A scientist wants to conduct an experiment to maximize crystal growth. Previous research has determined that catalyst exposure time, catalyst percentage, and temperature explain much of the variability in crystal growth.
The scientist generates the default central composite design for three factors and two blocks, assigns the factor levels, and randomizes the design.
The first table gives a summary of the design, which includes the total number of factors, runs, blocks, and replicates.
The design table displays the factor settings for each experimental run using coded factor names and levels. For example, in the first run, Factor A and Factor C are at level 1 and Factor B is at level −1. With 3 factors, the design has 20 runs. In the worksheet, Minitab displays the names of the factors and the uncoded levels.
Minitab randomizes the design by default, so when you create this design, the run order will not match the order in the example output.
Factors: | 3 | Replicates: | 1 |
---|---|---|---|
Base runs: | 20 | Total runs: | 20 |
Base blocks: | 2 | Total blocks: | 2 |
Cube points: | 8 |
---|---|
Center points in cube: | 4 |
Axial points: | 6 |
Center points in axial: | 2 |
Run | Blk | A | B | C |
---|---|---|---|---|
1 | 1 | 1.000 | -1.000 | 1.000 |
2 | 1 | -1.000 | -1.000 | -1.000 |
3 | 1 | 1.000 | -1.000 | -1.000 |
4 | 1 | -1.000 | -1.000 | 1.000 |
5 | 1 | 1.000 | 1.000 | 1.000 |
6 | 1 | -1.000 | 1.000 | 1.000 |
7 | 1 | -1.000 | 1.000 | -1.000 |
8 | 1 | 0.000 | 0.000 | 0.000 |
9 | 1 | 1.000 | 1.000 | -1.000 |
10 | 1 | 0.000 | 0.000 | 0.000 |
11 | 1 | 0.000 | 0.000 | 0.000 |
12 | 1 | 0.000 | 0.000 | 0.000 |
13 | 2 | 0.000 | 0.000 | 0.000 |
14 | 2 | 1.633 | 0.000 | 0.000 |
15 | 2 | 0.000 | 0.000 | 0.000 |
16 | 2 | 0.000 | -1.633 | 0.000 |
17 | 2 | 0.000 | 0.000 | -1.633 |
18 | 2 | -1.633 | 0.000 | 0.000 |
19 | 2 | 0.000 | 1.633 | 0.000 |
20 | 2 | 0.000 | 0.000 | 1.633 |