SWELLING INDEX
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Response 1 Swelling Index
ANOVA for selected factorial model
Analysis of variance table [Partial sum of squares - Type III]
Sum of Mean F p-value
Source Squares df Square Value Prob > F
Model 0.75 3 0.25 0.82 0.5190 not significant
A-PVP 0.59 1 0.59 1.93 0.2023
B-Kitosan 0.086 1 0.086 0.28 0.6099
AB 0.075 1 0.075 0.25 0.6336
Pure Error 2.44 8 0.30
Cor Total 3.18 11
The Model F-value of 0.82 implies the model is not significant relative to the noise. There is
a
51.90 % chance that a F-value this large could occur due to noise.
Values of "Prob > F" less than 0.0500 indicate model terms are significant.
In this case there are no significant model terms.
Values greater than 0.1000 indicate the model terms are not significant.
If there are many insignificant model terms (not counting those required to support
hierarchy),
model reduction may improve your model.
Std. Dev. 0.55 R-Squared 0.2349
Mean 3.84 Adj R-Squared -0.0520
C.V. % 14.35 Pred R-Squared -0.7214
PRESS 5.48 Adeq Precision 1.920
-2 Log Likelihood 14.92 BIC 24.86
AICc 28.64
A negative "Pred R-Squared" implies that the overall mean may be a better predictor of your
response than the current model.
"Adeq Precision" measures the signal to noise ratio. A ratio of 1.92 indicates an inadequate
signal and we should not use this model to navigate the design space.
Coefficient Standard 95% CI 95% CI
Factor Estimate df Error Low High VIF
Intercept 3.84 1 0.16 3.48 4.21
A-PVP 0.22 1 0.16 -0.15 0.59 1.00
B-Kitosan -0.085 1 0.16 -0.45 0.28 1.00
AB 0.079 1 0.16 -0.29 0.45 1.00
Final Equation in Terms of Coded Factors:
Swelling Index =
+3.84
+0.22 * A
-0.085 * B
+0.079 * AB
The equation in terms of coded factors can be used to make predictions about the response for
given levels of each factor. By default, the high levels of the factors are coded as +1 and the
low levels of the factors are coded as -1. The coded equation is useful for identifying the
relative impact of the factors by comparing the factor coefficients.
Final Equation in Terms of Actual Factors:
Swelling Index =
+5.33917
-0.24450 * PVP
-0.081183 * Kitosan
+0.015783 * PVP * Kitosan
The equation in terms of actual factors can be used to make predictions about the response for
given levels of each factor. Here, the levels should be specified in the original units for
each factor. This equation should not be used to determine the relative impact of each factor
because the coefficients are scaled to accommodate the units of each factor and the intercept
is not at the center of the design space.
Proceed to Diagnostic Plots (the next icon in progression). Be sure to look at the:
1) Normal probability plot of the studentized residuals to check for normality of residuals.
2) Studentized residuals versus predicted values to check for constant error.
3) Externally Studentized Residuals to look for outliers, i.e., influential values.
4) Box-Cox plot for power transformations.
If all the model statistics and diagnostic plots are OK, finish up with the Model Graphs icon.
KEKUARTAN
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Response 2 Kekuatan Mucoadhesive
ANOVA for selected factorial model
Analysis of variance table [Partial sum of squares - Type III]
Sum of Mean F p-value
Source Squares df Square Value Prob > F
Model 560.06 3 186.69 0.60 0.6337 not significant
A-PVP 485.14 1 485.14 1.56 0.2476
B-Kitosan 38.52 1 38.52 0.12 0.7343
AB 36.40 1 36.40 0.12 0.7414
Pure Error 2495.07 8 311.88
Cor Total 3055.13 11
The Model F-value of 0.60 implies the model is not significant relative to the noise. There is
a
63.37 % chance that a F-value this large could occur due to noise.
Values of "Prob > F" less than 0.0500 indicate model terms are significant.
In this case there are no significant model terms.
Values greater than 0.1000 indicate the model terms are not significant.
If there are many insignificant model terms (not counting those required to support
hierarchy),
model reduction may improve your model.
Std. Dev. 17.66 R-Squared 0.1833
Mean 45.06 Adj R-Squared -0.1229
C.V. % 39.19 Pred R-Squared -0.8375
PRESS 5613.90 Adeq Precision 1.599
-2 Log Likelihood 98.10 BIC 108.04
AICc 111.81
A negative "Pred R-Squared" implies that the overall mean may be a better predictor of your
response than the current model.
"Adeq Precision" measures the signal to noise ratio. A ratio of 1.60 indicates an inadequate
signal and we should not use this model to navigate the design space.
