10.4. FourParameter Logistic Model
This procedure features two implementations of the 4PL method, (1) as described in United States Pharmacopoeia (2010) chapters <1032>, <1033>, <1034> and (2) according to European Pharmacopoeia (19972017). It is possible to estimate the Full and Reduced USP models including plate effects, equivalence tests, outlier detection and multiple potency estimates with confidence limits. 96well ELISA, as well as unbalanced designs are supported.
10.4.1. 4PL Variable Selection
The required data format is as described in section 10.0.1. Data Preparation (also see 10.0.2. Doses, Dilutions and Potency). Response data is stacked in a single column, a second column contains the dose level for each response value and an optional categorical data column indicates which preparation a particular doseresponse pair belongs to. Another optional categorical data column can be selected to track plate membership for USP models.
Designs can be unbalanced, i.e. the number of replicates for each dosepreparation combination may be different, dose levels for standard and test preparations may be different, there can be more than one test preparation, but the first preparation in the data matrix should always be the standard (or reference). When the optional [Plate] column is selected, the program assumes that all dosetreatment groups (or cells) have an equal number of replicates, or in other words, a balanced design is assumed.
To run an analysis, it is sufficient to select one [Data] and one [Dose] variable. In this case, a single sigmoid curve will be fitted to data. If a [Preparation] variable (which should be a numeric or string factor or categorical data variable) is also selected, then you will have the option of fitting a Full Model (where a separate sigmoid curve is fitted for each preparation) or a Reduced Model (where parallel sigmoid curves are fitted for selected preparations). If multiple plates have been used in the bioassay, this information can also be entered in a separate factor variable [Plate].
The next dialogue asks for the following convergence and model options.
Tolerance: This value is used to control the sensitivity of the maximum likelihood procedure employed. Under normal circumstances, you do not need to edit this value. If convergence cannot be achieved, then larger values of this parameter can be tried by removing one or more zeros.
Maximum Number of Iterations: When convergence cannot be achieved with the default value of 100 function evaluations, a higher value can be tried.
Transform Response: It is possible to transform the response variable by one of e (natural), 10 or 2based logarithms or leave it untransformed (default).
Transform Dose: It is possible to transform the dose variable by one of natural (default), 10 or 2based logarithms or leave it untransformed.
Confidence Level for Regression Coefficients: While the general level of confidence (default 95%) is set on the Variable Selection Dialogue, confidence level for Regression Coefficients and Equivalence Tests can be set here separately. The default is 90%.
In the next dialogue, the model to be estimated is selected. This dialogue will not appear if a [Preparation] variable is not selected or if the number of preparations is only one.
EP: Parallel sigmoid curves are fitted for all preparations. Validity tests and confidence limits of estimated potency are calculated according to European Pharmacopoeia (19972017). See sections 10.3.2.4. Potency and 10.4.2.1. EP (European Pharmacopoeia).
Full Model USP: This is also known as the nonparallel or unconstrained model. A separate sigmoid curve is fitted for each preparation.
Reduced Model USP: Also known as the parallel or constrained model. Parallel sigmoid curves are fitted for all selected preparations.
If Reduced Model is selected, then a further dialogue will allow you to select the test preparations to include in the model. Goodness of Fit and Equivalence Tests options can be used to determine which preparations should be included. The first preparation is considered to be the standard (or reference).
10.4.2. 4PL Output Options
The 4parameter logistic function is given as:
where:
A is upper asymptote,
D is lower asymptote,
B is Hill slope,
C is ED50 and
x is the random variable dose.
In the output, these parameters will be labelled by their above string literals. If this is not the terminology you are used to, you can change these by entering the following line in Documents\Unistat10\Unistat10.ini file under the [Bioassay] section:
4plLabels=i
The parameter labels used will be:
for i = 0: Upper asymptote, Lower asymptote, Hill slope, ED50 (default),
for i = 1: A, D, B, EC50,
for i = 2: Top, Bottom, Hill, EC50.
10.4.2.1. EP (European Pharmacopoeia)
