The first part of the ANOVA table is a summary of the significance of the global model. If the pvalue for the global model is smaller than 0.05, it means that the model explains more of the variations of the response variable than could be expected from random phenomena. In other words, the model is significant at the 5% level. The smaller the pvalue, the more significant (and useful) the model is.
The error pvalue should be large as the error should be random and not be explaining the variation in the response.
Total error should also have a small pvalue, so that the model is valid.
This table plot gives an overview of the significance of all effects for all responses. There are three values per effect and per response:
Effect Summary table 


Significance levels and associated codes 
The sign and significance level of each effect is given as a code:
NS: non significant. ?: possible effect at the significance level 10%.
Note: If some of the design variables have more than 2 levels, the Effects Overview table contains stars (*) instead of ”+” and ”–” signs.
Analyze this table by:
Checking the Response Variables
Look for responses which are not significantly explained by any of the design variables (gray columns). This may be because there are errors in the data, these responses have very little variation, these responses are very noisy, or their variations are caused by noncontrolled conditions which have not been included in the design.
Checking the Design Variables
Look for rows which contain many ”+” or ”–” signs and are green: these main effects or interactions dominate. This is how to detect the most important variables.
This plot is used to find the settings of the design variables which give an optimal response value, and to study the general shape of the response surface fitted by the Response Surface model or the Regression model. It shows one response variable at a time.
This plot can appear in various layouts. The most relevant are:
Interpretation: contour plotLook at this plot as a map which tells how to reach the experimental objective. The plot has two axes: two predictor variables are studied over their range of variation; the remaining ones are kept constant. The constant levels are indicated in the RS table. 
Interpretation: landscape plotLook at this plot to study the 3D shape of the response surface. Here it is obvious whether there is a maximum, a minimum or a saddle point. 
Response surface plot, with contour layout  Response surface plot, with landscape layout 
The response values are displayed as contour lines, i.e. lines that show where the response variable has the same predicted value. Clicking on a line, or on any spot within the map, will show the predicted response value for that point, and the coordinates of the point (i.e. the settings of the two predictor variables giving that particular response value).
To interpret several responses together, print out their contour plots on color transparencies and superimpose the maps.
This table is used to set the parameters of the response surface.
Design variables
In a response surface only two design variables can vary the others are fixed.
To select the variables to vary tick/untick the box in the Display column.
To set the value for the fixed variable enter the value manually in the column Value to display. By default this value is the average value.
For category variables select one of the levels using the dropdown list.
Response variables
Only one response variable can be plot at a time. Select the variable to plot by ticking/unticking them.
Once all the modifications are done, click the Generate Surface button to generate a new response surface.
Response surface table
This table presents the effect values for all variables as well as their significance levels and pvalues.
PLSANOVA Summary 


Significance levels and associated codes 
NS: non significant.
?: possible effect at the significance level 10%.