Coefficient Standard 95% CI 95% CI
Factor Estimate df Error Low High VIF
Intercept 45.06 1 5.10 33.30 56.81
A-PVP 6.36 1 5.10 -5.40 18.11 1.00
B-Kitosan -1.79 1 5.10 -13.55 9.96 1.00
AB 1.74 1 5.10 -10.01 13.50 1.00
Final Equation in Terms of Coded Factors:
Kekuatan Mucoadhesive =
+45.06
+6.36 * A
-1.79 * B
+1.74 * AB
The equation in terms of coded factors can be used to make predictions about the response for
given levels of each factor. By default, the high levels of the factors are coded as +1 and the
low levels of the factors are coded as -1. The coded equation is useful for identifying the
relative impact of the factors by comparing the factor coefficients.
Final Equation in Terms of Actual Factors:
Kekuatan Mucoadhesive =
+75.15833
-4.65833 * PVP
-1.76167 * Kitosan
+0.34833 * PVP * Kitosan
The equation in terms of actual factors can be used to make predictions about the response for
given levels of each factor. Here, the levels should be specified in the original units for
each factor. This equation should not be used to determine the relative impact of each factor
because the coefficients are scaled to accommodate the units of each factor and the intercept
is not at the center of the design space.
Proceed to Diagnostic Plots (the next icon in progression). Be sure to look at the:
1) Normal probability plot of the studentized residuals to check for normality of residuals.
2) Studentized residuals versus predicted values to check for constant error.
3) Externally Studentized Residuals to look for outliers, i.e., influential values.
4) Box-Cox plot for power transformations.
If all the model statistics and diagnostic plots are OK, finish up with the Model Graphs icon.
DURASI
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Response 3 Waktu Tinggal
ANOVA for selected factorial model
Analysis of variance table [Partial sum of squares - Type III]
Sum of Mean F p-value
Source Squares df Square Value Prob > F
Model 2886.25 3 962.08 1.01 0.4368 not significant
A-PVP 2494.08 1 2494.08 2.62 0.1441
B-Kitosan 290.08 1 290.08 0.30 0.5959
AB 102.08 1 102.08 0.11 0.7517
Pure Error 7612.67 8 951.58
Cor Total 10498.92 11
The Model F-value of 1.01 implies the model is not significant relative to the noise. There is
a
43.68 % chance that a F-value this large could occur due to noise.
Values of "Prob > F" less than 0.0500 indicate model terms are significant.
In this case there are no significant model terms.
Values greater than 0.1000 indicate the model terms are not significant.
If there are many insignificant model terms (not counting those required to support
hierarchy),
model reduction may improve your model.
Std. Dev. 30.85 R-Squared 0.2749
Mean 310.92 Adj R-Squared 0.0030
C.V. % 9.92 Pred R-Squared -0.6315
PRESS 17128.50 Adeq Precision 2.171
-2 Log Likelihood 111.49 BIC 121.43
AICc 125.20
A negative "Pred R-Squared" implies that the overall mean may be a better predictor of your
response than the current model.
"Adeq Precision" measures the signal to noise ratio. A ratio of 2.17 indicates an inadequate
signal and we should not use this model to navigate the design space.
Coefficient Standard 95% CI 95% CI
Factor Estimate df Error Low High VIF
Intercept 310.92 1 8.90 290.38 331.45
A-PVP 14.42 1 8.90 -6.12 34.95 1.00
B-Kitosan -4.92 1 8.90 -25.45 15.62 1.00
AB 2.92 1 8.90 -17.62 23.45 1.00
Final Equation in Terms of Coded Factors:
Waktu Tinggal =
+310.92
+14.42 * A
-4.92 * B
+2.92 * AB
The equation in terms of coded factors can be used to make predictions about the response for
given levels of each factor. By default, the high levels of the factors are coded as +1 and the
low levels of the factors are coded as -1. The coded equation is useful for identifying the
relative impact of the factors by comparing the factor coefficients.
Final Equation in Terms of Actual Factors:
Waktu Tinggal =
+372.91667
-5.91667 * PVP
-3.71667 * Kitosan
+0.58333 * PVP * Kitosan
The equation in terms of actual factors can be used to make predictions about the response for
given levels of each factor. Here, the levels should be specified in the original units for
each factor. This equation should not be used to determine the relative impact of each factor
because the coefficients are scaled to accommodate the units of each factor and the intercept
is not at the center of the design space.
Proceed to Diagnostic Plots (the next icon in progression). Be sure to look at the:
1) Normal probability plot of the studentized residuals to check for normality of residuals.
2) Studentized residuals versus predicted values to check for constant error.
3) Externally Studentized Residuals to look for outliers, i.e., influential values.
4) Box-Cox plot for power transformations.
If all the model statistics and diagnostic plots are OK, finish up with the Model Graphs icon.
DESIRABILITY