Parallel sigmoid curves are fitted for all preparations. Validity tests (nonlinearity and nonparallelism) and confidence limits of estimated potency are calculated according to European Pharmacopoeia (19972017) (see section 10.3.2.4. Potency). Here, confidence limits of estimated potency are the fiducial limits instead of the standard tinterval as recommended by United States Pharmacopoeia (2010).
The estimated residual variance (s^{2}) used in the calculation of fiducial limits is based on the residual mean square of Completely Randomised Design.
For the details of individual output options see section 10.4.2.3. Reduced Model USP.
10.4.2.2. Full Model USP
A separate sigmoid curve is fitted for each preparation. Therefore, this model has 4k parameters, where k is the number of preparations including the standard (or reference).
Regression Coefficients: The tstatistic is defined as:
and has a tdistribution with:
df = N – (4 * k)
degrees of freedom and N is the total number of observations used in the analysis, including all preparations. When a plate factor is selected, a Satterthwaite approximation of degrees of freedom is used as recommended by USP <1034>.
Confidence intervals for regression coefficients are computed as:
where each coefficient’s standard error σ_{j} is the square root of the diagonal element of the covariance matrix:
where X is the Jacobian matrix. See Seber, G A. F. and Wild, C. J 2003.
Correlation Matrix of Regression Coefficients: This is a symmetric matrix with unity diagonal elements. It gives the correlation between regression coefficients and is obtained by dividing each element of (X’X)^{1} matrix by the square root of the diagonal elements corresponding to its row and column.
Case (Diagnostic) Statistics:
Predictions (Interpolations): If, for a case, all variables are nonmissing, but only the response variable is missing, then the estimated response and its confidence and prediction intervals will be calculated. When a case is predicted, its label will be prefixed by an asterisk (*). Such cases are not included in the estimation of the model. Therefore, it will be a good idea to include the cases for which predictions are to be made in the data matrix during the data preparation phase.
Confidence and Prediction Intervals: Approximate standard errors are calculated using the delta method (Van Der Vaart, 1998). Defining the column vector of partial derivatives with respect to each parameter as:
for each given level of dose calculate:
The standard error for actual Y (confidence) for that dose level is:
and the standard error for mean of Y (prediction) is:
The 95% intervals are then calculated as:
Effective Dose: ED50 is readily estimated as a model parameter together with its standard error and 90% confidence limits. If you need to obtain other effective dose values for the currently estimated model (i.e. the socalled ED_{anything}), you can enter or edit the ED values from this dialogue, which pops up when you click the [Opt] button situated to the left of the Effective Dose option.
For a given percentage point P% the effective dose is calculated as:
Approximate standard errors are calculated using the delta method (Van Der Vaart, 1998). Defining the column vector of partial derivatives with respect to each parameter as:
for each given level of effective dose calculate:
The standard error for that effective dose is:
and the 90% intervals are then calculated as:
Goodness of Fit tests: The following measures of model fit (or lack of fit) are provided. The third option Parallelism Tests is not available for the Full Model or if a [Preparation] column was not selected.
ANOVA of Regression: For each preparation the total sum of squares is:
with N – 1 degrees of freedom and the residual sum of squares is:
with 3 degrees of freedom (i.e. four parameters less one).
The Rsquared value is calculated as
Measures of Variability: Let σ^{2} be the total variance of residuals. Then:
where the degrees of freedom is the same as in the Regression Coefficients output.
Upper limit of geometric standard deviation (GSD) is:
upper limit of the percent geometric coefficient of variation (%GCV) is:
and upper limit of the percent coefficient of variation (%CV) is calculated as:
If a [Plate] column is selected, the total variance is broken into within and between plate components and the onesided upper 95% limits are also computed for within plate standard deviation.
Equivalence Tests: These tests are used to decide which test preparations are to be included in the Reduced Model as recommended by USP <1034>, instead of the two parametric tests of parallelism which are available under the Parallelism Tests output option.
Mean Difference from Standard: For each model parameter other than ED50 (i.e. A, D and B), the difference between the standard and each test preparation and their confidence limits are tabulated. For instance, for the upper asymptote A:
The 90% intervals are then calculated as:
The criteria against which an accept or reject decision is to be made (i.e. the socalled goalposts) are not included here. These criteria must be based on the specific properties of the bioassay.
