Once the experiments are completed, the results can be added to the `doe` variable. The number of provided results must match with the number of runs. The results must be provided position-wise to the plan. Then, the first provided result is related to the first row presented with `show_plan()`, and so on. In the following lines, 16 results are provided, as the generated DOE requires 16 runs:

`from doenova import doe_ff
doe = doe_2f()
plan = doe.make_plan(3,0,2,0,1)
doe.insert_results([3.3,3.6,5.6,5.3,7.8,7.0,5.6,5.2,
2.3,2.4,5.6,5.1,3.2,2.9,4.8,4.4])`

If the DOE was created by importing a `xlsx` file containing the results, the previous line must be ignored.

Once the results are provided, the ANOVA table can be calculated:

`anova_comps = doe.anova()`

By doing the previous line, the ANOVA table is automatically presented in the console. The ANOVA table components are also being found in the `anova_comps` dictionary variable.

By default, all factors and possible combinations of factors are considered in the ANOVA table. It is possible to only select some factors or combinations. For this, a Numpy Array must be created prior calling the `anova` function. The number of columns must match with the number of factors in the DOE. Each row of this Numpy Array corresponds to one selection of a single factor or a combination of factors. For example,

sel = np.array([[1,0,0,0],

[0,0,1,0]])

[0,1,0,1]])

The `[1,0,0,0]` row first says that the first factor must be included in the ANOVA table. The `[0,0,1,0]` second row says that the third factor must be included in the ANOVA table. The `[0,1,0,1]` third row says that the interaction between the second and the fourth factors must be considered.

The `sel` variable must be input as an argument when calling the `anova` function:

`anova_comps = doe.anova(sel)`

The updated ANOVA table does not include the second and fourth factors (without interactions) as it was not specified in the `sel` variable.

The second step of the interactive designer can also be used to create the `sel` variable.

For 2-level factorial and PB deisgns, a regression model is automatically calculated when calling the `anova` function. Only the selected variables and interactions of variables will be considered in the model. If no `sel` variable was provided, the model will only consider 1-order interactions.

To perform a prediction from the model, use the following command:

`doe.predict_from_model([.5,.6,.2,-.5])`

In the example above, the DOE contains 4 factors. The number of numbers to input in the `predict_from_model` function depends on the number of factors.