between the two factors. Estimates for these parameters are given by the following equations... 

There should be sufficientiy large numbers of animals to allow a quantitative determination of the average response and the range of responses, including the demonstration of hypersensitive populations. When objective procedures are undertaken, these should be sufficient to allow vaUd statistical comparison to be made between treated and control groups. 

The water residue level is also determined from the relative responses of the analytes to the internal standards. The sample residue levels are calculated by comparison with an average response factor determined by triplicate analysis of a five-point calibration curve. Samples receive 5ng of each internal standard (0.1 ngmL ) and are concentrated 50-fold by Ci8 SPE before analysis to achieve adequate instrumental sensitivity. The calculations to determine the residue level in water are outlined in Section 7.3.3. 

Calculate an average value from the response factors obtained from the calibration curve analyses. Use the average response factor in the following equation to determine the residue levels in the sample ... 

Famoxadone, IN-JS940, and IN-KZ007 residues are measured in soil (p-g kg ), sediment (p-gkg ), and water (pgL ). Quantification is based on analyte response in calibration standards and sample extract analyses determined as pg mL Calibration standard runs are analyzed before and after every 1 samples in each analytical set. Analyte quantification is based on (1) linear regression analysis of (y-axis) analyte concentration (lagmL Q and (x-axis) analyte peak area response or (2) the average response factor determined from the appropriate calibration standards. The SLOPE and INTERCEPT functions of Microsoft Excel are used to determine slope and intercept. The AVERAGE and STDEV functions of Microsoft Excel are used to determine average response factors and standard deviations. 

Calculation to determine qg kg found in soil and sediment test samples by average response factor analysis ... 

SW = sample weight (0.010 kg) of soil or sediment extracted S V = sample volume (0.20 L) of water extracted RFavg. = average response factor [peak area/(pg mL )] for analyte... 

Quantification is performed by comparing the sample response with an average response factor determined from the standard analyses. Internal standards are used... 

Quantification is based on the use of a three-point calibration curve analyzed in triplicate using ISs to adjust for instrument response. The average response factor from the calibration curve is used for all subsequent analyses. 

Perform quantifications using the average response from a three point calibration curve analyzed in triplicate ... 

Figure 7. Simultaneously monitoring vapor signatures of 1000 sensors for 2,4-DNT, 1,3-DNB, and TNT vapor strips at 8% saturated vapor levels. The (noisy) responses for 250 individual sensors are compared to the averaged response profile for 1000 individual sensors. Reprinted with permission from ref. 12a. Copyright 2000 American Chemical Society.

Figure 8. Seventy-six sensor beads (Jupiter C4/Nile Red) monitored to show that the average responses for three consecutive 0.38-s exposures of 50% saturated vapor levels result in reproducible and high-speed response profiles. The sensors are positioned on the distal tip of an optical imaging fiber and relative analyte concentrations are 0.5 and 18700 ppm for 1,3-DNB and toluene, respectively. Reprinted with permission from ref 12a. Copyright 2000 American Chemical Society.

Due to all this, the panels functioned very well for the full period with an average response rate of 80%. 

Fenvalerate Data. Calibration data for the GC measurement of Fenvalerate were furnished by D. Kurtz (17). Average responses for five replicates at each of five standard concentrations are given in Table III. It should be noted that the stated responses are not raw observations, but rather on-line computer generated peak area estimates (cm ). (Had we started with the raw data [chromatograms], the problem would actually have been two-dimensional, including as variables retention time and concentration.) The stated uncertainties in the peak areas are based on a linear fit (o a+bx) of the replication standard deviations to concentration and the "local slopes" [first differences] in the last column of Table III are presented... 

FIGURE 15 An example of a main effect plot.The average responses at low level and high level of the factors are plotted. 

The condition is that the instantaneous current is sampled during the last few microseconds of the pulse [2,3]. This procedure was assumed in the theoretical calculations presented in Figs. 2.1 and 2.2, and Tables 2.1 and 2.2. Usually, the current is sampled during a certain portion of the pulse and then averaged. The average response corresponds to an instantaneous current sampled in the middle of the sampling window. For niisw = 25 mV and uAE = —5 mV, this relationship is [12] ... 

The offset parameter (P = 7.072 at the 100.(KX)% level of confidence) does not represent an average response, but rather corresponds to the clearings at day zero when all of the other factor effects have been removed. It represents a reference point to which the factor effects can be added. 

In the classical factorial design literature, a factor effect is defined as the difference in average response between the experiments carried out at the high level of the factor and the experiments carried out at the low level of the factor. Thus, in a 2 full factorial design, the main effect of A would be calculated as ... 

In a similar way, the classical interaction effects AB, AC, BC, and ABC can be defined as the difference in average response between the experiments carried out at the high level of the interaction and the experiments carried out at the low level of the interaction. Again, the high level of an interaction is indicated by a plus sign in its column in Table 14.3 (either both of the individual factors are at a high level, or both of the individual factors are at a low level). The low level of a two-factor interaction is indicated by a minus sign in its column in Table 14.3 (one but not both of the individual factors is at a low level). Thus, the classical two-factor interaction effects are easily calculated ... 

Starting with a dimensional response metric, ES is the a measure of the average response in standard deviation units calculated as MC — ME/SD, j, where ME represents the mean of the experimental treatment, MC represents the mean of the control treatment, and represents pooling of the stan-... 

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