Difference Plots: Three plots for parameters A, D and B are displayed with error bars. Each plot will have k – 1 points, one for each test preparation.
Outlier Plot: Standardised residuals are plotted against a randomized Xaxis. If a [Plate] factor has been selected, the residuals are marked by their plate membership, otherwise by preparation.
Homogeneity of Variance Plot: Studentised residuals are plotted against estimated (fitted) response values. Residuals are marked by their preparation membership. If the horizontal spread of points indicates a bias, the response variable may need to be transformed to improve the homogeneity of variance.
DoseResponse Plot: A doseresponse curve will be drawn for each preparation. Selecting Edit → XY DoseResponse Plot… (or doubleclicking on the graph area) will pop a dialogue where what is displayed on the plot can be controlled.
For each curve it is possible to draw lines for ED50, asymptotes, and their confidence limits with or without values By default, confidence limits for actual Y values (i.e. the confidence interval) are drawn for each curve. If you wish to display the prediction interval instead, enter the following line in Documents\Unistat10\Unistat10.ini file under the [Bioassay] section:
4plPredictionInterval=1
Here are a few examples of what can be achieved:
10.4.2.3. Reduced Model USP
USP <1034> recommends that the best subset of preparations to be included in Reduced Model should be selected using Equivalence Tests as described in Full Model output above.
This model assumes that all preparations have the same A, D and B coefficients and only differ in parameter C (i.e. ED50). Therefore, the Reduced Model has 3 + k’ parameters to estimate; A, D, B, C_{j} j = 1, …, k’, where k’ is the number of preparations selected, including the standard.
Regression Coefficients: The tstatistic is defined as in Full Model, except for the degrees of freedom, which is for Reduced Model:
df = N’ – (3 + k)
where N’ is the total number of observations used in Reduced Model.
Correlation Matrix of Regression Coefficients: Correlations between all 3 + k’ parameters are displayed.
Case (Diagnostic) Statistics: These are as described in Full Model.
Effective Dose: You can obtain effective dose values other than 50% and their confidence limits as described in Full Model.
Goodness of Fit tests: ANOVA of Regression and Measures of Variability are as described in Full Model.
Parallelism Tests: This option is only available for Reduced Model when it is run immediately after a Full Model. Although these tests are not recommended by USP, they are included here for the sake of completeness. Both F and chisquared statistics are based on the residual sum of squares of Full and Reduced models:
where:
df1 = (4 * k’) – (3 + k’)
df2 = N’ – (4 * k’)
k’ is the number of preparations selected in Reduced Model.
and:
where:
df = N’.
Potency: In transformed (logged) scale, the relative potency for test preparation j is found as:
and its confidence limits as:
where the variance of relative potency for test preparation j is:
The relative potency and its confidence limits are then transferred (antilogged) back to the original scale.
Weights (which are needed when various potencies are to be combined) are computed as:
and % Precision is:
If the data column [Dose] contains the actual dose levels administered in original dose units, we will obtain the estimated potency and its confidence limits in the same units. If, however, the [Dose] column contains unitless relative dose levels, then we may need to perform further calculations to obtain the estimated potency in original units. To do that you can enter assigned potency of the standard, assumed potency of each test preparation and predilutions for all preparations including the standard in a data column and select it as [Dilution] variable. UNISTAT will then calculate the estimated potency as described in section 10.0.2. Doses, Dilutions and Potency. Also see section 10.0.3. Potency Calculation Example.
DoseResponse Plot: This has the same options as the doseresponse plot described for the Full Model in the previous section.
10.4.3. 4PL Examples
Example 1
Data is given in Table 5.4.1.I. on p. 592 of European Pharmacopoeia (2008).
Open BIOPHARMA6 and select Bioassay → FourParameter Logistic Model. From the Variable Selection Dialogue select columns C39, C40 and L41 respectively as [Data], [Dose] and [Preparation]. Click [Next] and leave all entries on the Convergence Criteria dialogue unchanged. On the next two dialogues select EP: Fit parallel sigmoids and check Preparation T. On Output Options Dialogue check only the Regression Results and Potency options.
Click [Finish] to obtain the following output. Note that the potency reported here is relative potency.
FourParameter Logistic Model
EP
Regression Results
Valid Number of Cases: 40, 0 Omitted

