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BioMed Research International
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2019
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Article
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Tab 5
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Research Article
Artificial Neural Networks (ANNs) and Response Surface Methodology (RSM) Approach for Modelling the Optimization of Chromium (VI) Reduction by Newly Isolated
Acinetobacter radioresistens
Strain NS-MIE from Agricultural Soil
Table 5
Analysis of variance (ANOVA).
Sources
Sum of squares
df
Mean squares
F-value
P>F
Model
5697.40
9
633.04
158.45
< 0.0001
significant
A-Nutrient broth
2116.31
1
2116.31
529.69
< 0.0001
B-pH
1677.11
1
1677.11
419.77
< 0.0001
C-Chromate conc.
1483.92
1
1483.92
371.41
< 0.0001
AB
25.42
1
25.42
6.36
0.0397
AC
0.76
1
0.76
0.19
0.6755
BC
7.40
1
7.40
1.85
0.2158
A
2
298.19
1
298.19
74.63
< 0.0001
B
2
11.06
1
11.06
2.77
0.1401
C
2
52.07
1
52.07
13.03
0.0086
Residual
27.97
7
4.00
4.61
Lack of Fit
21.69
3
7.23
0.0869
Not significant
Pure Error
6.27
4
1.57
R
2
0.9974
Adjusted R
2
0.9888
P > F less than 0.05 = statistically significant.