The experimental data was analyzed

The experimental data was analyzed by multiple regression
analysis through the least square method. Sequential sum of
squares and model summary statistics was carried out on the
experimental data to evaluate the adequacy of various models
(linear, interactive (2FI), quadratic and cubic). Regression
coefficients of linear, quadratic, and interaction involved in
the model and their effects were analyzed by Pareto analysis
of variance (ANOVA) and ANOVA tables were generated. All
the terms in the model were tested by student’s F-test and
significance of the F-values at probability levels (p≤0.05)
were analyzed. The experimental data were analysed with
various statistical analysis such as sum of squares (SS), determination
coefficient (R2
), adjusted determination of coefficient
(Ra
2
), predicted determination of coefficient (Rp
2
) and
coefficient of variation (CV) to reflect the statistical significance
of the fitted polynomial equation. All the statistical
analyses were done with the help of Stat ease Design Expert
8.0.7.1 statistical software package (Stat-Ease Inc.,
Minneapolis, USA). After fitting the data to the models, the
models were used for the construction of three dimensional
(3D) response surface plots to predict the relationships between
independent and dependent variables.

ผลลัพธ์ (อังกฤษ) 2:

The Experimental Data was analyzed by multiple regression
analysis Through The least Square Method. Sequential sum of
Squares and Model Summary Statistics was carried out on The
Experimental Data to evaluate The adequacy of Various models
(Linear, Interactive (2FI), Quadratic and Cubic). Regression
coefficients of Linear, Quadratic, and Interaction Involved in
The Pareto Model and their effects were analyzed by analysis
of variance (ANOVA) and ANOVA were Generated Tables. All
in The Model The terms were tested by Student's F-test and
significance of The F-values ​​at levels Probability (P≤0.05)
were analyzed. The Experimental Data were analyzed with
Various statistical analysis Such As sum of Squares (SS), determination
coefficient (R2
), adjusted determination of coefficient
(Ra
2
), predicted determination of coefficient (Rp
2
) and
coefficient of variation (CV) to Reflect. The statistical significance
of The fitted polynomial Equation. The all statistical
analyzes were done with The Help of Stat Ease Design Expert
8.0.7.1 Package statistical software (Stat-Ease Inc.,
Minneapolis, USA). After The Fitting Data to The models, The
models were Used for The Construction of Three dimensional
(3D) Response surface plots to Predict The Relationships between
independent and dependent variables.