Coefficient 
Standard Error 
tStatistic 
2Tail Probability 
Lower 90% 
Upper 90% 
Upper asymptote 
3.1960 
0.0327 
97.7429 
0.0000 
3.1407 
3.2512 
Lower asymptote 
0.1455 
0.0143 
10.2042 
0.0000 
0.1214 
0.1695 
Hill slope 
1.1244 
0.0294 
38.2340 
0.0000 
1.0747 
1.1741 
Standard S ED50 
0.1348 
0.0037 
36.4278 
0.0000 
0.1285 
0.1410 
Preparation T ED50 
0.0924 
0.0026 
35.7697 
0.0000 
0.0880 
0.0967 
Total Residual Variance = 
0.0012 
Degrees of Freedom = 
35 
Validity of Assay

ChiSquare 
DoF 
Probability 
Nonlinearity 
8.8935 
16 
0.9177 
Nonparallelism 
0.0458 
1 
0.8306 
Potency
Estimated Potency 
Lower 95% 
Upper 95% 

Preparation T 
1.4589 
1.3920 
1.5290 
Create a new column of data as follows and select it as [Dilution] variable. The standard has an assigned potency of 0.4 IU / ml.
0.4 IU/ml
1
FourParameter Logistic Model
EP
Potency
Assigned potency of Standard S: 0.4 IU/ml
Estimated Potency 
Lower 95% 
Upper 95% 

Preparation T 
0.5835 
0.5568 
0.6116 
Example 2
Open the file 4PL and select Bioassay → FourParameter Logistic Model. From the Variable Selection Dialogue select columns C1 to C4 respectively as [Data], [Dose], [Preparation] and [Plate]. Click [Next], enter 1 for Transform Response, 3 for Transform Dose and leave other entries unchanged. On the next dialogue select Full Model: Fit a separate sigmoid on each preparation. Leave Output Options Dialogue unchanged and then click [Finish].
FourParameter Logistic Model
Full Model USP
Regression Results

Coefficient 
Standard Error 
tStatistic 
2Tail Prob 
Lower 90% 
Upper 90% 
Standard A 
14851.5573 
721.5338 
20.5833 
0.0000 
13377.0205 
16326.0941 
D 
161.5165 
7.8716 
20.5189 
0.0000 
145.4300 
177.6030 
B 
1.4833 
0.0782 
18.9697 
0.0000 
1.6431 
1.3235 
EC50 
0.0111 
0.0004 
25.8819 
0.0000 
0.0102 
0.0120 
Test A A 
14945.5751 
577.3904 
25.8847 
0.0000 
13765.6117 
16125.5385 
D 
158.2746 
10.9899 
14.4018 
0.0000 
135.8155 
180.7337 
B 
1.5004 
0.0828 
18.1187 
0.0000 
1.6696 
1.3312 
EC50 
0.0316 
0.0013 
24.2711 
0.0000 
0.0289 
0.0343 
Test B A 
14027.4183 
838.3262 
16.7326 
0.0000 
12314.2027 
15740.6339 
D 
262.6613 
10.8566 
24.1937 
0.0000 
240.4746 
284.8480 
B 
1.4996 
0.0917 
16.3570 
0.0000 
1.6869 
1.3122 
EC50 
0.0056 
0.0003 
22.2848 
0.0000 
0.0051 
0.0061 
Test C A 
14995.9650 
578.6668 
25.9147 
0.0000 
13813.3931 
16178.5369 
D 
159.0249 
7.1096 
22.3675 
0.0000 
144.4955 
173.5542 
B 
2.0018 
0.1060 
18.8896 
0.0000 
2.2184 
1.7852 
EC50 
0.0156 
0.0005 
33.5103 
0.0000 
0.0147 
0.0166 
Total Residual Variance = 
0.0451 
Degrees of Freedom = 
368 
Satterthwaite DoF = 
4.7019 
Correlation Matrix of Regression Coefficients

