For the simultaneous production of response surface analysis output by rsm used in combination with graphics in the second-order polynomial approach. The predictor variables must be named “Factor1”, “Factor2”, etc., while the response variable must be named “Response”. The output includes regression model fitting and plot of the fitted response surface.
the matrix of experimental data that contains columns with the uncoded levels for each experimental factor and the observed values for the response variable in the rightmost column.
The user will be prompted to enter “1” for a 3-D plot of the response surface, or “2” to plot the contour of the predicted variance of the response
“Data.For.Analysis”, includes the data set and the coding coefficients for the transformation of the independent factors
“Response.Surface.Summary”, includes the response surface for variable, hypothesis tests for linear, quadratic, and crossproduct terms, lack of fit test, parameter estimates, the factor ANOVA table, canonical analysis, and eigenvectors
Mead, R., Gilmour, S. G., and Mead, A. 2012. Statistical Principles for the Design of Experiments: Applications to Real Experiments. Cambridge University Press, Cambridge.
Panneton, B., Philion, H., Dutilleul, P., Theriault, R., and Khelifi, M. 1999. Full factorial design versus central composite design: Statistical comparison and experimental implications for spray droplet deposition. Transactions of the American Society of Agricultural Engineers 42:877-883.
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