A 
D 
B 
EC50 
Standard A 
1.0000 
0.2339 
0.5689 
0.4288 
D 
0.2339 
1.0000 
0.5698 
0.4309 
B 
0.5689 
0.5698 
1.0000 
0.0015 
EC50 
0.4288 
0.4309 
0.0015 
1.0000 
Test A A 
1.0000 
0.2417 
0.4974 
0.2356 
D 
0.2417 
1.0000 
0.6673 
0.6290 
B 
0.4974 
0.6673 
1.0000 
0.2818 
EC50 
0.2356 
0.6290 
0.2818 
1.0000 
Test B A 
1.0000 
0.2347 
0.6275 
0.5546 
D 
0.2347 
1.0000 
0.5172 
0.3068 
B 
0.6275 
0.5172 
1.0000 
0.1771 
EC50 
0.5546 
0.3068 
0.1771 
1.0000 
Test C A 
1.0000 
0.1421 
0.4344 
0.3558 
D 
0.1421 
1.0000 
0.4831 
0.4325 
B 
0.4344 
0.4831 
1.0000 
0.0532 
EC50 
0.3558 
0.4325 
0.0532 
1.0000 
Case (Diagnostic) Statistics

LogE(Response) 
Log2(Dose) 
Preparation 
Plate 
Estimated Response 
1 
4.5962 
1.0000 
Standard 
Plate 1 
5.1005 
2 
4.9461 
1.0000 
Standard 
Plate 1 
5.1005 
3 
5.2660 
1.0000 
Standard 
Plate 2 
5.1005 
4 
5.1606 
1.0000 
Standard 
Plate 2 
5.1005 
5 
5.2318 
1.0000 
Standard 
Plate 3 
5.1005 
6 
5.0652 
1.0000 
Standard 
Plate 3 
5.1005 
7 
5.2122 
1.0000 
Standard 
Plate 4 
5.1005 
8 
5.3453 
1.0000 
Standard 
Plate 4 
5.1005 

… 
… 
… 
… 
… 

95% lb Actual Y 
95% lb Mean of Y 
95% ub Mean of Y 
95% ub Actual Y 
Residuals 
1 
0.5007 
4.1345 
6.0666 
9.7004 
0.5043 
2 
0.5007 
4.1345 
6.0666 
9.7004 
0.1544 
3 
0.5007 
4.1345 
6.0666 
9.7004 
0.1655 
4 
0.5007 
4.1345 
6.0666 
9.7004 
0.0601 
5 
0.5007 
4.1345 
6.0666 
9.7004 
0.1312 
6 
0.5007 
4.1345 
6.0666 
9.7004 
0.0353 
7 
0.5007 
4.1345 
6.0666 
9.7004 
0.1117 
8 
0.5007 
4.1345 
6.0666 
9.7004 
0.2447 

… 
… 
… 
… 
… 
Effective Dose

Dose 
Standard Error 
Lower 90% 
Upper 90% 
Standard ED10 
0.0025 
0.0002 
0.0021 
0.0030 
ED20 
0.0044 
0.0003 
0.0038 
0.0049 
ED50 
0.0111 
0.0004 
0.0102 
0.0120 
ED80 
0.0283 
0.0018 
0.0247 
0.0319 
ED90 
0.0489 
0.0043 
0.0402 
0.0576 
Test A ED10 
0.0073 
0.0006 
0.0061 
0.0085 
ED20 
0.0125 
0.0007 
0.0111 
0.0140 
ED50 
0.0316 
0.0013 
0.0289 
0.0343 
ED80 
0.0796 
0.0059 
0.0676 
0.0916 
ED90 
0.1367 
0.0137 
0.1086 
0.1647 
Test B ED10 
0.0013 
0.0001 
0.0010 
0.0016 
ED20 
0.0022 
0.0002 
0.0019 
0.0026 
ED50 
0.0056 
0.0003 
0.0051 
0.0061 
ED80 
0.0141 
0.0009 
0.0122 
0.0160 
ED90 
0.0242 
0.0022 
0.0196 
0.0287 
Test C ED10 
0.0052 
0.0003 
0.0045 
0.0059 
ED20 
0.0078 
0.0004 
0.0071 
0.0085 
ED50 
0.0156 
0.0005 
0.0147 
0.0166 
ED80 
0.0312 
0.0015 
0.0281 
0.0343 
ED90 
0.0468 
0.0031 
0.0404 
0.0531 
ANOVA of Regression
Due To 
Sum of Squares 
DoF 
Mean Square 
FStat 
Prob 
Standard Regression 
332.193 
3 
110.731 
2665.488 
0.0000 
Error 
3.822 
92 
0.042 


Total 
336.015 
95 
3.537 


Rsquared 
0.989 




Test A Regression 
307.441 
3 
102.480 
3200.335 
0.0000 
Error 
2.946 
92 
0.032 


Total 
310.387 
95 
3.267 


Rsquared 
0.991 




Test B Regression 
248.561 
3 
82.854 
2184.435 
0.0000 
Error 
3.489 
92 
0.038 


Total 
252.050 
95 
2.653 


Rsquared 
0.986 




Test C Regression 
373.567 
3 
124.522 
3189.062 
0.0000 
Error 
3.592 
92 
0.039 


Total 
377.159 
95 
3.970 


Rsquared 
0.990 




Measures of Variability

Standard Deviation 
Upper 95% Standard Deviation 
Upper 95% GSD 
Upper 95% %GCV 
Upper 95% %CV 
Within Plate 
0.0958 
0.1019 
1.1072 
10.7249 
10.2144 
Between Plate 
0.1894 




Total 
0.2123 
0.5621 
1.7543 
75.4291 
60.9521 
Mean Difference from Standard

Mean Difference 
Lower 90% 
Upper 90% 
A Test A 
94.0178 
1794.5193 
1982.5549 
Test B 
824.1390 
3084.5298 
1436.2518 
Test C 
144.4077 
1745.7603 
2034.5757 
D Test A 
3.2419 
30.8677 
24.3840 
Test B 
101.1448 
73.7400 
128.5496 
Test C 
2.4916 
24.1683 
19.1850 
B Test A 
0.0171 
0.2157 
0.2498 
Test B 
0.0163 
0.2300 
0.2625 
Test C 
0.5185 
0.2493 
0.7876 
Click the Last Dialogue button to obtain the Full Model Output Options Dialogue and click [Back]. Select Reduced Model: Fit parallel sigmoids on selected preparations. On the next dialogue check only Test A, as it is the only test preparation meeting the selection criteria. Leave output options unchanged and then click [Finish].
FourParameter Logistic Model
Reduced Model USP
Regression Results

Coefficient 
Standard Error 
tStatistic 
2Tail Prob 
Lower 90% 
Upper 90% 
A 
14901.5591 
444.0137 
33.5610 
0.0000 
13997.7665 
15805.3517 
D 
160.4999 
6.2979 
25.4846 
0.0000 
147.6804 
173.3193 
B 
1.4941 
0.0551 
27.1126 
0.0000 
1.6063 
1.3820 
Standard EC50 
0.0111 
0.0004 
29.9367 
0.0000 
0.0104 
0.0119 
Test A EC50 
0.0314 
0.0010 
30.0153 
0.0000 
0.0293 
0.0336 
Total Residual Variance = 
0.0440 
Degrees of Freedom = 
187 
Satterthwaite DoF = 
4.7821 
Correlation Matrix of Regression Coefficients

A 
D 
B 
Standard EC50 
Test A EC50 
A 
1.0000 
0.2272 
0.5248 
0.2760 
0.2792 
D 
0.2272 
1.0000 
0.6047 
0.4291 
0.4232 
B 
0.5248 
0.6047 
1.0000 
0.1069 
0.1030 
Standard EC50 
0.2760 
0.4291 
0.1069 
1.0000 
0.3294 
Test A EC50 
0.2792 
0.4232 
0.1030 
0.3294 
1.0000 
Case (Diagnostic) Statistics

LogE(Response) 
Log2(Dose) 
Preparation 
Plate 
Estimated Response 
1 
4.5962 
1.0000 
Standard 
Plate 1 
5.0936 
2 
4.9461 
1.0000 
Standard 
Plate 1 
5.0936 
3 
5.2660 
1.0000 
Standard 
Plate 2 
5.0936 
4 
… 
… 
… 
… 


95% lb Actual Y 
95% lb Mean of Y 
95% ub Mean of Y 
95% ub Actual Y 
Residuals 
1 
1.9426 
4.5478 
5.6395 
8.2446 
0.4974 
2 
1.9426 
4.5478 
5.6395 
8.2446 
0.1475 
3 
1.9426 
4.5478 
5.6395 
8.2446 
0.1723 
4 

… 
… 
… 
… 
Effective Dose

Dose 
Standard Error 
Lower 90% 
Upper 90% 
Standard ED10 
0.0026 
0.0002 
0.0022 
0.0029 
ED20 
0.0044 
0.0002 
0.0040 
0.0048 
ED50 
0.0111 
0.0004 
0.0104 
0.0119 
ED80 
0.0281 
0.0014 
0.0253 
0.0310 
ED90 
0.0484 
0.0032 
0.0419 
0.0550 
Test A ED10 
0.0072 
0.0004 
0.0063 
0.0081 
ED20 
0.0124 
0.0006 
0.0113 
0.0136 
ED50 
0.0314 
0.0010 
0.0293 
0.0336 
ED80 
0.0795 
0.0040 
0.0714 
0.0877 
ED90 
0.1368 
0.0091 
0.1183 
0.1554 
ANOVA of Regression
Due To 
Sum of Squares 
DoF 
Mean Square 
FStat 
Prob 
Regression 
654.740 
4 
163.685 
4514.212 
0.0000 
Error 
6.781 
187 
0.036 


Total 
661.520 
191 
3.463 


Rsquared 
0.990 




Measures of Variability

Standard Deviation 
Upper 95% Standard Deviation 
Upper 95% GSD 
Upper 95% %GCV 
Upper 95% %CV 
Within Plate 
0.0968 
0.1058 
1.1116 
11.1636 
10.6130 
Between Plate 
0.1862 




Total 
0.2098 
0.5535 
1.7393 
73.9327 
59.8725 
Parallelism Tests

Test Statistic 
RightTail Probability 
ChiSquare(96) 
0.0127 
1.0000 
F(3,376) 
0.1150 
0.9512 
Potency

Estimated Potency 
Lower 95% 
Upper 95% 
Test A 
2.8258 
2.5551 
3.1251 